design of consumer experiments using conjoint analysis

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What Is Conjoint Analysis & How Can You Use It?

• 18 Dec 2020

For a business to run effectively, its leadership needs a firm understanding of the value its products or services bring to consumers. This understanding allows for a more informed strategy across the board—from long-term planning to pricing and sales.

In today’s business environment, most products and services include multiple features and functions by default. So, how do businesses go about learning which ones their customers value most? Is it possible to assign a specific value to each feature a product offers?

This is where conjoint analysis becomes an essential tool.

Here’s an overview of conjoint analysis, why it’s important, and steps you can take to analyze your products or services.

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What Is Conjoint Analysis?

Conjoint analysis is a form of statistical analysis that firms use in market research to understand how customers value different components or features of their products or services. It’s based on the principle that any product can be broken down into a set of attributes that ultimately impact users’ perceived value of an item or service.

Conjoint analysis is typically conducted via a specialized survey that asks consumers to rank the importance of the specific features in question. Analyzing the results allows the firm to then assign a value to each one.

Learn about conjoint analysis in the video below, and subscribe to our YouTube channel for more explainer content!

Types of Conjoint Analysis

Conjoint analysis can take various forms. Some of the most common include:

• Choice-Based Conjoint (CBC) Analysis: This is one of the most common forms of conjoint analysis and is used to identify how a respondent values combinations of features.
• Adaptive Conjoint Analysis (ACA): This form of analysis customizes each respondent's survey experience based on their answers to early questions. It’s often leveraged in studies where several features or attributes are being evaluated to streamline the process and extract the most valuable insights from each respondent.
• Full-Profile Conjoint Analysis: This form of analysis presents the respondent with a series of full product descriptions and asks them to select the one they’d be most inclined to buy.
• MaxDiff Conjoint Analysis: This form of analysis presents multiple options to the respondent, which they’re asked to organize on a scale of “best” to “worst” (or “most likely to buy” to “least likely to buy”).

The type of conjoint analysis a company uses is determined by the goals driving its analysis (i.e., what does it hope to learn?) and, potentially, the type of product or service being evaluated. It’s possible to combine multiple conjoint analysis types into “hybrid models” to take advantage of the benefits of each.

What Is Conjoint Analysis Used For?

The insights a company gleans from conjoint analysis of its product features can be leveraged in several ways. Most often, conjoint analysis impacts pricing strategy, sales and marketing efforts, and research and development plans.

Conjoint Analysis in Pricing

Conjoint analysis works by asking users to directly compare different features to determine how they value each one. When a company understands how its customers value its products or services’ features, it can use the information to develop its pricing strategy.

For example, a software company hoping to take advantage of network effects to scale its business might pursue a “freemium” model wherein its users access its product at no charge. If the company determines through conjoint analysis that its users highly value one feature above the others, it might choose to place that feature behind a paywall.

As such, conjoint analysis is an excellent means of understanding what product attributes determine a customer’s willingness to pay . It’s a method of learning what features a customer is willing to pay for and whether they’d be willing to pay more.

Conjoint Analysis in Sales & Marketing

Conjoint analysis can inform more than just a company’s pricing strategy; it can also inform how it markets and sells its offerings. When a company knows which features its customers value most, it can lean into them in its advertisements, marketing copy, and promotions.

On the other hand, a company may find that its customers aren’t uniform in assigning value to different features. In such a case, conjoint analysis can be a powerful means of segmenting customers based on their interests and how they value features—allowing for more targeted communication.

For example, an online store selling chocolate may find through conjoint analysis that its customers primarily value two features: Quality and the fact that a portion of each sale goes toward funding environmental sustainability efforts. The company can then use that information to send different messaging and appeal to each segment's specific value.

Conjoint Analysis in Research & Development

Conjoint analysis can also inform a company’s research and development pipeline. The insights gleaned can help determine which new features are added to its products or services, along with whether there’s enough market demand for an entirely new product.

For example, consider a smartphone manufacturer that conducts a conjoint analysis and discovers its customers value larger screens over all other features. With this information, the company might logically conclude that the best use of its product development budget and resources would be to develop larger screens. If, however, future analyses reveal that customer value has shifted to a different feature—for example, audio quality—the company may use that information to pivot its product development plans.

Additionally, a company may use conjoint analysis to narrow down its product or service’s features. Returning to the smartphone example: There’s only so much space within a smartphone for components. How a phone manufacturer’s customers value different features can inform which components make it into the end product—and which are cut.

One example is Apple’s 2016 decision to remove the headphone jack from the iPhone to free up space for other components. It’s reasonable to assume this decision was reached after analysis revealed that customers valued other features above a headphone jack.

Leveraging Conjoint Analysis for Your Business

Conjoint analysis is an incredibly useful tool you can leverage at your company. By using it to understand which product or service features your customers value over others, you can make more informed decisions about pricing, product development, and sales and marketing activities.

Are you interested in learning more about how customers perceive and realize value from the products they buy, and how you can use that information to better inform your business? Explore Economics for Managers — one of our online strategy courses —and download our free e-book on how to formulate a successful business strategy.

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What is Conjoint Analysis?

Conjoint analysis is a popular method of product and pricing research that uncovers consumers’ preferences, which is useful when a company wants to:

• Select product features.
• Assess consumers’ sensitivity to price changes.
• Forecast its volumes and market share.
• Predict adoption of new products or services.

Conjoint analysis is frequently used across different industries for all types of products, such as consumer goods, electrical goods, life insurance plans, retirement housing, luxury goods, and air travel. It is applicable in various instances that centre around discovering what type of product consumers are likely to buy and what consumers value the most (and least) about a product. As such, it is a familiar tool for marketers, product managers, and pricing specialists.

Businesses of all sizes can benefit from conjoint analysis, including even local grocery stores and restaurants — and its scope is not just limited to consumer contexts, for example, charities can use conjoint analysis’ techniques to find out donor preferences, while HR departments can use it to build optimal compensation packages .

How does conjoint analysis work?

Conjoint analysis works by breaking a product or service down into its components ( attributes and levels ) and testing different combinations of these components to identify consumer preferences .

For example, consider a conjoint study on smartphones. The smartphone is broken down into four attributes which are each assigned different possible variations to create levels:

Each choice task then presents a respondent with different possible smartphones, each created by combining different levels for each attribute:

Going further than simply asking respondents what they like in a product, or what features they find most important, conjoint analysis employs a more realistic approach: asking each respondent to choose between potential product concepts (or alternatives) formed through the combination of attributes and levels. These combinations are carefully assembled into choice sets (or questions). Each respondent is typically presented with 8 to 12 questions . The process of assembling attributes and levels into product concepts and then into choice sets is called experimental design and requires extensive statistical and mathematical analysis (done automatically by Conjointly or manually by researchers).

Using survey results, it is possible to calculate a numerical value that measures how much each attribute and level influenced the respondent’s choices. Each of these values is called a “ preference score ” (AKA “partworth utility” or “utility score”). The below example shows preference scores for attributes and levels of a mobile phone plan.

Preference scores are used to build simulators that forecast market shares for a set of different products offered to the market. By using the simulator to model (i.e. simulate ) respondents’ decisions, we can identify the specific features and pricing that balance value to the customer with cost to the company and forecast potential demand in a competitive market situation. The below example shows how different data amounts in a mobile plan will affect a company’s market share.

Consider you are launching a new product and wish to address several research questions. Through the below example, we demonstrate how various outputs from your Conjointly survey report can be used to gain insights.

