Customer Segmentation Hypotheses
Define segments by needs, not demographics alone.
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Develop segmentation hypotheses. Product and current customers: [PRODUCT_CUSTOMERS]. Data available (usage, surveys, sales notes): [DATA]. Business goal: [GOAL]. Propose four to six segments defined by needs, context and behaviour, with a name, description, job to be done, size estimate method (not a guessed number), how to identify them in data, and how they might value the product differently. Create a table of segments and a scoring of attractiveness (size, need intensity, fit, accessibility, strategic value) with weights I can change. Recommend which segment to focus on first as a hypothesis and the tests to confirm. Flag segments built on weak evidence. Add a validation plan for the preferred segment with five interview screening questions, two data cuts to run in existing analytics, and a signal that would make me drop the segment. Include guidance for avoiding segments that are so narrow they cannot support the business, and a note on revisiting the segmentation every six months or after major product changes. Add a worked example of two invented segments for a bookkeeping tool (sole traders who invoice weekly; small agencies with part-time bookkeepers) written with name, job to be done, behaviours that identify them in data, willingness to pay as a hypothesis, and the evidence strength rated weak, moderate or strong. Show the attractiveness scoring with weights and arithmetic. Label it an illustration. Add a data plan to test the segments with existing information: which fields to combine, which cuts to run, and what pattern would support or weaken each hypothesis. Include a section on naming segments by need and not by demographic labels, and a rule for retiring a segment if it cannot be identified in the data after one round of work. Finish with the single question to ask in the first customer call to find out which segment a prospect belongs to, written so the answer does not reveal the preferred segment.
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