research trends meaning

• A/B Monadic Test
• A/B Pre-Roll Test
• Key Driver Analysis
• Multiple Implicit
• Penalty Reward
• Price Sensitivity
• Segmentation
• Single Implicit
• Category Exploration
• Competitive Landscape
• Consumer Segmentation
• Innovation & Renovation
• Product Portfolio
• Marketing Creatives
• Advertising
• Shelf Optimization
• Performance Monitoring
• Better Brand Health Tracking
• Ad Tracking
• Trend Tracking
• Satisfaction Tracking
• AI Insights
• Case Studies

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What Is Trend Analysis in Research? Types, Methods, and Examples

Trends are everywhere. They are central to how businesses craft their product development, marketing, and dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268031">social media strategies, and how consumers go about purchasing decisions.

Trends are sometimes driven by dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268011">external factors (like a shortage of a certain product that creates a trend for something new), and other times trends are driven by internal consumer wants/needs (like plant-based dairy alternatives). Businesses that pay attention to and understand current/evolving trends (through dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trend analysis research ) are able to use dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268028">informed dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268003">decision dropdown#toggle" data-dropdown-menu-id-param="menu_term_289268003" data-dropdown-placement-param="top" data-term-id="289268003">-making in their operations. This article looks at different dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268012">types of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trend analysis , how to conduct it, and how to act on emerging trends to stay ahead of the competition.

Table of contents

• What is trend analysis?
• Importance of trend analysis in market research
• Types of trend analysis in research

Advanced methods for trend analysis

• How to do trend analysis

How to identify existing trends from your analysis

• How to use trends analysis for virtually any type of research 
• Example of trend analysis in market research
• Advantages of trend analysis
• Use quantilope for automated trend analysis

What is dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trend analysis ?

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">Trend analysis is the process of using dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267999">historical data as well as current dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268001">data sets to determine how consumers behave and how businesses react; the same is true of the inverse: how businesses behave and how consumers react. dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">Trend analysis focuses on dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268004">market trends over a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268006">period of time and can be used as an ongoing resource to keep ahead of market changes.

Whether it’s used in the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268010">short term or the long term, dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trend analysis can reveal changes in consumer needs as well as changes in industry activity. These aren’t always going to be huge, industry-wide trends; they can be smaller ones too - such as small dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268021">fluctuations in consumer loyalty or satisfaction with a particular product, or dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268042">downtrends and dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268041">uptrends in certain product usage. Trends can also be temporary - around for a while and then gone in a flash, as is often the case with fashion or some hairstyles (unless they make a comeback...like flare jeans and bucket hats). Some trends might gain momentum slowly and grow steadily over time, like tech usage or certain diets. Businesses use dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trend analysis findings to act on emerging trends as well as to predict dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268039">future trends and plan dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268032">new products or marketing activity accordingly. Back to table of contents  

Importance of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trend analysis in market research

Trend analysis research empowers businesses to unlock valuable insights across various facets of their operations and market landscape. By examining historical and current data patterns, companies can gain a deeper understanding of their own performance over time - be it  financial metrics like revenue and profit margins, operational efficiency, or customer satisfaction trends.

Beyond internal usage, trend analysis research helps grasp the competitive landscape. By tracking rivals' performance and strategies, companies can identify opportunities to differentiate themselves and gain a competitive edge in their category. For instance, analyzing trends around competitive product launches or marketing strategies can point out what captures consumers' interest and what ends up being a 'miss' so that businesses can emulate or avoid those elements in their own initiatives. 

Trend analysis is key to understanding consumers. By examining patterns in purchasing decisions, preferences, and engagement with various brands, businesses can tailor their offerings to meet evolving customer needs and desires. This could involve developing new products or services , refining marketing messages, or optimizing customer experience. Trend analysis might even point out technological advancements that could disrupt entire markets or industries. By staying ahead of these trends, businesses can proactively adapt their strategies and capitalize on new opportunities for growth and innovation. Back to table of contents  

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268012">Types of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trend analysis in research

There are various dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268012">types of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trend analysis available through market research. Below we’ll touch on a few of the most popular types that can guide businesses’ dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268003">decision making for different needs .

Consumer dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trend analysis

This dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268012">type of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trend analysis relates to how consumers behave, think, and purchase within a certain sphere or market landscape. It could uncover consideration and usage of a product or service, consumer behavior in a specific product category, consumers’ dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268031">social media usage, or how consumers feel about political, social, or environmental issues. Information gathered from consumer dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trend analysis helps businesses leverage those consumer preferences in their current business operations and identify new growth opportunities.

Competitor dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trend analysis

Knowing where competitors are winning and losing is crucial information to feed into business decisions. Analyzing how competitors have performed at certain points in time, such as the launch of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268032">new products or advertising campaigns, reveals how positively the target market reacts to those types of business activities. This dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268012">type of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trend analysis helps identify strategies that will encourage consumers to choose your business over competitors, as well as to find new opportunities where competitors are weak.

Historical dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trend analysis

Looking at past dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268000">data points and tracking how consumer attitudes, consumer behaviors, or industry activities have changed in relation can provide valuable context for dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268013">future events . Say for example you sell beauty products and you’ve seen the popularity of vitamins in body cream grow over the past two years; this is a good indication that the trend will continue, which will help shape dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268032">new product development and future marketing messages.

Temporal dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trend analysis

Temporal dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trend analysis looks at a specific dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268006">period of time to see how consumer trends have changed in that dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268018">time frame alone. You could take one or more periods of time and compare them, or even analyze based on dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268009">seasonality (e.g. summer, the holiday dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268009">season ). This dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268012">type of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trend analysis helps identify trends at a set time which can be helpful when planning inventory stock, pricing strategies, or product promotions for similar time periods in the future.

Geographic dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trend analysis

Geographic dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trend analysis looks at changes within geographical locations and compares them with each other. For example, how have skincare preferences evolved in Asia, and how does this compare with preferences in North America? Trends in one region could give clues as to how trends will develop in another - especially today with global dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268031">social media platforms like Instagram and TikTok that can spread geographical trends in record speed. This dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268012">type of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trend analysis is useful for international businesses looking to shape their offer in each location they operate in.

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268040">Demographic dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trend analysis

Knowing your target market is essential to running a successful business. You need to keep tabs on what your consumers want and need, and how those differ based on factors like age, gender, region, etc. Older consumers may have different dietary needs than younger ones; the same goes for cosmetics, footwear, haircare, technology, and so on. This dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268012">type of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trend analysis is great for understanding how a particular dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268040">demographic group has changed over time so brands can appeal to that audience with the right communication and product portfolios.

Economic dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trend analysis

Inflation and the general cost of living are examples of economic trends that give businesses a good idea of current consumer buying power and their likely willingness to spend. Economic trended dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268001">data sets are typically available publicly, along with a company’s own internal dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268008">financial statements . This type of data is helpful to reference when setting new price points or making upcoming production decisions.

Technological dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trend analysis

Technology is continuously evolving, and there’s no doubt it will continue to do so. In recent years alone it seems to be evolving faster than ever with things like self-driving cars, virtual reality, and the rise of AI. Technological dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trend analysis empowers organizations to make dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268028">informed decisions and gain a competitive advantage. Businesses can use technology trends to operate more efficiently, foster new innovations, and to understand consumer expectations better. Back to table of contents  

Trends are constantly shifting which can be a challenge for businesses to stay ahead. Those that want to act on (rather than react to) consumer or marketplace trends use advanced methodologies to go beyond standard usage and attitude metrics. Advanced methods provide deeper insights around why trends emerge, which are likely to endure, and how businesses can act on them for future success. 

Below are a few examples of advanced methods used for trend analysis - all of which are available on quantilope's Consumer Intelligence Platform: 

MaxDiff (Maximum Difference Scaling):

Pinpoint the most impactful features or aspects driving a specific trend with MaxDiff . Is it sustainability, convenience, or design that's impacting the way consumers feel currently (and over time)? 

TURF (Total Unduplicated Reach and Frequency):

When multiple trends emerge (say a change in feature preferences, marketing message relevancy, etc.) it can be hard to pinpoint which trends to focus the most attention on. TURF analysis helps businesses determine the optimal combination of elements to maximize your reach. Which trends, when paired together, create the most compelling offering for your target audience?

Choice-Based Conjoint analysis:

Quantify the value consumers place on emerging trends relative to existing product attributes. Is the trend worth investing in? How much are consumers willing to pay for products or services that align with that trend (e.g. sustainability, minimalism, personalization, etc.)

Price Sensitivity Meter (PSM):

Understand how much consumers are willing to pay for products or services related to a new or existing trend. Does the trend come with a premium price, or is it rather price-sensitive?

Implicit Association Tests (SIAT and MIAT):

Uncover subconscious connections between consumers and emerging trends. Are there hidden emotional drivers influencing the trend's popularity? What intrinsic associations arise related to the trend in question?

The above advanced methods just touch the surface of what businesses have at their disposal when it comes to leveraging these tools to explore trends. For more on this, check out quantilope's guide on the Importance of Advanced Methodologies in Consumer Research .  Back to table of contents  

How to do dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268025">trend dropdown#toggle" data-dropdown-menu-id-param="menu_term_289268005" data-dropdown-placement-param="top" data-term-id="289268005"> analysis

Below are a few simple steps to getting started with your trend analysis research study : 

1. Define your goals

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268004">Market dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trend dropdown#toggle" data-dropdown-menu-id-param="menu_term_289267998" data-dropdown-placement-param="top" data-term-id="289267998"> analysis requires a clear starting point and a clear end point. In other words, what do you know already and what do you hope to find out? The latter will determine your end goal(s).

Your goals will guide your dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trend analysis throughout each stage - from initial survey setup to final analysis. When you start looking through your dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268001">data set , your end research goal will help you focus on the trends that actually impact your business.

2. Invest in regular dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trends analysis

Identifying trends doesn’t happen overnight. Trends appear over continuous dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268018">timeframes - known as ‘waves’ in trend research. You need to collect data on an ongoing basis to find those trends, and the best way of doing so is setting up a consistent research tracker. Monthly, quarterly, twice-yearly, or annual tracking surveys are some of the most commonly-used cadences to identify trends over time. The frequency of your tracker will depend on how dynamic your industry is; CPG product preferences can change all the time whereas something like home/car insurance may be less wavering.

3. Find an easy-to-use survey tool

An intuitive survey tool - like an online research platform , can speed up your dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268005">data analysis process to act on insights faster. Easy-to-use survey tools offer things like research expert consultation, drag & drop modules, automated advanced methods , real-time reporting, and easily designed dashboard reports that can be shared around without the risk of version control. 

4. Identify your sample

For quality data, you need to find the right people and ask the right questions. This means launching a survey among respondents who accurately reflect your target audience and asking questions that relate to your previously-defined goals; the right survey tool will make sure you can achieve both of these by offering things like survey templates and panel agnostic capabilities.

5. Field and analyze your data

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268005">Data analysis will highlight trends that arise from consumer behavior, competitive behavior, or general industry behavior. A good dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268005">data analysis platform will allow you to review results in real-time, as respondents complete your survey - rather than having to wait until the end of fieldwork for a data processing team to send over a final cross-tab file. As you review your data, you can cut dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268020">metrics by different parameters and dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268040">demographics to understand various trend perspectives. Your final data will go into a dashboard or report to share with dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268016">stakeholders for next steps.

6. Act on your findings

Once you’ve analyzed and reported on your trended data findings, it’s time to take action. This might mean immediate action, like putting a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268032">new product into market, or waiting for another wave of data to confirm a suspected trend. Regardless, the insights you’ve gained from your dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trend analysis can feed into future business dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268003">decision-making to stay ‘on trend’ and ahead of competitors. Back to table of contents  

Uncovering trends in your data is a critical step to understand the dynamics of your market, category, or brand. Whether you're starting a new trend analysis study or tracking the evolution of established patterns from an existing tracker, trended insights are invaluable in shaping your strategic decision-making.

The first few waves of your trended insights study are exciting; with these results, focus on identifying emerging trends (i.e. shifts in your data) that hint at changing consumer behavior, preferences, or market forces. Recognizing these early signals can give you a competitive advantage, allowing you to adapt and innovate ahead of the curve. Once you have several waves of trended insights available, your goal might be to delve deeper into the trends you've earlier identified.  

Regardless of where you are in your trend analysis, below are a few key considerations to keep in mind:

Look for patterns: Scrutinize your captured data for recurring patterns. These could be increases or decreases in anything from sales figures, customer demographics, or customer preferences - just to name a few examples. Identifying these general patterns will serve as a starting point for deeper analysis.

Isolate anomalies: Don't dismiss data points that seem unusual or unexpected. These anomalies could be early indicators of emerging trends. Keep an eye on these data points to investigate further once you have new data available to see if it might become a long-term trend.

Compare with benchmarks: Compare any new data with industry benchmarks or historical data. This will help you determine whether any observed patterns are unique to your business or part of a wider industry trend.

Visualize your data: Sometimes the easiest way to identify patterns and shifts is through chart visualizations rather than staring solely at the numbers. Create graphs, charts, or other visual representations of your data to see trends more clearly and even make them easier to communicate/share with others.

Consult with others: Seek input from other team members or research consultants (if applicable). Other viewpoints may be able to identify trends you didn't see or add new context to see things from a different angle. 

By keeping the above steps in mind, you can effectively identify new and existing trends from your analysis and use this information to make informed business decisions.  Back to table of contents  

How to use dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trends analysis for virtually any type of research

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">Trend analysis can be used to uncover almost any trends. Above we’ve already mentioned the benefits in exploring trends amongst consumers, competitors, and dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268040">demographics , along with using dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trend analysis to uncover geographic, economic, and technological changes. Other use cases of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trend analysis include:

Customer satisfaction. Understanding satisfaction levels with regards to a product or service, and how this relates to a brand’s activity or competitor performance. Part of this measurement might be tracking a brand’s NPS score over time.

Employee satisfaction. Identifying how employee turnover or loyalty relates to the company ethos or other factors.

Customer spend. Tracking how different customer types allocate budget to a product over time reveals trends in disposable cash levels as well as their willingness to spend. This feeds into determining dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268032">new product price points and planning dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268032">new product offers.

Financial dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268021">fluctuations and forecasts. Pinpointing where sales have peaked or dipped, and whether there has been an dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268034">upward trend or downward trend since then, provides crucial information on when businesses should explore new opportunities. It also helps predict how business activity will shape future growth.

The customer experience. Part of understanding your target audience means appreciating how their experience of your brand correlates to prevailing trends. This is separate from overall satisfaction; a customer might be satisfied with the end product or service but not the process in finding or purchasing it. Back to table of contents  

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268027">Examples of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trend analysis in market research

Companies can use dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trend analysis to inform their spend, product development, advertising, and just about all other areas of business operations. Below are three examples of trend analysis findings from various quantilope syndicated studies. 

DTC Mattress Trends

quantilope runs an annual direct-to-consumer mattress tracker  that identifies trends around in-store vs. online mattress purchasing, direct-to-consumer mattress buying, the popularity of certain mattress brands, and so on. Over the past few years, consumers’ shopping experiences (in general) have shifted heavily online - and this tracker showed that mattresses were no exception. Between 2019 and 2020 onward, the study showed a significant dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268034">upward trend in online mattress purchasing.

Soda Trends

quantilope's quarterly Better Brand Health Tracking (BBHT) study in the soda category tracks metrics around 10 major soda brands. Aside from standard brand funnel metrics like awareness and usage, the BBHT model leverages Category Entry Points (CEPs) , Mental Availability Metrics, and Mental Advantage analysis to provide modernized, actionable insights at both the category and brand level.  Recently, the study has pointed to seasonal trends around soda - particularly diet varieties. In the warmer months of the year, trends for diet sodas like Diet Coke and Diet Pepsi significantly rise. As of wave 5 (April '24), Diet Coke's Mental Market Share (MMS - one of four major Mental Availability metrics) was the highest it's been since the start of tracking a year ago (9%).

With the simultaneous, statistically-significant rise in Diet Pepsi's MMS (7%), this is trend that soda brands should watch over time to plan for future seasonal campaigns, inventory needs, and more. 

To explore more of this study's data, check out the BBHT soda blog post here . 

Consumer Trends 

quantilope's Consumer NOW Index study ran for two years - from July 2020 to June 2022. Over that time, the study tracked trends around overall consumer well-being, shopping behaviors, social media platforms, consumers' finances, travel insights, food trends, and work environments. 

The study's chart visualizations clearly show where there were changes in trends over time - providing an understanding of the general market and consumer sentiment. As one example, the below chart shows that TikTok usage significantly increased between July '21 and the most recent wave of the study about a year later. The same can't be said for any other platform.  

As another example from this study, we can see the change in consumer trends over time on where they choose to stay when booking travel: 

Back to table of contents

Advantages of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trend analysis

Trend analysis empowers businesses to make informed decisions, stay ahead of the curve, and thrive in a competitive landscape. Below are just a few key advantages of running this type of research. 

Proactive decision making:

Trend analysis helps you spot emerging trends before they become mainstream, giving you a head start in adapting your strategies. By understanding the underlying drivers of trends, you can make better decisions about new product development, marketing, and resource allocation.

Competitive advantage:

Staying ahead of trends allows you to offer innovative products and services that set you apart from competitors. By anticipating shifts in consumer preferences, you can position yourself as a trendsetter (rather than a follower) and gain a competitive edge in your market. 

Risk mitigation:

Trend analysis helps you identify potential risks, such as declining demand for certain products or shifting consumer attitudes. By understanding changing trends, you can proactively adapt your business strategies to mitigate risks and avoid obsolescence.

Improved resource allocation:

Trend analysis guides you in allocating resources effectively, ensuring that your investments align with emerging opportunities. By focusing on trends with the highest potential, you can avoid wasting resources on products or services that are losing relevance.

Enhanced marketing and sales:

Understanding trends enables you to create highly targeted marketing campaigns that resonate with your target audience. By aligning your products and services with emerging trends, you can attract new customers and drive sales growth.

Innovation and growth:

Trend analysis can inspire new product ideas and innovations that cater to evolving consumer needs. Identifying emerging markets or opportunities for growth can help your business expand into new areas.

Customer satisfaction and loyalty:

Use quantilope for automated dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trend analysis  .

quantilope offers intuitive and affordable dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trend analysis through its tracking solutions.

Choose between a category tracker or quantilope’s new Better brand Health Tracking approach that uses industry-praised concepts such as Category Entry Points and Mental Availability . Either way, quantilope users will start with the option to customize a pre-built survey template or build their own tracking study from scratch. Building your tracker is made easy through a library of pre-programmed questions and advanced dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268033">methodologies that you simply drag & drop into your survey builder. The platform even offers an AI co-pilot, quinn , to assist you in your survey creation, analysis, and reporting processes. Findings are available in real-time, with the option to start building report charts long before fieldwork wraps up. Once it does, all charts are automatically updated with final data and statistical testing. Cut the data any way you like, by any other variable within your dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289267998">trend analysis survey. Store all final charts in the reporting tab of the platform to use in a final dashboard deliverable, which is shareable with dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268016">stakeholders through a single link.

Subsequent waves of your dropdown#toggle" data-dropdown-placement-param="top" data-term-id="289268025">trend data research can be set live on the platform with a few clicks of a button, as often as you choose. Trended data is automatically added to existing charts in real-time, so you never have to go back to square one.

For more on how quantilope can help your business ahead of trends (and the competition), get in touch below!

Get in touch to learn more about trend analysis with quantilope!

Related posts, quantilope academy is now open to the broader insights community, quantilope & greenbook webinar: tapping into consumers' subconscious through implicit research, master the art of tracking with quantilope's certification course, van westendorp price sensitivity meter questions.

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The Methodological Basis of Defining Research Trends and Fronts

N. a. mazov.

1 Trofimuk Institute of Petroleum Geology and Geophysics, Siberian Branch, Russian Academy of Sciences, 630090 Novosibirsk, Russia

2 State Public Scientific Technological Library, Siberian Branch, Russian Academy of Sciences, 630102 Novosibirsk, Russia

V. N. Gureev

3 Novosibirsk State Technical University, 630087 Novosibirsk, Russia

V. N. Glinskikh

The methodological and technical aspects of identifying research fronts and trends in the development of science are considered. Based on the literature data, a comparison of scientometric methods for finding research fronts was carried out: analysis of publication activity, direct citation analysis, co-citation analysis, bibliographic coupling, and content analysis. The advantages of the combined application of various approaches are shown, the role of expert assessment and verification of the results of scientometric analysis is emphasized. We revealed topical problems associated with the detection of scientific fronts by scientometric methods and showed promising directions in their solution.