• It is also possible to perform clustering based on raw conjoint utilities .

Why do conjoint analysis with Conjointly?

Conjointly automates the often complicated experimental design process using state-of-the-art methodology. This gives you control over specific settings , such as the number of concepts per choice set and the number of choice sets per respondent when you set up a conjoint analysis experiment. Respondents then complete the choice tasks within the conjoint survey – this typically requires a few hundred responses but may vary depending on the complexity of the study.

Once we’ve gathered the recommended sample size of respondents, Conjointly produces a survey report which contains several in-depth outputs. The outputs of Brand Specific Conjoint , Generic Conjoint , and Brand-Price Trade-Off include estimates of respondents’ preferences, overall sample profile, segmentation and interactive simulations. Conjointly estimates and charts preference shares, revenue projections, and price elasticity using simulators.

There are many types/flavours of conjoint analysis , classified by response type, questioning approach, design type, and adaptivity of the design. All flavours of conjoint analysis have the same basics but not all are as effective as others. That’s why Conjointly offers two key conjoint designs, called generic and brand-specific, and uses the most tested, developed, and theoretically sound response type – choice-based conjoint analysis (CBC). CBC’s predictive power far surpasses its alternatives , such as SIMALTO and self-explicated conjoint, making it the ideal choice for your next experiment.

Don’t have a large marketing budget or the scope to conduct conjoint analysis? That’s OK: Conjointly does full conjoint analysis for you, affordably . Unlike desktop software tools, Conjointly does not require you to deep dive into the advanced methodology of conjoint analysis. Your business can rely on the full functionality of the software to deliver high-quality analysis and powerfully accurate results. Conjointly embodies an agile approach that puts you in control of the research process without the need.

Conjointly is made unique by the following characteristics:

We are the home of conjoint analysis. Conjointly offers complete set of outputs and features through an accessible interface.

Quick to set up. Setting up your experiment is fast and hassle-free with a simple wizard, which helps you choose appropriate settings and suggests your minimum sample size. You won’t need to customise or test any survey – the system does that for you. Conjointly can send participants invites on your behalf or generate a shareable link for you.

Easy on respondents. Experiment participants only need a few minutes to complete a survey and can answer questions with ease on their mobile phone, tablet, or computer.

Smart analytics done for you. Behind the scenes, Conjointly uses state-of-the-art analytics to crunch the numbers, and check validity of reporting. Outputs are ready for any application of conjoint analysis (pricing, feature selection, product testing, new market entry, cannibalisation analysis, etc.) in any industry (telecommunications, SaaS, FMCG, automotive, financial services, HR, etc.).

Our market research experts are always ready to support your studies. Schedule a consultation if you need any assistance.

What is the difference between conjoint and discrete choice experiments?

Conjoint analysis is a survey-based quantitative research technique of presenting respondents with several options (each described in terms of feature and price levels) and measuring their response to these options.

When the measured response is their choice between these options (rather than ranking or rating each of these options), it is called choice-based conjoint (which is the most commonly-used type of discrete choice experiments).

Discrete choice analysis is examination of datasets that contain choices made by people from among several alternatives. Commonly, we want to understand what drove people to make these choices. For example, how does weather affect people’s choice of eating out, ordering food delivery, or cooking at home. Discrete choice analysis can be done on historical data (e.g. sales data) or from experiments (including survey-based experiments).

Choice-based conjoint is an example of discrete choice experimentation.

History of conjoint analysis

Conjoint analysis has its roots in academic research from the 1960s and has been used commercially since the 1970s. In 1964, two mathematicians, Duncan Luce and John Tukey published a rather indigestible (by modern standards) article called ‘Simultaneous conjoint measurement: A new type of fundamental measurement’ . In abstract terms, they sketched the idea of “measuring the intrinsic goodness of certain characteristics of objects by measuring the goodness of an object as a whole”.

The article did not mention data collection, products, features, prices, or other elements that we associate with conjoint analysis today, but it spurred academic interest in the topic and perhaps gave rise to the name “conjoint”. It not only kick-started the topic but also set the tone for future developments in the area. Over time, it has become technical to the point of inaccessibility to most people, led by American academics with a strong emphasis on the statistical workings of survey research.

Green and Srinivasin (1978) agree that the theory of conjoint measurement was developed in Luce and Tukey’s paper but that “the first detailed, consumer-orientated” approach was Green and Rao’s (1971) ‘Conjoint Measurement for Quantifying Judgmental Data’ . In 1974, Professor Paul E. Green penned ‘On the Design of Choice Experiments Involving Multifactor Alternatives’ , cementing the impact of conjoint analysis in market research.

Over the next few decades, conjoint analysis became an increasingly popular method across the globe with notable studies in the 1980s and 90s highlighting its growing adoption and development during this time (Wittink & Cattin 1989; Wittink, Vriens, and Burhenne 1994 cited in Green, Kreiger & Wind 2001) .

Conjoint surveys are continuously developing on a range of software platforms, through which many different flavours of conjoint analysis can be enjoyed. Today, conjoint analysis thrives as a widespread tool built on a robust methodology and is used by market researchers daily as an indispensable tool for understanding consumer trade-offs.

Example outputs of Generic Conjoint on ice-cream

This is a simple conjoint analysis report for a Generic Conjoint test on ice-cream. You can also take this survey yourself . We tested three features:

• Flavour (Fudge, Vanilla, Strawberry, and Mango)
• Size (from 120g to 200g)
• Price (from $1.95 to $3.50)

We collected over 1,500 good quality responses in this test (even though this report would be robust enough with a hundred complete answers). It turns out that variation of price was a more important driver of people’s decision-making than differences in both flavour and size of the cone combined:

Unsurprisingly, people preferred larger and cheaper cones. Fudge and vanilla were the two top flavours:

But when we look at confidence intervals, we notice that we are much less certain about average preferences for flavours than for size or price:

It is probably because if we simulate preference shares for four concepts with varied flavours but fixed price and size, we observe that the distribution of people who pick different options is not extremely skewed towards one flavour:

But when we do simulation analysis with different price points, we clearly see that more people prefer to pay a lower price. Even though some still stick with a higher price, probably due to price-quality inference.

Another useful output of the study is marginal willingness to pay , which shows the equivalent amount of money for upgrade from the less preferred to the more preferred features:

If you want to pick the topmost preferred combination of product features, you can take a look at the following ranking as well:

It looks like a large dollop of modestly-priced Frosty Vanilla is the winner today.

A simple conjoint analysis example in Excel

To further your understanding, you can download a conjoint analysis example in Excel , also available on Google Sheets (which you can copy to edit). This example covers:

• Inputs for a conjoint study
• Questions presented to respondents
• Calculations of preference scores (relative preferences and importance scores of attributes)

This example is limited to:

• Ten choice-based responses (in real conjoint tests, we collect ~12 choices from 100 to 2,000 respondents);
• Four attributes with two levels each (in real conjoint tests, we can have up to a dozen attributes and up to several dozen levels);
• A multiple linear regression (in real conjoint tests, we use hierarchical Bayesian multinomial logit );
• A fractional factorial design .

The best way to learn more about conjoint analysis is to set up your own study, which you can do when you sign up . You can also read about:

• Alternatives to conjoint (such as MaxDiff and Claims Test )
• Common mistakes and practical tips for setting up conjoint studies
• Key takeaways from our Conjoint Analysis 101 webinar

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Choice-Based Conjoint Analysis Guide [Example Questions and Case Study]

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579035">In this article, we take a look at the benefits of choice-based conjoint (CBC), how and when to conduct a CBC study, what CBC questions look like, and an example of a CBC project.

