Does this MCP only calculate retention lift?

No. While calculating retention lift is a core function, you can also check the statistical correlation between usage and retention, estimate the feature's ROI, and summarize impact by user segment.

What kind of data does the MCP need to run?

The MCP requires data points like usage group retention rates, non-usage group retention rates, feature costs, and projected churn values to run its calculations accurately.

Can I compare different user groups?

Yes. The get_segmented_impact_summary capability allows you to compare how different user tiers or segments are affected by the AI features.

Is this for general analytics or specific AI features?

It is specifically designed to measure the impact of AI-driven features. It helps you quantify the value of your AI investments, not general product usage.

How do I calculate the retention boost from a specific feature?

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

Can I see how different user segments are affected by AI features?

Yes, the get_segmented_impact_summary capability provides adoption rates and average lift for specific user segments like Enterprise or Free tiers.

How is the ROI of an AI feature determined?

The estimate_feature_roi capability calculates the ratio of prevented churn value against the annual maintenance cost, adjusting for the feature's maturity stage.

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Connect AI Feature Retention Analyzer to ChatGPT and Claude

Set it up once and it works in ChatGPT, Claude, Cursor, and any other client you love. It all runs safely in the background, so you can relax and focus on what really matters. Prove the value and ROI of your product features.

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Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

Your AI does the work. We keep it safe and sound.

No other AI tool gives you this. Your workflow runs in the client you already love, your account stays protected, and you decide what the agent can and cannot do. It is the whole reason to make the switch.

Each Connector lives inside our infrastructure, the same place where your workflow runs. The three things below are already part of it, working quietly in the background so you never have to think about them. Nothing to add, nothing to set up, nothing to worry about.

V8 Isolate01

Each Connector runs inside its own sealed environment.

Each Connector runs inside its own sealed V8 runtime — no access to your files, your system, or other servers. A Connector never sees past its own request.

Kill Switch02

One switch stops every agent. Instantly.

One action deactivates every Connector, revokes every token, and ends every session at once. AI Governance keeps the record.

FinOps per User03

Spending limits that pause instead of surprise.

Every user keeps their own connections and credentials, isolated from everyone else. Anything that would cross your spending limit is held for your approval, not billed.

Three controls, built into the layer your AI connects through.

Curated set · 4 capabilities

We ship only the actions that matter.

Capability set01 / 01

01-04

4 capabilities in this set.

Part of 4 available through Retention Analytics.

01
Get segmented impact summary
Provides a summary of AI feature impact across different user segments
02
Analyze usage correlation
Measures the statistical link between AI feature usage frequency and user retention
03
Calculate retention lift
Calculates the percentage increase in retention attributed to the AI feature
04
Estimate feature roi
Estimates the ROI of an AI feature based on cost and prevented churn value

Real work

What teams actually run

Justifying New Features

A Product Manager needs to prove the value of a new AI summarizer. They use this MCP to calculate the ROI based on projected churn savings.

Optimizing the Roadmap

A team wants to know which user segment benefits most from AI capabilities. They run a segmented impact summary to guide the next development cycle.

Measuring Feature Success

After launching a new autocomplete feature, a Data Analyst uses the retention lift capability to quantify the immediate positive impact.

Identifying Usage Patterns

You want to know if high usage correlates with long-term retention. You run the usage correlation capability to find the statistical link.

One connector, every AI

Retention Analytics works with the most popular AI clients.

These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.

Building your own app? The connector is yours to use.

You don't need a client to put Retention Analytics to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.

Observed, not estimated

906ms average. Fast in production.

Retention Analytics is checked daily against the live service.

Daily averagePeak 1040ms

Oct 3Today

Fastest day: 702ms
Slowest day: 1040ms
14-day trend: Slowing+14%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 4 capabilities arrive ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of Retention Analytics, so you can see the experience inside your AI.

It does not authenticate your account with Retention Analytics. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

Retention Analytics Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_MSHt7rUhQY5iWnADCndbfeH29Pk3aonUpoBsOFDO/mcp

Claude Desktop

Follow the steps below to connect in seconds.

1In Claude Desktop, open Settings → Connectors.
2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
3Click Add and start a new chat — Retention Analytics capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "ai-feature-retention-analyzer-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_MSHt7rUhQY5iWnADCndbfeH29Pk3aonUpoBsOFDO/mcp"
    }
  }
}

Claude
ChatGPT
Cursor
VS Code
Windsurf
Claude Code
JetBrains
Cline

Step-by-step instructions for each client are in the guide. How to connect

Guided setup for Claude? Set up Retention Analytics in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Retention Analytics owners hand off.

Product Managers and Data Analysts use this MCP to move beyond gut feelings. They need concrete data showing which AI features are worth the investment. This connector gives you the metrics to prove value to stakeholders and guide the product roadmap.

01
Product Manager
Determines which AI features to build next by quantifying retention lift and ROI.
02
Data Analyst
Checks the statistical link between usage frequency and long-term user stickiness.
03
Business Intelligence Lead
Compares feature performance across different user segments to optimize resource allocation.

FAQ

Questions Retention Analytics owners ask.

01
Does this MCP only calculate retention lift?
No. While calculating retention lift is a core function, you can also check the statistical correlation between usage and retention, estimate the feature's ROI, and summarize impact by user segment.
02
What kind of data does the MCP need to run?
The MCP requires data points like usage group retention rates, non-usage group retention rates, feature costs, and projected churn values to run its calculations accurately.
03
Can I compare different user groups?
Yes. The get_segmented_impact_summary capability allows you to compare how different user tiers or segments are affected by the AI features.
04
Is this for general analytics or specific AI features?
It is specifically designed to measure the impact of AI-driven features. It helps you quantify the value of your AI investments, not general product usage.
05
How do I calculate the retention boost from a specific feature?
You can use the calculate_retention_lift capability by providing the feature ID, the cohort size, and the retention rates for both the usage and non-usage groups.
06
Can I see how different user segments are affected by AI features?
Yes, the get_segmented_impact_summary capability provides adoption rates and average lift for specific user segments like Enterprise or Free tiers.
07
How is the ROI of an AI feature determined?
The estimate_feature_roi capability calculates the ratio of prevented churn value against the annual maintenance cost, adjusting for the feature's maturity stage.

Explore

More in Business Intelligence

Connect Retention Analytics to Claude, Cursor, ChatGPT & more

Your AI does the work. We keep it safe and sound.

Each Connector runs inside its own sealed environment.

One switch stops every agent. Instantly.

Spending limits that pause instead of surprise.

We ship only the actions that matter.

01-04

Get segmented impact summary

Analyze usage correlation

Calculate retention lift

Estimate feature roi

What teams actually run

Justifying New Features

Optimizing the Roadmap

Measuring Feature Success

Identifying Usage Patterns

Building your own app? The connector is yours to use.

Retention Analytics Connector

Claude Desktop

Product Manager

Data Analyst

Business Intelligence Lead

Does this MCP only calculate retention lift?

What kind of data does the MCP need to run?

Can I compare different user groups?

Is this for general analytics or specific AI features?

How do I calculate the retention boost from a specific feature?

Can I see how different user segments are affected by AI features?

How is the ROI of an AI feature determined?

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