INTRODUCTION

The search for scientific trends and research fronts, that is, topical or promising research, is one of the most significant problems in science policy, scientometrics, and the history and philosophy of science and is of decisive importance at the stages of planning scientific activities. The topic of scientific trends and fronts is obvious if it is dictated by socio-political, environmental, and economic factors or threats to national health [ 1 ]. These can be natural disasters, terrorist attacks [ 2 ], economic crises, or the appearance of dangerous diseases in the human population, such as the outbreak of influenza A/H1N1 pandemic in 2009 [ 3 ] or SARS Cov2 in 2019–2020. In these cases, the scientific community, states, research and funding organizations are actively and consistently involved in the search for solutions to emerging problems. The fronts of science are much less obvious in the absence of such events; they then themselves become an object of study, requiring the development and use of methodological foundations and appropriate tools to identify them.

Scientific trends and fronts, as a rule, are the object of research of science itself, and their identification is an attempt to search for new growth points, as represented by the most promising ideas and developments that are important for the further development of science and technology. In other words, a search is carried out for changing objects of research in their relation to existing knowledge and to each other [ 4 ]. When identifying research trends and fronts it is predominantly scientometric methods that are used.

In a continuation of previous studies in the field of scientific trends in various fields of knowledge [ 5 – 7 ] and in the absence of reviews on the topic of detecting research fronts, we further consider the concepts of research trends and fronts, classify approaches, and describe the tools for their detection, as well as study the current issues that are pending their decision. When reviewing the literature, the Scopus and RJ “Informatika” databases of VINITI were used without restrictions on time and types of documents. The request included the following keywords: “research front”, “research trend”, and “research focus”. Additionally, sources from lists of references based on search results were used.

A METHODOLOGY FOR IDENTIFICATION OF RESEARCH TRENDS AND FRONTS

In general, a research front is understood as the situation where the interests and needs of society coincide with the current scientific results [ 8 ]. The key object of analysis in identifying research fronts is the groups of scientific publications and their interrelationships. According to the classical definition of D. Price, a research front is a densely cited network of recently published papers [ 9 ]. In a more detailed definition, a research front is understood as a group of recently published articles with a common topic, which are strictly connected by a network of citations among themselves and weakly connected with publications outside the group [ 10 ]. At the same time, strong links between citations within a group are characteristic of a research front at the initial stage of its development, while at later stages, with an increase in the number of citations, including from other scientific areas, this connection weakens. The strength of citation links between publications of clusters is determined by predetermined threshold values that are unique for each scientific field. The sizes of research fronts also depend on the discipline, which usually ranges from a few publications to several dozen. As an example, in the latest report on research fronts from Clarivate Analytics the spread is from 2 to 50 articles [ 11 ]; sometimes a minimum threshold is set, for example, 10 publications [ 12 ].

The concept of a research trend is close in meaning to a research front. A research trend is the collective action of a group of researchers, each of which begins to pay considerable attention to a specific scientific topic: read scientific publications on this topic, refer to them, and publish the results of their own research [ 4 ]. At times the concepts of the research front and research trend are used synonymously [ 13 ].

The main types of research fronts according to the common classification of G. Small [ 8 ] are shown in Fig. 1 . The method for identifying the stage of a research front involves comparing clusters of publications for two or more equal consecutive periods of time.

Types of research fronts.

The Clarivate Analytics together with the Chinese Academy of Sciences, in its periodic reports distinguishes only two types of research fronts: key ( key hot fronts ) and incipient ( emerging fronts ) [ 11 ]. Research fronts are also revealed by the Elsevier company based on SciVal data, where the most promising topics are determined by the Prominence indicator.

Under the influence of various factors, the research fronts of the extensive phase can turn into an intensive one, for example, when new promising research methods appear, with increased funding for the field, when there is an urgent need to develop a topic under the influence of external factors, etc. [ 1 ,  12 ]. As a result of the development of a research front, according to G. Small, it can either develop into a new discipline, or be absorbed by a broader field, which adapts the achievements of a research front to a wide group of studies [ 8 ]. In the first case, this indicates the growth of a scientific front, in the second, it indicates its influence on science. As a rule, scientific fronts of interdisciplinary research develop in separate directions, while the absorbed research fronts have little to do with interdisciplinarity, but are gaining citations faster.

Study on research fronts is significant from both fundamental and applied points of view. At the theoretical level, they determine the vector of development of scientific progress and allow tracing the origin and evolution of one field or another, the division and merging of areas of knowledge, contribute to the spread of knowledge between scientific disciplines [ 14 ], and allow adjusting organizational processes when new knowledge meets traditional paradigms that dictate research topics, standards and regulations [ 15 ]. The identification of research fronts is of practical interest for a wide range of stakeholders involved in the definition of priority areas of scientific research and their funding.

To date, three main scientometric approaches are widely used to identify research trends and fronts: analysis of the dynamics of changes in scientific production, citation analysis with its varieties, and content analysis, as well as their various combinations.

Analysis of Publication Activity to Identify Research Trends

Analysis of publication activity is usually used to identify research trends, while citation analysis is used to identify research fronts [ 4 , 16 ]. When analyzing scientific production, expressed by the number of publications, one resorts to models of the growth of scientific knowledge:

(1) in the first model, the growth of knowledge is considered as the cumulative development of new ideas based on previous recent scientific achievements;

(2) the second model assumes that the development of new ideas is based on the entire body of human knowledge, and not only on recent achievements. According to this model, there is a selective choice of grounds for a new idea from all of human scientific experience;

(3) the third model is based on the theory of scientific revolutions by T. Kuhn [ 17 ] and presupposes an intensive growth of knowledge interrupted by periods of calm.

There is no consensus about which of the proposed models most closely corresponds to reality, especially since each of them, to one degree or another, explains the ongoing scientific events in various disciplines. Each of these paradigms can correspond to some mathematical model of the growth of scientific literature, for example, linear or exponential [ 18 ]. In natural science disciplines, exponential growth often prevails; when identifying scientific trends researchers therefore turn to D. Price on the exponential growth and obsolescence of scientific literature [ 19 , 20 ]. The scattering law is used to identify a scientific information trend according to S. Bradford [ 21 ], which allows identification of the core of scientific journals of a given subject.

An example of a study using this method is the work to identify research trends in the field of tourism [ 22 ]. A circle of authors and organizations that form a research trend on this topic was determined according to zones of concentration and dispersion of Bradford’s scientific information, as well as the analysis of the scientific productivity and authoritativeness of publications. The analysis of research trends in the field of borehole geophysics was carried out by the authors of this work: the leading positions of this field in the field of earth sciences were identified, the most productive authors were detected and the redistribution of leading positions between countries over the past 20 years was shown [ 7 ]. Further identification of research trends and fronts in the field of geophysics is extremely important, since it is associated with the search for new research areas, primarily for the creation of innovative technologies. In the field of borehole geophysics, “cheap” logging technologies will be the most demanded by both large and small service companies in the near future, which is due to the end of time of “expensive” oil.

Citation Analysis to Identify Research Fronts

The main method in identifying research fronts is citation analysis, which makes it possible to trace the growth of interest and relevance of a particular topic by the dynamics of changes in the number of citations of publications of a particular field. Citation analysis is considered more objective in comparison with expert assessment, since it takes the opinion of the entire scientific world community of scientists expressed in references [ 23 ]. The approach is based on the observation that recent scientific publications are the most cited. Thus, the identification of thematic clusters of the most cited publications allows us to identify the research front of the corresponding discipline [ 9 ]. The response time to published papers varies across disciplines, but on average is 2–5 years, during which half of simultaneously published publications are cited [ 24 ]. Within the framework of citation analysis, where both cited and citing publications are clustered, a research front is understood as:

(a) a group of the most cited publications identified by direct citation analysis [ 4 , 9 ];

(b) a group of co-cited publications identified by co-citation analysis, positions 6 and 7 in Fig. 2b [ 25 – 27 ]. The cluster of a research front, in addition to co-cited publications, may include citing publications, positions 1, 6 and 7 in Fig. 2b [ 28 ]. This definition of research fronts was used by E. Garfield [ 29 ]; this approach is still implemented by the Clarivate Analytics in periodic reports on research fronts using Web of Science databases [ 11 ]. There is also a third approach, where a research front refers to publications that cited a cluster of co-cited publications, position 1 in Fig. 2b [ 30 ];

The principles of clustering publications used in identifying research fronts. A, direct citation analysis; B, co-citation analysis; B, bibliographic coupling. The top row usually represents recently published publications, the bottom row represents publications of the last 2–5 years. Citation analysis can cover out-of-sample publications.

(c) a group of publications with similar references, identified by the bibliographic coupling method, positions 3 and 4 in Fig. 2b . According to this approach, the articles of a research front themselves may not have citations [ 2 , 12 , 31 – 33 ];

(d) with the joint application of the indicated approaches, a research front is understood, for example, as a group of co-cited publications plus a group of publications with similar references [ 30 , 34 – 37 ], a group of co-cited publications plus publications citing this group [ 38 ], or several groups of publications based on the results of all three approaches [ 28 , 39 , 40 ]. As a rule, when used together, each method is used separately, after which the results are compared or combined. However, it is possible to build complex combined approaches: for example, clustering by bibliographic coupling of those publications in which clusters of co-cited publications are cited; this is then clustering of the first and second levels [ 30 ].

The formal similarity with the clusters of publications of research fronts is demonstrated by artificially created groups of articles united by chief editors, for example, within the framework of special issues of journals, where articles of each issue abundantly cite each other. When analyzing research fronts, groups of publications united by similar publication models are usually excluded from the analysis [ 8 ].

When describing research fronts, the concept of an intellectual base ( knowledge base , knowledge foundation , intellectual base , or intellectual structure ) is used, which means literature cited by publications of a research front [ 2 , 4 , 41 ]. Many studies demonstrate the thematic proximity of an intellectual base and research fronts [ 13 , 31 , 36 , 42 ]. When analyzing co-citation, sometimes confusion of these concepts occurs; while some researchers understand co-cited publications as a research front, others consider them as an intellectual base, and the citing publications as a front (see Figure 2B ). In general, the scientometric task is to identify the points of intellectual displacement (research fronts) in the relatively stable scientific literature (intellectual base).

Co-citation analysis was simultaneously proposed by I.V. Marshakova and G. Small [ 43 , 44 ]: two documents are considered co-cited and thematically related if they both appear in the reference list of a third document (with which the two cited documents also have a thematic relationship) and the citation rate is defined as the frequency with which two documents are cited together. Researchers usually choose a small group of publications that are highly cited within a given period of time as a basis for clustering. This could be 1 or 10% of the highly cited articles, the top 10, top 20 articles, etc.

This approach to the search for scientific fronts has a drawback associated with the nature of citation [ 45 ]. Accordingly, the ability to take new publications into account, which are often of the greatest interest in the search for scientific fronts, is limited [ 46 ]. In other words, co-citation is suitable for identifying a research front at a relatively late stage, and not at the very moment of its emergence [ 8 ]. According to one of the developers of the method of G. Small, the analysis of socializing does not cover the entirety of publications on a scientific front, but rather informs about the emergence of such a front; it is designed to do a quick screening of the scientific landscape rather than a definitive delineation of some specific area [ 8 ]. The approach does not depend on the vocabulary and language of publications.

The bibliographic coupling method proposed by M. Kessler [ 47 , 48 ] presupposes that two works have a meaningful relationship to each other and are thematically related if they have one or more similar references. Thus, a research front consists of publications that jointly cite other publications. Since references to the analyzed papers are not important and only their reference lists are investigated, the method is free from lag (especially if it is applied not to journal publications, but to preprints) and allows one to analyze newly published papers.

The main idea of the method is as follows: (1) a separate bibliographic reference used in two publications is called one unit of coupling between these publications; (2) several publications form a linked group G if each member of the group has at least one coupling unit with the test paper P 0 ; and (3) the coupling strength between P 0 and any member of G is measured by the number of coupling units (n) between them. Like co-citation analysis, the bibliographic coupling method is independent of the vocabulary and language of publications and can be automated. In comparison with the analysis of co-citation analysis, the method of bibliographic coupling is used less often to search for scientific fronts [ 28 , 32 ].

One essential criterion for the study of research fronts is the choice of the citation window. The problem of choosing a citation window received full coverage in [ 32 ]: the model of a traditional static 5-year citation window was compared with a sliding overlapping citation window, as well as with the half-life of highly cited articles. Research with a static citation window was found to be the least labor-intensive; however, the most labor-intensive method with a sliding citation window helped to find more research fronts. At the same time, some of the emerging research fronts identified by the two methods did not intersect, which is why the joint use of static and sliding citation windows was recognized as the most effective.

Since the main scientometric approaches to identifying research fronts involve a procedure for clustering bibliographic data, the results of the analysis can be influenced by clustering methods and the choice of threshold values for the measure of similarity between the grouped elements [ 30 , 31 ]. The object of citation analysis can be both the publications themselves and the authors of these publications, journals and, less often, subject categories [ 49 ].

Co-citation analysis is used to search for scientific fronts in various fields of knowledge: HIV/AIDS [ 15 ], scientific collaboration [ 13 ], library and information science [ 27 ]. The method of bibliographic coupling was used to study the historical development of research fronts in the field of anthrax research [ 12 ]. The joint use of methods of co-citation analysis and bibliographic coupling was carried out to search for scientific fronts in the library and information science [ 36 ] and in the field of battery research [ 37 ]. Author’s citations and content analysis of links were used to identify research fronts in the field of bacterial infections [ 23 ].

The experience of identifying research fronts not for a discipline as a whole, but for an individual organization is remarkable: in [ 49 ], the intellectual base was studied by co-citation analysis; the corpus of publications cited by the organization, on the basis of which a research fronts of the organization itself were further identified. Similar studies of the publication activity and citations of a particular organization were carried out by the authors of this work for more effective information support of scientific projects [ 50 , 51 ], while the developed methods were also applicable for identifying research trends and fronts. The search for scientific fronts can also be carried out for a separate journal: for example, the Journal of the American Society for Information Science. Using the methods of bibliographic coupling and citation analysis, research fronts were identified and a significant closeness of the intellectual base with them was shown [ 31 ].

Content Analysis to Identify Research Fronts

Methods for semantic analysis of metadata and full texts of scientific publications, including neural network technologies [ 52 , 53 ] and algorithms for detecting rapidly spreading, so-called burst terms, which express new phenomena, are widely used in identifying research fronts [ 2 , 14 , 42 , 54 ]. Content analysis investigates the frequency of the use of words in metadata and full texts and, separately, keywords, as well as their joint occurrence in publications. Analysis of the frequency and co-occurrence of keywords is carried out:

(a) on the metadata of publications; in this case, author’s or additional keywords assigned in systems are investigated (for example, KeyWords Plus [ 55 , 56 ] extracted from lists of cited literature) and words from various subject thesauri and authoritative dictionaries (for example, MeSH ), as well as automatically extracted keywords from titles and annotations;

(b) on full texts, where keywords and terms are also extracted and semantically analyzed using software tools.

Some researchers refer to the results of keyword co-occurrence analysis as a research focus, while the research front is considered to be the result of co-citation analysis [ 57 ].

To search for scientific fronts in the field of informatics and accounting, the content analysis method identified topics with growing and dying interest, as well as those that have lost their relevance [ 14 ]. To extract keywords, entity linking method was used that takes the context of the keyword into account. An approach based on the combined use of searching by association rules, keyword analysis and rapidly spreading terms is presented based on the example of anticancer developments in nanomedicine [ 54 ]. Using linguistic methods for searching for the semantic similarity of texts, the identification of research fronts was described in [ 46 ]: a method of comparing phrases and fragments of identical content, not necessarily expressed by the same keywords, was presented. Cluster analysis of author’s keywords was carried out to search for scientific fronts in the field of social sciences: the result of a study in five countries was a map of national science, indicating promising areas [ 1 ].

Content analysis is often combined with citation analysis methods to identify scientific fronts. Thus, research fronts in the field of artificial intelligence were identified through the combined use of methods of bibliographic coupling and content analysis of keywords [ 58 ]. Methods of bibliographic coupling (by co-authors and documents) and content analysis were used to search for scientific fronts in the field of business [ 41 ]. A co-occurrence analysis method combined with co-citation analysis has been used to find research fronts in library and information science in Spain [ 42 ]. The same two methods were used to analyze co-citation fronts in astrophysical research [ 59 ]. A more sophisticated analysis of a research fronts of the interdisciplinary direction is presented using the example of magnetic nanoparticles, where co-citation and co-word networds were studied based on a sample of the 500 most-cited publications [ 60 ].

THE EFFICIENCY OF DIFFERENT TYPES OF SCIENTOMETRIC ANALYSIS IN REVEALING RESEARCH FRONTS

A researcher’s choice of a particular scientometric method is arbitrary in most cases, while in some situations it is necessary to correlate the method with the goals of the study and take the complexity of the calculations into account [ 28 , 39 ]. Different methods are more or less applicable to one type of research front or another. Thus, the emerging research fronts are better identified by the method of bibliographic coupling, which does not have a time delay. If topological clustering is preferable for research, then citation analysis turns out to be more applicable [ 39 ]. If it is necessary to cluster based on the textual similarity of publications, content analysis has proven itself better, in which the frequency analysis of words from metadata or full texts gives better results in comparison with the frequency analysis of an author’s keywords.

The choice of the approach has a significant impact on the results, as shown by the example of publications on environmental protection: the intersection of the results obtained in the co-citation analysis and the method of bibliographic coupling was only 33–41%, which in fact indicated different research fronts [ 30 ]. Comparison of methods of co-citation analysis and bibliographing coupling was carried out by M. Huang et al., who studies the methodological foundations of the search for scientific fronts [ 32 – 34 ]. In a series of publications, the advantages of the bibliographic coupling were shown: with its use, a greater number of fronts were identified, and several fronts were found at an earlier date [ 34 ]. The advantages of bibliographic coupling were disclosed in [ 39 ], although it was indicated that in certain narrow areas the method of direct citation analysis may be preferable, since significant publications may have few thematic links in their field but gain a large number of citations from related fields.

A comparison of direct citation analysis, co-citation analysis, and bibliographic coupling was carried out in [ 61 ] using the example of research fronts in the field of carbon nanotubes, gallium nitride, and complex network: the direct citation method showed the best results for identifying the early stages of the formation of new topics and contributed to the identification of a larger number research fronts. The next most effective methods were the method of bibliographic coupling and co-citation analysis. Another example of comparing all three methods of citation analysis is the study of scientific fronts in biomedicine, where they were additionally compared with textual analysis [ 28 ]. To test the best approach, information on grants was analyzed: since publications on a grant are thematically similar by default, a search was made for the highest concentration of publications on specific grants in each of the clusters.

Weighted Approaches to Improve the Accuracy in Identifying Research Fronts

Over time, increasingly sophisticated approaches to defining research fronts are being developed, with the goal of increasing the accuracy of clustering. One of the trends in this field is the construction of weighted citation networks. With the assignment of weight to the publications of the cluster forming scientific fronts, a series of studies was carried out by K. Fujita et al., proving the benefits of weighted citation networks [ 39 , 40 , 53 ]. The weight of the publication, automatically determined using neural network training technologies, takes the year of publication, the number of citations of the publication, the field of knowledge, and the strength of the links between the reference list of publications and keywords into account [ 39 , 53 ]. A significant advantage of the research of this group is that various bibliometric methods are widely combined here.

The analysis of collective dynamics of knowledge networks represented by weighted citation and keyword networks, which takes both incoming and outgoing connections between network elements into account, was presented in [ 4 ], which shows the advantages of this method over the analysis of direct citation networks, since it more closely approaches identifying research trends in small areas of knowledge. For more accurate clustering, the PageRank algorithm is used to assign different weights to publications of different significance levels: not only are the most cited publications recognized as the most significant in a cluster, but also publications cited by other equally significant publications of the cluster [ 35 ].

An analysis of links that establishes the relationship between the cited publications, taking their importance and position in the citation network into account, was carried out to search for research fronts in the field of shareholder activism: during the analyzed period, the development of this field was reconstructed by means of research fronts, including seven stages, from the theoretical origin of the concept to its practical implementation [ 62 ]. A weighted approach was used in the search for scientific fronts in chemical technology: 29 clusters were identified containing an average of 5.3 publications; for each cluster, the Price index was calculated, which quantitatively characterizes the degree of novelty of the field [ 38 , 63 ].

