Sunflower Intelligence
Marketing & Business Strategy

M05 · Intermediate

Market Segmentation and Positioning

10 lessons

M05 · Intermediate · 10 lessons in four modules · about 13 hours

Market Segmentation and Positioning

Segment, target and position: find groups of customers who want different things, choose the ones to serve, and decide how to stand out to them, with data rather than hunches.

What you will learn

  • Explain segmentation, targeting and positioning (STP), and judge whether segments are usable: measurable, substantial, accessible, differentiable and actionable.
  • Segment a market by demographic, geographic, psychographic and behavioural bases, and say what each base can and can’t reveal about why people buy.
  • Segment customers on their needs with cluster analysis (standardised variables, k-means and Ward’s method), choosing the number of segments by the elbow, the silhouette and what the segments mean, and checking their stability.
  • Profile, name and size segments with indexes and cross-tabs, test whether they differ, and build a typing tool that assigns new customers to a segment.
  • Evaluate segments’ attractiveness and fit with a weighted scorecard, test how sensitive the choice is to the weights, and choose a targeting strategy.
  • Build buyer personas from segment data rather than imagination, and state their limits.
  • Build and read perceptual maps from attribute ratings, place ideal points, and judge whether a gap on the map is an opportunity.
  • Write a positioning statement with a frame of reference, points of parity and points of difference, choose how to differentiate, and plan a repositioning with its risks.

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 Segmentation bases

  1. Why segment?A market is rarely one crowd wanting one thing. Learn what a market segment is, how segmentation, targeting and positioning fit together, when segmenting pays and when it costs more than it earns, and how to judge whether a set of segments is usable. Then read Lantern’s needs study to decide whether Harbour City has one coffee market or several.75 min · not started
  2. Demographic and geographic segmentationAge, household, income and where people live are the oldest bases for segmenting a market, because they are easy to count and to reach. Learn what each variable captures, how geodemographic systems such as Acorn classify neighbourhoods, and how to test with cross-tabs whether a demographic really separates buyers. Lantern’s study shows why demographics describe who buys far better than why.65 min · not started
  3. Psychographic and behavioural segmentationPeople of the same age and postcode can want very different things. Segment instead by how people live and what they value (psychographics), or by what they do and seek: the benefits they want, how much they buy, when and how loyally. Learn why these bases explain choices better than demographics and why they are harder to reach, check the 80/20 claim on Lantern’s heavy coffee drinkers, and use an attitude scale as a segmentation base.75 min · not started
  4. Module 1 test12 questions on this module. 70% passes.not taken

Module 2 Segmenting with data

  1. Cluster analysisSegment customers on their needs with data: choose and standardise the variables, run k-means (and Ward’s method as a check), pick the number of segments from the elbow, the silhouette and what the segments mean, and test whether the answer is stable. Lantern’s 640-person needs study is the data.95 min · not started
  2. Profiling segmentsTurn a cluster solution into segments a business can use: profile them with indexes against the average, name them honestly, describe who they are and what they buy with cross-tabs and B04’s tests, size them in people and in spend, judge what an opt-in panel can say about sizes, and build a typing tool that assigns new customers from a few golden questions.85 min · not started
  3. Module 2 test10 questions on this module. 70% passes.not taken

Module 3 Targeting

  1. Evaluating segmentsA segment is worth targeting only if it is attractive and you can win it. Score segments on size, spending, growth, competition and fit with a weighted scorecard, find the weight that would change the winner, and screen out targets that would be wrong to pursue.85 min · not started
  2. Targeting strategiesDecide how many segments to serve and how differently: one offer for everyone, a separate offer for each segment, everything on one segment, or offers shaped for places and people. Count what a new offer takes from the old ones, and build a buyer persona from segment data that says what it stands for and what it can’t.85 min · not started
  3. Module 3 test11 questions on this module. 70% passes.not taken

