[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-cc66ce80-d73f-40d5-907c-8668fafb3884":3,"$fj06bG7VjRe2_cGApNKj_os9Suy1Vled_YbVEu9irT9E":43},{"id":4,"title":5,"description":6,"categoryId":7,"moduleId":8,"tags":9,"prompt":10,"icon":11,"source":12,"sourceUrl":13,"authorId":14,"authorName":15,"isPublic":16,"stars":17,"runs":18,"createdAt":19,"updatedAt":19,"module":20,"category":27,"packages":34},"cc66ce80-d73f-40d5-907c-8668fafb3884","product-analytics","在定义产品关键绩效指标、构建指标仪表板、运行群体或留存分析、或解释产品各个阶段的功能采用趋势时使用。","cat_prod_data","mod_productivity","alirezarezvani,productivity","---\nname: product-analytics\ndescription: Use when defining product KPIs, building metric dashboards, running cohort or retention analysis, or interpreting feature adoption trends across product stages.\n---\n\n# Product Analytics\n\nDefine, track, and interpret product metrics across discovery, growth, and mature product stages.\n\n## When To Use\n\nUse this skill for:\n- Metric framework selection (AARRR, North Star, HEART)\n- KPI definition by product stage (pre-PMF, growth, mature)\n- Dashboard design and metric hierarchy\n- Cohort and retention analysis\n- Feature adoption and funnel interpretation\n\n## Workflow\n\n1. Select metric framework\n- AARRR for growth loops and funnel visibility\n- North Star for cross-functional strategic alignment\n- HEART for UX quality and user experience measurement\n\n2. Define stage-appropriate KPIs\n- Pre-PMF: activation, early retention, qualitative success\n- Growth: acquisition efficiency, expansion, conversion velocity\n- Mature: retention depth, revenue quality, operational efficiency\n\n3. Design dashboard layers\n- Executive layer: 5-7 directional metrics\n- Product health layer: acquisition, activation, retention, engagement\n- Feature layer: adoption, depth, repeat usage, outcome correlation\n\n4. Run cohort + retention analysis\n- Segment by signup cohort or feature exposure cohort\n- Compare retention curves, not single-point snapshots\n- Identify inflection points around onboarding and first value moment\n\n5. Interpret and act\n- Connect metric movement to product changes and release timeline\n- Distinguish signal from noise using period-over-period context\n- Propose one clear product action per major metric risk\u002Fopportunity\n\n## KPI Guidance By Stage\n\n### Pre-PMF\n- Activation rate\n- Week-1 retention\n- Time-to-first-value\n- Problem-solution fit interview score\n\n### Growth\n- Funnel conversion by stage\n- Monthly retained users\n- Feature adoption among new cohorts\n- Expansion \u002F upsell proxy metrics\n\n### Mature\n- Net revenue retention aligned product metrics\n- Power-user share and depth of use\n- Churn risk indicators by segment\n- Reliability and support-deflection product metrics\n\n## Dashboard Design Principles\n\n- Show trends, not isolated point estimates.\n- Keep one owner per KPI.\n- Pair each KPI with target, threshold, and decision rule.\n- Use cohort and segment filters by default.\n- Prefer comparable time windows (weekly vs weekly, monthly vs monthly).\n\nSee:\n- `references\u002Fmetrics-frameworks.md`\n- `references\u002Fdashboard-templates.md`\n\n## Cohort Analysis Method\n\n1. Define cohort anchor event (signup, activation, first purchase).\n2. Define retained behavior (active day, key action, repeat session).\n3. Build retention matrix by cohort week\u002Fmonth and age period.\n4. Compare curve shape across cohorts.\n5. Flag early drop points and investigate journey friction.\n\n## Retention Curve Interpretation\n\n- Sharp early drop, low plateau: onboarding mismatch or weak initial value.\n- Moderate drop, stable plateau: healthy core audience with predictable churn.\n- Flattening at low level: product used occasionally, revisit value metric.\n- Improving newer cohorts: onboarding or positioning improvements are working.