Analyze users grouped by signup period or another shared characteristic to compare retention and feature adoption over time. Produce charts, highlight unusual changes, and recommend follow-up research or experiments to investigate the patterns.
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Perform cohort analysis on user engagement data — retention curves, feature adoption trends, and segment-level insights. Use when analyzing user retention by cohort, studying feature adoption over time, investigating churn patterns, or identifying engagement trends.
安装前可以查看交给 AI 代理的指令内容。
Analyze user engagement and retention patterns by cohort to identify trends in user behavior, feature adoption, and long-term engagement. Combine quantitative insights with qualitative research recommendations.
Example 1: Upload CSV Data
Upload cohort_engagement.csv with columns: cohort_month, weeks_active,
user_id, feature_x_usage, engagement_score
Request: "Analyze retention patterns and identify why Q4 2025 cohorts
underperform compared to Q3"
Example 2: Describe Data Format
"I have monthly user cohorts from Jan-Dec 2025. Each row shows:
cohort date, user ID, purchase frequency, and support tickets.
Analyze which cohorts show best long-term retention."
Example 3: Feature Adoption Analysis
Upload feature_usage.xlsx with cohort adoption data.
Request: "Compare adoption curves for our new feature across cohorts.
Which cohorts adopted fastest? Any patterns?"
You'll receive:
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Analyze A/B test results to decide whether to launch a change, extend testing, stop, or investigate further. Check sample size, statistical significance, uncertainty ranges, and effects on other important metrics, then summarize the recommendation and next steps.
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Organize customer feature requests into themes and rank the top three against product goals, customer value, effort, and risk. For each recommendation, explain the underlying need, possible alternatives, uncertain assumptions, and simple ways to test them.
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Compare growth options across existing and new products and markets using the Ansoff Matrix, a four-part planning framework. Assess opportunities, resources, competition, and risks, then develop market launch plans and a phased roadmap for priority options.
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Choose the first customer segment for a product launch by comparing urgent needs, willingness to pay, achievable market share, and referral potential. Get scored recommendations, a priority segment, a 90-day acquisition plan, and expansion options.
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Plan low-effort experiments to check assumptions about an existing product before building a feature. Compare prototypes, A/B tests, and other methods, with a clear assumption, procedure, metric, and success threshold for each experiment.
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Turn a new product idea into a testable hypothesis and two or three simple experiments. Define target customers, expected actions, measurement methods, and success thresholds to assess demand through behavior such as sign-ups, pre-orders, or paid commitments.
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