# Retention Cohort Interpreter > 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'. - Skill: `kirkruglov/retention-cohort-interpreter` (Agent Skill, multi-file: 5 files) - Install (CLI): `npx skillmds@latest add kirkruglov/retention-cohort-interpreter` - Raw SKILL.md: https://api.skillmd.com/api/skills/kirkruglov/retention-cohort-interpreter/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Data & Analytics - Author: KirKruglov (https://skillmd.com/u/kirkruglov) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/kirkruglov/retention-cohort-interpreter --- # 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 --- ## 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 --- ## 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." --- ## 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. --- ## Output Format Structured markdown response: ```markdown ## 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] --- ### Curve Health: [Healthy / Needs Work / Critical] [1–2 sentences summarizing overall pattern with specific data points] --- ### 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 | --- ### Benchmark Comparison - **Reference:** [product type] — typical [key period] retention: X%–Y% - **Your curve:** [above / at / below] benchmark at [key period]: Z% - [1 sentence interpretation] --- ### 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] --- ### 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] ```