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Writing a Research Strategy

This page is focused on providing practical tips and suggestions for preparing The Research Strategy, the primary component of an application's Research Plan along with the Specific Aims. The guidance on this page is primarily geared towards an R01-style application, however, much of it is useful for other grant types as well.

Developing the Research Strategy

The primary audience for your application is your peer review group. When writing your Research Strategy, your goal is to present a well-organized, visually appealing, and readable description of your proposed project and the rationale for pursuing it. Your writing should be streamlined and organized so your reviewers can readily grasp the information. If it's a key point, repeat it, then repeat it again. Add more emphasis by putting the text in bold , or bold italics . If writing is not your forte, get help. For more information, please visit  W riting For Reviewers .

How to Organize the Research Strategy Section

How to organize a Research Strategy is largely up to the applicant. Start by following the NIH application instructions and guidelines for formatting attachments such as the research plan section.

It is generally structured as follows:

Significance

For Preliminary Studies (for new applications) or a Progress Report (for renewal and revision applications).

• You can either include preliminary studies or progress report information as a subsection of Approach or integrate it into any or all of the three main sections.
• If you do the latter, be sure to mark the information clearly, for example, with a bold subhead.

 Helpful tips to consider when formatting:

• Organize using bold headers or an outline or numbering system—or both—that are used consistently throughout.
• Start each section with the appropriate header: Significance, Innovation, or Approach.
• Organize the Approach section around the Specific Aims.

For most applications, you need to address Rigor ous Study Design  by describing the experimental design and methods you propose and how they will achieve robust and unbiased results. See the NIH guidance for elaboration on the 4 major areas of rigor and transparency emphasized in grant review. These requirements apply to research grant, career development, fellowship, and training applications.

Tips for Drafting Sections of the Research Strategy

Although you will emphasize your project's significance throughout the application, the Significance section should give the most details. The farther removed your reviewers are from your field, the more information you'll need to provide on basic biology, importance of the area, research opportunities, and new findings. Reviewing the potentially relevant study section rosters may give you some ideas as to general reviewer expertise. You will also need to describe the prior and preliminary studies that provide a strong scientific rationale for pursuing the proposed studies, emphasizing the strengths and weaknesses in the rigor and transparency of these key studies.

This section gives you the chance to explain how your application is conceptually and/or technically innovative. Some examples as to how you might do this could include but not limited to:

• Demonstrate the proposed research is new and unique, e.g., explores new scientific avenues, has a novel hypothesis, will create new knowledge.
• Explain how the proposed work can refine, improve, or propose a new application of an existing concept or method.

If your proposal is paradigm-shifting or challenges commonly held beliefs, be sure that you include sufficient evidence in your preliminary data to convince reviewers, including strong rationale, data supporting the approach, and clear feasibility. Your job is to make the reviewers feel confident that the risk is worth taking.

For projects predominantly focused on innovation and outside-the-box research, investigators may wish to consider mechanisms other than R01s for example (e.g., exploratory/developmental research (R21) grants, NIH Director's Pioneer Award Program (DP1), and NIH Director's New Innovator Award Program (DP2).

The Approach section is where the experimental design is described. Expect your assigned reviewers to scrutinize your approach: they will want to know what you plan to do, how you plan to do it, and whether you can do it. NIH data show that of the peer review criteria, approach has the highest correlation with the overall impact score. Importantly, elements of rigorous study design should be addressed in this section, such as plans for minimization of bias (e.g. methods for blinding and treatment randomization) and consideration of relevant biological variables. Likewise, be sure to lay out a plan for alternative experiments and approaches in case you get uninterpretable or surprising results, and also consider limitations of the study and alternative interpretations. Point out any procedures, situations, or materials that may be hazardous to personnel and precautions to be exercised. A full discussion on the use of select agents should appear in the Select Agent Research attachment. Consider including a timeline demonstrating anticipated completion of the Aims. 

Here are some pointers to consider when organizing your Approach section:

• Enter a bold header for each Specific Aim.
• Under each aim, describe the experiments.
• If you get result X, you will follow pathway X; if you get result Y, you will follow pathway Y.
• Consider illustrating this with a flowchart.

Preliminary Studies

If submitting a new application to a NOFO that allows preliminary data, it is strongly encouraged to include preliminary studies. Preliminary studies demonstrate competency in the methods and interpretation. Well-designed and robust preliminary studies also serve to provide a strong scientific rationale for the proposed follow-up experiments. Reviewers also use preliminary studies together with the biosketches to assess the investigator review criterion, which reflects the competence of the research team. Provide alternative interpretations to your data to show reviewers you've thought through problems in-depth and are prepared to meet future challenges. As noted above, preliminary data can be put anywhere in the Research Strategy, but just make sure reviewers will be able to distinguish it from the proposed studies. Alternatively, it can be a separate section with its own header.

Progress Reports

If applying for a renewal or a revision (a competing supplement to an existing grant), include a progress report for reviewers.

Create a header so reviewers can easily find it and include the following information:

• Project period beginning and end dates.
• Summary of the importance and robustness of the completed findings in relation to the Specific Aims.
• Account of published and unpublished results, highlighting progress toward achieving your Specific Aims.

Other Helpful Tips

Referencing publications.

References show breadth of knowledge of the field and provide a scientific foundation for your application. If a critical work is omitted, reviewers may assume the applicant is not aware of it or deliberately ignoring it.

Throughout the application, reference all relevant publications for the concepts underlying your research and your methods. Remember the strengths and weaknesses in the rigor of the key studies you cite for justifying your proposal will need to be discussed in the Significance and/or Approach sections.

