Feature Cannibalization Impact Analysis
Analyze whether a new product feature is stealing usage, revenue, or engagement from existing features and recommend product or packaging actions.
Prompt Template
You are a senior product analyst. Analyze whether [new feature] is cannibalizing or complementing [existing feature/product/workflow]. Data available: - Feature launch date: [date] - User cohorts: [new users, existing users, plan tiers, industries, regions] - Metrics: [activation, feature usage, session frequency, conversion, retention, expansion, support contacts, revenue] - Data tables or fields: [events, accounts, subscriptions, revenue, experiments] - Observation window: [pre/post period] - Known seasonality or campaigns: [context] - Business concern: [revenue cannibalization, lower usage of old feature, plan downgrade, support confusion] Deliver: 1. Analysis plan with hypotheses for cannibalization vs complementarity 2. Cohort and segment definitions 3. Metrics to compare before and after launch 4. Suggested SQL or pseudocode for usage overlap, substitution rate, and revenue impact 5. Visualization recommendations 6. Confounders and validity checks 7. Decision framework: keep, reposition, bundle, retire, educate, or change pricing 8. Executive summary template with recommended action If data is insufficient, list the minimum instrumentation needed before making a decision.
Example Output
Feature Cannibalization Readout — AI Summary vs Manual Notes
Hypothesis
AI Summary is substituting manual note creation for existing teams, but complementing onboarding for new teams by increasing total documented meetings.
Core metrics
- Manual notes per active account, pre/post launch
- AI summaries created per active account
- Total documented meetings = manual notes + AI summaries
- Retention and expansion by usage pattern
SQL sketch
select account_id,
count_if(event_name = 'manual_note_created') as manual_notes,
count_if(event_name = 'ai_summary_created') as ai_summaries,
count(distinct meeting_id) as documented_meetings
from product_events
where event_date between :start_date and :end_date
group by 1;
Recommendation
If total documented meetings rose while manual notes fell, position AI Summary as a workflow upgrade. If total documentation is flat and expansion drops in power-user segments, review packaging and education before retiring the old workflow.
Tips for Best Results
- 💡Separate usage cannibalization from revenue cannibalization; one can be healthy while the other is risky.
- 💡Compare total workflow completion, not just the old feature's decline.
- 💡Segment new and existing users because adoption patterns often tell different stories.
- 💡Add instrumentation for feature source, plan tier, and account age if overlap is hard to measure.
Frequently Asked Questions
What is the Feature Cannibalization Impact Analysis prompt?
Analyze whether a new product feature is stealing usage, revenue, or engagement from existing features and recommend product or packaging actions. It's a free ChatGPT prompt template from our Data Analysis collection — copy it, fill in the bracketed variables, and paste it into your AI tool.
Which AI tools work with this prompt?
It's written and tested for ChatGPT, Claude and Gemini. Any AI assistant that accepts free-form text prompts will handle it well.
How do I customize this ChatGPT prompt?
Replace the bracketed variables — such as [new feature], [existing feature/product/workflow], [date] — with your own details before running it. Separate usage cannibalization from revenue cannibalization; one can be healthy while the other is risky.
Is this prompt free to use?
Yes. Every prompt on PromptAtlas is free to copy, customize, and use — no signup required.
Related Prompts
Cohort Retention Analysis Explainer
Interpret your cohort retention data to understand user behavior, identify drop-off patterns, and improve retention strategies.
Revenue Forecasting Model Builder
Build a structured revenue forecast with multiple scenarios, key assumptions, and sensitivity analysis to predict and plan for future growth.
Product Usage Analytics Interpreter
Interpret raw product analytics data — feature adoption, session patterns, power user behaviors — and translate findings into actionable product decisions with prioritized recommendations.