Map at scale: why people stay, leave and pay

You will be able to

• Explain how loop 2’s questions differ from loop 1’s: stay, leave and pay
• Build a weekly retention cohort table from your events, every cell shown as a count with its base
• Use the AARRR stages to locate your biggest leak, and tell vanity metrics from actionable ones
• Plan interviews with churned users, power users and people who nearly left
• Map one outcome, its opportunities, solutions and experiments on an opportunity solution tree

OUTPUT · Retention table + metrics tree + opportunity solution tree

You will be able to

• Combine sources (events, support messages, reviews, interviews) and say what each one misses
• Tag feedback at volume into themes with counts and exact quotes, and audit a sample of the tagging
• Judge where AI belongs in a product and where a rule, a person or nothing is better
• Describe one AI opportunity: job, input, judgement, output and the cost of a mistake
• Run the job by hand with Claude on ten real, anonymised inputs before writing any code

OUTPUT · Insight report + AI Opportunity Card (with ten hand runs)