Read more about Bibliography and References Cited at Additional Application Elements .

Graphics can illustrate complex information in a small space and add visual interest to your application. Including schematics, tables, illustrations, graphs, and other types of graphics can enhance applications. Consider adding a timetable or flowchart to illustrate your experimental plan, including decision trees with alternative experimental pathways to help your reviewers understand your plans.

Video may enhance your application beyond what graphics alone can achieve. If you plan to send one or more videos, you'll need to meet certain requirements and include key information in your Research Strategy. State in your cover letter that a video will be included in your application (don't attach your files to the application). After you apply and get assignment information from the Commons, ask your assigned Scientific Review Officer (SRO) how your business official should send the files. Your video files are due at least one month before the peer review meeting.

However, you can't count on all reviewers being able to see or hear video, so you'll want to be strategic in how you incorporate it into your application by taking the following steps:

• Caption any narration in the video.
• Include key images from the video
• Write a description of the video, so the text would make sense even without the video.

Tracking for Your Budget

As you design your experiments, keep a running tab of the following essential data:

• Who. A list of people who will help (for the Key Personnel section later).
• What. A list of equipment and supplies for the experiments
• Time. Notes on how long each step takes. Timing directly affects the budget as well as how many Specific Aims can realistically be achieved.

Jotting this information down will help when Creating a Budget  and complete other sections later.

Review and Finalize Your Research Plan

Critically review the research plan through the lens of a reviewer to identify potential questions or weak spots.

Enlist others to review your application with a fresh eye. Include people who aren't familiar with the research to make sure the proposed work is clear to someone outside the field.

When finalizing the details of the Research Strategy, revisit and revise the Specific Aims as needed. Please see Writing Specific Aims . 

Want to contact NINDS staff? Please visit our Find Your NINDS Program Officer page to learn more about contacting Program Officer, Grants Management Specialists, Scientific Review Officers, and Health Program Specialists. Find NINDS Program Officer

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• What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on September 5, 2024 by Pritha Bhandari.

A research design is a strategy for answering your   research question  using empirical data. Creating a research design means making decisions about:

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.

You might have to write up a research design as a standalone assignment, or it might be part of a larger   research proposal or other project. In either case, you should carefully consider which methods are most appropriate and feasible for answering your question.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, other interesting articles, frequently asked questions about research design.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types.

• Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
• Descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

• Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalize your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study , your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question .

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

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As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method , you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample—by mail, online, by phone, or in person?

If you’re using a probability sampling method , it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method , how will you avoid research bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organizing and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymize and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarize your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarize your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Research Strategy

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• Mina Tajvidi 3 &
• Azhdar Karami 3 , 4  

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This chapter sets out the various steps that are necessary in executing this study and thereby satisfying its objectives. It aims to explain in detail all aspects of the research, with particular reference to all of the key theoretical and practical issues involved. This chapter discusses the research design and methodology and the survey. Various research philosophies and approaches presented in literature will be discussed with focuses on their application to this study. This chapter is composed of seven sections which cover the research objectives and questions, research philosophy, research approach, research strategies, research choices, research time horizons, and research techniques and procedures. Each sub-section of this chapter covers one of these aspects of the research. The first sub-section addresses the objective and questions of the study. The first sub-section addresses the objective and questions of the study. The second sub-section presents the research methods and strategy and includes research philosophy, research approach, research design, data collection process and constructing the questionnaire. The third sub-section addresses the conceptual framework and research variables. The fourth sub-section presents the characteristics of SMEs. The fifth sub-section reveals the proposed model and hypotheses. The sixth sub-section presents the research choice and data analysis.

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Acquaah, M. (2007). Managerial social capital, strategic orientation, and organizational performance in an emerging economy. Strategic Management Journal , 28 , 1235–1255.

Article   Google Scholar  

Acur, N., Kandemir, D., and Boer, H. (2012). Strategic alignment and new product development: Drivers and performance effects. Journal of Product Innovation Management , 29 (2), 304–318.

Anderson, K. S. and Sandmann, L. (2009). Toward a model of empowering practices in youth—adult partnerships. Journal of Extension , 47 (2), 1–8.

Google Scholar  

Ansoff, H. I. (1969). Business Strategy: Selected Readings . Baltimore, MD: Penguin Books.

Bahemia, H. and Squire, B. (2010). A contingent perspective of open innovation in new product development projects. International Journal of Innovation Management , 14 (4), 603–627.

Barkham, R., Gudgin, G., Hart, M., and Hanvey, E. (1996). The Determinants of Small Firm Growth: An Inter-Regional Study in the United Kingdom 1986–90 . London: Jessica Kingsley Publishers.

Becker, B. E. and Huselid, M. A. (2010). SHRM and job design: Narrowing the divide. Journal of Organizational Behavior , 31 (2–3), 379–388.

Berry, M. M. and Taggart, J. H. (1998). Combining technology and corporate strategy in small high tech firms. Research Policy , 26 (7), 883–895.

Blaikie, N. (1993). Approaches to Social Enquiry . Cambridge: Polity.

Blaxter, L., Hughes, C., and Tight, M. (2010). How to Research , (4th edn). Maidenhead: McGraw-Hill International.

Borch, O. J., Huse, M., and Senneseth, K. (1999). Resource configuration, competitive strategies, and corporate entrepreneurship: An empirical examination of small firms. Entrepreneurship Theory and Practice , 24 (1), 49–70.

Bryman, A. and Bell, E. (2003). Business Research Methods . Oxford: Oxford University Press.

