What the Cohort, RFM and repeat-purchase analysis prompt does

Act as a retention analytics and lifecycle segmentation lead.

The prompt will, at minimum:

  • Validate the supplied datasets, definitions, time window, market scope and source-of-truth ownership before assessing cohort, rfm and repeat-purchase analysis
  • Examine customer identity, cohort definitions, order cadence, recency, frequency, monetary value, refunds, first-product effects, channel acquisition, repeat interval, retention curve and segment stability; retain original record identifiers and show how each finding was derived
  • Segment results only where the data supports the split; expose missingness, sample bias, seasonality, policy changes, promotions, migrations and other confounders rather than hiding them in averages
  • Recompute every material metric from supplied values, disclose formulas, denominators, exclusions and scenario assumptions, and never invent benchmarks or competitor performance
  • Turn the evidence into evidence-based lifecycle segments, retention priorities and measurable tests; assign owner, priority, dependency, expected signal, verification method and human-approval point to each action

Who it is for

Gökhan Güzel's e-commerce prompt for ChatGPT users: marketers, founders, agencies and consultants who need an auditable, evidence-based deliverable instead of generic advice.

What you get

  • Executive summary and data-quality report
  • Cohort, RFM and repeat-purchase analysis methodology and evidence ledger
  • Segmented findings, calculations and scoring
  • Prioritised action backlog with owners and validation criteria
  • Sources, limitations, confidence and QA report

Variables

PlaceholderPurpose
{{analysis_period}}Date, time or period value; state ISO format, time zone, start/end boundary and comparison period
{{brand_name}}Verified identifier or text value; state exact spelling, source, status and validity scope
{{channel_data}}Structured dataset or source file; state fields, data types, period, units, currency, time zone and provenance
{{cohort_definition}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{customer_id_rules}}Approved rule, policy or constraint; state owner, version, scope, jurisdiction and effective date
{{customer_order_data}}Structured dataset or source file; state fields, data types, period, units, currency, time zone and provenance
{{lifecycle_events}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{product_taxonomy}}Verified identifier or text value; state exact spelling, source, status and validity scope
{{refund_data}}Structured dataset or source file; state fields, data types, period, units, currency, time zone and provenance
{{rfm_rules}}Approved rule, policy or constraint; state owner, version, scope, jurisdiction and effective date
{{success_metrics}}Numeric value or table; state formula, numerator, denominator, unit, currency, tax treatment, period and source
{{target_market}}Target market

How to use

Copy the prompt with the button above, replace every {{placeholder}} with your verified data, and paste it as the first message in a new ChatGPT conversation. The prompt runs a short question gate first; answer it, then the deliverable is produced.

Run Cohort, RFM and repeat-purchase analysis in ChatGPT

Open a new ChatGPT chat, paste the filled-in Cohort, RFM and repeat-purchase analysis prompt and answer the short question gate. ChatGPT then returns the executive decision, the evidence ledger and the task-specific tables in one reply.

Source

Original file in the multilingual prompt library on GitHub: