Sunflower Intelligence
Marketing & Business Strategy

M04 · Intermediate

Market Research

10 lessons

M04 · Intermediate · 10 lessons in four modules · about 13 hours

Market Research

Plan research around a decision, use secondary sources and qualitative work, design questionnaires and samples, size markets, research competitors, and turn survey data into honest insight.

What you will learn

  • Turn a business decision into research objectives and questions, choose an exploratory, descriptive or causal design, and write a research brief that weighs the cost of research against what the decision is worth.
  • Find, evaluate and combine internal and external secondary data, judging its source, definitions, coverage and timeliness.
  • Plan depth interviews and focus groups, and analyse what people say with a codebook, while knowing what qualitative research can’t show.
  • Design questionnaires that measure what they intend, avoiding leading, loaded and double-barrelled questions, order effects and response styles.
  • Choose a probability or non-probability sampling design, allocate a stratified sample, and estimate results with their margin of error.
  • Diagnose survey error (coverage, sampling, non-response and measurement), calculate response rates, and weight responses, knowing what weighting can’t fix.
  • Size a market from the top down and the bottom up (TAM, SAM and SOM), and research competitors’ offers and prices ethically.
  • Analyse survey data in Excel (frequencies, cross-tabs, Net Promoter Score with its uncertainty, weighted results) and turn findings into insights and recommendations with their limits stated.

Prerequisites

How it works

Each lesson teaches the idea from first principles, works examples step by step, lets you explore it in an interactive, then has you practise with feedback. A lesson is complete when you have worked its guided problems and scored 80% on its knowledge check. Mastery is tracked separately and comes back for review before you are likely to forget. Each module ends with a test; the course ends with a project and a final assessment.

Fictional case

Lantern Coffee Co. A fictional coffee business founded by Ana Lim. It starts as a catering and barista-training service, opens its own cafés and an online shop for beans, and plans a canned cold brew. Every name and figure in it is invented for teaching.

Not sure where to start?

The diagnostic has 12 questions across the course. It doesn’t count toward anything; it suggests the module to start from. Take the diagnostic.

Modules and lessons

Module 1 Planning research

  1. The research processResearch earns its cost only through a decision. Learn to turn a business decision into a research problem and measurable objectives, choose an exploratory, descriptive or causal design, write a brief and read a proposal, put a ceiling on what research is worth, and follow the professional codes that protect the people you study.80 min · not started
  2. Secondary researchBefore collecting anything new, find out what is already known. Learn to search a business’s own records and outside sources (official statistics, trade associations, syndicated and panel data, company filings, digital traces), judge each source by who made it and why, its method, definitions, coverage, timeliness and consistency, and combine sources so that each checks the others.70 min · not started
  3. Qualitative researchSurveys count; qualitative research explains. Learn when depth interviews, focus groups and observation fit, how to recruit with a screener and write a discussion guide, how many interviews are enough, and how to code transcripts into themes with a codebook, check that two coders agree and report what people said without turning it into numbers it can’t support.75 min · not started
  4. Module 1 test10 questions on this module. 70% passes.not taken

Module 2 Surveys and samples

  1. Questionnaire designA survey can only be as good as its questions. Turn research objectives into questions that each earn their place, choose the right question types and rating scales, write options and wording that don’t push answers, guard against response styles and order effects, and pretest the result before anyone sees it, and check that multi-item scales are reliable and valid.85 min · not started
  2. Sampling methodsWho could be in your survey decides what it can tell you. Define the target population, frame and sampling unit; choose among simple random, systematic, stratified, cluster and multistage designs and know what non-probability samples can and can’t support; allocate a stratified sample and estimate from it with a standard error; and size a sample for the subgroups you need to read.70 min · not started
  3. Survey errorA margin of error measures one kind of error. Learn the others (coverage, non-response, measurement, processing) as one system, calculate response rates by AAPOR’s definitions, see why a response rate is not a measure of bias, weight responses back to the population and count what weighting costs, and read stated intentions for what they are.80 min · not started
  4. Module 2 test12 questions on this module. 70% passes.not taken

Module 3 Markets and competitors

  1. Market sizingHow big is the market, and how much of it can you win? Define a market by its products, customers, place and period; separate TAM, SAM and SOM; estimate from the top down and the bottom up; use survey intent with an explicit conversion assumption; and report a range with the assumption that drives it, not one hopeful number.80 min · not started
  2. Competitor researchFind out what rivals offer, charge and plan, from sources you can use honestly: websites, price lists, reviews, job advertisements, trade shows, filings, customers and store checks. Run a shelf-price audit and summarise it by brand and channel, know where the professional codes and the law draw the line on mystery shopping and confidential information, and turn the findings into decisions about your own offer.70 min · not started
  3. Module 3 test11 questions on this module. 70% passes.not taken