Burns, A. and Bush, R. F. (1998). Marketing Research (2nd edition). Upper Saddle River, NJ: Prentice Hall.

Burns, P. (2007). Entrepreneurship and Small Business (2nd edition). Basingstoke: Palgrave.

Calantone, R., Garcia, R., and Dröge, C. (2003). The effects of environmental turbulence on new product development strategy planning. Journal of Product Innovation Management , 20 (2), 90–103.

Cameron, R. and Molina-Azorin, J. F. (2011). The acceptance of mixed methods in business and management research. International Journal of Organizational Analysis , 19 (3), 256–271.

Chadee, D. and Roxas, B. (2013). Institutional environment, innovation capacity and firm performance in Russia. Critical Perspectives on International Business , 9 (1/2), 19–39.

Chen, J., Damanpour, F., and Reilly, R. R. (2010). Understanding antecedents of new product development speed: A meta-analysis. Journal of Operations Management , 28 (1), 17–33.

Cooper, D. R. and Schindler, P. S. (2003). Business Research Methods . New York: McGrow Hill.

Creswell, J. W. and Clark, V. L. P. (2007). Designing and Conducting Mixed Methods Research . Thousand Oaks, CA: Sage.

Crick, D. and Spence, M. (2005). The internationalisation of “high performing” UK high-tech SMEs: A study of planned and unplanned strategies. International Business Review , 14 (2), 167–185.

Crotty, M. (1998). The Foundations of Social Research: Meaning and Perspective in the Research Process . London: Sage Publication.

Delahaye, B. L. (2005). Knowledge management in a SME. International Journal of Organisational Behaviour , 9(3), 604–614.

Demick, D. H. and O’Reilly, A. J. (2000). Supporting SME internationalisation: A collaborative project for accelerated export development. Irish Marketing Review , 13 , 34–45.

Easterby-Smith, M., Thorpe, R., and Jackson, P. (2012). Management Research . London: Sage Publications.

Eisenhardt, K. M. and Martin, J. A. (2000). Dynamic capabilities: What are they? Strategic Management Journal , 21 (10–11), 1105–1121.

Eldabi, T., Irani, Z., Paul, R. J., and Love, P. E. (2002). Quantitative and qualitative decision-making methods in simulation modelling. Management Decision , 40 (1), 64–73.

Elfring, T. and Hulsink, W. (2003). Networks in entrepreneurship: The case of high-technology firms. Small Business Economics , 21 (4), 409–422.

Eng, L. L. and Shackell, M. (2001). The implications of long-term performance plans and institutional ownership for firms’ research and development (R&D) investments. Journal of Accounting, Auditing & Finance , 16 (2), 117–139.

European Commission (2005). The New SME Definition. User Guide and Model Declaration . Brussels: Enterprise and Industry Publications.

Ferreira, J. (2003). Estudo do crescimento e desempenho das pequenas empresas: A influência da orientação estratégica empreendedor . Unpublished doctoral thesis, Universidade da Beira Interior, Covilhã, Portugal.

Fraenkel, J. R., and Wallen, N. E. (2000). How to Design and Evaluate Research in Education . New York: McGraw-Hill.

Frankfort-Nachmias, C. and Nachmias, D. (2000). Research Methods in the Social Sciences . New York: Worth.

Gill, J. and Johnson, P. (2010). Research Methods for Managers . London: Sage.

Greene, J. C., Caracelli, V. J., and Graham, W. F. (1989). Toward a conceptual framework for mixed-method evaluation designs. Educational Evaluation and Policy Analysis , 11 (3), 255–274.

Gronum, S., Verreynne, M. L., and Kastelle, T. (2012). The role of networks in small and medium-sized enterprise innovation and firm performance. Journal of Small Business Management , 50 (2), 257–282.

Guba, E. G. and Lincoln, Y. S. (1994). Competing paradigms in qualitative research. In N. K. Denzin & Y. S. Lincoln, eds. Handbook of Qualitative Research , 105–117. Thousand Oaks, CA: Sage.

Hair, J. F., Bush, R. P., and Ortinau, D. J. (2000). Marketing Research: A Practical Approach for the New Millennium . Boston: Irwin\McGraw-Hill.

Hammersley, M. (2000). The relevance of qualitative research. Oxford Review of Education , 26 (3–4), 393–405.

Hassanain, M. A. and Al-Saadi, S. (2005). A framework model for outsourcing asset management services. Facilities , 23 (1/2), 73–81.

Hurley, R. F. and Hult, G. T. M. (1998). Innovation, market orientation, and organizational learning: An integration and empirical examination. Journal of Marketing , 62 (3), 42–54.

Johnson, G., Whittington, R., Scholes, K., and Pyle, S. (2011). Exploring Strategy: Text & Cases . Harlow: Financial Times Prentice Hall.

Karami, A. (2011). Management Research (Custom Publication). UK: Palgrave McMillan.

Karia, M., Bathula, H., and Abbott, M. (2015). An experiential learning approach to teaching business planning: Connecting students to the real world. In M. Li and Y. Zhao eds, Exploring Learning & Teaching in Higher Education , 123–144. Berlin, Heidelberg: Springer.

Koc, T. and Ceylan, C. (2007). Factors impacting the innovative capacity in large-scale companies. Technovation , 27 (3), 105–114.

Kodama, M. (2007). Innovation and knowledge creation through leadership-based strategic community: Case study on high-tech company in Japan. Technovation , 27 (3), 115–132.

Krishnaswamy, A. (2004). Participatory research: Strategies and tools. Practitioner: Newsletter of the National Network of Forest Practitioners , 22 , 17–22.

