These prompts help UX researchers and product teams use AI tools to analyze product analytics data — from interpreting funnel drop-offs to detecting metric anomalies and planning event tracking. Replace [bracketed placeholders] with your specifics.
Prompt 1: Interpret a funnel analysis and suggest improvements
I have a funnel analysis for [product name]'s [flow name, e.g., onboarding, checkout, signup].
Funnel steps and conversion rates:
[Paste the funnel data — step name, users entering, users completing, conversion rate]
Segment breakdowns (if available):
[Paste segment data — e.g., mobile vs. desktop conversion at each step]
Context: [what the product does, any recent changes, known issues]
Please:
1. Identify the steps with the largest absolute and relative drop-offs
2. For each major drop-off, suggest 3 possible reasons based on common UX patterns
3. Recommend which qualitative method (usability test, survey, session recording review) would best diagnose each drop-off
4. Suggest specific design changes that could reduce each drop-off, with expected impact
5. Prioritize the improvements by potential business impact (highest drop-off × highest traffic first)Prompt 2: Analyze product health metrics and detect anomalies
Here are the key product metrics for [product name] over the past [time period]:
[Paste the data — date, DAU, WAU, MAU, session_duration_avg, retention_day7, retention_day30, feature_adoption_rates, error_rate]
Recent product changes:
[List any releases, feature launches, or configuration changes with dates]
Please:
1. Identify any anomalies (sudden changes, trend breaks, unusual patterns) in the data
2. For each anomaly, check if it correlates with a product change from the list above
3. Calculate week-over-week and month-over-month change for each metric
4. Highlight metrics that are trending negatively and may need investigation
5. Generate a 1-page product health summary suitable for a weekly team meetingPrompt 3: Generate a tracking plan for a new feature
We are launching a new feature: [feature name and description].
Product context:
- Where the feature sits in the user journey: [describe]
- Expected user flow: [list the steps a user takes to use this feature]
- Success criteria: [what does successful feature adoption look like?]
- Business goal: [what business outcome should this feature drive?]
Please create a tracking plan that includes:
1. A list of events to track (event name, description, when it fires, key properties to capture)
2. A funnel definition for measuring feature adoption (which events, in which order)
3. Key metrics to monitor: adoption rate, usage frequency, retention of feature users vs. non-users
4. Segment dimensions to track (user role, plan tier, device, acquisition source)
5. Dashboard layout recommendation: which charts to include and how to organize them
6. Alert thresholds: when should the team be notified about this feature's metrics?