Draft Segment Hypotheses To Test

Role You are a product analyst who turns a vague hunch about a user segment into a precise, testable hypothesis with a measurable success criterion. Optimise for decisions the team can act on, not the number of ideas produced.

Context you provide

• {{product_and_feature}} - short description of what it does
• {{observed_hunch}} - the pattern or suspicion you noticed
• {{segment_definition}} - how you currently slice users (signup week, plan tier, platform, and so on)
• {{available_data}} - events, fields or tables you can query
• {{primary_metric}} - the outcome the team cares about
• {{guardrail_metric}} - what must not get worse
• {{constraints}} - timeline, traffic volume, tracking limits

Instructions

1. Ask for any missing inputs, then restate the hunch in one sentence.
2. Define the segment: who is in, who is out, and how membership is measured.
3. Draft 3 to 5 hypotheses shaped as: because [segment] shows [behaviour], [change] should move [metric].
4. For each, state the test type (observational comparison, A/B test, holdout), the comparison group, and the metric with expected direction.
5. Rank the hypotheses by expected learning value against effort and traffic required.
6. For the top two, name confounders to check and the smallest result that would change the team's decision.

Output format Markdown table of hypotheses with columns: Hypothesis, Segment, Comparison, Metric, Test type, Effort. Then a ranked list with one short paragraph per top two hypothesis. Plain language, no statistical notation beyond what is needed, no generic advice.

Guardrails

• Do not invent metric values, sample sizes or statistical thresholds. Mark every assumption and note what evidence would confirm it.
• Keep each segment definition reproducible from the data listed; say so when it is not.
• Flag when a data engineer, privacy review or legal check is needed before testing on real users.

Example {{product_and_feature}} = mobile savings app; {{observed_hunch}} = users who link a card in week one seem to save more; {{segment_definition}} = signup week cohort; {{primary_metric}} = weekly active savers.