Leshem, S. and Trafford, V. (2007). Overlooking the conceptual framework. Innovations in Education and Teaching International , 44 (1), 93–105.

Liu, P. L., Chen, W. C., and Tsai, C. H. (2005). An empirical study on the correlation between the knowledge management method and new product development strategy on product performance in Taiwan’s industries. Technovation , 25 (6), 637–644.

Lu, J. W. and Beamish, P. W. (2001). The internationalization and performance of SMEs. Strategic Management Journal , 22 (6–7), 565–586.

Macionis, J. J. and Gerber, L. M. (2011). Sociology (7th Canadian edn). Toronto, ON: Prentice Hall.

Maidique, M. A. and Zirger, B. J. (1989). A study of success and failure in product innovation: The case of the US electronics industry. IEEE Transactions on Engineering Management , 31 (4), 192–203.

March-Chorda, I., Gunasekaran, A., and Lloria-Aramburo, B. (2002). Product development process in Spanish SMEs: An empirical research. Technovation , 22 (5), 301–312.

Marques, C. and Ferreira, J. (2009). SME innovative capacity, competitive advantage and performance in a “traditional” industrial region of Portugal. Journal of Technology Management and Innovation , 4 (4), 53–68.

Marques, C. and Monteiro-Barata, J. (2006). Determinants of the innovation process: An empirical test for the Portuguese manufacturing industry. Management Research: Journal of the Iberoamerican Academy of Management , 4 (2), 113–126.

McGrath, J. E. and Johnson, B. A. (2003). Methodology makes meaning: How both qualitative and quantitative paradigms shape evidence and its interpretation. In P. M. Camic, J. E. Rhodes, and L. Yardley, eds. Qualitative Research in Psychology: Expanding Perspectives in Methodology and Design , 31–48. Washington, DC: American Psychological Association.

Chapter   Google Scholar  

McNeill, C. (ed.) (1990). Craniomandibular Disorders: Guidelines for Evaluation, Diagnosis, and Management . Chicago, IL: Quintessence Publishing Co., Inc.

Merriam, S. B. and Simpson, E. L. (2000). A Guide to Research for Educators and Trainers of Adults (2nd edn). Malabar, FL: Krieger.

Mogollon, R. and Vaquero, A. (2004). El comportamiento innovador y los resultados de la empresa: Un análisis empírico. Proceedings of the XVIII Congreso Anual y XIV Congreso Hispano-Francês , AEDEM, Ourense, Spain.

Morel, L. and Boly, V. (2006). New Product Development Process (NPDP): Updating the identification stage practices. International Journal of Product Development , 3 (2), 232–251.

Nicholas, J., Ledwith, A., and Perks, H. (2011). New product development best practice in SME and large organisations: Theory vs practice. European Journal of Innovation Management , 14 (2), 227–251.

Niglas, K. (2004). The Combined Use of Qualitative and Quantitative Methods in Educational Research . Dissertation, Faculty of Educational Sciences, Tallinn Pedagogical University, Tallinn, Estonia.

O’Cathain, A. (2009). Editorial: Mixed methods research in the health sciences: A quiet revolution. Journal of Mixed Methods Research , 3 (1), 3–6.

Parker, H. (2000). Interfirm collaboration and the new product development process. Industrial Management & Data Systems , 100 (6), 255–260.

Phelan, S. E., Ferreira, M., and Salvador, R. (2002). The first twenty years of the Strategic Management Journal . Strategic Management Journal , 23 (12), 1161–1168.

Picard, C. A. (2000) The Many Meanings of Mediation: A Sociological Study of Mediation in Canada . Unpublished doctoral dissertation, Department of Sociology, Carleton University, Ottawa.

Ponterotto, J. G. (2005). Qualitative research in counseling psychology: A primer on research paradigms and philosophy of science. Journal of Counseling Psychology , 52 (2), 126–136.

Prajogo, D. I. and Ahmed, P. K. (2006). Relationships between innovation stimulus, innovation capacity, and innovation performance. R&D Management , 36 (5), 499–515.

Rauch, A., Wiklund, J., Lumpkin, G. T., and Frese, M. (2009). Entrepreneurial orientation and business performance: An assessment of past research and suggestions for the future. Entrepreneurship Theory and Practice , 33 (3), 761–787.

Robson, C. (2007). How to Do a Research Project. A guide for undergraduatestudents . USA: Blackwell Publishing.

Rocco, T. S. and Plakhotnik, M. S. (2009). Literature reviews, conceptual frameworks, and theoretical frameworks: Terms, functions, and distinctions. Human Resource Development Review , 8 (1), 120–130.

Romijn, H. and Albaladejo, M. (2002). Determinants of innovation capability in small electronics and software firms in southeast England. Research Policy , 31 (7), 1053–1067.

Rothwell, R. and Dodgson, M. (1991). External linkages and innovation in small and medium-sized enterprises. R&D Management , 21 (2), 125–138.

Rothwell, R. and Dodgson, M. (1994). Innovation and size of firm. In M. Dodgson and R. Rothwell, eds, The Handbook of Industrial Innovation . Cheltenham: Edward Elgar.

Rowley, J. and Slack, F. (2004). Conducting a literature review. Management Research News , 27 (6), 31–39.

Sarantakos, S. (1993). Social Research . South Melbourne: Macmillan.

Book   Google Scholar  

Saunders, M., Lewis, P., and Tornhill, A. (2007). Research Methods for Business Students , (4th edn). Essex, England; Pearson Education Ltd.