Together with the fundamental applicability of each of the approaches in identifying research trends and fronts, the results of most studies show that the least-accurate results are obtained by the direct citation analysis, although in certain situations it shows advantages over other approaches [ 39 , 61 ]. In the accuracy of its results the combination of the co-citation analysis and the bibliographic coupling is significantly superior to direct citation analysis, which does not take thematic links between publications into account [ 34 , 39 ]. The most accurate results in most cases are yielded by the method of bibliographic coupling; co-citation analysis lags slightly behind. The best results are achieved with the combined use of different approaches (and, if possible, different data sets), which should take the variability of publication activity and citation models in different disciplines into account, but such approaches are more laborious and time consuming [ 28 ]. Many researchers, for example [ 1 , 2 , 64 ], noted the importance of involving subject experts in the qualitative assessment of the results of scientometric analysis.

Software for Revealing Research Fronts

Significant attention is paid to the study of research fronts by software developers for visualization and mapping of science [ 65 , 66 ]. The visualization of bibliographic information is especially valuable for experts because it allows real-time detection of unexpected trends, gaps in scientific knowledge, statistical biases, and other important characteristics of research fronts [ 67 ]. VOSviewer [ 22 , 41 , 57 , 68 , 69 ] and CiteSpace [ 2 , 13 , 26 , 42 , 60 ] are most often used; however, ready-made programs are often seen as having limitations, since their functionality is standardized and often does not support innovative approaches [ 35 ]. Therefore, sometimes less common software products are used, for example, Cytoscape [ 15 ] or BibTechMon [ 37 ], including programs written for a specific study [ 12 ].

One of the most functional software for identifying research fronts is CiteSpace [ 2 ]. The capabilities of the program are presented by its developer using examples of the fields of “mass extinction” and “terrorism.” Research fronts are understood as emerging transitional clusters of ideas, expressed by small groups (several dozen positions) of co-cited publications. At the same time, the study solved the problem of identifying new fields on the basis of linguistic analysis of terms from the metadata of publications (although some researchers insist on involving experts in the designation of new fields [ 12 , 23 ]).

Experience in using VOSviewer was presented by the scientific library of Kent State University (United States): the methods of bibliographic coupling, citation analysis and content analysis were used to identify research fronts in the field of the Internet of things [ 69 ]. Dynamic keyword analysis in VOSviewer allowed them show changes in research fronts in this area over time.

The Problem of the Reliability of the Results of Scientometric Analysis in Identifying Research Fronts

Since the definition of research fronts is based on an array of scientific publications, the question of the legitimacy of such an approach often arises. In addition to the general criticism of bibliometric approaches, there are somewhat fair statements about the devaluation of the institute of scientific publications associated with an increase in the number of duplicate works, plagiarism, and “predatory” journals, as well as the frequent absence of descriptions of research methods in publications, which prevents their reproducibility. Another critisism concerns the role of publications in rewarding a scientist for his/her work instead of spread of knowledge and a shift of the central channels of scientific communication towards “invisible colleges”. Taken together, this leads to the main question of how much one can rely on bibliometric research of publications to identify research trends and fronts.

Other problems of identifying research fronts are associated with journal articles and, more broadly, with the market for periodicals and its internal standards. As an example, reputable international journals are more willing to publish research results on popular and global topics. Accordingly, in such a limited array of publications, research fronts that are important at the regional or national levels may not be found.

The cautious attitude of reviewers and editorial boards to advanced ideas and developments, often at odds with the scientific tradition, remains an unresolved issue [ 70 ]. Modern publishing standards often imply a comprehensive coverage of a scientific problem and a description of a ready-made set of its solutions [ 71 ]. However, precisely in relation to research fronts, at the initial stages of developing a new problem, these requirements are the least feasible and force authors to bypass key issues, whose discussion is most important for understanding the essence of the problem and its causal mechanisms [ 64 , 71 ]. At times, the overestimated requirements of the editors of journals for breakthrough work lead to the rejection of publications that are significant for science and society. One illustrative example is the article by A.K. Geim and K.S. Novoselov on a new material, graphene, that was rejected by Nature 1 (it was later published by Science ).

Another problem of using journal publications as a basis for searching scientific fronts includes the time lag from the submission of the manuscript to the editorial office to its publication. This adds to the subsequent delay in distributing the journal to libraries or indexing it in bibliographic databases. On average, the delay due to the technological publishing processes is estimated at 1 year [ 24 ]. Even if we compare this period with the total time from the birth of a scientific idea to its publication, which, for example, is 4 years in medicine [ 59 ], the publication delay appear to be significant.

The databases for the selection of publications themselves have a significant impact on the identification of research fronts [ 27 ]. Most of research is based on publications indexed in Web of Science , and less often, Scopus . In addition to the delay in indexing, such systems have limitations in terms of regional and linguistic coverage of sources; the accuracy of bibliographic metadata is not always high [ 72 ]. Despite the annually expanding indexing of conference proceedings, where advanced scientific ideas are discussed much earlier than in print, international databases still tend to predominantly cover journal articles. The need for verification of automatically processed data was already noted in early works, caused by many discrepancies in the spelling of author’s names, variations in the abbreviation of the names of journals, etc. [ 31 ]. (For more detail on the problems of identifying bibliographic objects, see [ 73 , 74 ].)

Some questions remain open, others are eventually answered. Thus, in recent years, reviewers have paid more attention to the transparency of the methodological part of the articles; more and more often initial data are provided in the form of appendices to publications, which significantly increase the reliability and reproducibility of the results. Ethics committees are working to improve the research and publication culture of authors, preventing unfair approaches to the publication of scientific results [ 75 ].

At the philosophical level, the role of publications in the system of scientific information and the degree of their applicability to identifying research fronts are analyzed. The transformation of the main properties of a research front into the form of bibliometric indicators has been substantiated, including such front characteristics as novelty, relevance, interdisciplinarity, risk factors, and a combination of fundamental and applied significance [ 64 ]. The central place of publications in scientific research fronts is proved, since in addition to the main function of information delivery, they stabilize unstable networks of various scientific practices and elements [ 76 ]. The role of scientific publications is also demonstrated in the reconstruction of the evolutionary development of science: based on the example of research fronts in scientometrics and the historical processes of the intellectual organization of knowledge in this area, their codification and structuring with a simultaneous decrease in entropy have been shown [ 77 ]. Based on the example of one area of biomedical sciences, the methodology for constructing a time scale, which allows one to visualize the development of a research front and predict the emergence of new fronts, was presented [ 12 ]. On the basis of the theory of the aging of scientific literature, the speed of dissemination of scientific ideas is investigated and the depth of research fronts was revealed [ 24 ].

The problem of publishing breakthrough articles, whose material, methodology and results differ significantly from the scientific tradition, finds its solution in the widespread dissemination of open science, the publication of preprints, the development of repositories and models of open peer review. Publication of preprints solves the lag problem. This issue is partially resolved by the development of the system of “articles in print” that are published before the formation of printed issues, as well as early indexing of such publications in bibliographic databases. One possible solution to the problem of publication lag may include the analysis of network publications, whose rate of appearance is significantly higher, as shown by the example of the search for scientific fronts in the field of XML research [ 78 ]. In this case, unlike journal databases, special systems are used, for example, CiteSeer . It is proposed to solve the problem of publication delay of journal articles by analyzing information about the dates of the publication process (the time of receipt of the manuscript, its approval, and publication); clustering of publications taking these dates into account gives more accurate results in identifying research fronts [ 59 ].

CONCLUSIONS

Over a relatively short period of studying research trends and fronts, a significant complication of the methodology is noticeable: combined approaches, neural networks, a wide range of bibliographic and network databases, and special software is increasingly used. Scientometric methods show their promise due to their rapid adaptation to the changing conditions of the functioning of science and new publication models for the dissemination of scientific information.

The review of research carried out in this article shows that scientometric tools for identifying research fronts have proven themselves well as a source of reliable and objective information for subsequent expert assessment in various fields of knowledge. A wide methodological arsenal of various types of citation analysis and content analysis has been developed. The improvement of the approaches goes in the direction of specifying citation windows, objects of analysis, and identifying the advantages and disadvantages of each of the approaches, taking the types of scientific fronts and research goals into account.

We see the immediate tasks on identifying research fronts and trends as follows. The problem of the initial distrust of the scientific community in breakthrough developments, whose results or methods do not agree well with scientific tradition, awaits a solution. A scientometric solution to this problem is outlined in a broader analysis of network publications. The second task is to increase the speed of identifying new fronts, if possible at the stage of publishing preliminary data on new fields. This requires a further search for methods to neutralize the effect of publication lag.

This study was carried out with the financial support of the Russian Foundation for Basic Research (project no. 19-011-00531).

CONFLICT OF INTEREST

The authors declare that they have no conflicts of interest.

1 Information from the seminar conducted by the editor of Nature Nanotechnology on November 28, 2017, Exhibition Center SB RAS, Novosibirsk.

Contributor Information

N. A. Mazov, Email: ur.sarbs.ggpi@ANvozaM .

V. N. Gureev, Email: ur.sarbs.ggpi@NVveyeruG .

V. N. Glinskikh, Email: ur.sarbs.ggpi@NVhkiksnilG .

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Trend research: how to identify relevant trends

Trend research is an important part of business strategy and can help companies understand their customers' needs and develop innovative solutions. But how can you identify relevant trends?

There are different methods for having your finger on the pulse, whether that be by observing changes in society, analyzing data, or following developments in technology. However, successful trend research also requires creativity and keeping an eye on unexpected changes. This is the only way companies can pick up on tomorrow's trends early on and adapt to them.

Defining the word 'trend'  A trend is an assumed development in the future that causes and changes something enduringly over the long term. Current developments move in a different direction or intensify even more. Trends in one industry usually have far-reaching effects. Nutrition trends, for example, induce changes in the retail, hospitality, tourism, leisure, and agriculture sectors. Those who miss out on the trends risk losing out and being overtaken by the competition. That is why companies should not neglect trend research and should actively seek out new developments.

Identify trends: identifying opportunities and risks

Change brings risks and opportunities. A change can mean a risk for a company if the market is restructured and there is no longer a demand for its own product. Yet change also brings many opportunities and possibilities for new innovations. 

If a company takes a proactive role, it can help shape the future, launch products that stand the test of time, and not be surprised by destructive changes.

This is the task of trend research and innovation management. Potential for the future is to be identified today, and solutions are developed that will be needed tomorrow. The future is something that is shaped, rather than the future doing the shaping. However, this requires good, solid information on possible trends, future developments and their effects.

What is trend research?  Trend research is a method of predicting future developments and changes in various fields. To this end, data from various sources such as surveys, statistics, or expert interviews are used. The results of trend research can help companies to adapt their products or services to the needs of their customers and to react to new trends in good time.  

Trend management

Trends are ever-present and influence our lives in many ways. However, they are not just short-lived hype, but can also have long-term effects. Therefore, it is essential in innovation management to establish trend management. Effective trend management enables companies to adapt to new challenges at an early stage and thus position themselves for the long term. It is therefore an important component for a successful innovation strategy and should not be neglected.

Since trends are highly multidimensional, the art of trend management lies in "disentangling the threads". Many trends that you don't see in your own industry at first glance may nevertheless have relevance for your own business due to chain and interaction effects.

•   Identifying trends  – "What trends are there or will there be in the future? And which of these are relevant to us?"
• Analysis of the effects  and possible projections and scenarios – "In which directions can a trend develop? What impacts can a trend generally have?"
• Analysis of the implications  on one's own industry or company and deducing of  search areas and innovation fields  – "What does the trend mean for our company?" 

How does trend management work? 

Trend management is an important component of innovation management. It is about recognizing trends at an early stage and making them useful for one's own organization. There are two approaches to this:

• External trend scouts or experts monitor trends for the organization. This knowledge is then processed internally and combined with the company's own ideas.
• Trend management is managed internally. Information is collected and processed from various sources; e.g. at trade fairs, new technologies, changes in customer behavior, analysis of social media channels.

Trend research systematically searches for relevant developments on the market 

The  process  of trend research is not high science. It involves many and intensive analyses of:

• Megatrends : There are many publications on megatrends. Megatrends are long-term developments over several decades that have a formative effect on all areas of society and the economy globally.
• Environment : Changes in the corporate environment also have an indirect and direct effect on the company's own organization. Analysis of the environment includes customers, users, customers of customers, suppliers, administration, adjacent companies and industries (e.g. upstream and downstream), etc.

Once you have identified the relevant trends, answer the following questions:

• What impact do the megatrends have on our company, our business, and our industry?
• What impact do the megatrends have on our environment (e.g. customers, laws)?
• What major changes and developments will there be?
• What do these mean for our company, our business, and our industry?
• What trends are there in our industry?
• Which global trends (macro trends) are still relevant for my company, and most importantly: why?

With a method toward the relevant trends

The basis of trend identification is asking the right questions as shown above. Answering them is nothing less than a creative process that requires a great deal of knowledge, experience, and analytical and creative thinking.

Two methodological approaches to trend research:

• Primary research: Use interviews with customers, lead users, experts (science and industry), users, employees and suppliers to this end. Workshops with experts and stakeholders or a Delphi study are also suitable.
• Secondary research: Access information that is available. There are a variety of trend reports for a wide range of industries that can definitely be used as a source and basis for trend identification.

Tip: As part of primary research, first determining the available trends by means of secondary research is also recommended. In addition, research using AI tools, such as ChatGPT, helps to get an initial feel for the trend landscape in an industry.

Broad benefits of trend research

It is never possible to predict exactly which trends or developments will result in drastic changes for a company. However, trend management, as part of strategic innovation management, makes a significant contribution to turning visions into reality.

Of course, the results of trend research are not the end of the story. To ensure that your company is fit for the future, use the findings of trend research as follows:

• Deduce search areas 
• Develop a roadmap ( more about roadmapping )

Even beyond the innovation strategy, the clarity gained about possible future scenarios helps you, for example, with the development of a corporate strategy.

Every corporate decision also always has to do with the future. It is therefore important to disseminate the results of trend research widely within the company and to initiate a discussion about them. If employees actively engage with the future, a creative process is set in motion that fills the idea funnel with better ideas. 

Filling the idea funnel is only one aspect of achieving continuous innovation. Those responsible for innovation must create the framework for their organization to become holistically innovative. Find out how in the following article: House of Innovation, this is how CEOs make their company innovative .

Paulinde Schmidt

More articles, successfully apply build-measure-learn when developing ideas, what are regenerative innovations, sustainability and innovation: what matters in innovation management., successful innovation projects with a method for more innovative strength, what types of innovation exist, innovative yarns: 5 trends you should know, from production to the clothes rail: 6 trends in the textile industry, origami engineering: the art of folding in space travel and robotics, 3 mistakes that make every innovation strategy fail, how the lead user method has developed further.

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What Is Trend Analysis in Research? Types, Methods, and Examples

Mar 7, 2024

In the fast-paced world where change is the only constant, understanding trends has become crucial for businesses, policymakers, and researchers alike. Trend analysis stands at the forefront of this understanding, providing insights that guide decision-making and strategic planning. In this comprehensive guide, we delve into what trend analysis is, its types, methodologies, and practical applications, with a special focus on market research . As we explore the advantages and disadvantages, we'll illustrate how Market Xcel, a market research company with over 23 years of experience, empowers you to excel in your research endeavours.

What is Trend Analysis?

Trend analysis is a research method used to identify consistent patterns or trends over time within data sets. It serves as a crucial tool in forecasting future movements, understanding past behaviours, and making informed decisions. By analyzing trends, businesses and researchers can spot opportunities, anticipate changes, and navigate challenges effectively.

Types of Trend Analysis

Trend analysis can be categorized into several types, each with its unique focus and application. The primary types include statistical trend analysis, which uses numerical data to identify trends over time; qualitative trend analysis, which focuses on non-numerical data to understand patterns; and quantitative trend analysis, which combines both numerical and non-numerical data. Additionally, longitudinal and cross-sectional trend analysis offer insights into data collected over a long period and at a specific point in time, respectively.

How to Conduct Trend Data Analysis

Conducting trend data analysis involves several steps. Firstly, collecting relevant data is crucial. This is followed by cleaning the data to ensure its accuracy. Next, analysts choose the appropriate method of trend analysis based on the data type and research objectives. The process then involves analyzing the data using statistical tools and software, identifying patterns, and interpreting the results to make informed predictions or decisions.

How to Use Trend Analysis for Virtually Any Type of Research

Trend analysis is versatile, finding applications in various fields such as economics, healthcare, technology, and more. It aids in research trend identification, trend spotting, and trend forecasting, providing valuable insights regardless of the research domain. By utilizing trend data, researchers can uncover underlying patterns, predict future occurrences, and develop strategies to address potential challenges or leverage opportunities.

Example of Trend Analysis in Market Research

In market research, trend analysis plays a pivotal role in understanding consumer behaviour , market dynamics, and competitive landscapes. For instance, a company might use trend analysis to monitor the rising popularity of sustainable products. By analyzing sales data and consumer feedback over time, the company can forecast future demand, adjust its product offerings, and strategize its marketing efforts to align with consumer preferences.

Advantages and Disadvantages of Trend Analysis

Trend analysis offers numerous advantages, including the ability to forecast future trends , make informed decisions, and identify new opportunities. It also helps in risk management and strategic planning by providing a forward-looking view based on historical data. However, it's not without its disadvantages. Trend analysis may not account for sudden market shifts or unpredictable events, and misinterpretation of data can lead to incorrect conclusions. Additionally, it relies heavily on the quality and availability of historical data.

Trend analysis is an indispensable tool across various research fields, offering a roadmap to navigate the complexities of change. At Market Xcel, we understand the importance of harnessing the power of trend analysis to stay ahead in today's dynamic environment. With over 23 years of market research expertise, we are equipped with the knowledge, tools, and methodologies to help you ace your research. Whether it's through identifying emerging trends, conducting comprehensive trend data analysis, or leveraging insights for strategic decision-making, we're here to guide you every step of the way. Trust us to be your partner in navigating the ever-evolving market landscape, ensuring your research is not just current but future-ready.

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What is Trend Analysis? Definition, Formula, Examples

Appinio Research · 13.02.2024 · 38min read

Have you ever wondered how to uncover hidden insights within your data, predict future trends, and make informed decisions that can steer your business or projects toward success? In this guide on trend analysis, we'll unravel the intricacies of this powerful tool, helping you navigate the world of data patterns, forecasts, and informed strategies. Whether you're a data scientist, a business analyst, or simply curious about understanding and leveraging trends, this guide will equip you with the knowledge and techniques to harness the potential of trend analysis to your advantage.

What is Trend Analysis?

Trend analysis is a statistical technique used to identify and analyze patterns or trends in data over time. It involves examining historical data to uncover insights into past trends and predict future developments. Understanding the components of trend analysis is essential for conducting effective analysis:

Components of Trend Analysis

• Trend : The overall direction in which data is moving over time. Trends can be upward (positive), downward (negative), or flat (no significant change).
• Seasonality : Regular, predictable fluctuations in data that occur at fixed intervals, such as daily, weekly, or yearly patterns.
• Cyclical Patterns : Longer-term fluctuations in data that occur over multiple years, often driven by economic cycles or other external factors.
• Irregular or Random Fluctuations : Unpredictable variations in data that do not follow a discernible pattern. These fluctuations may be due to random events or measurement errors.

Understanding these components allows analysts to differentiate between various types of trends and apply appropriate methods for analysis.

Importance of Trend Analysis

Trend analysis is a crucial tool for decision-making and planning across diverse fields. Here are several reasons why trend analysis is essential:

• Strategic Planning : Trend analysis helps organizations identify emerging opportunities and threats, guiding strategic planning and resource allocation.
• Risk Management : By identifying trends and potential future scenarios, trend analysis enables organizations to mitigate risks and adapt to changing market conditions.
• Performance Evaluation : Trend analysis allows organizations to assess their performance over time, track progress toward goals, and identify areas for improvement.
• Forecasting : Trend analysis provides insights into future trends and developments, helping organizations anticipate changes and make proactive decisions.
• Resource Optimization : By understanding trends in demand, resource utilization, and consumer behavior, organizations can optimize operations and allocate resources efficiently.
• Informed Decision-Making : Trend analysis provides decision-makers with data-driven insights, reducing uncertainty and enabling informed decision-making.
• Competitive Advantage : Organizations that effectively utilize trend analysis gain a competitive advantage by staying ahead of market trends and customer preferences.
• Continuous Improvement : Trend analysis fosters a culture of continuous improvement by encouraging organizations to monitor performance, learn from past trends, and adapt strategies accordingly.