Module 4 Positioning

  1. Perceptual mapsDraw how customers see competing brands: plot two ratings against each other, or summarise many ratings with principal components. Read the dimensions, the attribute arrows and the segments’ ideal points, measure distances on the map, and judge whether an empty space is an opportunity or empty for a reason.80 min · not started
  2. Positioning statementsChoose the frame of reference an offer competes in, the points of parity it must match and the points of difference it can own, and write them into a positioning statement with a reason to believe. Then test the statement against M01’s four tests and with customers in a concept test.70 min · not started
  3. Differentiation and repositioningChoose how to make an offer different (product, services, channels, people, image), test which differences are worth promoting, and place the offer among the five value positions. Then plan a repositioning on a perceptual map with fixed axes, work out how far customers’ ratings would have to move, and track the result with a repeated survey.70 min · not started
  4. Module 4 test12 questions on this module. 70% passes.not taken

Project and final assessment

  1. Project: Positioning Lantern’s barista workshopsSegment the people who come to, or might come to, Lantern’s barista workshops, choose which segment to serve, build a persona from the data, map Lantern against a college course, tasting evenings and an online course, and write the positioning statement and the changes that would make it true.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, section 5.1, “Market Segmentation and Consumer Markets”. OpenStax. openstax.org/books/principles-marketing/pages/5-1-market-seg…
  2. Smith, W. R. (1956). Product differentiation and market segmentation as alternative marketing strategies. Journal of Marketing, 21(1), 3–8. JSTOR 1247695. (Quoted here from France and Ghose, 2019.) jstor.org/stable/1247695 (not yet verified)
  3. France, S. L. & Ghose, S. (2019). Marketing analytics: methods, practice, implementation, and links to other fields. Expert Systems with Applications; preprint arXiv:1801.09185. Section 4 quotes Smith (1956) and reviews a priori and post hoc segmentation and the criteria for segments. arxiv.org/abs/1801.09185
  4. Wind, Y. (2011). Yoram “Jerry” Wind’s contributions to marketing. Review of Marketing Research, Special Issue: Marketing Legends, 8, 269–315 (Table 5 reproduces Wind’s 1978 review of segmentation research). faculty.wharton.upenn.edu/wp-content/uploads/2012/02/Rev.Mkt…
  5. Shapiro, B. P. & Bonoma, T. V. (1984). How to segment industrial markets. Harvard Business Review, May 1984 (the nested approach: demographics, operating variables, purchasing approaches, situational factors and personal characteristics; summary checked in the publisher’s synopsis on Bookshare). hbr.org/1984/05/how-to-segment-industrial-markets (not yet verified)
  6. CACI. “What is Acorn?” (postcodes, categories, groups and types; data sources and uses). acorn.caci.co.uk/what-is-acorn
  7. CACI. “How Acorn works.” acorn.caci.co.uk/how-acorn-works
  8. Haley, R. I. (1968). Benefit segmentation: a decision-oriented research tool. Journal of Marketing, 32(3). doi:10.1177/002224296803200306 (abstract: descriptive characteristics of buyers are poor predictors of future buying). doi.org/10.1177/002224296803200306
  9. SRI International. “VALS™ market research” (history: launched 1978, new VALS 1989, now offered by Strategic Business Insights). sri.com/hoi/vals-market-research
  10. Sharp, B., Romaniuk, J. & Graham, C. (2019). Marketing’s 60/20 Pareto Law. Technical report, SSRN (Ehrenberg-Bass Institute and London South Bank University). doi:10.2139/ssrn.3498097. openresearch.lsbu.ac.uk/item/88vw1
  11. Kotler, P. (1986). Principles of Marketing, 3rd ed., Table 10-1, “Major segmentation variables for consumer markets”, as reproduced in a University of Washington course handout. faculty.washington.edu/harryb/courses/INFO%20Course/segmenta…
  12. Lastovicka, J. L. & Joachimsthaler, E. A. (1988). Improving the detection of personality-behavior relationships in consumer research. Journal of Consumer Research (March 1988). doi:10.1086/209138 (abstract checked in the Synapse index). doi.org/10.1086/209138