Table of Contents: 

• What is choice-based conjoint analysis?

Benefits of a choice-based conjoint study 

How to execute a choice-based conjoint analysis .

• When to use choice-based conjoint analysis for your business   
• Examples of choice-based conjoint analysis questions  
• Example of choice-based conjoint analysis study

How quantilope can help with your next choice-based conjoint analysis 

What is choice-based conjoint analysis? 

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579035">Choice-based conjoint analysis ( dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579033">CBC ) , also known as dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579086">discrete dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579041">choice dropdown#toggle" data-dropdown-menu-id-param="menu_term_281579086" data-dropdown-placement-param="top" data-term-id="281579086"> modeling , is an advanced dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579036">market dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579070">research dropdown#toggle" data-dropdown-menu-id-param="menu_term_281579070" data-dropdown-placement-param="top" data-term-id="281579070"> method that identifies consumers’ preferences when considering a product or service. This is done by asking research dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondents to make dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579039">trade-offs between competing products, each of which has a variety of attributes. Asking consumers to choose their preferred product reveals the importance of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579058">different attributes in determining consumers’ dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579072">willingness to pay . dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579054">Product attributes might include brand, design features, price, or style; dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579038">attribute levels (within each attribute) might be Ford and Toyota, built-in nav system, heated seats, sporty or family style, etc.

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579033">CBC is the most commonly used type of conjoint analysis. It differs from other conjoint approaches in that it presents consumers with full product profiles (rather than just asking them to rate attributes separately, as in two-attribute dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579039">trade-off analysis) and it allows for the inclusion of price as a determining attribute (which is not an ideal use case for another type of conjoint - dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579068">adaptive conjoint analysis; this type of conjoint changes as each person answers the survey questions to consider their individual preferences ).

Back to Table of Contents

Authenticity

Because dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondents are presented with profiles that detail the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579058">different attributes contained within each product, this method mimics a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579064">real-world purchase scenario. Buying a product can be a complex process, with subconscious decisions made along the way, so asking dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondents to make product dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579039">trade-offs  reveals which attributes and dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579038">attribute levels truly drive the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579062">purchase decision .

Attribute valuation

Traditional surveys ask dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondents to rank or rate attributes and dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579038">attribute levels , which can give an indication of how important different features are when consumers make purchases. However, the problem with considering attributes in isolation is that this lacks the contextual information required to assess how likely a purchase will be. For example, bread buyers might say that they rate whole grains, added vitamins, and bread softness highly, but it can be difficult for them to say which of those attributes is more important than others.

Forcing consumers to make dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579039">trade-offs reveals the relative dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579050"> importance and value of each attribute. Some product profiles in a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579033">CBC might not even be chosen at all, revealing attributes that are of little or no importance to consumers (and therefore, not worth the investment). Each attribute’s value metric is known as a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579043">part-worth utility score, and is calculated for each dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579038">attribute level in a study. This is a great springboard for a needs-based dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579034">segmentation that defines what different consumer groups are looking for in a product. Knowing each attribute’s valuation is helpful for designing products and ensuring the whole product package is attractive to consumers. After determining the top attributes, you can use a conjoint analysis to ensure that when these different parts are combined, the product is still overall appealing.

The effect of price

Determining the optimal price level for a product is incredibly important, but can be difficult to measure in a research study as dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondents will almost always say that price is important to them and that they want the lowest price possible. Further, price isn’t a fixed attribute with a limited dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579069">number of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579038">attribute dropdown#toggle" data-dropdown-menu-id-param="menu_term_281579038" data-dropdown-placement-param="top" data-term-id="281579038"> levels - it can always be increased or (to a degree) decreased.

With choice-based conjoint, brands can test out different price levels in their product profiles to identify the overall price range that consumers will consider buying their product. This is one of the best ways to identify a ‘fair’ and justified price to charge for your products. Beyond determining price level, conjoint can also project revenue modeling to find a sweet spot between a price index and actual revenue.

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">Respondent enjoyment

Because a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579067">conjoint survey feels like a real-life scenario, dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondents enjoy the ability to choose between different product profiles rather than simply answering questions about separate attributes or giving individual rankings/scores. Back to Table of Contents

There are a number of factors to consider when designing and conducting a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579033">CBC survey. At the design stage, brands need to decide on the following:

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579056">Sample size:   This needs to be big enough to provide meaningful data on consumer preferences. The number of r dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">espondents needed will depend on the complexity of the design, but a general guideline is to have a few hundred dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondents for each product profile you’re measuring.

Choice type: Choose how dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondents will evaluate each set of product profiles (i.e. combinations of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579038">attribute levels ). You might want to force dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondents to make a single choice from the sets of products shown or provide them with a ‘none of the above’ option.

Number of profiles per set:  Decide how many product profiles should be shown per set. Too many profiles can become tedious for dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondents , while too few profiles won’t provide enough comparative data.

Number of sets per dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondent:  Similar to the above, decide how many overall ‘sets’ of products each dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondent will evaluate so that dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondents aren’t overwhelmed but still provide enough data for analysis.

Attributes: These are the features of each product or service you’re researching. These might include price, size, color, brand, and style. Aim for no more than six attributes to avoid overloading dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondents .

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579038">Attribute levels: The variations within each attribute - such as large, medium, and small; blue, red, white, and yellow. Again, aim for no more than six levels to keep dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondents engaged.

Once the above factors have been established, a brand will launch their choice-based dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579067">conjoint survey amongst their target audience. Randomized dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579059">choice sets of product profiles are shown to each dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondent , and dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondents choose their favorite product from each set. At the analysis stage, each dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579038">attribute level ’s dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579043">part-worth utility is calculated, as well as its dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579050">relative importance to other dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579038">attribute levels in the study.

quantilope offers a fully automated approach to dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579033">CBC , from survey design to final analysis. Its survey templates and pre-programmed dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579033">CBC method ensure all relevant information is included in a conjoint study. quantilope’s dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579077">CBC dropdown#toggle" data-dropdown-menu-id-param="menu_term_281579077" data-dropdown-placement-param="top" data-term-id="281579077"> analysis output includes a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579042">market simulator that projects how different product profiles would be received by consumers in the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579064">real world and identifies propositions with the highest consumer appeal and dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579072">willingness to pay . Back to Table of Contents

When to use dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579035">choice-based conjoint analysis for your business   

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579035">Choice-based conjoint analysis is used across a broad range of business areas, from consumer packaged goods (CPG) to services and healthcare. Wherever there is the possibility of different product or service propositions, dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579033">CBC is an excellent way to determine which profile would be most appealing and profitable.

If your business wants to explore any of the following, dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579033">CBC is a great dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579052">methodology to leverage:

Projected dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579046">market share

If you have an idea for a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579040">new product or a revamp of an existing one, it pays to know whether it will sell well once launched. A common use of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579033">CBC is to determine which feature combination will claim the largest dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579046">market share .

Nailing down dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579045">product features

If you’re at the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579047">product development stage and have an idea of features for your product but don’t know which will be most important to consumers, dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579033">CBC will tell you which ones, and with which combinations, to include for maximum consumer appeal.