Overall, trend analysis is an indispensable tool for organizations seeking to navigate a dynamic and ever-changing environment effectively. By understanding past trends and anticipating future developments, organizations can position themselves for success and achieve their objectives.

Data Collection for Trend Analysis

In trend analysis, the journey begins with effectively collecting and managing your data . Your ability to make accurate predictions and draw meaningful insights heavily relies on the quality and relevance of the data you collect. Here's a closer look at the critical steps involved in this process.

Identifying Relevant Data Sources

Before you embark on any trend analysis, it's essential to pinpoint the most pertinent data sources for your specific objectives. This step requires a deep understanding of your subject matter and a keen eye for potential data goldmines. Consider the following when identifying data sources:

• Internal Data : Start by looking within your organization. This could include databases, CRM systems, financial records, or historical sales data. Internal data is often readily accessible and can provide valuable insights.
• External Data : Expand your horizons by exploring external data sources. Depending on your analysis goals, these might encompass public datasets, industry reports, social media data, economic indicators, or even weather data.
• Surveys and Feedback : If your analysis pertains to customer behavior or opinions, consider conducting surveys, interviews, or collecting feedback directly from your target audience. Qualitative data can be invaluable.
• Web Scraping : In the digital age, web scraping tools can be used to gather data from websites, forums, or online reviews, providing a wealth of information for analysis.

As you navigate the complexities of data collection for trend analysis, consider the seamless integration of Appinio into your research toolkit.

With its intuitive platform and global reach, Appinio streamlines the gathering of real-time consumer insights, ensuring you have the data you need to drive informed decisions. Embrace the power of Appinio to unlock a world of possibilities in trend analysis.

Ready to experience the future of market research? Book a demo today and see how Appinio can revolutionize your approach to data-driven decision-making!

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Data Gathering and Preparation

Once you've identified your data sources, the next step is to collect and prepare the data for analysis. This process involves several crucial tasks:

• Data Cleaning : Raw data is often messy, containing errors, duplicates, missing values, and outliers. Data cleaning involves rectifying these issues to ensure the accuracy and integrity of your dataset.
• Data Transformation : Depending on your analysis goals, you may need to transform your data. This could involve aggregating data over time periods, converting units, or normalizing variables to make them comparable.
• Data Integration : If you're using data from multiple sources, integrate it into a single dataset. This requires matching and merging data based on common identifiers.
• Data Documentation : Keep detailed records of your data collection and preparation process. This documentation is invaluable for transparency and reproducibility.

Data Quality Assurance

Data quality is paramount in trend analysis. Poor-quality data can lead to erroneous conclusions and unreliable predictions. To ensure data quality, implement the following practices:

• Data Validation : Validate your data against predefined criteria to identify inconsistencies or errors. This includes checking for data type mismatches, range validations, and logical validations.
• Outlier Detection : Use statistical methods to identify outliers that may distort your analysis. Decide whether to remove, transform, or investigate these outliers based on their impact.
• Data Consistency : Ensure consistency in data formats, units, and measurements. Inconsistent data can lead to misinterpretation.
• Data Security and Privacy : Protect sensitive data through encryption and access controls. Compliance with data privacy regulations, such as GDPR or HIPAA, is crucial.
• Data Governance : Establish data governance policies and procedures within your organization to maintain data quality over time. This includes assigning responsibilities for data quality maintenance and documentation.

By diligently following these data collection and quality assurance steps, you set a solid foundation for meaningful trend analysis, allowing you to extract valuable insights confidently.

Types of Trends

Trend analysis is a versatile tool that can be applied to various types of data, depending on your specific objectives and the nature of the information you're working with. Understanding the different types of trends is crucial for tailoring your analysis approach.

Time Series Trends

Time series trends  are perhaps the most familiar and widely used type of trend analysis. This category focuses on data points collected sequentially over time. Time series data can exhibit various patterns and behaviors, including:

• Trends : These are long-term movements in data, indicating a consistent upward or downward direction. For example, monthly sales data for a retail store may exhibit an upward trend if sales are gradually increasing over several years.
• Seasonal Patterns : Seasonality involves repeating patterns within a specific time frame. For instance, ice cream sales tend to rise during the summer and drop during the winter.
• Cyclic Patterns : Cyclic patterns are longer-term fluctuations that don't have fixed durations. They often result from economic cycles and can be challenging to predict accurately.
• Random Noise : Random noise represents unpredictable variations in data. It's essential to filter out noise to identify meaningful trends.

Analyzing time series trends involves techniques like moving averages, exponential smoothing, and autoregressive models (ARIMA) . These methods help extract underlying trends and patterns from noisy time series data, facilitating better predictions and decision-making.

Cross-Sectional Trends

Cross-sectional trends , on the other hand, focus on data collected at a single point in time, often comparing different entities or groups. This type of analysis is prevalent in market research, social sciences, and many other fields.

• Comparative Analysis : Cross-sectional analysis allows you to compare different groups or entities at a specific moment. For instance, you might analyze the salaries of employees across various departments within a company to identify disparities or trends.
• Demographic Studies : In demographic research, cross-sectional data can reveal trends in population characteristics, such as income distribution, education levels, or healthcare access.
• Market Segmentation : In marketing, cross-sectional trends help identify consumer preferences and segment markets based on various attributes like age, gender, or location.

Analyzing cross-sectional trends often involves descriptive statistics, hypothesis testing, and data visualization techniques like bar charts, pie charts, and histograms to compare and contrast different groups.

Longitudinal Trends

Longitudinal trends , also known as panel data analysis, focus on changes within individual entities or subjects over an extended period. This type of analysis is prevalent in fields like healthcare, education, and social sciences. Here's a closer look at longitudinal trends:

• Individual Tracking : Longitudinal studies track the same subjects or entities over time to observe changes. For instance, a medical study may follow patients over several years to assess the effectiveness of a treatment.
• Growth and Development : Longitudinal analysis can reveal patterns of growth, development, or decline within individuals or entities. This is vital in understanding human development, product lifecycle, or organizational evolution.
• Event Impact : It allows for the evaluation of how specific events or interventions affect subjects over time. For example, assessing the long-term impact of an educational program on student performance.

Analyzing longitudinal trends often involves statistical methods like growth curve modeling, repeated measures analysis, and mixed-effects models to account for individual variations and changes over time.

Understanding these distinct types of trends equips you with the knowledge needed to choose the appropriate analysis methods and techniques based on your data and objectives. Whether you're dealing with time series, cross-sectional, or longitudinal data, the insights gained from trend analysis can drive informed decision-making and strategy development in various domains.

Trend Analysis Methods

Now that you have a solid foundation in understanding the types of trends, it's time to delve deeper into the various methods used for trend analysis. These methods serve as powerful tools to extract meaningful insights and make predictions based on historical data.

Moving Averages

Moving averages  are a fundamental technique in trend analysis, widely used in fields like finance, economics, and marketing. They help smooth out noisy data and identify underlying trends. Here's how moving averages work and how they can be applied:

• Smoothing Data : Moving averages involve calculating the average of a specified number of previous data points. This rolling average effectively filters out short-term fluctuations, highlighting longer-term trends.
• Types of Moving Averages : There are different types of moving averages, including Simple Moving Averages (SMA), Exponential Moving Averages (EMA), and Weighted Moving Averages (WMA). Each has its strengths and weaknesses.
• Application : Moving averages find applications in forecasting, trend identification, and anomaly detection. For example, in finance, analysts use moving averages to identify trends in stock prices and predict potential reversals.

Formula for Simple Moving Average (SMA):

SMA = (Sum of Data Points in a Period) / (Number of Data Points in the Period) 

Exponential Smoothing

Exponential smoothing  is another essential method for trend analysis, particularly suited for short-term forecasting and trend prediction. This technique assigns different weights to data points, with more significance given to recent observations. Here's how exponential smoothing works:

• Weighted Averaging : Exponential smoothing involves computing a weighted average of past data points with decreasing weights as you move further back in time. This reflects the belief that recent data is more relevant for predictions.
• Adaptive to Change : Exponential smoothing adapts to changes in data trends over time, making it valuable for scenarios where trends are subject to sudden shifts or fluctuations.
• Applications : This method is commonly used in demand forecasting, inventory management, and financial analysis for short-term predictions.

Formula for Exponential Smoothing (ETS):

Forecast(t+1) = α * Actual(t) + (1-α) * Forecast(t) 

Regression Analysis

Regression analysis  is a versatile statistical technique used to understand the relationship between one or more independent variables and a dependent variable. It's widely employed in trend analysis for various purposes:

• Linear Regression : Simple linear regression models the relationship between two variables using a straight line. It's used when you want to predict a continuous outcome variable based on one predictor variable.
• Multiple Regression : Multiple regression extends the concept to include multiple independent variables, enabling more complex trend analysis by considering numerous factors simultaneously.
• Applications : Regression analysis is used in fields like economics, marketing, and social sciences to identify trends, make predictions, and assess the impact of variables on an outcome.

Seasonal Decomposition

Seasonal decomposition  is a method used to break down time series data into its constituent components: trend, seasonality, and residuals. This helps you understand and analyze the different aspects of your data:

• Trend Component : The trend component represents the underlying long-term movement in the data, allowing you to identify overall trends.
• Seasonal Component : Seasonal decomposition helps isolate and quantify repeating patterns or seasonality within your data. This is crucial for understanding periodic fluctuations.
• Residual Component : The residual component captures the unexplained variations in your data, often considered as noise or random fluctuations.

Other Analytical Techniques

Apart from the core methods mentioned above, numerous other analytical techniques can be employed depending on your specific data and analysis goals. These may include:

• ARIMA Modeling : AutoRegressive Integrated Moving Average (ARIMA) models are used for time series forecasting. They combine autoregressive and moving average components to make predictions.
• Machine Learning Algorithms : Various machine learning algorithms, such as decision trees, random forests, and neural networks, can be applied for trend analysis, especially when dealing with complex datasets.
• Nonlinear Models : In cases where linear models don't fit the data, nonlinear models like polynomial regression or logistic regression may be appropriate.
• Time Series Clustering : Cluster analysis techniques can help group similar time series data, allowing for trend analysis within clusters.

The choice of trend analysis method depends on your data characteristics, objectives, and domain-specific considerations. By mastering these techniques, you'll be well-equipped to extract valuable insights from your data and make informed decisions.

Visualization of Trends

Visualizing trends is a crucial aspect of trend analysis, as it allows you to gain a deeper understanding of your data and convey insights effectively to stakeholders. We'll explore various methods and best practices for visualizing trends.

Graphical Representations

Graphical representations are perhaps the most intuitive and widely used way to visualize trends in data. They help you spot patterns, anomalies, and correlations at a glance. Here are some common graphical representations:

Line Charts

Line charts  are a fundamental tool for visualizing trends over time. They are beneficial for showcasing time series data. A line chart typically plots data points on the y-axis against time on the x-axis. The resulting line connects the data points, revealing trends and fluctuations.

Bar graphs  are effective for comparing data across categories or groups. You can use vertical or horizontal bars to represent data, making it easy to see variations and trends. Bar graphs are often used in market research, demographics, and sales analysis.

Scatter Plots

Scatter plots  are valuable for examining the relationships between two variables. Each data point is plotted on a two-dimensional grid, allowing you to visualize patterns, correlations, and outliers.

Area Charts

Area charts  are similar to line charts but provide a visual representation of the area beneath the lines. They are instrumental in showing cumulative data, such as the total sales over a period.

Heatmaps  use color gradients to represent data values within a matrix. They are excellent for visualizing large datasets and identifying patterns or trends in complex data.

Histograms  are used to depict the distribution of data. They divide data into bins and display the frequency or density of data points within each bin. Histograms are commonly used in statistical analysis .

Dashboards and Tools

While individual graphs and charts are valuable, creating interactive dashboards can provide a holistic view of trends. Dashboards allow you to combine multiple visualizations into a single interface, making it easier to explore and analyze your data. Some popular dashboard tools include:

• Appinio : Appinio's intuitive platform streamlines the process of gathering real-time consumer insights, making it a valuable addition to your toolkit for trend analysis. With its global reach and user-friendly interface, Appinio empowers you to visualize trends and make data-driven decisions effortlessly.
• Tableau : Tableau is a powerful data visualization tool that enables you to create interactive and shareable dashboards. It supports a wide range of data sources and offers drag-and-drop functionality.
• Power BI : Microsoft's Power BI offers robust dashboarding capabilities with seamless integration with other Microsoft products. It's known for its user-friendly interface and extensive data connectors.
• Google Data Studio : Google Data Studio is a free, cloud-based tool for creating interactive reports and dashboards. It integrates seamlessly with other Google services like Google Sheets and Google Analytics.

Interpretation of Visualizations

Creating visualizations is just the first step; interpreting them correctly is crucial. Here are some best practices for interpreting visualizations effectively:

• Understand the Data : Before interpreting a visualization, ensure you have a solid understanding of the data, its context, and the specific question you're trying to answer.
• Identify Trends : Look for patterns, trends, and anomalies in the data. Are there noticeable peaks, troughs, or recurring patterns? Do certain data points stand out?
• Correlations and Relationships : If you're working with multiple variables, analyze how they interact. Are there strong correlations or causal relationships?
• Context Matters : Always consider the broader context of your analysis. External factors, seasonal variations, or other variables may influence the observed trends.
• Be Critical : Question your findings and assumptions. Don't jump to conclusions based solely on visualizations; cross-reference them with other data sources and conduct further analysis if necessary.
• Effective Communication : When presenting visualizations to others, ensure that your message is clear and concise. Use labels, legends, and annotations to guide your audience's understanding.

By mastering the art of visualizing trends and interpreting visualizations effectively, you can unlock valuable insights from your data, share them with stakeholders, and make informed decisions based on a deeper understanding of the trends at hand.

How to Identify Patterns and Anomalies?

In trend analysis, recognizing patterns and detecting anomalies is akin to uncovering hidden gems within your data. These insights can lead to informed decision-making and a deeper understanding of underlying trends. Here are some techniques and best practices for identifying patterns and anomalies.

Pattern Recognition

Pattern recognition  involves identifying recurring structures or behaviors within your data. Patterns can take various forms, depending on your dataset and analysis goals. Here's a closer look at this crucial aspect of trend analysis:

• Types of Patterns : Patterns can manifest as trends (long-term movements), seasonality (repeating patterns), cycles (long-term fluctuations), or even more complex structures unique to your data.
• Visualization Tools : Data visualization tools and techniques, such as line charts, heatmaps, and scatter plots, are invaluable for spotting patterns. Visual representations can reveal trends that may not be apparent in raw data.
• Statistical Approaches : Statistical methods, such as time series decomposition or clustering, can help identify patterns. Decomposition separates data into trend, seasonality, and residuals while clustering groups similar data points based on patterns.
• Machine Learning : Machine learning algorithms, including clustering algorithms, neural networks, and decision trees, can be employed to automatically identify complex patterns in large datasets.

Outlier Detection

Outlier detection  is the process of identifying data points that deviate significantly from the norm or expected behavior. Outliers can distort your analysis and lead to inaccurate conclusions. Here's how to effectively detect and handle outliers:

• Visual Inspection : Start by visually inspecting your data using box plots, scatter plots, or histograms. Outliers often appear as data points far removed from the bulk of the data.
• X is the data point
• μ is the mean
• σ is the standard deviation
• Machine Learning : Machine learning models, such as Isolation Forests or One-Class SVMs, can be trained to detect outliers automatically. These models are advantageous for handling large and complex datasets.
• Domain Knowledge : Sometimes, outliers can be legitimate data points with meaningful insights. It's essential to consider domain knowledge and the specific context of your analysis before deciding whether to exclude or investigate outliers.

Statistical Significance

Ensuring that the trends and patterns you identify are statistically significant is crucial for drawing reliable conclusions. Statistical significance helps you differentiate between patterns that occur by chance and those with real-world relevance.

• Hypothesis Testing : Hypothesis testing is a common approach to assess statistical significance. It involves formulating null and alternative hypotheses and conducting tests (e.g., t-tests or chi-square tests) to determine if there's enough evidence to reject the null hypothesis.
• P-Values : P-values indicate the probability of observing the data if the null hypothesis is true. A low p-value (typically below 0.05) suggests that the observed results are statistically significant.
• Effect Sizes : In addition to statistical significance, consider the effect size, which quantifies the magnitude of the observed effect. A large effect size may be practically significant even if p-values are marginal.
• Multiple Comparisons : When conducting multiple tests or comparisons, be cautious of the multiple comparisons problem, which can inflate the likelihood of finding false positives. Adjustments like Bonferroni correction can be applied to mitigate this issue.

By employing these techniques for pattern recognition, outlier detection, and assessing statistical significance, you can confidently identify meaningful trends and anomalies within your data. These insights will serve as a solid foundation for making informed decisions and taking appropriate actions based on the patterns you've uncovered.

Forecasting Using Trends

Forecasting is a vital application of trend analysis, allowing us to peer into the future and make informed decisions based on historical data patterns.

Time Series Forecasting

Time series forecasting  is the art of predicting future values based on historical time series data. It's an indispensable tool in various domains, including finance, economics, and supply chain management. Here's a closer look at how time series forecasting works:

• Historical Data : Time series forecasting starts with historical data, typically collected at regular intervals (e.g., daily, monthly, annually). This data serves as the foundation for making predictions.
• Trend and Seasonality : Analysts often decompose time series data into trend, seasonal, and residual components. This decomposition helps identify underlying patterns, making it easier to create accurate forecasts.
• Moving Averages : Simple moving averages or weighted moving averages are often used for short-term forecasting.
• Exponential Smoothing : Exponential smoothing methods, such as Holt-Winters, are suitable for capturing trends and seasonality in the data.
• ARIMA Models : AutoRegressive Integrated Moving Average (ARIMA) models are powerful tools for forecasting, especially when dealing with non-stationary data.
• Prophet : Developed by Facebook, Prophet is a user-friendly tool for forecasting time series data that handles holidays, seasonality, and outliers effectively.
• Evaluation : To ensure the accuracy of your forecasts, it's essential to evaluate them using appropriate metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), or Root Mean Squared Error (RMSE).
• Continuous Monitoring : Time series forecasting is an ongoing process. Regularly update your models with new data to improve forecasting accuracy and adapt to changing trends.

Predictive Modeling

While time series forecasting focuses on one variable over time,  predictive modeling  expands the scope by considering multiple variables to make predictions. This approach is handy when dealing with complex datasets and scenarios. Here's how predictive modeling fits into trend analysis:

• Feature Selection : In predictive modeling, you'll typically work with multiple features (independent variables) that may influence the target variable (what you're trying to predict). Feature selection is crucial to identify the most relevant variables for your analysis.
• Machine Learning Algorithms : Predictive modeling often leverages machine learning algorithms, such as regression, decision trees, random forests, or neural networks. These algorithms can capture complex relationships between variables.
• Training and Testing : A crucial step in predictive modeling is splitting your dataset into training and testing sets. The training set is used to build and train the model, while the testing set evaluates its performance.
• Hyperparameter Tuning : Fine-tuning the model's hyperparameters is essential to achieve the best predictive performance. Techniques like cross-validation can help in this process.
• Evaluation : Similar to time series forecasting, predictive modeling requires evaluation metrics to assess model accuracy. Standard metrics include accuracy, precision, recall, F1-score, and ROC-AUC.

Forecast Evaluation

Evaluating your forecasts is a critical aspect of trend analysis. It ensures that your predictions are reliable and can be used for decision-making. Here's how you can effectively evaluate your forecasts:

• Mean Absolute Error (MAE) : The average of the absolute differences between predicted and actual values.
• Mean Squared Error (MSE) : The average of the squared differences between predicted and actual values.
• Root Mean Squared Error (RMSE) : The square root of the MSE, providing a measure in the original units of the data.
• Visual Inspection : Visualizing your forecasts alongside the actual data can help identify patterns of overestimation or underestimation and detect any systematic errors.
• Residual Analysis : Analyzing the residuals (the differences between predicted and actual values) can reveal whether your forecasts exhibit bias or randomness.
• Forecasting Intervals : Consider constructing prediction intervals (e.g., 95% prediction intervals) to provide a range of possible outcomes, accounting for uncertainty.
• Benchmarking : Compare your forecasts to benchmark models or historical averages to determine if your model adds value.