Sawyer, O., McGee, J., and Peterson, M. (2003). Perceived uncertainty and firm performance in SMEs. International Small Business Journal , 21 (3), 269–289.

Schumpeter, J. A. (1911). Theorie der wirtschaftlichen Entwicklung. Eine Untersuchung ueber Unternehmergewinn, Kapital, Kredit, Zins und den Konjunkturzyklus . Berlin: Duncker und Humblot; translated by Redvers Opie, 1934 & 1963, The Theory of Economic Development: an Inquiry into Profits, Capital, Credit, Interest and the Business Cycle , Oxford: Oxford University Press.

Siegel, S. C. and Castellan, N. J. Jr (1988). Nonparametric Statistics for the Behavioural Sciences . New York: McGraw-Hill.

Silva, M., Raposo, M., and Ferrão, M. (2004). Capacidade inovadora empresarial: Estudo dos factores que influenciam an Inovação no Processo. Proceedings of the XVIII Congreso Anual y XIV Congreso Hispano-Frances de AEDEM , Ourense, Spain.

Silva, M. J., Leitao, J., and Raposo, M. (2008). Barriers to innovation faced by manufacturing firms in Portugal: How to overcome it for fostering business excellence? International Journal of Business Excellence , 1 (1), 92–105.

Simsek, T., Kocabas, F., Zheng, J., DeBerardinis, R. J., Mahmoud, A. I., Olson, E. N. et al. (2010). The distinct metabolic profile of hematopoietic stem cells reflects their location in a hypoxic niche. Cell Stem Cell , 7 (3), 380–390.

Slater, S. F., Olson, E. M., and Hult, G. T. M. (2006). The moderating influence of strategic orientation on the strategy formation capability—performance relationship. Strategic Management Journal , 27 (12), 1221–1231.

Smith, D. (2003). Strategic alliance and competitive advantages in the European aerospace industry. the case of BMW Rolls-Royce GmbH. European Business Review , 15 (4), 262–276.

Tajvidi, M., Karami, A., and Tajvidi, R. (2010). An Empirical Study of Entrepreneurship Effect on the productivity Index in Industrial Manufacturing Sector in Iran . International Conference on Innovation and Entrepreneurship, November 11–12, Izmir Economic University, Izmir, Turkey.

Tashakkori, A. and Teddlie, C. (2003). Issues and dilemmas in teaching research methods courses in social and behavioural sciences: US perspective. International Journal of Social Research Methodology , 6 (1), 61–77.

Tingling, P., Parent, M., and Wade, M. (2003). Extending the capabilities of Internet-based research: Lessons from the field. Internet Research , 13 (3), 223–235.

Verworn, B. (2009). A structural equation model of the impact of the “fuzzy front end” on the success of new product development. Research Policy , 38 (10), 1571–1581.

Von Zedtwitz, M. and Gassmann, O. (2002). Market versus technology drive in R&D internationalization: Four different patterns of managing research and development. Research Policy , 31 (4), 569–588.

Wong, P. K., Ho, Y. P., and Autio, E. (2005). Entrepreneurship, innovation and economic growth: Evidence from GEM data. Small Business Economics , 24 (3), 335–350.

Zahra, S. A. and Garvis, D. M. (2000). International corporate entrepreneurship and firm performance: The moderating effect of international environmental hostility. Journal of Business Venturing , 15 (5), 469–492.

Zikmund, W. G. (1991). Exploring Marketing Research (fourth edition). Chicago, IL: Dryden Press.

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Research strategy sets the general direction of research. It is one of the elements of research methodology and includes the process in which research is carried out. The right research strategy must be selected based on research objectives as well as questions, the amount of resources available and time, the philosophical foundations of the researcher, and the scope of existing knowledge about the studied area (Wedawatta G., Ingirige B., Amaratunga D. 2011, p. 3-4).

• 1 Types of research strategies
• 2 Quantitative and qualitative research strategies
• 3 Criteria to choose a research strategy
• 4 Research strategy impact on limitations of the research results
• 5 References

Types of research strategies

There are different research strategies, but they are largely convergent. Therefore, it is important to choose the most favorable strategy for a particular study. Part of the research strategies used in management and business are (Wedawatta G., Ingirige B., Amaratunga D. 2011, p. 4):

• experiment,
• action research,
• case study,
• grounded theory,
• ethnography,
• cross sectional studies.

Surveys are used to obtain data that will guide research groups (from choosing samples to questions and topics). It is a successful method that helps generate a large amount of data from many people. Respondents can be selected based on a different number of features, such as: sex, age, race, sexuality, social class or demographic questions. These questions are usually placed first in the survey. Such information may be needed by a researcher who does not know which focus groups to use (homogeneous or heterogeneous) (Hesse-Biber S., Leavy P. 2011, p. 173).

The experiment is a detailed study using the general empirical method. Processes and phenomena are tested in controlled, rigorous conditions. The main principle in each experiment is to change the selected individual component in the test procedure, the remaining components are fixed - they do not change. During a given experiment, the researcher introduces a new factor to the process, modifying its rate. This factor is called an independent variable or an experimental factor. On the other hand, factors changed under the influence of an independent variable are dependent variables (Novikov A., Novikov D. 2013, p. 56-56).

Action research

Action research is systematic research that helps people solve everyday problems, looking for effective solutions. The aim of this research is to involve complex dynamics in all social contexts. Continuous cycles of designed research seek to solve problems that occur in specific situations and locations, which allows for obtaining measures that increase efficiency and effectiveness of work , in: social organizations and agencies, human and health services, companies, schools. Thanks to this, it builds knowledge that strengthens social and professional practices and increases the well-being of people who take part in it (Stringer E. 2014, p. 1).