  13. Dolnicar, S., Grün, B. & Leisch, F. (2018). Market Segmentation Analysis: Understanding It, Doing It, and Making It Useful. Springer (Management for Professionals), open access under CC BY 4.0. Chapter 2 (natural, reproducible and constructive segmentation), Step 5, “Extracting segments”, and Step 6, “Profiling segments” (statistical criteria are no substitute for user assessment). link.springer.com/book/10.1007%2F978-981-10-8818-6
  14. Dolnicar, S. (2021). Data-driven market segmentation analysis. In Women’s Voices in Tourism Research, chapter 37. The University of Queensland. (States the guideline of 100 respondents per segmentation variable from Dolnicar, Grün & Leisch, 2016, and the earlier 70; summarises natural, reproducible and constructive segmentation.) uq.pressbooks.pub/tourismknowledge/chapter/market-segmentati…
  15. Dolnicar, S., Grün, B. & Leisch, F. (2016). Increasing sample size compensates for data problems in segmentation studies. Journal of Business Research, 69(2), 992–999. doi:10.1016/j.jbusres.2015.09.004. (The simulations behind the 100-per-variable guideline; the figure itself is given in Dolnicar, 2021, not in this abstract.) ideas.repec.org/a/eee/jbrese/v69y2016i2p992-999.html
  16. Dolnicar, S. & Leisch, F. (2010). Evaluation of structure and reproducibility of cluster solutions using the bootstrap. Marketing Letters, 21(1), 83–101. doi:10.1007/s11002-009-9083-4. ideas.repec.org/a/kap/mktlet/v21y2010i1p83-101.html
  17. Arthur, D. & Vassilvitskii, S. (2007). k-means++: the advantages of careful seeding. Proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms, 1027–1035. haralick.org/ML/kMeansPP-soda.pdf
  18. Ward, J. H., Jr. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58, 236–244. doi:10.1080/01621459.1963.10500845. doi.org/10.1080/01621459.1963.10500845
  19. Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65. doi:10.1016/0377-0427(87)90125-7. doi.org/10.1016/0377-0427(87)90125-7
  20. Kaufman, L. & Rousseeuw, P. J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis. Wiley. Their rough scale for the mean silhouette as reproduced in Emamat, M. S. M. M. et al. (2022), Using ELECTRE-TRI and FlowSort methods in a stock portfolio selection context, Financial Innovation, 8(1), table 16 (the book itself was not consulted). jfin-swufe.springeropen.com/articles/10.1186/s40854-021-0031…
  21. Hubert, L. & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2, 193–218; the adjusted Rand index as documented in scikit-learn 1.9 (adjusted_rand_score). scikit-learn.org/stable/modules/generated/sklearn.metrics.ad…
  22. Dolnicar, S., Grün, B. & Leisch, F. (2018). Step 6: Profiling segments (pp. 183–197) and Step 7: Describing segments (pp. 199–236). In Market Segmentation Analysis. Springer (Management for Professionals), open access. ideas.repec.org/h/spr/mgmchp/978-981-10-8818-6_8.html
  23. Baker, R., Blumberg, S. J., Brick, J. M. et al. (2010). AAPOR report on online panels. Public Opinion Quarterly, 74(4), 711–781. doi:10.1093/poq/nfq048. aapor.org/wp-content/uploads/2022/11/nfq048.pdf
  24. Bock, T. How to write golden questions for market segmentation. Q Research Software (undated web article). qresearchsoftware.com/how-to-write-golden-questions-for-mark…
  25. Horn, E. (n.d.). The Type Is Right: Maximizing the Impact of Segmentation Typing Tools. Decision Analyst (undated web article; the page carries a 2023 copyright notice). decisionanalyst.com/blog/segmentationtypingtools
  26. Principles of Marketing – H5P Edition (2022), section 5.3, “Selecting Target Markets and Target-Market Strategies”. BCcampus, CC BY-NC-SA 4.0 (author removed at the original publisher’s request). The features of an attractive market: size, growth, competition, accessibility, resources and fit with the firm’s mission and objectives. opentextbc.ca/principlesofmarketingh5p/chapter/selecting-tar…
  27. Smith, N. C. & Cooper-Martin, E. (1997). Ethics and target marketing: the role of product harm and consumer vulnerability. Journal of Marketing, 61(3), 1–20. Confirmed from the University of Birmingham research portal’s record and abstract; the full text was not consulted. research.birmingham.ac.uk/en/publications/ethics-and-target-…
  28. American Marketing Association. “AMA Statement of Ethics”: the three ethical norms and the value of equity, including recognising the needs of vulnerable market segments. ama.org/ama-statement-of-ethics