By rigorously applying time series forecasting, predictive modeling techniques , and thorough forecast evaluation, you can harness the power of trend analysis to make accurate predictions and informed decisions that can drive success in various domains.

Examples of Trend Analysis

To truly grasp the power and practical application of trend analysis, let's delve into a few real-world examples that showcase its relevance and impact across various domains:

Financial Market Trends

Financial analysts and traders heavily rely on trend analysis to make investment decisions. By examining historical stock prices, they can identify trends such as bullish (upward) or bearish (downward) markets.

Technical indicators like moving averages and Relative Strength Index (RSI) help traders spot entry and exit points. Additionally, trend analysis can be used to predict broader economic trends, helping policymakers and investors make strategic choices.

Epidemiological Trends

In the field of public health, trend analysis plays a critical role in monitoring and managing disease outbreaks. Epidemiologists track the spread of diseases like COVID-19 by analyzing infection rates, hospitalizations, and mortality data over time. This information guides the implementation of public health measures and vaccine distribution strategies.

Retail Sales and Consumer Behavior

Retailers use trend analysis to understand consumer behavior and optimize their business strategies. By analyzing sales data, they can identify seasonal buying patterns, determine the effectiveness of marketing campaigns, and forecast future demand. This enables them to adjust inventory levels, pricing, and promotional efforts accordingly.

Climate Change and Environmental Trends

Scientists and environmentalists utilize trend analysis to study long-term climate patterns and assess the impact of climate change. They can identify trends such as rising global temperatures and sea levels by analyzing temperature, precipitation, and greenhouse gas concentration data. This information is essential for policymakers and organizations working to mitigate climate change.

Social Media Engagement

Marketing professionals and social media managers use trend analysis to monitor online conversations and engagement. By tracking metrics like likes, shares, and comments, they can identify trending topics and content that resonates with their target audience. This helps them tailor their social media strategies for maximum impact.

These examples illustrate the versatility and significance of trend analysis in diverse fields. Whether you're making financial decisions, safeguarding public health, optimizing business strategies, addressing climate change, enhancing social media engagement, or improving education, trend analysis equips you with the insights needed to make informed choices and drive positive outcomes. Identifying, interpreting, and acting upon trends is a valuable skill that empowers individuals and organizations to thrive in an ever-changing world.

Trend Analysis Challenges

Trend analysis, while a powerful tool for deriving insights from data, is not without its challenges and potential pitfalls. Being aware of these challenges is crucial for conducting effective trend analysis.

Here's a list of common trend analysis challenges and pitfalls to watch out for:

• Data Quality : Inaccurate or incomplete data can lead to erroneous conclusions. Ensure data is clean, consistent, and relevant.
• Overfitting : Overfitting occurs when a model is too complex and fits the noise in the data rather than the underlying trend. It can result in poor generalization to new data.
• Assumption Violation : Many trend analysis methods make assumptions about data distribution or stationarity. Violating these assumptions can lead to incorrect results.
• Missing Data : Dealing with missing data is a common challenge. Ignoring missing data or using inappropriate imputation methods can skew results.
• Outliers : Outliers can significantly impact trend analysis. Failing to detect and handle outliers can lead to inaccurate trend identification.
• Selection Bias : Biased sampling or selection of data can introduce bias into trend analysis, leading to non-representative results.
• Data Snooping Bias : Repeated testing and tuning on the same dataset can lead to overly optimistic results. To mitigate this bias, use separate datasets for training, validation, and testing.
• Model Complexity : Using overly complex models can lead to difficulties in interpretation and may not necessarily yield better results.
• Overemphasis on Short-Term Trends : Focusing solely on short-term trends can lead to neglecting important long-term patterns and insights.
• Lack of Domain Knowledge : Trend analysis should be complemented with domain knowledge to ensure that trends are interpreted correctly and aligned with business objectives.

Best Practices for Effective Trend Analysis

To conduct effective trend analysis and mitigate the challenges and pitfalls mentioned above:

• Clearly Define Objectives : Begin with a clear understanding of your analysis goals and objectives. Define what you want to achieve with your trend analysis.
• Data Preprocessing : Invest time in data preprocessing, including data cleaning, transformation, and handling missing values. Quality data is the foundation of reliable analysis.
• Exploratory Data Analysis (EDA) : Use exploratory data analysis techniques to gain insights into your data's distribution, relationships, and potential outliers before applying trend analysis methods.
• Time Series Decomposition : When dealing with time series data, consider decomposing it into trend, seasonality, and residuals to better understand underlying patterns.
• Cross-Validation : Implement cross-validation techniques to assess the performance of your models and ensure they generalize well to new data.
• Benchmarking : Compare your analysis results against benchmark models or historical averages to gauge the added value of your trend analysis.
• Interpretability : Choose models and methods that are interpretable and align with your audience's level of understanding. Transparent models are often preferred.
• Regular Updates : Trend analysis is not a one-time task. Periodically update your analysis to capture evolving trends and changing patterns.
• Validation : Ensure the reliability of your analysis by seeking validation from domain experts or peers, especially when making critical decisions based on trends.
• Documentation : Maintain detailed documentation of your data sources, preprocessing steps, model choices, and assumptions. This documentation is invaluable for reproducibility.
• Continuous Learning : Stay informed about emerging trends in data analysis, machine learning, and statistical techniques to continually improve your trend analysis skills.

By adhering to these best practices and remaining vigilant about potential challenges and pitfalls, you can enhance the effectiveness and reliability of your trend analysis, ultimately leading to more informed decision-making and actionable insights.

Conclusion for Trend Analysis

Trend analysis is your compass in the vast sea of data. It helps you navigate uncertainty by identifying patterns, predicting future developments, and making well-informed decisions. By following the methods, best practices, and avoiding common pitfalls outlined in this guide, you can harness the power of trends and turn data into actionable insights. Remember, whether you're steering a business, solving real-world problems, or just satisfying your curiosity, trend analysis is a valuable tool that can guide you toward success. Now armed with the knowledge and skills needed to decipher data trends, you can embark on a journey of discovery, continuously learning, adapting, and making data-driven choices. As you traverse this landscape, keep in mind that trends are the threads connecting the past, present, and future, allowing you to confidently shape your path and navigate toward your desired destination.

How to Conduct Trend Analysis in Minutes?

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Trend Research: How to Find Relevant Trends

Most people discover trends by browsing social media, reading industry forums, or talking to industry experts. Yet this process is time-consuming, and you might still overlook the most important emerging trends.

Even after discovering a few trend ideas, you'll have to weed out the fads and buzzwords from legitimate trends worth investing in.

In this post, we'll show you how to research trends using a simple three-step process that makes it easy to quickly discover new trends, analyze their stability and growth potential, and monitor important activity.

Step 1: Identify Emerging Trends

You can find emerging trends in industry forums, podcasts, newsletters, and blog posts, but identifying just one or two trends can take hours.

A more efficient method is using a trend discovery tool that provides a database of emerging trends that you can filter by industry:

These tools make the discovery process faster, but trend suggestion quality varies depending on how each tool identifies and classifies trends.

So here are a few questions you can use to evaluate the trend quality of any trend discovery tool:

• How does it discover trends? Ideally, you want it to discover trends with AI or ML rather than a human analyst. Humans can only scan so much content, whereas AI and ML can scan millions of data points. So tools leveraging AI and ML are more likely to identify under-the-radar trends consistently.
• How does it define a trend? Does the tool qualify any popular topic as a trend? Or does it look at each topic's growth trajectory from the past several months and only qualify topics with steady compounding growth? Trend discovery tools that classify any popular topic as a trend may contain fads. And, you'll likely only see current trends rather than emerging trends.
• Are the trends relevant to your industry? Does it include pop culture trends, or does an analyst check each trend for business relevancy?

We struggled to find a trend discovery tool that consistently identified emerging trends relevant to business use cases. So we built our own trend discovery tool to meet our needs. Here's how Exploding Topics identifies and qualifies trends:

To discover trends with Exploding Topics, first select one of the dozens of B2B or B2C categories, ranging from artificial intelligence and sustainability to fitness and fashion:

Then, you’ll see hundreds of trending topics. Each topic is a brand name, product keyword, or industry topic.

The graph you see represents the topic’s Google Search volume trend, making it easy to gauge a topic’s growth trajectory at a glance:

To find the most promising trends, set the Status filter to "Exploding." You can also adjust the time period (e.g., three months, six months, one year, two years, five years, etc.), and the database automatically sorts topics with the strongest growth rate over that time period.

There are also a few other filter options, including:

• Growth: Fastest growth rate
• Trend Line: Most consistent compounding growth
• Discovered: The most recently discovered topics
• Search Volume: Topics with the highest search volume

You can click on any topic to see a forecast of its growth, the social media channel it’s trending on, and a number of related trending topics.

To save the topic, click "Track Topic" and add it to a Project.

Projects are files you create, and they make it easy to save and track trend ideas.

You can view all of your Projects in the Trend Tracking dashboard, and Exploding Topics will continue to update each topic's growth trajectory with real-time data.

This way, you never have to worry about managing spreadsheets.

Another trend discovery tool in Exploding Topics is the Meta Trends feature, which lists trending niches within broader markets.

Click on any of these meta trends to see a collection of the trending brands, products, and keywords within that niche.

Exploding Topics also has some other helpful trend discovery features.

The Trending Products dashboard is designed specifically for ecommerce brands and provides a database of trending product ideas.

You can sort the database by category (e.g., beauty, tech, fitness, health, etc.) and various other metrics, including:

• Monthly sales
• Review stars

You'll also see a graph of each product keyword's Google search volume trend.

Another feature is the Trending Startups database, which provides a list of growing brands.

This database is great for investors and entrepreneurs who want to find growing startups.

You can filter the database by category, growth rate, and other specific metrics, like total funding, employee headcount, and location.

If you want to see for yourself how Exploding Topics expedites the trend discovery process, you can sign up for Exploding Topics Pro for $1 .

If you'd rather manually research trends, here are a few tips.

First, you can use a tool like SparkToro to quickly generate a list of your industry's most popular podcasts, websites, forums, and social accounts.

Then, you can use an RSS feed like Feedly to create a curated news feed of content from the top blogs, newsletters, X accounts, and subreddits.

The downside is that Feedly only gives you a list of content. You might spot some trends from the titles of each piece of content, but it won't provide a specific list of trends.

Instead, you'll probably have to read the content to find new trends, and there's also the chance you may overlook emerging trends.

Another option is to use a tool like BuzzSumo . It offers a trend discovery feature that surfaces the most popular content in your industry.

This tool is great for content creators who want to view current industry trends and news, but it isn't designed for entrepreneurs or investors researching emerging market trends.

Step 2: Analyze Trend Growth

Once you have a list of trends, the next step is to weed out the fads from steady trends with long-term growth potential.

One easy way to quickly gauge a trend's growth is by looking at its historical Google Search volume data.

Steady compounding growth for that trend keyword is a good sign that demand will continue growing.

You’ll see a Google Search volume graph for each trend you find in Exploding Topics.

If you heard about a trend elsewhere and want to check its growth, you can type it into the Trends Search feature to instantly generate a report of its Google Search volume:

You can also use Google Trends.

Market forecast data can also help you gauge a market’s demand and stability.

Websites like Grand View Research , IBIS World , and MarketResearch.com offer some free market reports with this data.

The easiest way to find them is to Google the industry and “market report.”

Most provide a graph with market size data from the past several years and a forecast.

These reports also usually provide analysis to help you understand factors driving market growth.

For example, this report on the pet supplement market shows that demand is rising because there are more pet owners than ever before and pet owners are spending more money on their pets.

You can also gauge trend growth by looking at the growth patterns of the industry's leading brands.

To find leading brands, search the trend term and "brands" or "companies."

Then, you'll probably see a list of the top brands in the industry either directly from Google or in the organic search results:

If you're researching a trending ecommerce product, you can find leading brands by searching the trend keyword on Amazon:

Then, scroll down to see the list of top brands in the sidebar:

Once you have a list of brand names, type them into Google Trends to see an overview of their growth trend:

If most of these brands are growing, the trend is probably also growing.

If you're using Exploding Topics, you can also search each brand name in the Trends Search feature. Then, click "Track Topic" and save it to a Project.

For example, you can create a Project for all Pet Supplement brands.

Each trend saved to a Project is updated with real time data, so you can monitor growth just by glancing at the Trend Tracking dashboard:

This makes it easy to quickly gauge trend growth across the top brands in the industry.

Step 3: Monitor Trend Activity

All the trends you saved in Projects are automatically updated with real-time data, so you can just glance at the Trend Tracking dashboard to monitor trend growth.

If you're manually tracking trends, you can keep a spreadsheet with all the trends you want to track. Each month, you can type all of the trends into Google Trends and then download a CSV.

You can also use Google Alerts to monitor trends. This will help you stay up-to-date with the latest news on any of these trends, and it can help you identify industry influencers.

First, create an alert for the general trend term:

You can also create alerts for each of the top brands in the industry.

You can also use a competitive intelligence tool like Kompyte , AlphaSense , or Klue to monitor trends.

These tools make it easy to track any brand, and you can set alerts to be notified about:

• Marketing messaging changes
• Product launches
• Marketing strategy

You can also track hashtags for key trends you're monitoring.

For example, if you're monitoring the trend "ketamine therapy," you can track "#ketaminetherapy" in a tool like BrandMentions . This tool offers a free report that shows the most popular content across the web with that hashtag and other related hashtags.

If you're using the paid version of BrandMentions, you can schedule weekly reports for your tracked hashtags.

This is an easy way to stay up-to-date with the latest trend news and participate in important trend conversations.

Start Researching Trends In Your Industry Today

There are many methods to research trends, but most strategies are time-consuming, and you might overlook important emerging opportunities.

And even if you find a new trending topic, it's difficult to distinguish fads from long-term trends.

That's why we built Exploding Topics.

We wanted a method to quickly identify emerging trends and passively track them.

If this is a solution you're looking for, consider trying Exploding Topics Pro for $1 today.

Find Thousands of Trending Topics With Our Platform

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How to use trend analysis in research?

A relationship between two quantitative entities is established using trend analysis . The future of this relationship is set on the basis of the trend in the past and thus known as trend analysis. Businessmen get a fair idea about the future market trends with such an analytical research strategy.

Leading with data from the past is a concrete way of going ahead with the business and that’s exactly what trend analysis does. An accumulated statistical data is to be followed to a level of saturation, which indicates it’s high time to analyze newly recorded data. Trend analysis in research can get you the insights you need for better decision-making and business planning.

Applications of trend analysis in research:

The market direction will drive the trend which can either rise or fall. If the market is headed towards a particular direction, logically, the trend analysis in research will suggest that the longer the market moves in that direction, the better it is to set a trend.

Comparing the statistical historical data and predicting what the future market response is going to be like will set the ball rolling for the market. Loads and loads of research go into establishing the factor which determines that if there’s profit in the market, it will sustain for X amount of time. The application of trend analysis in research can also be used to understand whether a trend will set another trend in momentum.

The drawback of this is that even if you put tremendous time and effort into calculating humongous historical statistics and data, it can’t be assured that this data will give you accurate results but is definitely helpful in strategizing and better/informed decision making.

Thorough trend analysis in research can lead to trustworthy and reliable conclusions rather than mere assumptions. Such insights help us connect the dots and thereby help conclude the factor that will affect consumer behavior in the near future. It isn’t a future prediction tool, it’s an effective tool for analysis and for creating a plan of what the future holds for the organization by analyzing the consumer behavior over a defined period to understand their future behavior over a timeline.

Effective trend analysis in research will give you an idea about new entries in the market and can be a guide to strategizing a plan to maintain market position or improve it. Expansion of business can happen on the basis of this trend analysis as well. Understanding the factors affecting your market position becomes easy by adopting trend analysis for a business’s market research .

Looking for making informed decisions, planning optimistically, strategizing better than your competitors? Look no further! Leverage a powerful market research survey software to implement trend analysis in your research and get the best results.  

Learn More: Market Research Survey Software

Learn about the various market research techniques:

• Quantitative Market Research
• Qualitative Market Research

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Trend Market Research

Trend Market Research is the art of looking at what’s popular in the market right now. It is a stepping-stone to trend forecasting.  Marketers use trend forecasting to look at the direction of today’s hot new products. Trend forecasting gives marketers an idea of what will be selling in the next six months to a year. It gives companies a sense of the market response to their products and services.

How to spot trends.

It’s essential to understand the size of the market. It’s also important to know the market trends. This knowledge is vital for marketing and strategic decision-making. Market researchers can now use analytics and other tools to spot trends. The name of this process is market trend analytics. It establishes whether a market is stagnant, growing, or in decline. It also shows how fast that movement is happening.

Social media listening programs analyze how people speak about companies online. These programs have turned out to be one of the major market research trends. Soon marketers will need even more extensive and accurate social media research systems. One of these systems is the social media insight program. Marketers use these programs to analyze social media data.

Another way to track trends is to use “cool hunters.” These are people whose tastes are ahead of the curve. Trend analysis companies pay them hundreds of dollars to help them find the Next Big Trend. Cool hunting is one of the newest developments in trend analysis. No one knows how many cool hunters are prowling America’s streets and shopping malls.

Using “Spotters” in Key Cities to Uncover Trends

All products, services, and technologies started off as trends. This is also true of the material that people want to read and watch, and the activities in which they wish to participate. Brands that do not pay attention risk becoming invisible. As a result, companies are engaging trend spotters in key cities. Trend spotters will help them find out the direction consumer tastes are taking.

Knowing what consumers are going to want next is critical to succeeding in business. Trend spotters go beyond traditional market research methods. Historical data on consumer statements and choices are not always useful. In fact, using only such data can make them miss out on opportunities. 

Trend spotters read a lot: magazines, newspapers, and blogs. They also pay attention to social media. They look at new products and services that become popular out of the blue and create a lot of buzz. Trend spotting differs from cool hunting. Trend spotters are looking for shifts in mood and mindset. These shifts will have long-term effects on consumer behavior and society in general.

Emerging Global Trends

Consumer confidence is currently at a near-record high. Japan, Italy, China, and France are experiencing significant gains in confidence. In comparison, confidence in the US, the UK, and India is declining. The positive outlook is helping to boost the sale of fast moving consumer goods in some markets. But inflationary pressure remains. Many would-be shoppers continue to focus on saving rather than spending.

How Researchers Look Into the Future

Market researchers predict future opportunities, sales, risks, and consumer behavior. All the predictions might not be correct, but they do help companies to plan and make policies. It also helps them to take advantage of opportunities and to avoid future risks.

Retailers use predictive research to see which items a consumer will buy together. This tool enables them to use suggestive selling. It is necessary if marketers want to keep up with current trends and gain a competitive edge. It’s vital that they understand market research and use it to their advantage. This understanding will help them reach out to consumers and increase their sales.

Researchers can also analyze the trends that are a fit with a company’s objectives.  Amid an explosion of trends, researchers can identify the trends that are a good fit with a company’s resources, strategy and positioning.

SIS frequently does trend research.  We do so by speaking with influencers, surveying consumers about new trends, in Focus Groups, cool hunting, ethnography and digital communities.

An example of our work was the publication of “China’s Generation Y” by Michael Stanat, Director of Global Operations. The trailblazing research explores and identified trends facing China’s Millennials.

SIS conducts Thought Leader and Decision Maker research often through video or telephone interviews.  These high-level interviews identify trends and strategies for companies.

Contact us for your next Trend Market Research project .

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A Beginner’s Guide to Understanding Industry Trends

by Mike Vestil  

This article provides an in-depth look at the importance of understanding and staying ahead of industry trends for business success. Readers will learn the definition and different types of trends, including technology, consumer demand, regulatory, and more.

Further, the article explores various methods to identify and analyze trends, such as data analysis, competitor insights, and expert reports. It also discusses impactful tools for monitoring trends, the effect of trends on business strategy, and how to capitalize on industry trends. Finally, through case studies, readers will gain insights into companies that have successfully adapted to trends and those that have faced challenges due to lagging behind.

Understanding Industry Trends

Definition of industry trends.

Industry trends refer to the general direction that a specific industry or market is moving towards, influenced by various factors like technology, consumer demand, economic factors, and more. They are a collective pattern observed in the responses, behaviors, and performance of the players within a particular industry. These trends can be short-term or long-term and can dictate how businesses within the industry strategize their operations, product development, and marketing efforts.