Case study is one of the most popular research methods used by researchers in industrial marketing . The reason for this may partly be the nature of the subject. The main object of the study are relationships and organizations that have a complex structure and are difficult to access than (for example) consumer markets. The study of a single or small number of entities brings a lot of qualitative data that can be described as a case study, providing insight into their nature of phenomena (Easton G. 2010, p. 118).

Grounded theory

Grounded theory is recognized as a qualitative research method. It aims to develop a theory that is based on the systematic collection and analysis of data. From other quantitative methods, it is distinguished by a specific approach to the development of theory. Grounded theory believes that there should be a continuous relationship between two activities: collecting and analyzing data. It allows the appearance of rich and original results, closely related to data, and offers well-marked data analysis procedures. It guarantees the researchers self-confidence, because they can indicate a large number of cases in the data that are associated with the concept produced (Urquhart C., Lehmann† H., Myers M. 2010, p. 357-358)

Ethnography

Ethnography favors nuanced, contextually rich and involved nuanced examples of qualitative social research, the driving force of the produced data are daily interactions. It includes a combination of different field techniques (interviews, observations, audiovisual recording, note taking, research of autochthonous literature) inscribed in the participants' observation. It is based on the conviction and trust that data is created thanks to the deep interaction between researchers. Ethnographers often treat data as a gift from informers with all the mutual consequences that suggest the exchange of these gifts (Falzon M. 2016, p. 1).

Cross sectional studies

Cross sectional study is one of the types of observational studies. At the same time, the researcher measures the exposure and the result of the study participants in it. Participants of the study are selected according to the inclusion and exclusion criteria adopted for a given study. After selecting the participants, the researcher assesses the exposure and results following the task. Cross-sectional studies are used to assess the incidence of diseases in clinical samples and for population studies (Setia M. 2016, p. 261)

Quantitative and qualitative research strategies

Qualitative and quantitative research strategies are two different approaches to conducting research.

Quantitative research is a method of gathering and analyzing numerical data to understand patterns and trends. This approach is often used in social sciences and natural sciences to examine cause-and-effect relationships, and it relies on statistical analysis of data. Surveys, experiments, and observational studies are some examples of quantitative research methods.

On the other hand, qualitative research is a method of gathering and analyzing non-numerical data, such as words, images, and observations. It is often used in fields such as sociology, anthropology, and psychology, and it aims to understand the meaning and experiences of individuals and groups. Some examples of qualitative research methods include ethnography, case study, and grounded theory.

Both qualitative and quantitative research strategies have their own advantages and disadvantages, and the choice of which approach to use will depend on the research question and the overall goals of the study. In some cases, a mixed-methods approach, which combines both qualitative and quantitative methods, may be most appropriate.

Criteria to choose a research strategy

There are several criteria to consider when choosing a research strategy. These include:

• Relevance : The research strategy should be relevant to the research question and the purpose of the study.
• Feasibility : The research strategy should be feasible to implement within the given time and budget constraints.
• Validity : The research strategy should be able to provide valid and reliable results that can be generalized to the population of interest .
• Ethical considerations : The research strategy should take into account any ethical considerations, such as informed consent and the protection of participants' rights.
• Study design : The research strategy should be appropriate for the study design, whether it be quantitative, qualitative or mixed-methods.
• Data collection methods : The research strategy should include appropriate methods for collecting and analyzing data, such as surveys, interviews, or experiments.
• The sample size : The research strategy should consider the sample size to be able to generalize the results to the population of interest.
• The resources available : The research strategy should be compatible with the resources available to the researcher, such as time, budget, and personnel.

Ultimately, the choice of research strategy will depend on the specific research question and the overall goals of the study.

Research strategy impact on limitations of the research results

The research strategy chosen can have a significant impact on the limitations of the research results. Each approach has its own strengths and weaknesses, and the choice of strategy can influence the reliability and validity of the data, as well as the generalizability of the findings.

For example, quantitative research is often considered to be more objective and reliable than qualitative research because it relies on numerical data and statistical analysis. However, quantitative research can be limited by the specific research question and the methods used, and the results may not fully capture the complexity of social phenomena.

On the other hand, qualitative research can provide rich, in-depth data that can give a detailed understanding of a particular phenomenon. However, qualitative research can be more subjective and may be influenced by the researcher's own biases. Additionally, the sample size in qualitative research is usually small, which can limit the generalizability of the findings.

A mixed-methods approach can overcome some of the limitations of both qualitative and quantitative research by combining the strengths of both methods. However, it also requires more resources and can be more complex to implement.

In summary, the limitations of the research results are closely tied to the research strategy chosen. Therefore, it is important to consider the strengths and weaknesses of different approaches when choosing a research strategy and to be aware of the limitations of the research results.