  29. McKinsey Quarterly (2008). “Enduring ideas: The GE–McKinsey nine-box matrix”. The idea of judging a business by the attractiveness of its market and its own strength there, on two separate dimensions, which the attractiveness–fit chart borrows for segments. mckinsey.com/business-functions/strategy-and-corporate-finan…
  30. Kotler, P. & Armstrong, G. (2018). Principles of Marketing, 17th ed., chapter 7. Pearson. The book was not consulted; its sections on undifferentiated, differentiated and concentrated marketing, micromarketing (local and individual) and choosing a targeting strategy were confirmed from the book’s table of contents as listed on O’Reilly Learning. oreilly.com/library/view/principles-of-marketing/97801344925…
  31. Ripley, M. L. Course notes on chapter 7 of Armstrong and Kotler, Marketing: An Introduction, “Factors to consider in choosing a target-marketing strategy”. York University. The source for the five considerations’ labels; the notes list them without explanation. yorku.ca/lripley/imUsegment.htm
  32. Pruitt, J. & Grudin, J. (2003). Personas: practice and theory. Proceedings of the 2003 Conference on Designing for User Experiences (DUX). Microsoft Research. Cooper’s 1999 book as the source that popularised personas; personas built on data; their risks. microsoft.com/en-us/research/publication/personas-practice-t…
  33. Greenacre, M. (2010). Biplots in Practice, chapter 6, “Principal Component Analysis Biplots” (form and covariance biplots; showing both rows and columns in principal coordinates “is not a biplot”, and projections degrade when the two singular values differ). Bilbao: Fundación BBVA. fbbva.es/wp-content/uploads/2017/05/dat/greenacre_c06_2010.p…
  34. Gabriel, K. R. (1971). The biplot graphic display of matrices with application to principal component analysis. Biometrika, 58(3), 453–467. Citation and abstract confirmed from the bibliography of Greenacre (2010); the paper itself was not consulted. fbbva.es/wp-content/uploads/2017/05/dat/greenacre_ap0B_2010.…
  35. NIST/SEMATECH (2012). e-Handbook of Statistical Methods, section 6.5.5.2, “Principal Components: Numerical Example” (eigenvalues of the correlation matrix, cumulative proportions, communalities). itl.nist.gov/div898/handbook/pmc/section5/pmc552.htm
  36. Keller, K. L., Sternthal, B. & Tybout, A. M. (2002). Three questions you need to ask about your brand. Harvard Business Review, 80(9), 80–86 (product R0209F). Citation and description confirmed from the HBR Store and Kellogg School of Management listings; the article’s content was read in a course copy. store.hbr.org/product/three-questions-you-need-to-ask-about-…
  37. Lumen Learning. Principles of Marketing, 8.13, “Reading: Defining Positioning and Differentiation” (the parts of a positioning statement; the value proposition as the end result of positioning). CC BY 4.0. biz.libretexts.org/Bookshelves/Marketing/Principles_of_Marke…
  38. Principles of Marketing – H5P Edition (2022), section 5.4, “Positioning and Repositioning Offerings” (taglines). BCcampus Open Education. opentextbc.ca/principlesofmarketingh5p/chapter/positioning-a…
  39. Kotler, P. & Armstrong, G. Principles of Marketing. Pearson (many editions), the chapter on segmentation, targeting, differentiation and positioning. The book was not consulted. Lecture slides based on the 2006 edition, chapter 7, confirm the ways to differentiate under four headings (product and services, distribution, personnel, image), the seven criteria for a difference worth promoting by name, with short definitions of important, distinctive, pre-emptive and affordable, and the positioning-statement form. The questions given here for superior, communicable and profitable are this course’s wording, and the slides do not show the value positions. slidetodoc.com/market-segmentation-targeting-positioning-kot…
  40. Sherk, R. (2004). Value positioning: more for more, more for the same, the same for less, less for much less, more for less. Teaching notes, Oregon Coast Community College. Confirms the five value positions and their names; it calls more for less “the winning value proposition” and does not cite Kotler and Armstrong. The lesson’s caution that more for less is hard to sustain comes from M01’s argument about cost advantage, not from this source. oregoncoast.edu/?p=26515
  41. Lumen Learning. Principles of Marketing, 8.4, “Reading: Repositioning” (when to reposition, the process and its pitfalls). CC BY 4.0. biz.libretexts.org/Bookshelves/Marketing/Principles_of_Marke…