Make your AI work with Retention Analytics
Connect your account once and let the AI you already use work with it, without building another integration or switching to a different AI. Determine the true business impact of new features on user growth and retention
4 live capabilities. One account. Your AI. Real work.
- Step 01
Connect
Link your account through Vinkius.
- Step 02
Authorize
You decide what your AI can access.
- Step 03
Pick your AI
Use it with the AI application you already use.
- Step 04
Get things done
Ask your AI to work with your connected account.
-
Works with
-
Waiting for input…
Why people use Retention Analytics
AI Feature Retention Analyzer: Measuring Feature Impact on User Retention
With this MCP, you feed the raw data once. Your agent handles the complex statistical modeling, instantly calculating the retention lift and providing a clear, single metric. You don't just get data; you get a definitive, actionable number that proves the feature's worth.
What Vinkius changes
The bottom line is, you get quantifiable proof of which AI features are actually worth keeping.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 7,300+ Connectors
- Real-world use case 01
Justifying a new AI feature to the board
The Product Manager needs to prove the 'Smart Summarizer' feature is worth the engineering time.
- Real-world use case 02
Debugging low retention in a specific user segment
The Data Analyst notices that enterprise users are churning despite high usage.
- Real-world use case 03
Optimizing the product roadmap for maximum stickiness
The Product Owner wants to know which features truly drive long-term value.
Complete set · 4capabilities
The complete Retention Analytics capability set.
These are the exact actions your AI can choose when you ask it to work with Retention Analytics.
01—04
4 capabilities in this set.
Part of 4 available through Retention Analytics.
- 01 Capability
Analyze usage correlation
Checks the statistical link between how often a user uses a feature and whether they stay with the service.
- 02 Capability
Calculate retention lift
Calculates the exact percentage boost in retention that a specific AI feature provides to your user base.
- 03 Capability
Estimate feature roi
Determines the financial return on investment for a feature by comparing its cost against the value of prevented churn.
- 04 Capability
Get segmented impact summary
Provides a summary of feature impact, allowing you to compare performance across different user tiers or segments.
Set up in minutes
One URL. Then ask Retention Analytics to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Retention Analytics from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_MSHt7rUhQY5iWnADCndbfeH29Pk3aonUpoBsOFDO/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it Retention Analytics, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Retention Analytics for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_MSHt7rUhQY5iWnADCndbfeH29Pk3aonUpoBsOFDO/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the Retention Analytics URL.
- Step 03
Save and start
Save the connection and enable Retention Analytics in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"ai-feature-retention-analyzer": {
"url": "https://edge.vinkius.com/vk_preview_MSHt7rUhQY5iWnADCndbfeH29Pk3aonUpoBsOFDO/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using Retention Analytics
Open Agent mode in chat and ask: "Using Retention Analytics, help me...". 4 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"ai-feature-retention-analyzer": {
"url": "https://edge.vinkius.com/vk_preview_MSHt7rUhQY5iWnADCndbfeH29Pk3aonUpoBsOFDO/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using Retention Analytics
Ask Copilot: "Using Retention Analytics, help me...". 4 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"ai-feature-retention-analyzer": {
"url": "https://edge.vinkius.com/vk_preview_MSHt7rUhQY5iWnADCndbfeH29Pk3aonUpoBsOFDO/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using Retention Analytics
Open Cascade and ask: "Using Retention Analytics, help me...". 4 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"ai-feature-retention-analyzer": {
"url": "https://edge.vinkius.com/vk_preview_MSHt7rUhQY5iWnADCndbfeH29Pk3aonUpoBsOFDO/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using Retention Analytics
Ask Cline: "Using Retention Analytics, help me...". 4 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add ai-feature-retention-analyzer --transport http "https://edge.vinkius.com/vk_preview_MSHt7rUhQY5iWnADCndbfeH29Pk3aonUpoBsOFDO/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using Retention Analytics
Ask Claude: "Using Retention Analytics, show me...". 4 tools are ready
Where the request belongs
Work Retention Analytics can move forward.
This MCP is essential for Product Managers and Data Analysts who are tired of building features that don't move the needle. If you spend your days presenting dashboards and trying to prove ROI, this capability gives you the hard numbers you need to justify your roadmap.
Product Manager
Uses this MCP to validate feature hypotheses, determining which AI additions will maximize user retention and justify engineering resources.
Data Analyst
Runs deep statistical checks to find the correlation between specific usage patterns and long-term customer stickiness.
VP of Product
Leverages the ROI estimates to build a business case for product investments, showing the financial impact of feature maturity.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse Connectors
AI SaaS Feature Churn Correlation
Quantify the impact of AI features on customer retention and calculate prevented churn revenue.
AI Feature Expansion Impact Analyzer
Quantify the financial and behavioral impact of AI features on SaaS expansion revenue and upsell conversion.
AI Engagement Scoring
Analyze AI feature engagement, adoption rates, and churn risk.
AI Feature Upsell Correlation
Quantify the impact of AI features on subscription upgrades.
AI Model Usage Analytics
Analyze AI model cost distribution and usage concentration across product features.
AI Feature Value Realization
Quantify the time to value and adoption efficiency of your AI features.
Bring your own AI
Change the model, client or framework. Keep Retention Analytics connected.
-
Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about Retention Analytics.
The practical details behind the request, access and result.
How does the AI Feature Retention Analyzer help me prove ROI?
It calculates the financial return on investment by comparing the feature's annual cost against the dollar value of the customer churn it prevents. This gives you a clear, hard number for executive reporting.
Can I see if my AI features work differently for different customer types?
Yes, the MCP provides a segmented impact summary. You can compare performance across different user tiers, helping you identify if a feature is only valuable to your most expensive customers.
What if I just want to know if using a feature is related to staying subscribed?
You can run a usage correlation analysis. This tells you the statistical strength of the link between how often a user uses the feature and their likelihood of continued retention.
Is this MCP better than just looking at general product analytics?
Yes. General analytics show what happened; this MCP shows why it happened. It isolates the impact of the AI feature specifically, proving its direct contribution to retention.
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.
One connection away
Give your agent a direct line to Retention Analytics.
Connect Retention Analytics once. Keep it beside 7,300+ managed Connectors when the next task needs more.
Explore every Connector No credit card required · Free tier available