The right price for a product

A crucial question for any business is how to price its offer. With dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579033">CBC , each product profile can include price as one of the attributes and the analysis will reveal the perceived value of product benefits (i.e. what consumers are willing to pay for the features a product has). It will also give a good idea of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579071">price sensitivity - i.e. how a product’s demand is affected by price - and how dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579046">market share will affect revenue at different price levels. Back to Table of Contents

Examples of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579035">choice-based conjoint analysis questions  

A dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579035">choice-based conjoint analysis questionnaire can look different depending on the product or service being tested, or on the survey platform used, but the general principle is always the same.

If you’re conducting a survey on smartphones, your dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579059">choice sets presented to each dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondent could look something like this:

The smartphone profile that a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondent opts for will give an insight into which features they place the most importance on. For example, they might sacrifice a better camera for a longer battery life, or choose a larger screen despite lower storage.

As another example, a conjoint question for hand soap could include the following attributes:

Will dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondents go for an antibacterial hand wash, whatever the price? Is size a key factor because they have a large family? Are all these profiles too expensive for hand soap, and a respondent would choose 'none of these options'?

As a third example, a restaurant conducting a conjoint questionnaire might include the following attributes to see which menu items are most appealing:

If the restaurant were planning a new menu, the conjoint data would help narrow in on which menu items are most appealing, what the atmosphere should be like, and how they should price their meals. Back to Table of Contents

Example of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579035">choice-based conjoint analysis study  

quantilope’s automated dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579035">choice-based conjoint analysis is a popular dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579052">methodology for many platform users and clients. One such client is PAX , a leading global cannabis brand that wanted to gather consumer insights around a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579040">new product offer in a growing market. dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579082">Product design and innovation were essential to PAX’s growth, so using a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579033">CBC to explore product formats and benefits was key.

Using quantilope’s dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579033">CBC , PAX was able to present a range of product possibilities to consumers and, by means of automated analysis, understand dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579057">attribute importance and benefit dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579066">configurations that would appeal to the most consumers.

“Two weeks after we signed on with quantilope I got a direct request from our CEO to run a Conjoint analysis. I would not have been able to do it without quantilope; my other option would have been to find a specialist and lose time requesting and reviewing proposals.” -Kristen Archibald, Sr. Consuemr Insights Manager at PAX

For more on this successful dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579044">conjoint study , access the full case study here . Back to Table of Contents

How quantilope can help with your next dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579035">choice-based conjoint analysis  

quantilope’s expertise in AI-driven dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579052">methodologies (including dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579035">choice-based conjoint analysis ) provides brands with the confidence needed to design a successful product or service offer.

Although dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579033">CBC is a sophisticated and complex dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579070">research method , quantilope makes the process seamless and straightforward. Simply select the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579033">CBC from quantilope’s list of pre-programmed advanced dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579052">methodologies , design your product profiles with dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579085">various attributes and dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579038">attribute levels , ‘configure’ the remaining setup in one click, and set your survey live.

Review your conjoint analysis data through a variety of charts that show things like an optimal price point, acceptable price range, average dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579043">part-worths , individual dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579057">attribute importance , and more. Then, merge all your findings into one interactive, shareable dashboard with automated significance testing.

For more information on how quantilope can help your business test dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579040">new product profiles and features through automated dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579035">choice-based conjoint analysis , get in touch with us below!

Get in touch to learn more about choice-based conjoint!

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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What is a conjoint analysis conjoint types & when to use them.

11 min read Conjoint analysis is a popular market research approach for measuring the value that consumers place on individual and packages of features of a product.

Conjoint analysis explained

Conjoint analysis can be defined as a popular survey-based statistical technique used in market research. It is the optimal approach for measuring the value that consumers place on features of a product or service. This commonly used approach combines real-life scenarios and statistical techniques with the modeling of actual market decisions.

Product testing and employee benefits packages are examples of where conjoint analysis is commonly used. Conjoint surveys will show respondents a series of packages where feature variables are different to better understand which features drive purchase decisions.

Note: For an in-depth guide to conjoint analysis, download our free eBook:   12 Business Decisions you can Optimize with Conjoint Analysis

Menu-based conjoint analysis

Menu-based conjoint analysis is an analysis technique that is fast gaining momentum in the marketing world. One reason is that menu-based conjoint analysis allows each respondent to package their own product or service.

Conjoint studies can help you determine pricing, product features, product configurations, bundling packages, or all of the above. Conjoint is helpful because it simulates real-world buying situations that ask respondents to trade one option for another.

For example, in a survey, the respondent is shown a list of features with associated prices. The respondent then chooses what they want in their ideal product while keeping price as a factor in their decision. For the person conducting the market research , key information can be gained by analyzing what was selected and what was left out. If feature A for $100 was included in the menu question but feature B for $100 was not, it can be assumed that this respondent prefers feature A over feature B.

The outcome of menu-based conjoint analysis is that we can identify the trade-offs consumers are willing to make. We can discover trends indicating must-have features versus luxury features.

Add in the fact that menu-based conjoint analysis is a more engaging and interactive process for the survey taker, and one can see why menu-based conjoint analysis is becoming an increasingly popular way to evaluate the utility of features.

The advanced functionality of Qualtrics allows for the perfect conjoint survey – built with the exact look and feel needed to provide a reliable, easy to understand experience for the respondent. This means better quality data for you.

  There are numerous conjoint methodologies available from Qualtrics.

• Full-Profile Conjoint Analysis
• Choice-Based/Discrete-Choice Conjoint Analysis
• Adaptive Conjoint Analysis
• Max-Diff Conjoint Analysis

To provide a sense of these options, the following discussion provides an overview of conjoint analysis methods.

Two-attribute tradeoff analysis

Perhaps the earliest conjoint data collection method involved presented a series of attribute-by-attribute (two attributes at a time) tradeoff tables where respondents ranked their preferences for the different combinations of the attribute levels. For example, if two attributes each had three levels, the table would have nine cells and the respondents would rank their tradeoff preferences from 1 to 9.

The two-factor-at-a-time approach makes few cognitive demands of the respondent and is simple to follow but it is both time-consuming and tedious. Moreover, respondents often lose their place in the table or develop some stylized pattern just to get the job done. Most importantly, however, the task is unrealistic in that real alternatives do not present themselves for evaluation two attributes at a time.

Full-profile conjoint analysis

Full-profile conjoint analysis takes the approach of displaying a large number of full product descriptions to the respondent. The evaluation of these packages yields large amounts of information for each customer/respondent. Full-profile conjoint analysis has been a popular approach to measure attribute utilities. In the full-profile conjoint task, different product descriptions (or even different actual products) are developed and presented to the respondent for acceptability or preference evaluations.

Each product profile represents a part of a fractional factorial experimental design that evenly matches the occurrence of each attribute with all other attributes. By controlling the attribute pairings, the researcher can correlate attributes with profile preferences and estimate the respondent’s utility for each level of each attribute tested. In the rating task, the respondent gives their preference or likelihood of purchase. While many features and levels may be studied, this type of conjoint is best used where a moderate number of profiles are presented, thereby minimizing respondent fatigue. The advanced functionality of Qualtrics employs experimental designs to reduce the number of evaluation requests within the survey. The output and analysis accumulated from full-profile conjoint surveys is similar to that of other conjoint models.

Adaptive conjoint analysis

Adaptive conjoint analysis varies the choice sets presented to respondents based on their preference. This adaption targets the respondent’s most preferred feature and levels, thereby making the conjoint exercise more efficient, wasting no questions on levels with little or no appeal. Every package shown is more competitive and will yield ‘smarter’ data.

