app-growth-investigator
Investigate growth, activation, retention, conversion, funnel bottlenecks, and product-value realization for apps and websites using analytics, database or warehouse, billing, release, and support data. Use when asked why users do not activate, return, convert, collaborate, or retain; to measure build impact; to explain churn-like behavior; or to find concrete growth levers from product data.
Security Vetted
Reviewed by AI agents and approved by humans.
Skill Instructions
# App Growth Investigator Use this skill to investigate an app or site like a sharp product operator, not a dashboard tourist. Find the behavior that explains why users get value, fail to get value, come back, convert, or disappear, then turn that into specific product questions and experiments. ## Core Mindset - Think in funnels. Find the constrained step before debating everything else. - Treat timing as signal. Ask when a behavior happens, not just how often. - Segment aggressively. Aggregates hide the actual mechanism. - Look for value failure, not just "traffic" or "churn." - Care about product-business-model fit. Sometimes the product is working but the packaging is wrong. - Turn every finding into a lever, not just an observation. ## Workflow 1. Frame the business question. - Examples: - "Why are new signups failing to activate?" - "Where is the biggest conversion leak?" - "Did the March 1 onboarding change improve first-week retention?" - "Are users getting one-off value without becoming retained users?" 2. Pick source authority before drawing conclusions. - Use the rules below. - Read `references/source-patterns.md` for common stack combinations. 3. Get release context before forming hypotheses. - Check recent pushes, experiments, pricing changes, copy changes, and untouched surfaces. - Prefer pre/post analysis by exact rollout or change date when the question is about impact. 4. Choose one funnel family for the job-to-be-done. - Read `references/funnel-patterns.md`. - Define one funnel for the specific user job, not one giant master funnel. 5. Identify the bottleneck. - Measure conversion rate, absolute user loss, and delay at each step. - Spend most of the analysis on the step with the strongest combination of volume loss, delay, and strategic importance to value realization. 6. Segment before concluding anything. - At minimum consider real vs test vs uncertain, new vs returning, signed-in vs anonymous when relevant, build or rollout cohorts, and acquisition or entry path when available. 7. Look for timing cliffs and "done in one sitting" behavior. - Ask where drop-off is concentrated: same session, same hour, day 0, day 1, day 7, or first billing cycle. - Check whether users appear to complete the job once and have no reason to return. 8. End with levers. - Every finding should suggest a messaging, onboarding, pricing, packaging, adoption, instrumentation, or release follow-up. ## Source Rules - Assign authority by claim: - product database or warehouse for entity truth - analytics events for interaction paths and timing - billing system for monetization truth - release or experiment history for rollout context - support feedback or surveys for qualitative evidence - logs when instrumentation is missing - When sources disagree, do not average them together. Quantify the gap and explain what each source can and cannot prove. - Exclude internal, test, bot, and automation traffic by default when possible. - Keep an `uncertain` bucket when attribution is incomplete instead of hiding ambiguity. - Always show denominators, time windows, and exclusion rules. - If a metric depends on a proxy, say so plainly. ## Output Shape Return a short report with: 1. Release context - What changed recently - What important surfaces have not changed 2. Funnel and bottleneck - Funnel definition - Biggest drop-off or delay - Magnitude of the loss 3. Key findings - 3-7 concrete insights with exact percentages, counts, and time windows 4. Interpretation - What the findings likely mean - Whether they point to friction, weak value, wrong packaging, traffic mix, or instrumentation debt 5. Recommended next checks - The next cuts or queries that would confirm or falsify the hypothesis 6. Product levers - Specific messaging, onboarding, pricing, packaging, feature, or instrumentation changes worth testing ## Reference Guide - Read `references/source-patterns.md` when choosing authority rules or reconciling multiple data systems. - Read `references/funnel-patterns.md` when selecting a funnel family or defining step-level metrics. - Read `references/app-shapes.md` when the product shape affects the interpretation of activation, retention, conversion, or repeat usage.