Importance of Identifying and Analyzing Trends

Identifying and analyzing industry trends is crucial for any business to stay ahead in a constantly changing and evolving market landscape. By understanding the trends, businesses can make sound decisions on various aspects such as R&D, production, sales, marketing, and overall business strategy. A good understanding of industry trends can help businesses:

• Keep up with competitors
• Identify potential markets and customers
• Predict and prepare for changes in consumer demand
• Discover new business opportunities
• Stay compliant with regulations
• Gain insights for innovation and product development

Different Types of Industry Trends

Industry trends can be broadly classified into the following categories:

1. Technology

Technology-based trends are innovations that drive changes in the industry. These could include the integration of artificial intelligence, automation, IoT, or the adoption of new software platforms. As technology advances, so do the expectations and demands of consumers and businesses in terms of product offerings and processes.

2. Consumer Demand

Consumer demand trends are driven by the changing preferences and needs of the customers. This could range from the desire for environmentally-friendly products to the preference of online services over traditional offerings. Businesses must stay in tune with these shifts to deliver relevant products and services to their audience.

3. Regulatory and Legal

Regulatory and legal trends involve changes in laws, rules, and regulations governing a specific industry. Compliance with these changes is necessary to avoid legal repercussions and maintain a positive brand reputation.

4. Economic Factors

Economic trends are driven by changes in the overall economic climate, such as currency fluctuations, interest rates, employment, and inflation. These factors can impact consumer spending, investment opportunities, and overall business performance.

5. Globalization and Geopolitics

Trends in globalization and geopolitics can greatly affect businesses as they involve political, social, and economic forces that impact an industry in a specific region or globally.

6. Demographics and Social Lifestyle

Demographic trends refer to changes in the composition and attitudes of the population, such as aging, urbanization, and growing cultural diversity. Social lifestyle trends involve changes in behavior, values, and cultural norms that influence consumer preferences.

Methods to Identify Industry Trends

Data analysis and market research.

Analyzing data and conducting market research help businesses to identify trends by providing insights into customer behaviors, preferences, and competitor strategies. This can be done through data mining, collecting data from various sources and understanding customer segments, buying patterns, market size, and growth rates.

Competitor Insights

It is important to keep track of competitor activities and strategies as their advancements may impact your business as well. Monitoring press releases, financial reports, product launches, and other company news can provide valuable insights into the industry trends.

Customer Feedback and Surveys

Understanding customer needs, preferences, and pain points can help businesses identify trends early. Conducting surveys and gathering customer feedback through various channels can provide valuable insights into rising expectations and preferences.

Expert Insights, Reports, and Publications

Another method of identifying trends is by following industry experts, news sources, and publications relevant to your industry. Analyst reports, government resources, trade publications, and white papers are excellent sources of information that can provide reliable and detailed insights on the latest trends.

Industry Events and Conferences

Attending industry events, trade shows, and conferences can provide valuable opportunities to network with experts, get insights into new trends, and observe emerging technologies and products firsthand.

Tools for Analyzing and Monitoring Trends

Industry and market reports.

Many organizations, such as research firms or consultancies, produce detailed reports on specific industries and markets. These reports are a valuable resource for understanding market dynamics, trends, and growth opportunities, as well as competitor analysis.

Data Analytics Tools

Data analytics tools can help businesses sort and analyze large volumes of data from various sources to identify trends and patterns. Examples of these tools include data visualization software, data mining tools, and business intelligence solutions.

News Aggregators and Alerts

Staying up-to-date with industry news can help businesses recognize trends as they arise. News aggregators and alerts, such as Google Alerts or Feedly, can be customized for your specific industry to find and deliver relevant news to your inbox or app.

Competitor Tracking Tools

Competitor tracking tools, like Owler or SEMRush, can give valuable insights into your competitors’ activities, such as news, funding, and product launches, helping you stay aware of their strategies and detect trends early on.

Social Media Listening Tools

Lastly, social media listening tools, such as Mention or Hootsuite, can help businesses track what customers and industry influencers are saying, providing insights into customer opinions, pain points, and preferences, as well as high-level industry trends.

Impact of Industry Trends on Businesses

Industry trends not only reflect the evolution of markets, customer needs, and technologies, but also help shape the business landscape. Organizations that can identify and successfully adapt to these trends often lead to improved market share, profitability, and long-term stability. However, businesses that struggle to embrace industry trends may face stagnant growth, reduced relevance, and an accelerated risk of failure. In this discussion, we will explore the impact of industry trends on various aspects of businesses, including strategy, innovation, competitive advantage, and revenue growth.

Effect on Business Strategy

The emergence of new industry trends can have a profound effect on business strategy. These trends often force businesses to re-evaluate and evolve their current approach, to ensure they remain competitive and are aligned with market changes. For instance, the rapid adoption of digital technologies and e-commerce has compelled traditional brick-and-mortar retailers to invest in online presence or partnerships with online platforms.

Aligning business strategies with constantly shifting industry trends requires ongoing assessment of the competitive landscape, recognising evolving customer needs, and anticipating future market changes. Additionally, businesses must be agile in identifying ways to capitalize on these trends, which could involve restructuring internal resources or pursuing mergers and acquisitions to quickly access new capabilities. Do check out our glossary section as well.

Innovation and Adaptability

Innovation is essential for businesses to differentiate themselves from competitors, capture customer interest, and excel in dynamic markets. Industry trends often serve as a catalyst for innovation, as companies must evolve to align with new market paradigms. Adaptability and innovation can be seen in various sectors, such as the rise of streaming services in the entertainment industry, electric vehicles in the automobile sector, and artificial intelligence in most industries.

Organizations that adopt innovative processes, practices, or technologies often stand out from competitors and are better positioned to capitalize on new growth opportunities. Moreover, a culture of innovation and adaptability enables businesses to be more resilient in dealing with unforeseen challenges or changes in market conditions.

Competitive Advantage

Recognizing and leveraging emerging industry trends can provide businesses with a significant competitive advantage. Companies that are ahead of the curve in understanding and seizing opportunities presented by industry trends often establish themselves as market leaders, while late adopters may struggle to catch up or face obsolescence.

A proactive approach to identifying and capitalizing on industry trends can provide businesses opportunities to create a defensible market position, build and strengthen brand differentiation, and develop new revenue streams. Furthermore, keeping abreast of industry trends helps organizations identify potential threats to their business and allows them to take appropriate mitigating actions.

Revenue Growth and Market Share

Successful implementation of strategies based on industry trends often leads to increased revenue growth and market share. As businesses innovate and diversify their product offerings, they can attract a wider customer base and open up new markets, creating additional sources of revenue.

The rapid growth of alternative sources of sustainable energy is an excellent example of how companies that invest in new technologies based on industry trends can increase their market share and revenue. Companies that fail to acknowledge these trends risk being left behind as competitors seize opportunities and customers ultimately shift their allegiance.

Risks and Challenges

Despite the potential benefits of aligning with industry trends, there are inherent risks and challenges that businesses need to be aware of. Identifying and acting on false or short-lived trends can lead to wasted resources and potential damage to brand reputation. Additionally, adapting to new industry trends may require significant investments in resources, infrastructure, and research, without any guarantee of success.

To mitigate these risks, businesses can invest in market research , customer foresight, and employ trend-watching tools to ensure they make informed decisions. Furthermore, by pursuing a flexible and adaptable approach, businesses can pivot their strategy as new trends emerge or fail, ensuring they stay relevant and competitive in their industry.

Capitalizing on Industry Trends

Capitalizing on industry trends is about recognizing and leveraging emerging opportunities in the market. In today’s fast-moving world where technology and innovation are key drivers of change, businesses need to be agile and adaptive to stay ahead of the competition. In this section, we will discuss the process of identifying, understanding and taking advantage of industry trends to maximize profits and growth.

Identifying Opportunities

The first step in capitalizing on industry trends is to identify opportunities that exist within the market. This requires staying informed about the industry, closely monitoring the activities of competitors, understanding customer preferences, and conducting comprehensive research about the latest innovations and technological advancements.

Some useful strategies for identifying opportunities include attending industry conferences and events, subscribing to industry publications, and engaging with thought leaders through social media networks and online forums. Businesses should also consider investing in market research and competitive intelligence tools to gather accurate and up-to-date information about industry trends and opportunities.

Once a business has identified a potential opportunity, it should validate its viability by conducting feasibility studies, market research, and by seeking opinions from experienced professionals and target customers.

Implementing Changes and New Offerings

Upon identifying a viable opportunity, businesses need to strategize and plan for implementing necessary changes within their organization. This could involve developing new products or services, altering existing processes, investing in new technologies, or repositioning marketing strategies to capitalize on the emerging trend.

Implementing changes also involves the allocation of resources, management of risks, and evaluation of the potential impact on business performance. Businesses should maintain open lines of communication across departments and ensure that employees are kept informed and involved in the process of change to encourage a smooth transition.

Collaborations and Partnerships

Forming strategic partnerships and collaborations can expedite entry into new markets and help businesses capitalize on industry trends more effectively. By collaborating with other organizations, a business not only shares the risk and reward of entering a new market but also gains access to new resources and specialized skills.

Strategic partnerships can take various forms, ranging from joint ventures and licensing agreements to co-development and co-marketing arrangements. In such instances, businesses should thoroughly evaluate potential partners for compatibility and reliability, and ensure that the terms of the partnership align with their long-term strategic goals.

Mergers and Acquisitions

In some cases, capturing the value of industry trends may necessitate the acquisition of other companies or mergers with competitors. Mergers and acquisitions can provide numerous benefits, including gaining market share, acquiring new technologies, and improving economies of scale.

However, businesses should carefully evaluate the risks associated with these transactions, including cultural and managerial differences, legal and regulatory concerns, and the possibility of overpaying for assets. A thorough due diligence process can help mitigate such risks and maximize potential benefits.

Case Studies: Success and Failure in Industry Trends

To better understand the importance of capitalizing on trends, it is essential to look at real-world examples. This section highlights successful and unsuccessful adaptations to industry trends.

Successful Trend Adaption: Companies that Flourished

Several companies have managed to capitalize on industry trends and emerged as leaders within their respective markets. For instance, Netflix was initially a DVD rental service but quickly pivoted to streaming content when consumer preferences shifted from physical media to online streaming. This adaptation allowed Netflix to become the dominant player in the global video streaming market.

Similarly, Apple foresaw the trend towards smartphones, and its innovative iPhone disrupted the entire mobile phone industry. Apple’s consistent efforts to introduce new products and services, such as the iPad, Apple Watch, and Apple Music, have helped the company to maintain a leading position in various industries.

In both these cases, the success of these companies can largely be attributed to their ability to understand the shifting trends in their industries and swiftly adapt their business models to accommodate these changes.

Failed Trend Adaption: Lessons Learned

On the other hand, several businesses have suffered due to their inability or reluctance to adapt to evolving industry trends. Kodak, once a pioneer in the photography industry, failed to adapt its product offerings when digital camera technology emerged, leading to its eventual bankruptcy. Similarly, Blockbuster, a once-dominant video rental chain, failed to recognize the trend towards streaming services, ultimately leading to its decline and subsequent liquidation.

These two examples serve as cautionary tales for businesses to recognize and embrace early signs of trend shifts in their industries, and their failure offers valuable lessons in the need for timely adaptation and innovation.

To summarize, capitalizing on industry trends requires businesses to monitor their environments, identify opportunities, evaluate risks and benefits, and adapt strategies when necessary. Companies should be open to change and embrace innovation to remain competitive and position themselves for long-term success. Studying the successes and failures of other businesses can offer valuable lessons for navigating industry trends and staying ahead of the curve.

Industry Trends — FAQ

1. what prominent industry trends have emerged recently due to technological advancements.

Some notable industry trends emerging from technological advancements include artificial intelligence (AI), Internet of Things (IoT), cybersecurity, 3D printing, and sustainable energy innovations. Companies utilize these technologies to create more efficient processes, reduce environmental impacts, and maintain a competitive edge.

2. How do industry trends impact the job market and career opportunities?

Industry trends can rapidly change the job market landscape, creating demand for new skill sets and career opportunities. For instance, the growing importance of data analysis, cybersecurity, and sustainable technologies have created high demand for skilled professionals in these areas while decreasing demand for traditional roles in some industries.

3. What are some successful strategies for staying current on industry trends?

To stay current on industry trends, businesses should consider attending conferences, reading industry-specific publications, following experts on social media, and subscribing to relevant newsletters. Additionally, networking with professionals in the industry can provide valuable insights into the latest developments and trends.

4. How can businesses adapt and respond to emerging industry trends?

Businesses can adapt and respond to emerging industry trends by embracing technology, investing in employee training, and adopting a flexible management approach. By partnering with technology innovators, seeking external consultation or collaboration, and fostering a culture of continuous improvement, businesses can keep up with industry changes.

5. In which industries is the impact of technology trends most pronounced?

The impact of technology trends is particularly pronounced in industries such as manufacturing, finance, healthcare, transportation, and entertainment. These sectors are experiencing significant transformations due to AI, automation, data analysis, and other technology-related advancements.

6. How do industry trends influence customer expectations and demands?

Industry trends shape customer expectations and demands by setting new benchmarks for quality, accessibility, and user experience. For example, innovations in mobile technology and IoT have led consumers to expect faster, more personalized service. Staying current with these trends is essential to maintain customer satisfaction in a competitive market.

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The Methodological Basis of Defining Research Trends and Fronts

• Published: 26 February 2021
• Volume 47 , pages 221–231, ( 2020 )

Cite this article

• N. A. Mazov 1 , 2 ,
• V. N. Gureev 1 , 3 &
• V. N. Glinskikh 1  

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The methodological and technical aspects of identifying research fronts and trends in the development of science are considered. Based on the literature data, a comparison of scientometric methods for finding research fronts was carried out: analysis of publication activity, direct citation analysis, co-citation analysis, bibliographic coupling, and content analysis. The advantages of the combined application of various approaches are shown, the role of expert assessment and verification of the results of scientometric analysis is emphasized. We revealed topical problems associated with the detection of scientific fronts by scientometric methods and showed promising directions in their solution.

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INTRODUCTION

The search for scientific trends and research fronts, that is, topical or promising research, is one of the most significant problems in science policy, scientometrics, and the history and philosophy of science and is of decisive importance at the stages of planning scientific activities. The topic of scientific trends and fronts is obvious if it is dictated by socio-political, environmental, and economic factors or threats to national health [ 1 ]. These can be natural disasters, terrorist attacks [ 2 ], economic crises, or the appearance of dangerous diseases in the human population, such as the outbreak of influenza A/H1N1 pandemic in 2009 [ 3 ] or SARS Cov2 in 2019–2020. In these cases, the scientific community, states, research and funding organizations are actively and consistently involved in the search for solutions to emerging problems. The fronts of science are much less obvious in the absence of such events; they then themselves become an object of study, requiring the development and use of methodological foundations and appropriate tools to identify them.

Scientific trends and fronts, as a rule, are the object of research of science itself, and their identification is an attempt to search for new growth points, as represented by the most promising ideas and developments that are important for the further development of science and technology. In other words, a search is carried out for changing objects of research in their relation to existing knowledge and to each other [ 4 ]. When identifying research trends and fronts it is predominantly scientometric methods that are used.

In a continuation of previous studies in the field of scientific trends in various fields of knowledge [ 5 – 7 ] and in the absence of reviews on the topic of detecting research fronts, we further consider the concepts of research trends and fronts, classify approaches, and describe the tools for their detection, as well as study the current issues that are pending their decision. When reviewing the literature, the Scopus and RJ “Informatika” databases of VINITI were used without restrictions on time and types of documents. The request included the following keywords: “research front”, “research trend”, and “research focus”. Additionally, sources from lists of references based on search results were used.

A METHODOLOGY FOR IDENTIFICATION OF RESEARCH TRENDS AND FRONTS

In general, a research front is understood as the situation where the interests and needs of society coincide with the current scientific results [ 8 ]. The key object of analysis in identifying research fronts is the groups of scientific publications and their interrelationships. According to the classical definition of D. Price, a research front is a densely cited network of recently published papers [ 9 ]. In a more detailed definition, a research front is understood as a group of recently published articles with a common topic, which are strictly connected by a network of citations among themselves and weakly connected with publications outside the group [ 10 ]. At the same time, strong links between citations within a group are characteristic of a research front at the initial stage of its development, while at later stages, with an increase in the number of citations, including from other scientific areas, this connection weakens. The strength of citation links between publications of clusters is determined by predetermined threshold values that are unique for each scientific field. The sizes of research fronts also depend on the discipline, which usually ranges from a few publications to several dozen. As an example, in the latest report on research fronts from Clarivate Analytics the spread is from 2 to 50 articles [ 11 ]; sometimes a minimum threshold is set, for example, 10 publications [ 12 ].

The concept of a research trend is close in meaning to a research front. A research trend is the collective action of a group of researchers, each of which begins to pay considerable attention to a specific scientific topic: read scientific publications on this topic, refer to them, and publish the results of their own research [ 4 ]. At times the concepts of the research front and research trend are used synonymously [ 13 ].

The main types of research fronts according to the common classification of G. Small [ 8 ] are shown in Fig. 1 . The method for identifying the stage of a research front involves comparing clusters of publications for two or more equal consecutive periods of time.

Types of research fronts.

The Clarivate Analytics together with the Chinese Academy of Sciences, in its periodic reports distinguishes only two types of research fronts: key ( key hot fronts ) and incipient ( emerging fronts ) [ 11 ]. Research fronts are also revealed by the Elsevier company based on SciVal data, where the most promising topics are determined by the Prominence indicator.

Under the influence of various factors, the research fronts of the extensive phase can turn into an intensive one, for example, when new promising research methods appear, with increased funding for the field, when there is an urgent need to develop a topic under the influence of external factors, etc. [ 1 ,  12 ]. As a result of the development of a research front, according to G. Small, it can either develop into a new discipline, or be absorbed by a broader field, which adapts the achievements of a research front to a wide group of studies [ 8 ]. In the first case, this indicates the growth of a scientific front, in the second, it indicates its influence on science. As a rule, scientific fronts of interdisciplinary research develop in separate directions, while the absorbed research fronts have little to do with interdisciplinarity, but are gaining citations faster.

Study on research fronts is significant from both fundamental and applied points of view. At the theoretical level, they determine the vector of development of scientific progress and allow tracing the origin and evolution of one field or another, the division and merging of areas of knowledge, contribute to the spread of knowledge between scientific disciplines [ 14 ], and allow adjusting organizational processes when new knowledge meets traditional paradigms that dictate research topics, standards and regulations [ 15 ]. The identification of research fronts is of practical interest for a wide range of stakeholders involved in the definition of priority areas of scientific research and their funding.

To date, three main scientometric approaches are widely used to identify research trends and fronts: analysis of the dynamics of changes in scientific production, citation analysis with its varieties, and content analysis, as well as their various combinations.

Analysis of Publication Activity to Identify Research Trends

Analysis of publication activity is usually used to identify research trends, while citation analysis is used to identify research fronts [ 4 , 16 ]. When analyzing scientific production, expressed by the number of publications, one resorts to models of the growth of scientific knowledge:

(1) in the first model, the growth of knowledge is considered as the cumulative development of new ideas based on previous recent scientific achievements;

(2) the second model assumes that the development of new ideas is based on the entire body of human knowledge, and not only on recent achievements. According to this model, there is a selective choice of grounds for a new idea from all of human scientific experience;

(3) the third model is based on the theory of scientific revolutions by T. Kuhn [ 17 ] and presupposes an intensive growth of knowledge interrupted by periods of calm.

There is no consensus about which of the proposed models most closely corresponds to reality, especially since each of them, to one degree or another, explains the ongoing scientific events in various disciplines. Each of these paradigms can correspond to some mathematical model of the growth of scientific literature, for example, linear or exponential [ 18 ]. In natural science disciplines, exponential growth often prevails; when identifying scientific trends researchers therefore turn to D. Price on the exponential growth and obsolescence of scientific literature [ 19 , 20 ]. The scattering law is used to identify a scientific information trend according to S. Bradford [ 21 ], which allows identification of the core of scientific journals of a given subject.