Adaptive conjoint analysis is often more engaging to the survey-taker and thus can produce more relevant data. It reduces the survey length without diminishing the power of the conjoint analysis metrics or simulations. There are multiple ways to adapt the conjoint scenarios to the respondent. Most commonly the design is based on the most important feature levels. As each package is presented for evaluation, the survey accounts for the choice and then makes the next question more efficient. A combination of full profile and feature evaluation methods can be utilized and is referred to as Hybrid Conjoint Analysis.

Choice-based conjoint

The Choice-based conjoint analysis (CBC) (also known as discrete-choice conjoint analysis) is the most common form of conjoint analysis. Choice-based conjoint requires the respondent to choose their most preferred full-profile concept. This choice is made repeatedly from sets of 3–5 full profile concepts.

This choice activity is thought to simulate an actual buying situation, thereby mimicking actual shopping behavior. The importance and preference for the attribute features and levels can be mathematically deduced from the trade-offs made when selecting one (or none) of the available choices. Choice-based conjoint designs are contingent on the number of features and levels. Often, that number is large and an experimental design is implemented to avoid respondent fatigue. Qualtrics provides extreme flexibility in utilizing experimental designs within the conjoint survey.

The output of a Choice-based conjoint analysis provides excellent estimates of the importance of the features, especially in regards to pricing. Results can estimate the value of each level and the combinations that make up optimal products. Simulators report the preference and value of a selected package and the expected choice share (surrogate for market share).

Self-explicated conjoint analysis

Self-explicated conjoint analysis offers a simple but surprisingly robust approach that is easy to implement and does not require the development of full-profile concepts. Self-explicated conjoint analysis is a hybrid approach that focuses on the evaluation of various attributes of a product. This conjoint analysis model asks explicitly about the preference for each feature level rather than the preference for a bundle of features.

Although the approach is different, the outcome is still the same in that it produces high-quality estimates of preference utilities.

• First, like ACA, factors and levels are presented to respondents for elimination if they are not acceptable in products under any condition
• For each feature, the respondent selects the levels they most and least prefer
• Next, the remaining levels of each feature are rated in relation to the most preferred and least preferred levels
• Finally, we measure how important the overall feature is in their preference. The relative importance of the most preferred level of each attribute is measured using a constant sum scale (allocate 100 points between the most desirable levels of each attribute).
• The attribute level desirability scores are then weighted by the attribute importance to provide utility values for each attribute level.

Self-explicated conjoint analysis does not require the statistical analysis or the heuristic logic required in many other conjoint approaches. This approach has been shown to provide results equal or superior to full-profile approaches, and places fewer demands on the respondent. There are some limitations to self-explicated conjoint analysis, including an inability to trade off price with other attribute bundles. In this situation, the respondent always prefers the lowest price, and other conjoint analysis models are more appropriate.

Max-diff conjoint analysis

Max-Diff conjoint analysis presents an assortment of packages to be selected under best/most preferred and worst/least preferred scenarios. Respondents can quickly indicate the best and worst items in a list, but often struggle to decipher their feelings for the ‘middle ground’. Max-Diff is often an easier task to undertake because consumers are well trained at making comparative judgments.

Max-Diff conjoint analysis is an ideal methodology when the decision task is to evaluate product choice. An experimental design is employed to balance and properly represent the sets of items. There are several approaches that can be taken with analyzing Max-Diff studies including: Hierarchical Bayes conjoint modeling to derive utility score estimations, best/worst counting analysis and TURF analysis.

Hierarchical Bayes analysis (HB)

Hierarchical Bayes Analysis (HB) is similarly used to estimate attribute level utilities from choice data. HB is particularly useful in situations where the data collection task is so large that the respondent cannot reasonably provide preference evaluations for all attribute levels. As part of the procedure to estimate attribute level utilities for each individual, hierarchical Bayes focuses individual respondent measurement on highly variable attributes and uses the sample’s attribute level averages when attribute-level variability is smaller. This approach again allows more attributes and levels to be estimated with smaller amounts of data collected from each individual respondent.

Conjoint is a highly effective analysis technique

Conjoint analysis methodology has withstood intense scrutiny from both academics and professional researchers for more than 30 years. It is widely used in consumer products, durable goods, pharmaceutical, transportation, and service industries, and ought to be a staple in your research toolkit.

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Choice-Based Conjoint Analysis

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• First Online: 03 December 2021
• Cite this reference work entry

• Felix Eggers 4 ,
• Henrik Sattler 5 ,
• Thorsten Teichert 5 &
• Franziska Völckner 6  

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Conjoint analysis is one of the most popular methods to measure preferences of individuals or groups. It determines, for instance, the degree how much consumers like or value specific products, which then leads to a purchase decision. In particular, the method discovers the utilities that (product) attributes add to the overall utility of a product (or stimuli). Conjoint analysis has emerged from the traditional rating- or ranking-based method in marketing to a general experimental method to study individual’s discrete choice behavior with the choice-based conjoint variant. It is therefore not limited to classical applications in marketing, such as new product development, pricing, branding, or market simulations, but can be applied to study research questions from related disciplines, for instance, how marketing managers choose their ad campaign, how managers select internationalization options, why consumers engage in or react to social media, etc. This chapter describes comprehensively the “state-of-the-art” of conjoint analysis and choice-based conjoint experiments and related estimation procedures.

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Addelman, S. (1962). Orthogonal main-effect plans for asymmetrical factorial experiments. Technometrics, 4 (1), 21–46.

Article   Google Scholar  

Allenby, G. M., Arora, N., & Ginter, J. L. (1995). Incorporating prior knowledge into the analysis of conjoint studies. Journal of Marketing Research, 32 (2), 152–162.

Allenby, G. M., Brazell, J. D., Howell, J. R., & Rossi, P. E. (2014). Economic valuation of product features. Quantitative Marketing and Economics, 12 (4), 421–456.

American Marketing Association. (2015). American Marketing Association AMA. https://www.ama.org/resources/Pages/Dictionary.aspx . Accessed 15 Nov 2015.

Arora, N., Allenby, G. M., & Ginter, J. L. (1998). A hierarchical Bayes model of primary and secondary demand. Marketing Science, 17 (1), 29–44.

Batsell, R. R., & Louviere, J. J. (1991). Experimental analysis of choice. Marketing Letters, 2 (3), 199–214.

Bauer, H., Herrmann, A., & Homberg, F. (1996). Analyse der Kundenwünsche zur Gestaltung eines Gebrauchsgutes mit Hilfe der Conjoint Analyse. Universität Mannheim, Lehrstuhl für ABWL und Marketing II, Working Paper Nr. 110.

Google Scholar  

Becker, G. M., Degroot, M. H., & Marschak, J. (1964). Measuring utility by a single-response sequential method. Behavioral Science, 9 (3), 226–232.

Brazell, J. D., Diener, C. G., Karniouchina, E., Moore, W. L., Séverin, V., & Uldry, P.-F. (2006). The no-choice option and dual response choice designs. Marketing Letters, 17 (4), 255–268.

Burmester, A., Eggers, F., Clement, M., & Prostka, T. (2016). Accepting or fighting unlicensed usage – Can firms reduce unlicensed usage by optimizing their timing and pricing strategies? International Journal of Research in Marketing, 33 (2), 434–356.

Chakraborty, G., Ball, D., Gaeth, G. J., & Jun, S. (2002). The ability of ratings and choice conjoint to predict market shares – A Monte Carlo simulation. Journal of Business Research, 55 (3), 237–249.

