MCP For Claude Marketplace
AI Feature Retention Analyzer

AI Feature Retention Analyzer MCP Connector for Claude

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Quantify the impact of AI features on user retention and ROI.

4 tools Official Updated Oct 1, 2026 Official Vinkius Partner
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This MCP server provides analytical tools to measure how AI-driven features influence user retention. It allows agents to calculate retention lift, determine the statistical correlation between usage frequency and retention, and estimate the economic ROI of AI features. Use calculate_retention_lift to find the percentage boost in retention, analyze_usage_correlation to check the strength of the relationship between usage and stickiness, and estimate_feature_roi to evaluate the cost-to-value ratio based on feature maturity. You can also use get_segmented_impact_summary to compare performance across different user tiers.

retentionai-impactroiuser-behavioranalytics

4 tools expose this connector's capabilities to your AI agent.

get_segmented_impact_summary

Provides a summary of AI feature impact across different user segments

analyze_usage_correlation

Measures the statistical link between AI feature usage frequency and user retention

calculate_retention_lift

Calculates the percentage increase in retention attributed to the AI feature

estimate_feature_roi

Estimates the ROI of an AI feature based on cost and prevented churn value

See how to talk to your AI agent using AI Feature Retention Analyzer.

What is the retention lift for the 'smart-autocomplete' feature if the usage group retention is 0.45 and the non-usage group is 0.35 with a cohort of 1000?

The retention lift for the smart-autocomplete feature is 28.57%.

Is there a strong link between using the 'ai-summarizer' and staying with the service? Here is the data: [{'frequency': 10, 'retained': true}, {'frequency': 1, 'retained': false}]

There is a strong positive correlation between usage frequency and retention.

Calculate the ROI for 'predictive-chat' which costs $5000 annually, prevents $20000 in churn, and is currently in the 'stable' stage.

The ROI for the predictive-chat feature is 300%.

You can use the `calculate_retention_lift` tool by providing the feature ID, the cohort size, and the retention rates for both the usage and non-usage groups.

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