An example of a study using this method is the work to identify research trends in the field of tourism [ 22 ]. A circle of authors and organizations that form a research trend on this topic was determined according to zones of concentration and dispersion of Bradford’s scientific information, as well as the analysis of the scientific productivity and authoritativeness of publications. The analysis of research trends in the field of borehole geophysics was carried out by the authors of this work: the leading positions of this field in the field of earth sciences were identified, the most productive authors were detected and the redistribution of leading positions between countries over the past 20 years was shown [ 7 ]. Further identification of research trends and fronts in the field of geophysics is extremely important, since it is associated with the search for new research areas, primarily for the creation of innovative technologies. In the field of borehole geophysics, “cheap” logging technologies will be the most demanded by both large and small service companies in the near future, which is due to the end of time of “expensive” oil.

Citation Analysis to Identify Research Fronts

The main method in identifying research fronts is citation analysis, which makes it possible to trace the growth of interest and relevance of a particular topic by the dynamics of changes in the number of citations of publications of a particular field. Citation analysis is considered more objective in comparison with expert assessment, since it takes the opinion of the entire scientific world community of scientists expressed in references [ 23 ]. The approach is based on the observation that recent scientific publications are the most cited. Thus, the identification of thematic clusters of the most cited publications allows us to identify the research front of the corresponding discipline [ 9 ]. The response time to published papers varies across disciplines, but on average is 2–5 years, during which half of simultaneously published publications are cited [ 24 ]. Within the framework of citation analysis, where both cited and citing publications are clustered, a research front is understood as:

(a) a group of the most cited publications identified by direct citation analysis [ 4 , 9 ];

(b) a group of co-cited publications identified by co-citation analysis, positions 6 and 7 in Fig. 2b [ 25 – 27 ]. The cluster of a research front, in addition to co-cited publications, may include citing publications, positions 1, 6 and 7 in Fig. 2b [ 28 ]. This definition of research fronts was used by E. Garfield [ 29 ]; this approach is still implemented by the Clarivate Analytics in periodic reports on research fronts using Web of Science databases [ 11 ]. There is also a third approach, where a research front refers to publications that cited a cluster of co-cited publications, position 1 in Fig. 2b [ 30 ];

The principles of clustering publications used in identifying research fronts. A, direct citation analysis; B, co-citation analysis; B, bibliographic coupling. The top row usually represents recently published publications, the bottom row represents publications of the last 2–5 years. Citation analysis can cover out-of-sample publications.

(c) a group of publications with similar references, identified by the bibliographic coupling method, positions 3 and 4 in Fig. 2b . According to this approach, the articles of a research front themselves may not have citations [ 2 , 12 , 31 – 33 ];

(d) with the joint application of the indicated approaches, a research front is understood, for example, as a group of co-cited publications plus a group of publications with similar references [ 30 , 34 – 37 ], a group of co-cited publications plus publications citing this group [ 38 ], or several groups of publications based on the results of all three approaches [ 28 , 39 , 40 ]. As a rule, when used together, each method is used separately, after which the results are compared or combined. However, it is possible to build complex combined approaches: for example, clustering by bibliographic coupling of those publications in which clusters of co-cited publications are cited; this is then clustering of the first and second levels [ 30 ].

The formal similarity with the clusters of publications of research fronts is demonstrated by artificially created groups of articles united by chief editors, for example, within the framework of special issues of journals, where articles of each issue abundantly cite each other. When analyzing research fronts, groups of publications united by similar publication models are usually excluded from the analysis [ 8 ].

When describing research fronts, the concept of an intellectual base ( knowledge base , knowledge foundation , intellectual base , or intellectual structure ) is used, which means literature cited by publications of a research front [ 2 , 4 , 41 ]. Many studies demonstrate the thematic proximity of an intellectual base and research fronts [ 13 , 31 , 36 , 42 ]. When analyzing co-citation, sometimes confusion of these concepts occurs; while some researchers understand co-cited publications as a research front, others consider them as an intellectual base, and the citing publications as a front (see Figure 2B ). In general, the scientometric task is to identify the points of intellectual displacement (research fronts) in the relatively stable scientific literature (intellectual base).

Co-citation analysis was simultaneously proposed by I.V. Marshakova and G. Small [ 43 , 44 ]: two documents are considered co-cited and thematically related if they both appear in the reference list of a third document (with which the two cited documents also have a thematic relationship) and the citation rate is defined as the frequency with which two documents are cited together. Researchers usually choose a small group of publications that are highly cited within a given period of time as a basis for clustering. This could be 1 or 10% of the highly cited articles, the top 10, top 20 articles, etc.

This approach to the search for scientific fronts has a drawback associated with the nature of citation [ 45 ]. Accordingly, the ability to take new publications into account, which are often of the greatest interest in the search for scientific fronts, is limited [ 46 ]. In other words, co-citation is suitable for identifying a research front at a relatively late stage, and not at the very moment of its emergence [ 8 ]. According to one of the developers of the method of G. Small, the analysis of socializing does not cover the entirety of publications on a scientific front, but rather informs about the emergence of such a front; it is designed to do a quick screening of the scientific landscape rather than a definitive delineation of some specific area [ 8 ]. The approach does not depend on the vocabulary and language of publications.

The bibliographic coupling method proposed by M. Kessler [ 47 , 48 ] presupposes that two works have a meaningful relationship to each other and are thematically related if they have one or more similar references. Thus, a research front consists of publications that jointly cite other publications. Since references to the analyzed papers are not important and only their reference lists are investigated, the method is free from lag (especially if it is applied not to journal publications, but to preprints) and allows one to analyze newly published papers.

The main idea of the method is as follows: (1) a separate bibliographic reference used in two publications is called one unit of coupling between these publications; (2) several publications form a linked group G if each member of the group has at least one coupling unit with the test paper P 0 ; and (3) the coupling strength between P 0 and any member of G is measured by the number of coupling units (n) between them. Like co-citation analysis, the bibliographic coupling method is independent of the vocabulary and language of publications and can be automated. In comparison with the analysis of co-citation analysis, the method of bibliographic coupling is used less often to search for scientific fronts [ 28 , 32 ].

One essential criterion for the study of research fronts is the choice of the citation window. The problem of choosing a citation window received full coverage in [ 32 ]: the model of a traditional static 5-year citation window was compared with a sliding overlapping citation window, as well as with the half-life of highly cited articles. Research with a static citation window was found to be the least labor-intensive; however, the most labor-intensive method with a sliding citation window helped to find more research fronts. At the same time, some of the emerging research fronts identified by the two methods did not intersect, which is why the joint use of static and sliding citation windows was recognized as the most effective.

Since the main scientometric approaches to identifying research fronts involve a procedure for clustering bibliographic data, the results of the analysis can be influenced by clustering methods and the choice of threshold values for the measure of similarity between the grouped elements [ 30 , 31 ]. The object of citation analysis can be both the publications themselves and the authors of these publications, journals and, less often, subject categories [ 49 ].

Co-citation analysis is used to search for scientific fronts in various fields of knowledge: HIV/AIDS [ 15 ], scientific collaboration [ 13 ], library and information science [ 27 ]. The method of bibliographic coupling was used to study the historical development of research fronts in the field of anthrax research [ 12 ]. The joint use of methods of co-citation analysis and bibliographic coupling was carried out to search for scientific fronts in the library and information science [ 36 ] and in the field of battery research [ 37 ]. Author’s citations and content analysis of links were used to identify research fronts in the field of bacterial infections [ 23 ].

The experience of identifying research fronts not for a discipline as a whole, but for an individual organization is remarkable: in [ 49 ], the intellectual base was studied by co-citation analysis; the corpus of publications cited by the organization, on the basis of which a research fronts of the organization itself were further identified. Similar studies of the publication activity and citations of a particular organization were carried out by the authors of this work for more effective information support of scientific projects [ 50 , 51 ], while the developed methods were also applicable for identifying research trends and fronts. The search for scientific fronts can also be carried out for a separate journal: for example, the Journal of the American Society for Information Science. Using the methods of bibliographic coupling and citation analysis, research fronts were identified and a significant closeness of the intellectual base with them was shown [ 31 ].

Content Analysis to Identify Research Fronts

Methods for semantic analysis of metadata and full texts of scientific publications, including neural network technologies [ 52 , 53 ] and algorithms for detecting rapidly spreading, so-called burst terms, which express new phenomena, are widely used in identifying research fronts [ 2 , 14 , 42 , 54 ]. Content analysis investigates the frequency of the use of words in metadata and full texts and, separately, keywords, as well as their joint occurrence in publications. Analysis of the frequency and co-occurrence of keywords is carried out:

(a) on the metadata of publications; in this case, author’s or additional keywords assigned in systems are investigated (for example, KeyWords Plus [ 55 , 56 ] extracted from lists of cited literature) and words from various subject thesauri and authoritative dictionaries (for example, MeSH ), as well as automatically extracted keywords from titles and annotations;

(b) on full texts, where keywords and terms are also extracted and semantically analyzed using software tools.

Some researchers refer to the results of keyword co-occurrence analysis as a research focus, while the research front is considered to be the result of co-citation analysis [ 57 ].

To search for scientific fronts in the field of informatics and accounting, the content analysis method identified topics with growing and dying interest, as well as those that have lost their relevance [ 14 ]. To extract keywords, entity linking method was used that takes the context of the keyword into account. An approach based on the combined use of searching by association rules, keyword analysis and rapidly spreading terms is presented based on the example of anticancer developments in nanomedicine [ 54 ]. Using linguistic methods for searching for the semantic similarity of texts, the identification of research fronts was described in [ 46 ]: a method of comparing phrases and fragments of identical content, not necessarily expressed by the same keywords, was presented. Cluster analysis of author’s keywords was carried out to search for scientific fronts in the field of social sciences: the result of a study in five countries was a map of national science, indicating promising areas [ 1 ].

Content analysis is often combined with citation analysis methods to identify scientific fronts. Thus, research fronts in the field of artificial intelligence were identified through the combined use of methods of bibliographic coupling and content analysis of keywords [ 58 ]. Methods of bibliographic coupling (by co-authors and documents) and content analysis were used to search for scientific fronts in the field of business [ 41 ]. A co-occurrence analysis method combined with co-citation analysis has been used to find research fronts in library and information science in Spain [ 42 ]. The same two methods were used to analyze co-citation fronts in astrophysical research [ 59 ]. A more sophisticated analysis of a research fronts of the interdisciplinary direction is presented using the example of magnetic nanoparticles, where co-citation and co-word networds were studied based on a sample of the 500 most-cited publications [ 60 ].

THE EFFICIENCY OF DIFFERENT TYPES OF SCIENTOMETRIC ANALYSIS IN REVEALING RESEARCH FRONTS

A researcher’s choice of a particular scientometric method is arbitrary in most cases, while in some situations it is necessary to correlate the method with the goals of the study and take the complexity of the calculations into account [ 28 , 39 ]. Different methods are more or less applicable to one type of research front or another. Thus, the emerging research fronts are better identified by the method of bibliographic coupling, which does not have a time delay. If topological clustering is preferable for research, then citation analysis turns out to be more applicable [ 39 ]. If it is necessary to cluster based on the textual similarity of publications, content analysis has proven itself better, in which the frequency analysis of words from metadata or full texts gives better results in comparison with the frequency analysis of an author’s keywords.

The choice of the approach has a significant impact on the results, as shown by the example of publications on environmental protection: the intersection of the results obtained in the co-citation analysis and the method of bibliographic coupling was only 33–41%, which in fact indicated different research fronts [ 30 ]. Comparison of methods of co-citation analysis and bibliographing coupling was carried out by M. Huang et al., who studies the methodological foundations of the search for scientific fronts [ 32 – 34 ]. In a series of publications, the advantages of the bibliographic coupling were shown: with its use, a greater number of fronts were identified, and several fronts were found at an earlier date [ 34 ]. The advantages of bibliographic coupling were disclosed in [ 39 ], although it was indicated that in certain narrow areas the method of direct citation analysis may be preferable, since significant publications may have few thematic links in their field but gain a large number of citations from related fields.

A comparison of direct citation analysis, co-citation analysis, and bibliographic coupling was carried out in [ 61 ] using the example of research fronts in the field of carbon nanotubes, gallium nitride, and complex network: the direct citation method showed the best results for identifying the early stages of the formation of new topics and contributed to the identification of a larger number research fronts. The next most effective methods were the method of bibliographic coupling and co-citation analysis. Another example of comparing all three methods of citation analysis is the study of scientific fronts in biomedicine, where they were additionally compared with textual analysis [ 28 ]. To test the best approach, information on grants was analyzed: since publications on a grant are thematically similar by default, a search was made for the highest concentration of publications on specific grants in each of the clusters.

Weighted Approaches to Improve the Accuracy in Identifying Research Fronts

Over time, increasingly sophisticated approaches to defining research fronts are being developed, with the goal of increasing the accuracy of clustering. One of the trends in this field is the construction of weighted citation networks. With the assignment of weight to the publications of the cluster forming scientific fronts, a series of studies was carried out by K. Fujita et al., proving the benefits of weighted citation networks [ 39 , 40 , 53 ]. The weight of the publication, automatically determined using neural network training technologies, takes the year of publication, the number of citations of the publication, the field of knowledge, and the strength of the links between the reference list of publications and keywords into account [ 39 , 53 ]. A significant advantage of the research of this group is that various bibliometric methods are widely combined here.

The analysis of collective dynamics of knowledge networks represented by weighted citation and keyword networks, which takes both incoming and outgoing connections between network elements into account, was presented in [ 4 ], which shows the advantages of this method over the analysis of direct citation networks, since it more closely approaches identifying research trends in small areas of knowledge. For more accurate clustering, the PageRank algorithm is used to assign different weights to publications of different significance levels: not only are the most cited publications recognized as the most significant in a cluster, but also publications cited by other equally significant publications of the cluster [ 35 ].

An analysis of links that establishes the relationship between the cited publications, taking their importance and position in the citation network into account, was carried out to search for research fronts in the field of shareholder activism: during the analyzed period, the development of this field was reconstructed by means of research fronts, including seven stages, from the theoretical origin of the concept to its practical implementation [ 62 ]. A weighted approach was used in the search for scientific fronts in chemical technology: 29 clusters were identified containing an average of 5.3 publications; for each cluster, the Price index was calculated, which quantitatively characterizes the degree of novelty of the field [ 38 , 63 ].

Together with the fundamental applicability of each of the approaches in identifying research trends and fronts, the results of most studies show that the least-accurate results are obtained by the direct citation analysis, although in certain situations it shows advantages over other approaches [ 39 , 61 ]. In the accuracy of its results the combination of the co-citation analysis and the bibliographic coupling is significantly superior to direct citation analysis, which does not take thematic links between publications into account [ 34 , 39 ]. The most accurate results in most cases are yielded by the method of bibliographic coupling; co-citation analysis lags slightly behind. The best results are achieved with the combined use of different approaches (and, if possible, different data sets), which should take the variability of publication activity and citation models in different disciplines into account, but such approaches are more laborious and time consuming [ 28 ]. Many researchers, for example [ 1 , 2 , 64 ], noted the importance of involving subject experts in the qualitative assessment of the results of scientometric analysis.

Software for Revealing Research Fronts

Significant attention is paid to the study of research fronts by software developers for visualization and mapping of science [ 65 , 66 ]. The visualization of bibliographic information is especially valuable for experts because it allows real-time detection of unexpected trends, gaps in scientific knowledge, statistical biases, and other important characteristics of research fronts [ 67 ]. VOSviewer [ 22 , 41 , 57 , 68 , 69 ] and CiteSpace [ 2 , 13 , 26 , 42 , 60 ] are most often used; however, ready-made programs are often seen as having limitations, since their functionality is standardized and often does not support innovative approaches [ 35 ]. Therefore, sometimes less common software products are used, for example, Cytoscape [ 15 ] or BibTechMon [ 37 ], including programs written for a specific study [ 12 ].

One of the most functional software for identifying research fronts is CiteSpace [ 2 ]. The capabilities of the program are presented by its developer using examples of the fields of “mass extinction” and “terrorism.” Research fronts are understood as emerging transitional clusters of ideas, expressed by small groups (several dozen positions) of co-cited publications. At the same time, the study solved the problem of identifying new fields on the basis of linguistic analysis of terms from the metadata of publications (although some researchers insist on involving experts in the designation of new fields [ 12 , 23 ]).

Experience in using VOSviewer was presented by the scientific library of Kent State University (United States): the methods of bibliographic coupling, citation analysis and content analysis were used to identify research fronts in the field of the Internet of things [ 69 ]. Dynamic keyword analysis in VOSviewer allowed them show changes in research fronts in this area over time.

The Problem of the Reliability of the Results of Scientometric Analysis in Identifying Research Fronts

Since the definition of research fronts is based on an array of scientific publications, the question of the legitimacy of such an approach often arises. In addition to the general criticism of bibliometric approaches, there are somewhat fair statements about the devaluation of the institute of scientific publications associated with an increase in the number of duplicate works, plagiarism, and “predatory” journals, as well as the frequent absence of descriptions of research methods in publications, which prevents their reproducibility. Another critisism concerns the role of publications in rewarding a scientist for his/her work instead of spread of knowledge and a shift of the central channels of scientific communication towards “invisible colleges”. Taken together, this leads to the main question of how much one can rely on bibliometric research of publications to identify research trends and fronts.

Other problems of identifying research fronts are associated with journal articles and, more broadly, with the market for periodicals and its internal standards. As an example, reputable international journals are more willing to publish research results on popular and global topics. Accordingly, in such a limited array of publications, research fronts that are important at the regional or national levels may not be found.

The cautious attitude of reviewers and editorial boards to advanced ideas and developments, often at odds with the scientific tradition, remains an unresolved issue [ 70 ]. Modern publishing standards often imply a comprehensive coverage of a scientific problem and a description of a ready-made set of its solutions [ 71 ]. However, precisely in relation to research fronts, at the initial stages of developing a new problem, these requirements are the least feasible and force authors to bypass key issues, whose discussion is most important for understanding the essence of the problem and its causal mechanisms [ 64 , 71 ]. At times, the overestimated requirements of the editors of journals for breakthrough work lead to the rejection of publications that are significant for science and society. One illustrative example is the article by A.K. Geim and K.S. Novoselov on a new material, graphene, that was rejected by Nature Footnote 1 (it was later published by Science ).

Another problem of using journal publications as a basis for searching scientific fronts includes the time lag from the submission of the manuscript to the editorial office to its publication. This adds to the subsequent delay in distributing the journal to libraries or indexing it in bibliographic databases. On average, the delay due to the technological publishing processes is estimated at 1 year [ 24 ]. Even if we compare this period with the total time from the birth of a scientific idea to its publication, which, for example, is 4 years in medicine [ 59 ], the publication delay appear to be significant.

The databases for the selection of publications themselves have a significant impact on the identification of research fronts [ 27 ]. Most of research is based on publications indexed in Web of Science , and less often, Scopus . In addition to the delay in indexing, such systems have limitations in terms of regional and linguistic coverage of sources; the accuracy of bibliographic metadata is not always high [ 72 ]. Despite the annually expanding indexing of conference proceedings, where advanced scientific ideas are discussed much earlier than in print, international databases still tend to predominantly cover journal articles. The need for verification of automatically processed data was already noted in early works, caused by many discrepancies in the spelling of author’s names, variations in the abbreviation of the names of journals, etc. [ 31 ]. (For more detail on the problems of identifying bibliographic objects, see [ 73 , 74 ].)

Some questions remain open, others are eventually answered. Thus, in recent years, reviewers have paid more attention to the transparency of the methodological part of the articles; more and more often initial data are provided in the form of appendices to publications, which significantly increase the reliability and reproducibility of the results. Ethics committees are working to improve the research and publication culture of authors, preventing unfair approaches to the publication of scientific results [ 75 ].

At the philosophical level, the role of publications in the system of scientific information and the degree of their applicability to identifying research fronts are analyzed. The transformation of the main properties of a research front into the form of bibliometric indicators has been substantiated, including such front characteristics as novelty, relevance, interdisciplinarity, risk factors, and a combination of fundamental and applied significance [ 64 ]. The central place of publications in scientific research fronts is proved, since in addition to the main function of information delivery, they stabilize unstable networks of various scientific practices and elements [ 76 ]. The role of scientific publications is also demonstrated in the reconstruction of the evolutionary development of science: based on the example of research fronts in scientometrics and the historical processes of the intellectual organization of knowledge in this area, their codification and structuring with a simultaneous decrease in entropy have been shown [ 77 ]. Based on the example of one area of biomedical sciences, the methodology for constructing a time scale, which allows one to visualize the development of a research front and predict the emergence of new fronts, was presented [ 12 ]. On the basis of the theory of the aging of scientific literature, the speed of dissemination of scientific ideas is investigated and the depth of research fronts was revealed [ 24 ].