Chen, M.-H., Shao, Q.-M., & Ibrahim, J. G. (2000). Monte Carlo methods in Bayesian computation . New York: Springer Series in Statistics.

Book   Google Scholar  

Croissant, Y. (2012). Estimation of multinomial logit models in R: The mlogit packages. R package version 0.2-2. http://cran.r-project.org/web/packages/mlogit/vignettes/mlogit.pdf .

De Bekker-Grob, E. W., Ryan, M., & Gerard, K. (2012). Discrete choice experiments in the health economics: A review of the literature. Health Economics, 21 (2), 145–172.

DeSarbo, W. S., Ramaswamy, V., & Cohen, S. (1995). Market segmentation with choice-based conjoint analysis. Marketing Letters, 6 (2), 137–147.

Ding, M. (2007). An incentive-aligned mechanism for conjoint analysis. Journal of Marketing Research, 44 (2), 214–223.

Ding, M., Grewal, R., & Liechty, J. (2005). Incentive-aligned conjoint analysis. Journal of Marketing Research, 42 (2), 67–82.

Ding, M., Park, Y.-H., & Bradlow, E. T. (2009). Barter markets for conjoint analysis. Management Science, 55 (6), 1003–1017.

Dong, S., Ding, M., & Huber, J. (2010). A simple mechanism to incentive-align conjoint experiments. International Journal of Research in Marketing, 27 (1), 25–32.

Eggers, F., & Sattler, H. (2009). Hybrid individualized two-level choice-based conjoint (HIT-CBC): A new method for measuring preference structures with many attribute levels. International Journal of Research in Marketing, 26 (2), 108–118.

Eggers, F., Hauser J. R., & Selove, M. (2016). The effects of incentive alignment, realistic images, video instructions, and ceteris paribus instructions on willingness to pay and price equilibria. Proceedings of the Sawtooth Software conference , 1–18 September.

Elrod, T., Louviere, J. J., & Davey, K. S. (1992). An empirical comparison of ratings-based and choice-based conjoint models. Journal of Marketing Research, 29 (3), 368–377.

Frischknecht, B., Eckert, C., Geweke, J., & Louviere, J. J. (2014). A simple method for estimating preference parameters for individuals. International Journal of Research in Marketing, 31 (1), 35–48.

Gensler, S., Hinz, O., Skiera, B., & Theysohn, S. (2012). Willingness-to-pay estimation with choice-based conjoint analysis: Addressing extreme response behavior with individually adapted designs. European Journal of Operational Research, 219 (2), 368–378.

Green, P. E., & Srinivasan, V. (1978). Conjoint analysis in consumer research: Issues and outlook. Journal of Consumer Research, 5 , 103–123.

Green, P. E., & Srinivasan, V. (1990). Conjoint analysis in marketing: New developments with implications for research and practice. Journal of Marketing, 54 , 3–19.

Haaijer, R., & Wedel, M. (2003). Conjoint experiments. general characteristics and alternative model specifications. In A. Gustafsson, A. Herrmann, & F. Huber (Eds.), Conjoint measurement: Methods and applications (3rd ed., pp. 371–412). Berlin: Springer.

Chapter   Google Scholar  

Haaijer, R., Wedel, M., Vriens, M., & Wansbek, T. (1998). Utility covariances and context effects in conjoint MNP models. Marketing Science, 17 (3), 236–252.

Haaijer, R., Kamakura, W. A., & Wedel, M. (2001). The “no-choice” alternative to conjoint choice experiments. International Journal of Market Research, 43 (1), 93–106.

Hartmann, A. (2004). Kaufentscheidungsprognose auf Basis von Befragungen. Modelle, Verfahren und Beurteilungskriterien . Wiesbaden: Gabler.

Hensher, D. A. (1994). Stated preference analysis of travel choices: The state of practice. Transportation, 21 (2), 107–133.

Hensher, D. A., & Johnson, L. W. (1981). Applied discrete choice modelling . New York: Wiley.

Huber, J., & Zwerina, K. (1996). The importance of utility balance in efficient choice designs. Journal of Marketing Research, 33 (3), 307–317.

Johnson, R. M. (1987). Adaptive conjoint analysis. In Sawtooth software conference proceedings . Ketchum: Sawtooth Software.

Johnson, R. M., & Orme, B. K. (1996). How many questions should you ask in choice-based conjoint studies? (Sawtooth software research paper series). Sequim: Sawtooth Software.

Kraus, S., Ambos, T. C., Eggers, F., & Cesinger, B. (2015). Distance and perceptions of risk in internationalization decisions. Journal of Business Research, 68 (7), 1501–1505.

Lindley, D. V., & Smith, A. F. (1972). Bayes estimates for the linear models. Journal of the Royal Statistical Society, Series B, 34 (1), 1–41.

Louviere, J. J., & Woodworth, G. (1983). Design and analysis of simulated consumer choice or allocation experiments. An approach based on aggregated data. Journal of Marketing Research, 20 (4), 350–367.

Louviere, J. J., Hensher, D. A., & Swait, J. D. (2000). Stated choice methods. Analysis and application . Cambridge: Cambridge University Press.

Louviere, J. J., Flynn, T. N., & Marley, A. A. J. (2015). Best-worst scaling: Theory, methods, and applications . Cambridge: Cambridge University Press.

Lusk, J. L., & Schroeder, T. C. (2004). Are choice experiments incentive compatible? A test with quality differentiated beef steaks. American Journal of Agricultural Economics, 86 (2), 467–482.

McFadden, D. (1981). Econometric models of probabilistic choice. In C. Manski & D. McFadden (Eds.), Structural analysis of discrete data (pp. 198–272). Cambridge: MIT-Press.

Meissner, M. Oppewal, H., & Huber, J. (2016). How many options? Behavioral responses to two versus five alternatives per choice. Proceedings of the Sawtooth Software conference , 1–18 September.

Miller, K. M., Hofstetter, R., Krohmer, H., & Zhang, Z. J. (2011). How should Consumersʼ willingness to pay be measured? An empirical comparison of state-of-the-art approaches. Journal of Marketing Research, 48 (1), 172–184.

Moore, W. L. (2004). A cross-validity comparison of rating-based and choice-based conjoint analysis models. International Journal of Research in Marketing, 21 (3), 299–312.

Moore, W. L., Gray-Lee, J., & Louviere, J. J. (1998). A cross-validity comparison of conjoint analysis and choice models at different levels of aggregation. Marketing Letters, 9 (2), 195–207.

Orme, B. (2001). Assessing the monetary value of attribute levels with conjoint analysis: Warnings and suggestions (Sawtooth software research paper series). Sequim: Sawtooth Software.

Orme, B. (2002). Formulating attributes and levels in conjoint analysis (Sawtooth software research paper series) . Sequim: Sawtooth Software.

Orme, B. K. (2016). Results of the 2017 Sawtooth Software User Survey . https://www.sawtoothsoftware.com/about-us/news-and-events/news/1693-results-of-2016-sawtooth-software-user-survey .

Orme, B., & Johnson, R.M. (2006). External effect adjustments in conjoint analysis (Sawtooth software research paper series). Sequim: Sawtooth Software.

Page, A. L., & Rosenbaum, H. F. (1992). Developing an effective concept testing program for consumer durables. Journal of Product Innovation Management, 9 , 267–277.

Park, Y.-H., Ding, M., & Rao, V. R. (2008). Eliciting preference for complex products: A web-based upgrading method. Journal of Marketing Research, 45 (5), 562–574.