• Easton G. (2010), Critical realism in case study research , Industrial Marketing Management, nr. 39, p. 118-128
• Falzon M. (2016), Multi-sited Ethnography: Theory, Praxis and Locality in Contemporary Research , Taylor & Francis Group, London, p. 1
• Gravetter F., Forzano L. (2017), Research Methods for the Behavioral Sciences , Cengage Learning, USA
• Hesse-Biber S., Leavy P. (2011), The Practice of Qualitative Research , SAGE Publications, USA, p. 173
• Novikov A., Novikov D. (2013), Research Methodology: From Philosophy of Science to Research Design , CRC Press, Boca Raton, p. 56-56
• Rainer A. (2011), The longitudinal, chronological case study research strategy: A definition, and an example from IBM Hursley Park , Information and Software Technology , Volume 53, Issue 7, p. 730-746
• Setia M. (2016), Methodology Series Module 3: Cross-sectional Studies , Indian J Dermatol, nr 61, p. 261-264
• Stringer E. (2014), Action Research , SAGE Publications, USA, p. 1
• Urquhart C., Lehmann† H., Myers M. (2010), Putting the ‘theory’ back into grounded theory: guidelines for grounded theory studies in information systems , Info Systems J, nr 20, p. 357-358
• Whitley B., Kite M. , Adams H. (2013), Principles of Research in Behavioral Science , Routledge, New York
• Wedawatta G., Ingirige B., Amaratunga D. (2011), Case study as a research strategy: Investigating extreme weather resilience of construction SMEs in the UK , the University of Salford, Manchester, p. 3-4

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This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

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What Is Strategy?

For starters, it’s not the same as operational effectiveness. by Michael E. Porter

Summary .   

Today’s dynamic markets and technologies have called into question the sustainability of competitive advantage. Under pressure to improve productivity, quality, and speed, managers have embraced tools such as TQM, benchmarking, and re-engineering. Dramatic operational improvements have resulted, but rarely have these gains translated into sustainable profitability. And gradually, the tools have taken the place of strategy. In his five-part article, Michael Porter explores how that shift has led to the rise of mutually destructive competitive battles that damage the profitability of many companies. As managers push to improve on all fronts, they move further away from viable competitive positions. Porter argues that operational effectiveness, although necessary to superior performance, is not sufficient, because its techniques are easy to imitate. In contrast, the essence of strategy is choosing a unique and valuable position rooted in systems of activities that are much more difficult to match. Porter thus traces the economic basis of competitive advantage down to the level of the specific activities a company performs. Using cases such as Ikea and Vanguard, he shows how making trade-offs among activities is critical to the sustainability of a strategy. Whereas managers often focus on individual components of success such as core competencies or critical resources, Porter shows how managing fit across all of a company’s activities enhances both competitive advantage and sustainability. While stressing the role of leadership in making and enforcing clear strategic choices, Porter also offers advice on how companies can reconnect with strategies that have become blurred over time.

[   I   ] Operational Effectiveness Is Not Strategy

For almost two decades, managers have been learning to play by a new set of rules. Companies must be flexible to respond rapidly to competitive and market changes. They must benchmark continuously to achieve best practice. They must outsource aggressively to gain efficiencies. And they must nurture a few core competencies in race to stay ahead of rivals.

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Phase #2: Clearly Define Your Research Strategy

Read about the 5 Phases of an effective marketing research process here Phase 1: Research Inventory Phase 2: Clearly Define 2014 Research Strategy

A Research Strategy is a step-by-step plan of action that gives direction to your thoughts and efforts, enabling you to conduct research systematically and on schedule to produce quality results and detailed reporting. This enables one to stay focused, reduce frustration, enhance quality and most importantly, save time and resources. The Research Strategy is the nuts and bolts of your application, describing the rationale for your research and the experiments you will do to accomplish your desired goals. This post will discuss an extremely important phase in the process; clearly defining your research strategy.

In phase 1 we focused on inventory of all research, current and past. This week we will use that data to identifying specific research targets for both customer segments and feedback criteria.

This defined research strategy will also act as a reference point for benchmarking purposes so your company can see if it yielded the desired results. It requires the clear communication of targets and criteria ensuring all relevant stakeholders move in the same direction.

Identify gaps in existing data

A good place to start this process is to first examine the results of Phase 1 where you took an inventory of existing data and current research projects. With this framework you can identify gaps in data sets and create a research strategy that will compliment your company’s existing information.

Include all departments to maximize resources

Understanding the needs of your company’s individual departments, like marketing , communications , human resources and others, will help you to avoid repetitive research efforts which are both a waste of resources and damaging to communication channels with your clients. So determine data needs by department to make sure everyone is involved, to insure the highest possible value to your research investment.

Map out your customer’s life cycle

Another approach is to map out the customer’s life cycle in three steps.

• information seeking
• purchase decision
• product use

With these three steps you can visualize when you are attempting to reach your customers and evaluate whether or not the timing is appropriate for the specific information you’re trying to collect. You can also set the frequency at which you are requesting feedback or information from your customers; it is wise to avoid over-saturating a customer base with research inquiries.

What are your company’s priorities?

Once you’ve identified a need for data by examining your research inventory (established in Phase 1) and you’ve mapped out the customer life cycle to pinpoint the ideal timing for a research project, consider your company’s overall priorities to determine if a research project will help accomplish your goals. If so, then you’ve done your due diligence and moving forward with a research project is a prudent way to increase the likelihood of future success. As we’ve mentioned before, due to an ever-changing environment and variables it is impossible to predict with absolute certainty how a market will react to your product or service. However, an educated perspective of your consumer base will always improve your chances of achieving company-wide goals.

Our series will continue with – Phase #3: Conduct Research . We will discuss effective ways to communicate with your target respondents and keys to developing a successful research instrument.

Jenny Dinnen is President of Sales and Marketing at MacKenzie Corporation. Driven to maximize customer's value and exceed expectations, Jenny carries a can-do attitude wherever she goes. She maintains open communication channels with both her clients and her staff to ensure all goals and objectives are being met in an expeditious manner. Jenny is a big-picture thinker who leads MacKenzie in developing strategies for growth while maintaining a focus on the core services that have made the company a success. Basically, when something needs to get done, go see Jenny. Before joining MacKenzie, Jenny worked at HD Supply as a Marketing Manager and Household Auto Finance in their marketing department. Jenny received her undergrad degree in Marketing from the University of Colorado (Boulder) and her MBA from the University of Redlands.