Module 4 From data to insight

  1. Analysing survey dataPrepare a survey export for analysis, then build frequency tables, cross-tabs, top-box scores and the Net Promoter Score with its margin of error in Excel. Weight results to the population with SUMPRODUCT, compare groups with B04’s tests, choose the median for skewed counts, and know when a base is too small to report.95 min · not started
  2. Insights and reportingTurn findings into insights and recommendations by combining survey, qualitative and secondary evidence; say what research can’t show and when only an experiment will do; and write a research report with honest charts, uncertainty in plain words and respondents kept anonymous.75 min · not started
  3. Module 4 test10 questions on this module. 70% passes.not taken

Project and final assessment

  1. Project: Office coffee for Harbour CityResearch whether Lantern should launch an office-coffee subscription: set the objectives, fix the questionnaire, sample the business directory, analyse the office survey, size the market from the top down and the bottom up, read the competition, code the interviews and write Ana a memo that separates what the research shows from what it can’t.not taken
  2. Final assessment24 questions across the whole course. 70% passes.not taken

The course is complete when every lesson is complete, every module test is passed, the project is finished and the final assessment is passed.

Resources

  • Glossary: every term this course defines.
  • Formulas: every formula with its symbols and units.
  • Review: flashcards and the questions you missed, when they are due.