Rao, V. R., & Sattler, H. (2003). Measurement of price effects with conjoint analysis: Separating informational and allocative effects of price. In Conjoint Measurement (pp. 47–66). Berlin/Heidelberg: Springer.

Rooderkerk, R. P., Van Heerde, H. J., & Bijmolt, T. H. (2011). Incorporating context effects into a choice model. Journal of Marketing Research, 48 (4), 767–780.

Sattler, H. (2005). Markenbewertung: State-of-the-Art. Zeitschrift für Betriebswirtschaft , 2 , 33–57.

Sattler, H. (2006). Methoden zur Messung von Präferenzen für Innovationen. Zeitschrift für Betriebswirtschaftliche Forschung, 54 (6), 154–176.

Sattler, H., Hartmann, A., & Kröger, S. (2004). Number of tasks in choice-based conjoint analysis. Conference proceedings of the 33rd EMAC conference . Murcia.

Sawtooth (1999). The choice-based conjoint (CBC) technical paper (Sawtooth software technical paper series). Sequim: Sawtooth Software.

Sawtooth. (2000). The CBC/HB system for hierarchical Bayes estimation version 4.0 (Sawtooth software technical paper series). Sequim: Sawtooth Software.

Sawtooth. (2004). The CBC latent class technical paper (version 3) (Sawtooth software technical paper series) . Sequim: Sawtooth Software.

Sawtooth. (2013). The MaxDiff system – Technical paper (Sawtooth software technical paper series). Orem: Sawtooth Software.

Sawtooth. (2014). ACBC – Technical paper (Sawtooth software technical paper series). Orem: Sawtooth Software.

Schlereth, C., & Skiera, B. (2016). Two new features in discrete choice experiments to improve willingness-to-pay estimation that result in SDR and SADR: Separated (adaptive) dual response. Management Science, 63 (3), 829–842.

Shocker, A. D., & Srinivasan, V. (1973). Linear programming techniques for multidimensional analysis of preference. Psychometrika , 337–369.

Sloan, N. J. A. (2015). A library of orthogonal arrays. http://neilsloane.com/oadir/ . Accessed 15 Nov 2015.

Srinivasan, V., & Park, C. S. (1997). Surprising robustness of the self-explicated approach to customer preference structure measurement. Journal of Marketing Research, 34 (2), 286–291.

Teichert, T. (2001a). Nutzenschätzung in Conjoint-Analysen: Theoretische Fundierung und empirische Aussagekraft . Wiesbaden: Springer.

Teichert, T. (2001b). Nutzenermittlung in wahlbasierten Conjoint-Analysen. Ein Vergleich zwischen Latent-Class- und hierarchischem Bayes-Verfahren. Zeitschrift für Betriebswirtschaftliche Forschung, 53 (8), 798–822.

Toubia, O., Simester, D. I., Hauser, J. R., & Dahan, E. (2003). Fast polyhedral adaptive conjoint estimation. Marketing Science, 22 (3), 273–303.

Toubia, O., Hauser, J. R., & Simester, D. I. (2004). Polyhedral methods for adaptive choice-based conjoint analysis. Journal of Marketing Research, 41 , 116–131.

Toubia, O., Hauser, J., & Garcia, R. (2007). Probabilistic polyhedral methods for adaptive choice-based conjoint analysis: Theory and application. Marketing Science, 26 (5), 596–610.

Toubia, O., de Jong, M. G., Stieger, D., & Füller, J. (2012). Measuring consumer preferences using conjoint poker. Marketing Science, 31 (1), 138–156.

Train, K. (2009). Discrete choice models with simulation (2nd ed.). Cambridge: Cambridge University Press.

Urban, G. L., & Hauser, J. R. (1993). Design and marketing of new products (2nd ed.). Englewood Cliffs: Prentice Hall.

Urban, G. L., Weinberg, B. D., & Hauser, J. R. (1996). Premarket forecasting of really-new products. Journal of Marketing, 60 (1), 47–60.

Verlegh, P. W. J., Schifferstein, H. N. J., & Wittink, D. R. (2002). Range and number-of-levels in derived and stated measures of attribute importance. Marketing Letters, 13 (1), 41–52.

Voeth, M. (1999). 25 Jahre conjointanalytische Forschung in Deutschland. Zeitschrift für Betriebswirtschaft , Ergänzungsheft 2, 153–176.

Vriens, M., Oppewal, H., & Wedel, M. (1998). Rating-based versus choice-based latent class conjoint models – An empirical comparison. Journal of the Market Research Society, 40 (3), 237–248.

Walker, J., & Ben-Akiva, M. (2002). Generalized random utility model. Mathematical Social Sciences, 43 (3), 303–343.

Wedel, M., & Kamakura, W. A. (2000). Market segmentation. conceptual and methodological foundations (2nd ed.). Boston: Springer.

Wedel, M., Kamakura, W. A., Arora, N., Bemmaor, A., Chiang, J., Elrod, T., Johnson, R. M., Lenk, P., Neslin, S., & Poulsen, C. S. (1999). Discrete and continuous representations of unobserved heterogeneity in choice modeling. Marketing Letters, 10 (3), 219–232.

Wertenbroch, K., & Skiera, B. (2002). Measuring consumers’ willingness to pay at the point of purchase. Journal of Marketing Research, 39 (2), 228–241.

Wittink, D. R., Vriens, M., & Burhenne, W. (1994). Commercial use of conjoint analysis in Europe: Results and critical reflections. International Journal of Research in Marketing, 11 , 41–52.

Wlömert, N., & Eggers, F. (2016). Predicting new service adoption with conjoint analysis: External validity of BDM-based incentive-aligned and dual-response choice designs. Marketing Letters, 27 (1), 195–210.

Zeithammer, R., & Lenk, P. (2009). Statistical benefits of choices from subsets. Journal of Marketing Research, 46 (6), 816–831.

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Felix Eggers

University of Hamburg, Hamburg, Germany

Henrik Sattler & Thorsten Teichert

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Franziska Völckner

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Appendix: R Code

The R code and dataset that correspond to the ebook reader example and estimated models can be found at: http://www.preferencelab.com/data/CBC.R . The estimation uses the mlogit package (Croissant 2012 ), which needs to be installed first. A less documented version of the R code can be found below (# indicates a comment):

