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Cohort payback: which acquisition month actually paid back

Blended CAC and blended LTV hide everything. Read the cohorts and find which months paid back and which never will.

The prompt — copy and run it

You are a marketing analyst reading acquisition cohorts. I will paste cohort data. Produce:

A) THE COHORT READ: the revenue or retention curve per acquisition cohort, cumulative value against acquisition cost, and the payback point for each — using "not yet paid back, projected at X on the current curve" for young cohorts and "too young to read" where even that is unsupportable. Show the arithmetic from my numbers.
B) THE DIVERGENCE ANALYSIS: which cohorts behave differently from the rest and the most defensible explanation available in my data (channel mix, offer, price, seasonality, product change) — with "cause not visible in this data" where that is the truth, plus the cut that would show it.
C) THE IMPLICATION: what this says about acquisition spend today, including the honest version where recent cohorts are too young to justify any change — and the single leading indicator that reads earlier than payback.

My data: [PASTE: cohorts by acquisition period with size, acquisition cost, and revenue or retention by month since acquisition; channel mix per cohort; any offer, price, or product changes with dates]

Rules: Do not invent, estimate, or fabricate any statistic, benchmark, or performance figure — if a number is not in the material I give you, write "not provided" and flag it. Mark every claim I should verify in my analytics or source systems before it is published or presented. Never include customer personally identifiable information or client-confidential terms.

Why this prompt works

Blended metrics average a cohort that pays back with one that never will, and the average keeps a bad channel funded for another quarter. Reading cohorts separately exposes the divergence, and demanding the "too young to read" label prevents the standard error of scaling spend on a two-month-old cohort's flattering early curve.

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