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Customers that leave a brand for a competitor usually have specific reasons for doing so; whether tangible or intangible. In the case of a lost customer, it’s extremely valuable to understand WHY and WHAT (if anything) would bring that customer back. Sometimes nothing can be done and the customer is simply lost; but other times a simple adjustment can make a big difference. It might feel awkward at first, but reaching out to lost customers is actually beneficial in more than a few ways. For starters, you show customers that you care about them and that you still value their opinion. This is a strong gesture and it’s a way to passively reengage consumers who might consider returning.

Digital platforms, both social and shopping, have forever changed the consumer experience. At the same time, in-person experiences are still relevant and need to be aligned with the online experiences to provide a seamless, fluid flow between the real and digital worlds. Furthermore, consumers want personalized experiences that adjust as quickly as their preferences and favorite trends. This can be an overwhelming ordeal, or it can be as straightforward as direct and open two-way communication with your customers. This is the power of an ongoing Voice of Customer satisfaction program. By understand what your customers want, listening to how they feel your brand is performing, and being open to improvement suggestions, you will have all the data-driven insights you need win customer loyalty.

In addition to marketing reach and visibility, it’s important to consider the impact of specific brand messages. If the goal is to shape or reinforce brand image, customer feedback is the only way to measure marketing effectiveness. If the goal is to motivate action, such as visiting a website or purchasing a particular item, then sales tracking is one way to measure marketing effectiveness. However, transaction data doesn’t address purchase decision drivers so it’s difficult to attribute a sale to a particular marketing campaign. Again, direct customer feedback is required to identify their decision drivers.

For up-and-coming brands, or even established brands looking to expand their products/services, it’s important to answer the question, “Are consumers aware of our brand?” This is the first step in developing a strong, long-term marketing strategy and will provide a reliable benchmark for future reference. By segmenting awareness and perception data, you’ll have a clear indication of your current standing within specific markets and customer demographics.

It’s easy to become narrow-sighted and overly focused on one objective to the point where surrounding activity is unnoticed; especially with the rapid progression of technology and emerging consumer trends. The most successful brands will occasionally take a step back for a high-level, impartial look at the overall market landscape for a refreshed perspective on how their business fits into the bigger picture. Detailed Market Mapping not only acts as a preventative measure by exposing threats and potential danger, it will also uncover secondary variables that may be flying under the radar. This dual-benefit will swing the competitive balance in your favor by equipping your organization with the insights needed to make confident decisions; both short-term and long-term.

There’s no way to predict the future with absolute certainty but there are ways of applying market insights to improve success probabilities. Rather than conducting a series of trial-and-error initiatives, conducting research prior to forward-thinking strategic development will support informed decision making today and establish benchmarks for comparative analysis in the future. Just like having a map on a road trip, Market Mapping defines the surrounding area so decision makers can see where they’re going, stay on track and safely reach their destination. A clearly illustrated framework of your competitive environment enables your team to run through possible scenarios and how the market might react under specific circumstances.

Consumers are complex and unique individuals whose purchase decisions are influenced by a broader spectrum of attributes than the traditional price, product, placement, and positioning. Of course these are still relevant, but modern consumers are looking for more. Factors that may not seem to align at first glance are now connected (i.e. Health Concerns and Retail). The Market Mapping process will not only uncover the pieces it will act as a guide to reveal how the pieces fit together. Brands that understand their surroundings and create action plans based on market insights are best equipped to take proactive rather than reactive measures.

Understanding the past is a great place to start when strategically planning for the future. Consider market conditions and consumer preferences as they exist today, then examine the historical timeline of economic, social, political and technological shifts that have impacted your industry. By highlighting the environmental factors that have shaped consumer behavior, your brand will be equipped with consumer insights explaining the why, how and when of significant market shifts.

Imagine each existing dataset is a brick. If the bricks are haphazardly tossed in a pile then standing on that pile won’t be easy, But if the bricks are organized and positioned in a way that they work together, then there’s a foundation to not only stand on but to build on. Treat your existing data inventory as building materials rather than the byproducts of past efforts. No matter when or why data was collected, it still holds considerable value. After completing the Data Inventory Assessment, ensuing efforts will continue building on a solid foundation rather than merely adding more bricks to the pile.

Every company has its missing puzzle pieces, but not every company is willing to do something about it. Actively searching to identify the gaps in what you know about your customers and your market is an important part of strategic brand development. There’s also the benefit of ensuring that any steps taken are covering new ground rather than repeating what’s already been done. Pursuing the missing pieces will build upon the bigger picture and add cohesive value, but the only way to pursue the missing pieces is by knowing with certainty which pieces are missing.

By organizing and analyzing the existing customer data, brands will gain a clear picture what is quantifiably known at that point in time. This process will also itemize the types of data being held and it will establish benchmarks for future comparative analytics. Identifying the metrics that are most beneficial to current goals and objectives will produce the actionable insights needed to guide decision making.

Market research and data analytics are commonly applied on a “need-to-know” basis; meaning individual projects or initiatives are launched to address a specific set of questions for short-term application. After serving their intended purpose, these data files usually go into the vault and are rarely (if ever) seen again. But our Data Inventory Assessment digs into this data vault and offers a second life to the dormant files within.

• Harvard Business School →
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• November – December 1996
• Harvard Business Review

What Is Strategy?

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About The Author

Michael E. Porter

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• August 2023
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