# load the library to estimate multinomial choice models. library(mlogit) # load (simulated) data about ebook readers cbc <- read.csv(url("http://www.preferencelab.com/data/ Ebook_Reader.csv")) # convert data for mlogit cbc <- mlogit.data(cbc, choice="Selected", shape="long", alt.var="Alt_id", id.var = "Resp_id") ### calculate models ### ### partworth model ### ml1 <- mlogit(Selected ~ Storage_4GB + Storage_8GB + Screen.size_5inch + Screen.size_6inch + Color_black + Color_white + Price_79 + Price_99 + Price_119 + None | 0, cbc) summary(ml1) # recover reference level estimates (effect-coding) # Storage_16GB -(coef(ml1)["Storage_4GB"] + coef(ml1)["Storage_8GB"]) # Screen.size_7inch -(coef(ml1)["Screen.size_5inch"] + coef(ml1)["Screen.size_6inch"]) # Color_silver -(coef(ml1)["Color_black"] + coef(ml1)["Color_white"]) # Price_139 -(coef(ml1)["Price_79"] + coef(ml1)["Price_99"] + coef(ml1)["Price_119"]) # standard errors of the effects are given by the # square root of the diagonal elements of the # variance-covariance matrix covMatrix <- vcov(ml1) sqrt(diag(covMatrix)) # with effect-coding, the standard error of the reference # level needs to consider the off-diagonal elements of the # corresponding attribute levels # Std. Error Storage_16GB sqrt(sum(covMatrix[1:2, 1:2])) # Std. Error Screen.size_7inch sqrt(sum(covMatrix[3:4, 3:4])) # Std. Error Color_silver sqrt(sum(covMatrix[5:6, 5:6])) # Std. Error Price_139 sqrt(sum(covMatrix[7:9, 7:9])) ### Vector model ### # Storage and Price follow a linear trend. Replacing # parameters leads to a more parsimonious model. ml2 <- mlogit(Selected ~ Storage + Screen.size_5inch + Screen.size_6inch + Color_black + Color_white + Price + None | 0, cbc) summary(ml2) # likelihood ratio test lrtest(ml2, ml1) # incremental willingness-to-pay for storage coef(ml2)["Storage"]/coef(ml2)["Price"] # WTP to upgrade from a black to a white ebook reader (coef(ml2)["Color_white"] - coef(ml2)["Color_black"])/coef(ml2)["Price"] ### Vector model for screen size has sig. worse fit ### ml3 <- mlogit(Selected ~ Storage + Screen.size + Color_black + Color_white + Price + None | 0, cbc) summary(ml3) lrtest(ml3, ml2) ### Testing an ideal point model for screen size ### ml4 <- mlogit(Selected ~ Storage + Screen.size + I(Screen.size**2) + Color_black + Color_white + Price + None | 0, cbc) summary(ml4) # same model fit because no differences in df lrtest(ml4, ml2) ### Adding interactions between screen size and color ### ml5 <- mlogit(Selected ~ Storage + Screen.size_5inch + Screen.size_6inch + Color_black + Color_white + Price + Screen.size_5inch * Color_black + Screen.size_6inch * Color_black + Screen.size_5inch * Color_white + Screen.size_6inch * Color_white + None| 0, cbc) summary(ml5) # likelihood ratio test lrtest(ml2, ml5)

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Eggers, F., Sattler, H., Teichert, T., Völckner, F. (2022). Choice-Based Conjoint Analysis. In: Homburg, C., Klarmann, M., Vomberg, A. (eds) Handbook of Market Research. Springer, Cham. https://doi.org/10.1007/978-3-319-57413-4_23

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Modeling Consumer Decisions: Conjoint Analysis

A guide to choice-based conjoint analysis for product development.

Ritvik Kuila

Towards Data Science

People spend a lot of time making decisions about some of the products and services they purchase. In fact, A recent study showed that the average person spends about 130 hours a year just deciding where to eat. Generally, consumers make purchase decisions by making trade-offs between the various attributes of a product based on the utility it provides them. As marketers or product managers, it is crucial to understand how consumers make these trade-offs and what utility each attribute provides.

To understand the concepts of attributes and utility, let us consider the example of purchasing a new smartphone. The factors one might consider while deciding to purchase are the RAM, Storage Capacity, Camera Specifications, Screen size & Resolution, Brand, Price, etc. These considered factors are called attributes, and consumers derive some utility from each of these attributes. The utility gained from each attribute is also called a part-worth. Each consumer is different and could gain a different utility from an attribute of a product. For example, a photography enthusiast may gain more utility from the Camera Specifications attribute than from other attributes such as RAM and Storage Capacity. In contrast, A gaming enthusiast would gain more utility from the RAM, Storage Capacity, and Screen Size/Resolution. To develop successful products, marketers/product managers must understand the attribute preferences of their customer base and quantify the utility that customers gain from the attributes. Conjoint Analysis is a statistical method used to understand the relative importance/preference of attributes and quantify the utility a consumer gains from each attribute of a product. It can thus be used to model the trade-offs a consumer might make while making a purchase decision.

There are two fundamental assumptions we make while performing a Conjoint Analysis:

• Consumers purchase the product which gives them the highest total utility (sum of individual attribute utilities)
• Consumers follow a compensatory decision-making process. Simply speaking, this means that a positive attribute of a product can compensate for a negative attribute, i.e., customers are willing to make trade-offs.

Market Research Design

The first step in Conjoint Analysis is to design a market research study. Participants for the study are selected by Stratified Random Sampling to be representative of the population or target audience of the product.

Let us once again consider the example of purchasing a smartphone. (Product teams spend a significant amount of time brainstorming the attributes of a product and often conduct focus groups to get more insights from consumers) For the sake of simplicity, let us assume the only attributes are Ram, Storage, Camera, Screen, Brand, and Price.

The questionnaire for this study is designed as shown below:

Participants of the study are given multiple choice sets and prompted to pick one option from each choice set. (I have only provided two random choice sets for the sake of simplicity. In an actual survey, participants are given anywhere between 10 and 20 choice sets based on the number of attributes of the product) The design of these choice sets is a complex task in itself, so I will not delve into that in this article. The questions are framed in the manner shown to simulate an actual decision-making process a consumer would go through. Each participant's response for each choice set is recorded and processed for modeling.

Statistical Model

The response of each participant is recorded and processed. A sample of what the resulting dataset might look like is as shown below:

Before creating the model, we need to ensure that we correctly code the continuous and categorical variables. In this example, I will consider all the attributes except the 'Brand' as continuous. We then run a Logistic Regression with 'Choice' as the dependant variable and the attributes as the independent variables. It would also be useful to force the intercept to 0 for this model because when all the dependent variables are 0, there should technically be 0 utility for the product. This can be done in R using this code:

Model Results and Interpretation

After we run the regression, we obtain the coefficients for each attribute. A sample of this is as shown below:

These coefficients can be interpreted as in a regular Logistic Regression. In this case, the log-odds that we model using Logistic Regression represent the utility the consumer gains from an attribute. So, A 1GB increase in 'RAM' results in a 2.1 unit increase in utility on average for our customers. Similarly, a 1$ increase in 'Price' results in a 0.08 unit decrease in utility on average for our customers. We also understand that customers value Brand 'C' more than Brands' A' and 'B'. (Brand 'D' is not included in the coefficients table as it is taken as the reference with coefficient 0)

Finally, we can calculate the total utility and probability of purchase for a product based on its attribute as shown below: (These results and calculations are based on random data that I created, not actual data. This might make some of the results seem illogical)

With the results of our model, we can test multiple specifications for the product attributes and arrive at the total utility and probability of purchase for our target customers. This is particularly useful when designing a new product to launch into the market.

Market Simulation

We can also use this method to simulate the market and estimate market share for a new product. Instead of running the Logistic Regression on the entire data of all the participants of the market research study, we run a Logistic Regression on each participant's responses. This gives us the total utility for a product and the probability of purchase for each participant in the sample. Since the sample is selected to be representative of the population, the results of the sample can be extrapolated to the entire population to arrive at an estimated market share. More complex methods such as Hierarchical Bayesian Models can also be used to arrive at more statistically significant results.

Conjoint Analysis is a powerful method to understand the product attributes that the consumers prefer in a particular environment. It can be used for designing a variety of products and even services. Professionally, I have used this method to understand the customers’ food preferences at a quick-service restaurant. It is also often used for Attribute-Based Pricing. While marketing decisions are a combination of art and science, this method is a powerful tool to remove subjectivity and personal biases while designing products or services. Needless to say, it is a method that will benefit every Marketing Analyst.

Written by Ritvik Kuila

Data Scientist with a passion for Applied Statistics and Marketing Science

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