How do I use the Cohort Retention Analysis prompt with Claude?

Copy the prompt template, replace the bracketed placeholders with your actual data, and paste it into Claude. The AI will analyze your data and produce structured results including specific recommendations you can act on immediately.

Customer Analytics

Cohort Retention Analysis

Group customers by acquisition month and track repeat purchase rates over time to see if your retention is actually improving.

What This Skill Does

This skill builds a monthly cohort retention table from your order data. It groups customers by the month of their first purchase and tracks what percentage come back to buy again in each subsequent month.

The output reveals whether your business is getting better at keeping customers over time, identifies the exact month where the biggest drop-off occurs (your "churn cliff"), and shows which acquisition cohorts are your strongest.

What You Need

Customer order data with at least: Customer ID and Order Date. More data (order value, acquisition channel) enables deeper analysis.

Prompt Template

Copy this prompt, replace the [BRACKETED] placeholders with your data, and paste into Claude.

Using this order data, build a monthly cohort retention table.

Group customers by their first purchase month. For each cohort, calculate the percentage who made at least one additional purchase in each subsequent month (Month 1 through Month 12).

Present as a retention table, then analyze:

1. Which cohorts have the best and worst Month-3 retention?
2. Is retention trending up or down across recent cohorts?
3. At which month does the largest drop-off occur (the "churn cliff")?
4. Three actionable recommendations to improve the weakest point in the retention curve.

Here is my order data:
[PASTE YOUR DATA]

Example Output

Here is what Claude produces when you use this skill with sample data.

**Cohort Retention Table (% making a repeat purchase):**

| Cohort | Size | M1 | M2 | M3 | M4 | M5 | M6 |
|--------|------|----|----|----|----|----|-----|
| Jan 2025 | 82 | 32% | 24% | 19% | 16% | 14% | 12% |
| Feb 2025 | 95 | 35% | 26% | 21% | 18% | 16% | 14% |
| Mar 2025 | 78 | 28% | 20% | 15% | 13% | 11% | 10% |
| Apr 2025 | 110 | 30% | 22% | 18% | 15% | 13% | — |
| May 2025 | 88 | 24% | 17% | 12% | 10% | — | — |
| Jun 2025 | 92 | 33% | 25% | 20% | — | — | — |
| Jul 2025 | 75 | 36% | 28% | — | — | — | — |
| Aug 2025 | 105 | 38% | 30% | — | — | — | — |

**The Churn Cliffs:**

Primary cliff: M0 → M1 — average 69% of customers lost
You lose nearly 7 in 10 customers within 30 days of first purchase. This is your #1 retention opportunity.

Secondary cliff: M2 → M3 — retention drops by ~25% relative
Customers who repurchased once still have a critical decision point at the 60-90 day mark.

**Trend:** Recent cohorts (Jun-Aug) show improved M1 retention (33-38% vs. 24-32% earlier), suggesting Q2/Q3 changes are working. Monitor as these cohorts mature.

**Recommendations:**

1. POST-PURCHASE NURTURE (fixes M0→M1 cliff)
Deploy a 5-email sequence in the first 30 days:
Day 1: Brand story | Day 3: Usage tips | Day 7: Social proof
Day 14: Cross-sell | Day 25: Replenishment + incentive
Expected impact: +8-12% M1 retention

2. LOYALTY TRIGGER (fixes M2→M3 cliff)
At day 60, send "Unlock loyalty rewards" email to 2x buyers. Offer free shipping + sample with 3rd purchase.
Expected impact: +5-8% M3 retention

3. INVESTIGATE MAY COHORT (12% M3 vs. 19-21% for adjacent months)
Audit: What channels drove May acquisition? Any fulfillment issues? Understanding this anomaly prevents repeating the pattern.

Tips for Best Results

You need at least 6 months of data for meaningful cohort analysis. 12+ months is ideal.

If cohort sizes vary dramatically (e.g., Black Friday cohorts), note the sizes alongside percentages.

Ask Claude to also calculate revenue per cohort — high-retention cohorts with low spend may need different treatment than low-retention, high-spend ones.

Skip the Prompt — Automate This

Finsi runs this analysis automatically on your live data. No prompting, no copy-pasting — just real-time insights and AI-powered recommendations.

See Retention Intelligence

Related Skills

Continue building your AI-powered e-commerce toolkit.

RFM Customer Segmentation

Churn Risk Scoring

Customer Lifetime Value Calculator

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