Analyze · Cohort & Channel ROI

Analyze Customer Cohort Retention

Calculate month-over-month retention for each client cohort and surface which intake periods produce the stickiest accounts.

foundermanagerIntermediate3-4 hours

When to use

Use when you have at least 6 months of signed-client data and want to know which intake cohorts are retaining vs churning. Best run quarterly before pricing or packaging changes. Helps founders decide whether a churn problem is a delivery issue or a wrong-fit-client issue.

The prompt

Prompt

You are a growth analyst for a digital marketing agency analyzing the agency's own GTM ROI. You translate cohort tables into retention curves and root-cause hypotheses.
Agency: [AGENCY_NAME] — [SERVICES] | Period: [PERIOD] | Data:
[COHORT_DATA] (rows = signup month, columns = M0, M1, M2 ... active client counts)
Calculate retention % at M1, M3, M6, and M12 for every cohort. Identify the strongest and weakest cohorts, quantify the gap, and hypothesize 2-3 reasons grounded in the numbers (intake channel, pricing tier, onboarding change).

- Show the formula: Retention(Mn) = Active(Mn) / Active(M0) × 100
- Flag any cohort with 15pp as a churn cliff
- Recommend one concrete experiment (offer, onboarding step, ICP filter) to lift the weakest cohort

1) Retention matrix table (cohort × M1/M3/M6/M12 with %). 2) "Best vs Worst" 2-row comparison. 3) Hypotheses (bulleted, each tied to a number). 4) One recommended experiment with expected lift.

Variables

[AGENCY_NAME]Your agency name
[SERVICES]Primary services sold (e.g., SEO + paid social retainers)
[PERIOD]Analysis window (e.g., Jan 2025 - Apr 2026)
[COHORT_DATA]Table of signup-month cohorts and active client counts by month-since-signup

Example input

Input

Agency: Northbeam Digital — SEO + paid search retainers | Period: Jan 2025 - Apr 2026 | Data:
Cohort | M0 | M1 | M3 | M6 | M12
Jan 2025 | 12 | 12 | 11 | 9 | 6
Apr 2025 | 9 | 9 | 8 | 5 | 3
Jul 2025 | 14 | 13 | 11 | 10 | -
Oct 2025 | 11 | 11 | 9 | - | -
Jan 2026 | 8 | 8 | - | - | -

Example output

Output

Retention matrix:
Jan'25: M1 100% / M3 92% / M6 75% / M12 50%
Apr'25: M1 100% / M3 89% / M6 56% / M12 33%
Jul'25: M1 93% / M3 79% / M6 71% / M12 early signal
Oct'25: M1 100% / M3 82% / M6 early signal
Jan'26: early signal only

Best vs Worst: Jan'25 (50% M12) vs Apr'25 (33% M12) — 17pp gap.

Churn cliff: Apr'25 lost 33pp between M3 and M6 — a contract-renewal cliff.

Hypotheses:
- Apr'25 cohort signed during the Q2 "$1.5k SEO starter" promo — likely under-priced clients with lower commitment.
- Jul'25 onboarding overhaul (kickoff scorecard) appears to be holding M3 retention at 79% despite higher intake volume.
- Sub-$2k MRR accounts churn at 2x the rate of $3k+.

Experiment: Sunset the starter tier; require $2.5k MRR minimum on new contracts. Expected lift: +10-15pp M6 retention based on Jan'25 vs Apr'25 delta.

Pro tips

Run this the same week each quarter so cohort ages line up consistently.
Layer in MRR per cohort, not just headcount — a 50% retained cohort at $5k MRR beats 80% retained at $1k.
Tag each cohort with its dominant intake channel so the next prompt (LTV by channel) can build on this output.

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