\n\n## Anti-Patterns\n\n| Anti-pattern | Fix |\n|---|---|\n| **Vanity metrics** — tracking pageviews or total signups without activation context | Always pair acquisition metrics with activation rate and retention |\n| **Single-point retention** — reporting \"30-day retention is 20%\" | Compare retention curves across cohorts, not isolated snapshots |\n| **Dashboard overload** — 30+ metrics on one screen | Executive layer: 5-7 metrics. Feature layer: per-feature only |\n| **No decision rule** — tracking a KPI with no threshold or action plan | Every KPI needs: target, threshold, owner, and \"if below X, then Y\" |\n| **Averaging across segments** — reporting blended metrics that hide segment differences | Always segment by cohort, plan tier, channel, or geography |\n| **Ignoring seasonality** — comparing this week to last week without adjusting | Use period-over-period with same-period-last-year context |\n\n## Tooling\n\n### `scripts\u002Fmetrics_calculator.py`\n\nCLI utility for retention, cohort, and funnel analysis from CSV data. Supports text and JSON output.\n\n```bash\n# Retention analysis\npython3 scripts\u002Fmetrics_calculator.py retention events.csv\npython3 scripts\u002Fmetrics_calculator.py retention events.csv --format json\n\n# Cohort matrix\npython3 scripts\u002Fmetrics_calculator.py cohort events.csv --cohort-grain month\npython3 scripts\u002Fmetrics_calculator.py cohort events.csv --cohort-grain week --format json\n\n# Funnel conversion\npython3 scripts\u002Fmetrics_calculator.py funnel funnel.csv --stages visit,signup,activate,pay\npython3 scripts\u002Fmetrics_calculator.py funnel funnel.csv --stages visit,signup,activate,pay --format json\n```\n\n**CSV format for retention\u002Fcohort:**\n```csv\nuser_id,cohort_date,activity_date\nu001,2026-01-01,2026-01-01\nu001,2026-01-01,2026-01-03\nu002,2026-01-02,2026-01-02\n```\n\n**CSV format for funnel:**\n```csv\nuser_id,stage\nu001,visit\nu001,signup\nu001,activate\nu002,visit\nu002,signup\n```\n\n## Cross-References\n\n- Related: `product-team\u002Fexperiment-designer` — for A\u002FB test planning after identifying metric opportunities\n- Related: `product-team\u002Fproduct-manager-toolkit` — for RICE prioritization of metric-driven features\n- Related: `product-team\u002Fproduct-discovery` — for assumption mapping when metrics reveal unknowns\n- Related: `finance\u002Fsaas-metrics-coach` — for SaaS-specific metrics (ARR, MRR, churn, LTV)\n","","imported","https:\u002F\u002Fgithub.com\u002Falirezarezvani\u002Fclaude-skills","user_system_seed","SkillOPIC",true,158,990,"2026-05-16 14:03:35",{"id":8,"name":21,"slug":22,"icon":23,"description":24,"sort":25,"createdAt":26},"效率工具","productivity","mdi-lightning-bolt-outline","文档处理、数据分析、自动化工作流",4,"2026-05-16 12:53:40",{"id":7,"name":28,"slug":29,"icon":30,"description":31,"moduleId":8,"sort":32,"skillCount":33,"createdAt":26},"数据分析","data-analysis","mdi-chart-bar","数据可视化、统计分析",2,30,[35],{"id":36,"skillId":4,"version":37,"fileName":38,"fileSize":39,"filePath":40,"fileHash":41,"manifest":42,"createdAt":19},"d0dbbe42-54b0-4697-aa33-03c5ed938296","1.0.0","product-analytics.zip",6831,"uploads\u002Fskills\u002Fcc66ce80-d73f-40d5-907c-8668fafb3884\u002Fproduct-analytics.zip","ce1c223a1c9291fa434b0f1e0699c25b5f41b249e44b7c1b3f22791d66c3ea06","[{\"path\":\"SKILL.md\",\"isDirectory\":false,\"size\":5501},{\"path\":\"references\u002Fdashboard-templates.md\",\"isDirectory\":false,\"size\":2014},{\"path\":\"references\u002Fmetrics-frameworks.md\",\"isDirectory\":false,\"size\":2425},{\"path\":\"scripts\u002Fmetrics_calculator.py\",\"isDirectory\":false,\"size\":7349}]",{"code":44,"message":45,"data":46},200,"success",{"items":47,"stats":48,"page":51},[],{"averageRating":49,"totalRatings":49,"ratingCounts":50},0,[49,49,49,49,49],{"limit":52,"offset":49,"hasMore":53,"nextOffset":52,"ratedOnly":16},15,false]