retention-cohort-interpreter

name	retention-cohort-interpreter
description	Interpret cohort retention tables into plain-language diagnosis: curve health, drop-off points, benchmark comparison, and next steps. Use when reviewing weekly/monthly retention data, preparing investor updates, or diagnosing churn. Triggers: 'analyze retention cohort', 'interpret cohort table', 'diagnose retention drop', 'проанализируй таблицу удержания', 'интерпретируй когортный анализ', 'диагностируй отвал по когортам'.
version	1.0.0

Retention Cohort Interpreter

This skill interprets cohort retention tables for product managers and analysts, translating raw retention data into actionable plain-language diagnostics. Paste any cohort table (CSV or markdown) and receive a structured report: curve health assessment, key drop-off windows, industry benchmark comparison, hypotheses, and prioritized next steps.

Input:

• Cohort retention table (pasted as CSV, markdown table, or space-separated numbers; rows = cohorts, columns = time periods, values = % retained or user counts)
• Optional: product type (mobile app / SaaS / marketplace / consumer), cohort definition, current goal context

Output:

• Structured markdown report with sections: Curve Health, Key Drop-off Points, Benchmark Comparison, Hypotheses, Recommended Next Steps

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Language Detection

Detect the user's language from their message:

• If Russian (or contains Cyrillic): respond in Russian
• If English (or other Latin-script language): respond in English
• If ambiguous: respond in the language of the trigger phrase used

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Instructions

Step 1: Validate and Parse Input

1. Check that a retention table is provided

   • If no table provided (description only, or question without data): stop and report: "Retention table required. Paste your cohort data as a CSV, markdown table, or plain numbers with headers."

2. Identify table structure

   • Rows = cohorts (signup week/month, acquisition channel, or similar)
   • Columns = time periods (D1, D7, D30 or Week 1, Week 2, etc.)
   • Values = retention % (0–100 or 0–1 scale) or absolute user counts

3. Validate structure

   • If single row or single column: stop and report: "Table structure not recognized. Expected: rows = cohorts, columns = time periods, values = retention % or user counts."
   • If non-numeric cell values (excluding headers): stop with same message

4. Detect value type

   • If values > 100 or the table clearly shows descending absolute counts (not percentages): treat as absolute counts; convert each value to % relative to the period-0 (first column) value for that cohort; note conversion in the output

5. Detect scale

   • If all values ≤ 1.0 (e.g., 0.45, 0.22): treat as 0–1 scale; multiply by 100 for display; note this assumption

Step 2: Compute Curve Descriptors

1. For each cohort row, identify the retention value at key benchmark periods:

   • For daily products: D1, D7, D30 (or closest available periods)
   • For weekly/monthly products: W1, W4, W12 or M1, M3, M6

2. Compute period-over-period deltas for each cohort:

   • Drop = value[period N] − value[period N+1]
   • Identify the 2–3 largest drops across all cohorts

3. Check for curve flattening (asymptotic floor):

   • If last 2–3 periods show drops < 2 pp: note as "curve is flattening — long-term floor likely around X%"

4. Compute average retention across cohorts for each time period (if multiple cohorts)

Edge Cases:

• If table has only 2–3 time periods: perform analysis on available data; add flag: "Limited periods — trends may not be conclusive. Consider extending observation window."
• If some cells are blank: skip missing cells; add flag: "Missing values in periods [X] may affect trend reliability."
• If one cohort has dramatically different values (outlier): flag it explicitly; separate its analysis from the aggregate trend

Step 3: Select Benchmark

1. Determine product type from user input, context clues, or column naming:

   • "D1, D7, D30" → likely mobile/consumer app
   • "Week 1, Week 4" or "M1, M3" → likely SaaS or B2B
   • If user stated product type explicitly: use that

2. Apply appropriate benchmark ranges:

   • Mobile / Consumer app: D1 ≥ 40% = good; D7 ≥ 20% = good; D30 ≥ 10% = good
   • B2C SaaS: M1 ≥ 60% = good; M3 ≥ 40% = good; M6 ≥ 30% = good
   • B2B SaaS: M1 ≥ 75% = good; M3 ≥ 65% = good; M6 ≥ 55% = good
   • Marketplace / Consumer platform: M1 ≥ 50% = good; M3 ≥ 30% = good

3. Compare curve to benchmark at a representative mid-period (e.g., D30 or M3)

   • State whether curve is above / at / below benchmark

Step 4: Diagnose Curve Health

1. Assign overall health label based on combined criteria:

   • Healthy: Curve meets or exceeds benchmark at key periods AND flattens above a meaningful floor
   • Needs Work: Curve is 10–30% below benchmark at key periods OR shows no sign of flattening
   • Critical: Curve is >30% below benchmark at key periods OR drops to near-zero before expected floor

2. Write 1–2 sentence narrative explaining the label, referencing specific data points

Step 5: Generate Hypotheses

1. Identify the dominant churn window (the period with the largest average drop across cohorts)

2. Generate exactly 3 hypotheses specific to that window:

   • Each hypothesis must be actionable and testable (not generic)
   • Link each hypothesis to a mechanism (e.g., onboarding gap, feature discovery failure, competitive alternative, habit loop not formed)

3. Order hypotheses from most likely (based on pattern) to exploratory

Step 6: Compile and Output Report

1. Assemble the full report using the Output Format below
2. Ensure all sections are populated; do not leave any section empty
3. If prediction of future retention is requested without historical basis: add note: "Forecasting requires more historical data. The analysis above is based on observed trends only."

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Negative Cases

• No data provided: Stop with "Retention table required. Paste your cohort data as CSV, markdown, or plain numbers with headers."
• Malformed table (single row/column, non-numeric): Stop with "Table structure not recognized. Expected: rows = cohorts, columns = time periods, values = retention % or user counts."
• Prediction request without data: Decline forecast, explain requirement for historical data; continue with analysis of what was provided.

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Output Format

Structured markdown response:

## Cohort Retention Diagnosis

**Product type:** [detected or stated]
**Cohorts analyzed:** [N]
**Periods covered:** [e.g., D1–D30 or W1–W12]
**Note:** [Any flags: scale conversion, missing values, limited periods — or omit if none]

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### Curve Health: [Healthy / Needs Work / Critical]

[1–2 sentences summarizing overall pattern with specific data points]

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### Key Drop-off Points

| Period | Avg Retention | Drop vs Previous | Severity |
|--------|---------------|------------------|----------|
| [e.g., D1→D7] | X% | -Y pp | High / Medium / Low |
| [e.g., D7→D30] | X% | -Y pp | High / Medium / Low |

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### Benchmark Comparison

- **Reference:** [product type] — typical [key period] retention: X%–Y%
- **Your curve:** [above / at / below] benchmark at [key period]: Z%
- [1 sentence interpretation]

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### Hypotheses for Primary Churn Window ([period])

1. **[Hypothesis 1 — most likely]:** [Specific, actionable explanation + mechanism]
2. **[Hypothesis 2]:** [Specific, actionable explanation + mechanism]
3. **[Hypothesis 3 — exploratory]:** [Specific, actionable explanation + mechanism]

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### Recommended Next Steps

1. [Specific investigation or experiment — tied to Hypothesis 1]
2. [Data cut or segment analysis to run]
3. [Stakeholder conversation or metric to instrument]