Elsewhere on this site

Sources used in this course

  1. Gomez Albrecht, M., Green, M. & Hoffman, L. (2023). Principles of Marketing, sections 6.1 “Marketing Research and Big Data” and 6.3 “Steps in a Successful Marketing Research Plan”. OpenStax. openstax.org/books/principles-marketing/pages/6-3-steps-in-a…
  2. ICC and ESOMAR (2025). ICC/ESOMAR International Code on Market, Opinion and Social Research and Data Analytics (2025 revision). Fundamental principles; Articles 1–4 and 9. community.esomar.org/uploads/public/knowledge-and-standards/…
  3. Market Research Society (2023). MRS Code of Conduct. Rules 4, 16, 30, 34 and 61–62, and the definition of a child. mrs.org.uk/pdf/MRS-code-of-conduct-2023.pdf
  4. Market Research Society (revised 2024). Regulations for Administering Incentives and Free Prize Draws. Rule 3 (no price discounts as incentives) and rule 4 (no client goods or services, or vouchers for them). mrs.org.uk/pdf/Regulations%20for%20Incentives%20and%20Prize%…
  5. Insights Association. Code of Standards and Ethics for Market Research and Data Analytics: summary of the code’s provisions on consent, non-research activities, children and public releases (page last updated September 2026). insightsassociation.org/About-Us/Code-of-Standards
  6. U.S. Census Bureau. “About County Business Patterns (CBP)”. census.gov/programs-surveys/cbp/about.html
  7. U.S. Census Bureau. “County Business Patterns (CBP) Glossary” (establishment; paid employment; nonemployers). census.gov/programs-surveys/cbp/about/glossary.html
  8. U.S. Census Bureau. “Using 1-Year or 5-Year American Community Survey Data”. ACS guidance for data users (1-year, 1-year supplemental and 5-year estimates). census.gov/programs-surveys/acs/guidance/estimates.html
  9. U.S. Census Bureau (2020). Understanding and Using American Community Survey Data: What All Data Users Need to Know, chapter 3 “Understanding and Using ACS Single-Year and Multiyear Estimates” (supplemental tables as simplified versions of popular tables) and chapter 7 “Understanding Error and Determining Statistical Significance” (margins of error published at the 90 percent confidence level). census.gov/content/dam/Census/library/publications/2020/acs/…
  10. Office for National Statistics. “Living Costs and Food Survey guide” (quality and methodology information). ons.gov.uk/peoplepopulationandcommunity/personalandhousehold…
  11. U.S. Securities and Exchange Commission, Investor.gov. “Using EDGAR to Research Investments”. investor.gov/research-before-you-invest/research/researching…
  12. Companies House. “Searching the Companies House register”. GOV.UK guidance. gov.uk/guidance/searching-the-companies-house-register
  13. Guest, G., Bunce, A. & Johnson, L. (2006). How many interviews are enough? An experiment with data saturation and variability. Field Methods, 18(1), 59–82. doi:10.1177/1525822X05279903 socthesis.fas.harvard.edu/file_url/134
  14. Hennink, M. & Kaiser, B. N. (2022). Sample sizes for saturation in qualitative research: a systematic review of empirical tests. Social Science & Medicine, 292, 114523. The findings are taken from the abstract. pubmed.ncbi.nlm.nih.gov/34785096
  15. Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. doi:10.1191/1478088706qp063oa. The record and abstract were consulted; the phase names were checked against a published reproduction of the paper’s table of phases. uwe-repository.worktribe.com/output/1043060/using-thematic-a…
  16. McHugh, M. L. (2012). Interrater reliability: the kappa statistic. Biochemia Medica, 22(3), 276–282. doi:10.11613/BM.2012.031 biochemia-medica.com/en/journal/22/3/10.11613/BM.2012.031/fu…
  17. Market Research Society (2023). MRS Code of Conduct. Rule 29 (informing participants about recording and observation) and rules 61–62 (reporting). mrs.org.uk/pdf/MRS-code-of-conduct-2023.pdf
  18. Albrecht, M. G., Green, M. & Hoffman, L. (2023). Principles of Marketing, section 6.3 “Steps in a Successful Marketing Research Plan” (question types; table 6.2 on survey modes). OpenStax. openstax.org/books/principles-marketing/pages/6-3-steps-in-a…
  19. Pew Research Center. “Writing Survey Questions”. Methods. pewresearch.org/writing-survey-questions
  20. Krosnick, J. A. & Presser, S. (2010). “Question and Questionnaire Design”. In P. V. Marsden & J. D. Wright (eds), Handbook of Survey Research, 2nd edn, chapter 9, from p. 263. Emerald. web.stanford.edu/dept/communication/faculty/krosnick/docs/20…
  21. U.S. Census Bureau. “Appendix A2: Questionnaire Testing and Evaluation Methods for Censuses and Surveys”. Statistical Quality Standards. census.gov/about/policies/quality/standards/appendixa2.html
  22. Reichheld, F. F. (2003). “The One Number You Need to Grow”. Harvard Business Review 81(12), 46–54. Paywalled; the question wording and the three groups quoted here were checked against a reprint of the article. hbr.org/2003/12/the-one-number-you-need-to-grow
  23. Cronbach, L. J. (1951). “Coefficient alpha and the internal structure of tests”. Psychometrika 16(3), 297–334. doi:10.1007/BF02310555 (record on IDEAS/RePEc). ideas.repec.org/a/spr/psycho/v16y1951i3p297-334.html
  24. Tavakol, M. & Dennick, R. (2011). “Making sense of Cronbach’s alpha”. International Journal of Medical Education 2, 53–55. ijme.net/archive/2/cronbachs-alpha.pdf
  25. Price, P. C., Jhangiani, R. & Chiang, I.-C. A. (2015). Research Methods in Psychology, 2nd Canadian edition, chapter 5, “Reliability and Validity of Measurement”. BCcampus. opentextbc.ca/researchmethods/chapter/reliability-and-validi…
  26. Bain & Company. “Measuring Your Net Promoter Score”. Net Promoter System (the question as Bain gives it today, the 0–10 scale, promoters, passives and detractors, and the calculation). nps.bain.com/about/measuring-your-net-promoter-score