The problem of publishing breakthrough articles, whose material, methodology and results differ significantly from the scientific tradition, finds its solution in the widespread dissemination of open science, the publication of preprints, the development of repositories and models of open peer review. Publication of preprints solves the lag problem. This issue is partially resolved by the development of the system of “articles in print” that are published before the formation of printed issues, as well as early indexing of such publications in bibliographic databases. One possible solution to the problem of publication lag may include the analysis of network publications, whose rate of appearance is significantly higher, as shown by the example of the search for scientific fronts in the field of XML research [ 78 ]. In this case, unlike journal databases, special systems are used, for example, CiteSeer . It is proposed to solve the problem of publication delay of journal articles by analyzing information about the dates of the publication process (the time of receipt of the manuscript, its approval, and publication); clustering of publications taking these dates into account gives more accurate results in identifying research fronts [ 59 ].

CONCLUSIONS

Over a relatively short period of studying research trends and fronts, a significant complication of the methodology is noticeable: combined approaches, neural networks, a wide range of bibliographic and network databases, and special software is increasingly used. Scientometric methods show their promise due to their rapid adaptation to the changing conditions of the functioning of science and new publication models for the dissemination of scientific information.

The review of research carried out in this article shows that scientometric tools for identifying research fronts have proven themselves well as a source of reliable and objective information for subsequent expert assessment in various fields of knowledge. A wide methodological arsenal of various types of citation analysis and content analysis has been developed. The improvement of the approaches goes in the direction of specifying citation windows, objects of analysis, and identifying the advantages and disadvantages of each of the approaches, taking the types of scientific fronts and research goals into account.

We see the immediate tasks on identifying research fronts and trends as follows. The problem of the initial distrust of the scientific community in breakthrough developments, whose results or methods do not agree well with scientific tradition, awaits a solution. A scientometric solution to this problem is outlined in a broader analysis of network publications. The second task is to increase the speed of identifying new fronts, if possible at the stage of publishing preliminary data on new fields. This requires a further search for methods to neutralize the effect of publication lag.

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This study was carried out with the financial support of the Russian Foundation for Basic Research (project no. 19-011-00531).

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Mazov, N.A., Gureev, V.N. & Glinskikh, V.N. The Methodological Basis of Defining Research Trends and Fronts. Sci. Tech. Inf. Proc. 47 , 221–231 (2020). https://doi.org/10.3103/S0147688220040036

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Trends and Skills for the Future of Research

May 7, 2019 

Technological advancements have made it easier than ever for organizations to access large amounts of data. However, with this information overload comes the challenge of managing, analyzing, and reporting on the data. Organizations are increasingly relying on professional researchers and research analysts to turn these large amounts of data into information they can use to make strategic decisions that will positively impact business operations.

Career Outlook for Researchers

Career opportunities in the research field are diverse and span a variety of industries. A social science researcher may focus on areas like healthcare and unemployment, conducting interviews and surveys to collect data for analysis. In a corporate environment, an operations research analyst can help his or her organization by reviewing business processes and identifying efficiencies, while a market research analyst may make production recommendations after examining consumer purchasing patterns.

The educational requirements for research jobs also vary by industry and the roles and responsibilities of the position. Most professional researchers have a bachelor’s degree in market research or a related field, such as a  Bachelor of Arts degree in Liberal Studies . Senior-level research positions typically require a graduate degree such as a master’s in business administration.

New Trends and Techniques for Researchers

Regardless of specific role or training, professional researchers need to understand the emerging trends and new techniques in this field to excel in their careers. Here’s what researchers should know about future research trends.

Predictive Analytics

Predictive analytics refers to a sophisticated form of analysis using current and historical data to forecast future outcomes. Although using analytics to draw predictions about the future is not a new practice, predictive analytics is at the forefront of data analysis because of the advanced techniques involved. Some of the tools used in this practice include machine learning, artificial intelligence, data mining, and statistical and mathematical algorithms. These advanced tools and models allow for the creation of more accurate and dependable future predictions of trends, behaviors, and actions.

Because accurate future studies and forecasting are essential to most business models, researchers with predictive analytics experience are in demand. The valuable information generated by predictive analytics can be used by organizations to make strategic decisions about operations and identify opportunities and risks. For example, the financial services sector could use this practice to forecast market trends or create credit risk reports. Or government and law enforcement agencies may look to gather data about community crime and use that information to develop proactive safety measures.

Researchers need to keep abreast of this cutting-edge form of analytics because of its increasing usage. According to a report by Zion Market Research, the predictive analytics market in 2016 was valued at approximately $3.49 billion and is expected to continue to grow.

Digital Tools

Advancements in digital tools continue to change the way researchers work. In fact, it can be a challenge for researchers to stay up-to-speed with the new resources available to them. Here are just a few digital tools and trends that support and simplify the work of researchers:

• Search faster and easier. Researchers can spend less time searching for the right information by using search engines and curator sites such as CiteULike, Google Scholar, and LazyScholar.
• Manage and share data. Code and data sharing are becoming more common among researchers, with sites like Code Ocean and Datahub providing data management, storage, and sharing.
• Manage references. Sites such as EndNote and CitationStyles help researchers electronically manage their bibliographies, citation styles, and references.
• Connect with fellow researchers. Sites such as Academia and Addgene help researchers get expert advice and identify opportunities to collaborate or share findings.

Data Visualization

From the widespread use of infographics in educational materials to storytelling on social media platforms through video and pictures, there is a clear trend toward more frequent visual communication in society. When applied to data analytics, visualization is the term often used to describe the practice of taking standard data and statistics and displaying them in a visually creative way.

Researchers who want their analysis effectively communicated should take note of this trend. For example, a simple research report that presents the findings in a large numerical spreadsheet may be hard to understand and confusing to the average person. If that same information was displayed in a graphic chart or by telling a story with images, readers would more likely have a clearer picture and understanding of the report’s main points.

Researchers who want to implement this trend in their practice should:

• Consider the visual options available — whether it’s an infographic, chart, or slideshow
• Focus on their audience and the key messages they need to convey
• Remember to ensure the visual will highlight the actual data instead of serving as a distraction

Are you interested in learning more about the research profession and the techniques involved in predictive analytics and data visualization? Explore the Marville University Bachelor of Arts degree in Liberal Studies , and learn how this online degree could be your first step to a new career as a research analyst.

All About Careers, Social Science Researcher

Bureau of Labor Statistics, Market Research Analysts

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CIO, “What Is Predictive Analytics, Transforming Data into Future Insights”

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EconoTimes, “Trends in Predictive Analytics”

Forbes, “Data Visualization, How to Tell a Story with Data”

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12 emerging trends for 2024

In 2024, psychology will play a major role in pointing the way toward a healthier, more just society

2024 will be a pivotal year for psychology. The U.S. presidential campaign, already infected with misinformation, needs psychological science’s debunking and prebunking strategies. Generative artificial intelligence—unleashed upon society with few guardrails—will desperately require social science insights as it progresses along its exciting and uncertain trajectory.

The ongoing crisis in mental health care access, the trauma for women and LGBTQ+ individuals whose bodily autonomy is threatened by ongoing legislation, and the backlash against racial equity work present unprecedented challenges and opportunities over the next year. There is hope though as mental health technology enters a second wave of investment, clinicians continue to innovate to reach more patients, new strategies to end addiction make promising headway, and neuroscience helps us to discover ways to protect brain health and treat brutal afflictions like Alzheimer’s.

Join us in exploring these 12 trends.

This election year, fighting misinformation is messier and more important than ever

Psychologists are using science communication to set the record straight. But it's ugly out there.

What psychologists need to know about the evolution of generative AI

Psychologists are exploring how this new technology can simplify or amplify their efforts—and leading the charge to bring behavioral insights into the creation and deployment of generative AI tools.

Higher education is struggling. Psychologists are navigating its uncertain future

Policymakers are taking aim at women and LGBTQ+ individuals

Psychologists persevere in EDI work despite growing backlash against racial equity efforts

More than three years after pledges to increase racial diversity, the story on the ground is far less optimistic than many had hoped.

Mental health care is in high demand. Here’s how psychologists are leveraging technology and their peers to meet the need

Amid a shortage of mental health providers, digital therapeutics could play an important role in providing support for underserved communities.

What do people really want in their work? Meaning and stability

Widespread volatility is strengthening employee resolve to advocate for security, purpose, and well-being on the job.

Psychologists are innovating to tackle substance use by building new alliances in treatment efforts

New interventions are improving chances of recovery from addictions.

There’s a strong push for more school psychologists

A combination of pre-existing shortages and a rise in school stressors has led to a major effort to train and hire more mental health professionals for school settings.

Monetizing mental health is harder than it looks

After several years of rapid growth of mental health technology companies, a slew of high-profile layoffs and ethical breaches are spurring better clinical and business practices.

What’s ahead for clinical practice?

As telehealth settles into a new normal, new policies around reimbursement and data privacy are still being hammered out.

Psychology is improving brain health and aging

Researchers are developing new interventions that can help prevent, identify, and manage cognitive decline.

Research Trends

Research Trends (ISSN 2213-4441) was an online publication, active between 2007 and 2014, providing objective insights into scientific trends based on bibliometrics analyses.

Published by Elsevier, the publication was led by and included contributions from thought leaders in bibliometrics and related fields. For many years, Henk F. Moed steered the publication, as Editor in Chief. Studies and articles reviewed emerging metrics and indicators, proposed ways to map science and considered the evolving dynamics of research, while also including foundational knowledge for those new to the field.

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Money blog: Smoke machines deployed in Tesco; big inflation moment forecast

The Money blog brings you personal finance and consumer news, plus all the latest on the economy. Let us know your thoughts on any of the stories we're covering in the comments box below.

Tuesday 18 June 2024 21:05, UK

• Big inflation moment forecast
• Fury as tickets for rock band halved due to poor sales - after many had already paid hundreds
• Smoke machines deployed in Tesco to fight break-ins
• London overtakes Paris to become Europe's largest stock exchange

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• Holiday money - where to buy it, how to avoid fees, and one thing you must not do
• Best of the Money blog - an archive

Ask a question or make a comment

By James Sillars , business reporter

We're entering what could be the most crucial 48 hours of the election campaign for the economy.

There are two closely watched events ahead: the inflation figures for May (released early on Wednesday), followed the next day by the Bank of England's last interest rate decision before polling day.

The latter hinges on the former, in terms of potential excitement.

The consensus view is that the rate of inflation will ease back to the Bank of England's target of 2% for the first time since spring 2021.

That should be enough for the Bank of England to act the following day, you may well think. Job done?

Borrowers across the country are crying out for a rate cut after several false dawns in the fixed-rate mortgage market since we first really started talking about the prospects for rate cuts at the start of the year.

A reduction from 5.25% to 5% by the independent central bank would also be welcome for the Conservatives.

But here's where, from the view of economists and financial markets, the fairy tale for voters and the government hangs in the balance.

Even if the inflation rate hits the Bank of England's target this week, just 9% of the market currently expects the Bank of England to cut on Thursday.

That figure could change if the inflation number comes in lower than expected but the prediction is based on the future path for inflation rather than the present number.

Bank policymakers have repeatedly voiced worries over indicators showing a pick-up in the pace of price increases during the second half of the year.

They are concerned too that wage growth, running stubbornly at 6% annually at the moment, risks stoking demand in the economy and therefore inflation further.

Without these factors falling out of consideration, the majority on the rate-setting committee will likely continue to say it's too early to release the chokehold on inflation.

There is also a school of thought that the Bank would be reluctant to act during an election campaign.

So, these two events ahead are unlikely to rock the boat politically, or light up your finances to the extent the Bank of England has seen enough to fire the starting gun.

There is certainly the chance of a surprise on Thursday but it would take a pretty big shift for that pistol to light up the race for Number 10.

Doctors are calling for the drink-drive limit to be reduced to the equivalent of a small glass of wine or beer.

The limit in England, Wales and Northern Ireland is the highest in Europe at 80mg of  alcohol  per 100ml of blood. In Scotland it is 50mg.

The British Medical Foundation, the trade union for doctors, has said it will lobby the next government to reduce the limit to 50mg - and 20mg for new and commercial drivers.

Read the full story here ...

Grocery inflation has eased for the 16th month in a row, according to industry data released ahead of the general election.

Kantar Worldpanel - which tracks supermarket till prices, sales and market share - said its measure of grocery inflation slowed to 2.1% in the four weeks to 9 June from 2.4% the previous month.

The UK had the lowest rates of business investment out of all G7 nations for a third year in a row, a new report has claimed.

The economies of the US, Canada, France, Germany, Italy and Japan are all said to have attracted higher levels of funding from the private sector - as a percentage of gross domestic product (GDP) - in 2022.

The Institute for Public Policy Research (IPPR), which carried out the research, said the ranking was important because investment in things like new factories, equipment and innovations helped boost economic activity, wages and household incomes.

Read the full story  here ...

House buyers or renters should be familiar with being handed an Energy Performance Certificate (EPC) when surveying a property. 

In theory, they offer insight as to how efficient a building is - except consumer champion Which? doesn't think so.

Its experts argue that the certificates are "unreliable" and that the next government urgently needs to reform the system. 

It may not seem like the end of the world - but access to grant funding, or green financial products such as loans or mortgages, is often available only to those who meet certain EPC-based criteria.

Additionally, a better EPC can make a big difference for owners, as it allows them to command a higher price if they choose to sell and may make the home more attractive to tenants.  

The consumer magazine selected homeowners and booked EPC assessments on their behalf. 

"Which? uncovered issues with the accuracy of the results and the recommendations that homeowners received," it said. 

"Most participants (eight out of 11) told Which? their EPC did not appear to be accurate - they said the descriptions of key aspects of their home like the windows, roofs and heating systems were incorrect."

An electric vehicle company looking to rival Tesla has filed for bankruptcy amid a wider sales slump in the industry. 

Fisker filed a bankruptcy petition in Delaware yesterday after after failing to secure investment, announcing weaker-than-expected earnings and plans to cut 15% of its workforce.

The company, started by James Bond car designer Henrik Fisker, announced plans in March to cut prices by as much as 39%, while its share price has plummeted by 99% in recent days.

This comes as electric vehicle sales in the US and Europe continue to drop. 

Smoke machines are the latest gadgets being introduced into supermarket shops to fight crime.

Tesco has deployed them in some stores to stop thieves breaking in after-hours, Sky News understands.

The 4ft security machines - arranged on the shop floor after closing -  fill the room with a dense fog if motion detectors are tripped.

"Warning: You're being watched. Smoke screen security fog in operation," reads a message on the front of the device, which is fitted with a CCTV camera.

Sky News understands the unit pictured above was not plugged in and has been removed after being mistakenly left out during opening times.

While the deterrent has been rolled out in some high-risk branches, they are not part of a universal policy.

A customer who saw the device said: "The size and visibility of the machine, along with the prominent camera, and the pair of eyes and 'We're watching you' decals, highlight its use as yet another part of the culture of fear visited on the most vulnerable in our society during this cost of living crisis."

Tesco has declined to comment.

Basically... it's a little "what it says on the tin", but an interest-only mortgage is an agreement where you pay only the interest owed on your loan each month.

Popular in the 1980s and 90s, and peaking just before the 2008 financial crisis, interest-only mortgages benefit those who are trying to keep their monthly payments down in the short term.

How does an interest-only mortgage work?

You only pay off the interest on the amount you borrow - not the loan itself.

This differs from more common repayment mortgages which see you pay off the interest and some of the capital on your home each month, eventually leading to the mortgage being paid off at the end of the term.

With interest-only, you'll have to pay off the total amount borrowed in full at the end of your mortgage term using savings, investments or other assets.

You can also find temporary arrangements if you are struggling financially.

Example:  You're looking at a house which requires you to borrow £100,000 over 25 years with a fixed interest rate of 3.5%.

Imagining this interest rate stays the same for the whole term, on a repayment mortgage plan your monthly cost would be  £501 , while on interest-only it would be significantly lower at  £292 .

The interest-only option is great for those who want to keep their outgoings in check - but it does mean that, as the capital isn't being paid down, the amount of interest ends up being higher than on the full repayment plan.

Therefore someone on an interest-only deal would owe  £187,579  (£87,579 interest plus £100,000 loan capital outstanding), while a repayment deal would see them pay back  £150,238  (£100,000 loan capital fully paid off plus £50,238 interest).

How easy are they to come by? 

As we touched on earlier, prior to the 2008 financial crisis interest-only mortgages were much easier to get hold of - some 40% of all mortgages taken out were interest-only around this time.

But the crash revealed that many loans were at risk with customers who would struggle to pay off the full loan later down the line.

Affordability criteria were introduced as a result, which caused their popularity to sharply decline. It's now quite difficult to borrow on an interest-only basis, with not all lenders offering them as an option.

Those that do will have strict terms, such as a high deposit and an approved plan to pay the loan back at the end of the term.

Research by the Financial Conduct Authority in August last year revealed that the number of interest-only and part-interest-only mortgages had halved since 2015.

What are the benefits? 

The biggie with interest-only mortgages is the reduced monthly payments, which can provide you with a financial safety net if you go through times when you're earning less.

There's also a chance that if you're in your property for a long time, you could sell it for more than you paid for it, meaning you've built up equity to help you pay off the lump sump.

What about the downsides?

You're not paying off any of the loan as you go, meaning you're not building up that equity that you would do with a repayment mortgage. You'll also end up paying more interest due to not making a dent in the capital.

Interest-only also means you need a solid plan for paying it off at the end of the term - this may include constant monitoring of investments and being strict with yourself to ensure you're putting money aside. With a repayment plan you don't need to think about this element.

Read other entries in our Basically series...

An invitation-only "Diamond" Deliveroo subscription has launched, offering priority delivery, dedicated customer care teams and access to restaurants unavailable to other consumers.

For £19.99 per month, users get 10% credit back on orders of £30 or more and an on-time promise - meaning if an order arrives more than 15 minutes late, customers get their money back.

Only the very top users of the app will be invited to subscribe - with members estimated to spend three times as much as regular customers and twice as likely to try a restaurant that was new to the platform.

"The enhanced loyalty programme will play an important role in driving growth for Deliveroo," the company said.

A London fish and chip shop has been named in the top 100 best cheap eats in Europe.

The Mayfair Chippy comes in at 87 in the respected  Opinionated About Dining list - which is widely shared by top chefs and which draws conclusions based on tens of thousands of reviews from foodies.

As far as chippies go, Mayfair isn't actually that cheap - a cod and chips will set you back over £19...

The top UK entry is bakery Fabrique at 25 - and most things in its central London branches cost less than a fiver.

Pollen Bakery in Manchester, where you can easily eat for less than £10, is at 54 - with St John's Bakery (Neals Yard), in London, famous for its donuts, is at 57.

Jolene bakery in Newington Green, London, which has a daily changing menu, is at 59 - ahead of ramen joint Kanada-Ya, which has branches in Angel, Soho and Covent Garden.

Lots of other London eateries make the lower end of the top 100 - which is topped by Oslo coffee bar Tim Wendelboe.

What's your favourite cheap eat across the UK? Tell us in the comments box and we may follow this up later

By Daniel Binns, business reporter

The stock market in London has crept up slightly this morning as investors wait for tomorrow's inflation data - followed by Thursday's interest rate decision by the Bank of England.

The figures will be the last major economic indicators to be released ahead of July's general election.

Commentators expect inflation to fall to the Bank's target of 2% on Wednesday, according to a poll of economists by Reuters.

While an interest rate cut is not expected this week, the forecast drop in inflation will help pave the way for a rate cut in August, experts say.

Fiona Cincotta, a senior market analyst at City Index, said investors were keenly waiting for tomorrow's data - but said there was a "sense of optimism".

Overall, the FTSE 100 is up almost 0.4% this morning, while the FTSE 250 has increased by just over 0.5%.

Among the top gainers is Whitbread, which owns brands such as Premier Inn. The hospitality firm is up nearly 4% after reporting its results for the first quarter.

Whitbread said sales rose 1% to £739m and that its performance was in line with expectations.

At the other end of the scale, industrial equipment rental company Ashtead Group has slipped nearly 4% after the firm downgraded its growth forecast.

On the currency markets, £1 buys $1.27 US or €1.18, similar to Monday.

The cost of oil is up this morning, with a barrel of Brent Crude priced at almost $84 (£66).

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