  27. Baker, R., Brick, J. M., Bates, N. A., Battaglia, M., Couper, M. P., Dever, J. A., Gile, K. J. & Tourangeau, R. (2013). Report of the AAPOR Task Force on Non-Probability Sampling. American Association for Public Opinion Research. aapor.org/wp-content/uploads/2022/11/NPS_TF_Report_Final_7_r…
  28. Stat Trek. “Sample Size: Stratified Random Samples” (the stratified estimate, its standard error, proportional and Neyman allocation, with a worked example). stattrek.com/sample-size/stratified-sample
  29. Zipf, G. & Valliant, R. (2025). “Design Effects and Effective Sample Size”. Vignette for the R package PracTools, CRAN. cran.r-project.org/web/packages/PracTools/vignettes/Design-e…
  30. Groves, R. M. & Lyberg, L. (2010). “Total Survey Error: Past, Present, and Future”. Public Opinion Quarterly 74(5), 849–879. doi:10.1093/poq/nfq065 (listed in the issue’s contents). academic.oup.com/poq/issue/74/5
  31. American Association for Public Opinion Research (2023). Standard Definitions: Final Dispositions of Case Codes and Outcome Rates for Surveys, 10th edition. aapor.org/wp-content/uploads/2023/05/Standards-Definitions-1…
  32. National Research Council (2013). Nonresponse in Social Science Surveys: A Research Agenda, chapter 2 “Nonresponse Bias”. The National Academies Press. nationalacademies.org/read/18293/chapter/4
  33. Mercer, A., Lau, A. & Kennedy, C. (2018). “For Weighting Online Opt-In Samples, What Matters Most?” Pew Research Center. pewresearch.org/methods/2018/01/26/for-weighting-online-opt-…
  34. Morwitz, V. G., Steckel, J. H. & Gupta, A. (2007). “When do purchase intentions predict sales?” International Journal of Forecasting 23(3), 347–364. academia.edu/35359803/When_do_purchase_intentions_predict_sa…
  35. Sun, B. & Morwitz, V. G. (2010). “Stated intentions and purchase behavior: A unified model”. International Journal of Research in Marketing 27(4), 356–366. english.ckgsb.edu.cn/wp-content/uploads/2017/12/intention.pd…
  36. TechTarget (2023). “What is TAM SAM SOM?” Written in 2019 and revised by TechTarget editors in 2023; definitions of TAM, SAM and SOM and of top-down and bottom-up sizing. techtarget.com/enterprise-software/definition/TAM-SAM-SOM
  37. The Carta Team (2023, 14 April). “Market size: TAM, SAM, & SOM”. Carta. carta.com/blog/market-size
  38. Armstrong, J. S., Morwitz, V. G. & Kumar, V. (2000). Sales forecasts for existing consumer products and services: do purchase intentions contribute to accuracy? International Journal of Forecasting, 16(3), 383–397. The sentences on bias are in the authors’ working paper (MPRA paper 81689, April 2000). mpra.ub.uni-muenchen.de/81689/1/MPRA_paper_81689.pdf
  39. U.S. Census Bureau. “County Business Patterns” (programme overview: establishments, employment and payroll by six-digit NAICS industry, for the nation, states, counties, metropolitan areas and ZIP codes, annually since 1964). census.gov/econ/overview/mu0800.html
  40. U.S. Census Bureau. “About County Business Patterns (CBP)” (annual statistics for establishments with paid employees). census.gov/programs-surveys/cbp/about.html
  41. Market Research Society (2023). MRS Code of Conduct, operative from 15 May 2023 (rule 29 and its comment on mystery shopping). mrs.org.uk/pdf/MRS-code-of-conduct-2023.pdf
  42. Market Research Society (2011). MRS Guidelines for Mystery Shopping (Research), interpreting the 2010 Code (competitor mystery shopping, rule B44). mrs.org.uk/pdf/2013-01-14%20Mystery%20Shopping%20Research%20…
  43. ESOMAR (2005). Mystery Shopping Studies, ESOMAR World Research Codes & Guidelines. ana.esomar.org/api/public/document/file_renderer/6672
  44. Strategic Consortium of Intelligence Professionals (SCIP). “SCIP Code of Ethics”. scip.org/page/CodeOfEthics
  45. 18 U.S.C. § 1839, Definitions (“trade secret” and “improper means”), Legal Information Institute, Cornell Law School. law.cornell.edu/uscode/text/18/1839
  46. Directive (EU) 2016/943 of 8 June 2016 on the protection of undisclosed know-how and business information (trade secrets), Chapter II, Articles 3 and 4, as published on legislation.gov.uk (title and dates confirmed on EUR-Lex). legislation.gov.uk/eudr/2016/943/chapter/II
  47. The Trade Secrets (Enforcement, etc.) Regulations 2018 (SI 2018/597), explanatory note: Chapter II of the directive is implemented in the UK by the common law and equity of breach of confidence, and the Regulations address the gaps. legislation.gov.uk. legislation.gov.uk/uksi/2018/597/note/made
  48. Bain & Company. “Measuring Your Net Promoter Score”: the question, the 0–10 scale, promoters, passives and detractors, and the calculation. nps.bain.com/about/measuring-your-net-promoter-score
  49. Reichheld, F. F. (2003). The one number you need to grow. Harvard Business Review, 81(12), 46–54. hbr.org/2003/12/the-one-number-you-need-to-grow
  50. Keiningham, T. L., Cooil, B., Andreassen, T. W. & Aksoy, L. (2007). A longitudinal examination of net promoter and firm revenue growth. Journal of Marketing, 71(3). doi:10.1509/jmkg.71.3.39. research.hub.ku.edu.tr/entities/publication/6c8605c0-154e-40…
  51. Rocks, B. (2016). Interval estimation for the “net promoter score”. The American Statistician, 70(4), 365–372. Preprint: arXiv:1601.07235. arxiv.org/abs/1601.07235v1
  52. Zhang, C. & Conrad, F. (2014). Speeding in web surveys: the tendency to answer very fast and its association with straightlining. Survey Research Methods, 8(2), 127–135. ojs.ub.uni-konstanz.de/srm/article/view/5453
  53. Parker, J. D., Talih, M., Malec, D. J. et al. (2017). National Center for Health Statistics Data Presentation Standards for Proportions. Vital and Health Statistics, Series 2, No. 175. stacks.cdc.gov/view/cdc/47786
  54. American Association for Public Opinion Research (2021). AAPOR Code of Professional Ethics and Practices, section III.A, “Disclosure Standards”. aapor.org/standards-and-ethics/disclosure-standards
  55. Market Research Society (2023). MRS Code of Conduct, effective 15 May 2023: rules 49–51 (participant anonymity) and 58–64 (reporting). mrs.org.uk/pdf/MRS-code-of-conduct-2023.pdf