• RISK
  • CAUSE
  • HUMAN
  • FIX

How to identify cohort attrition causes with your AI

Connect your accelerator data to your AI client. Once connected, your agent finds the specific reasons companies are leaving your program.

Ask AI about this page

Short answer

Why are companies dropping out of my accelerator?

Your AI client analyzes engagement patterns and exit data to pinpoint the exact friction points causing dropouts. You get a clear breakdown of the specific drivers behind attrition.

Where your findings land.

The outcomes of your analysis.

The AI sorts its findings into these four categories.

RISK

At-risk companies

The AI flags specific companies showing patterns similar to previous dropouts. You get a list of names to watch closely.

CAUSE

Primary attrition drivers

The agent identifies if the issue is curriculum, mentorship, or resource access. You see the root reason for the churn.

HUMAN

Required manual outreach

The AI identifies companies that need a personal check-in from you. It cannot perform these sensitive conversations itself.

FIX

Programmatic changes

You get specific suggestions for adjusting your curriculum or support model. This helps you prevent future dropouts.

The workflow

What your AI does when the data arrives.

Your agent handles the heavy lifting of data correlation and pattern matching.

  1. Data ingestion

    Your agent pulls engagement metrics and exit survey responses from your program management tools.

    analyze_attrition_drivers

  2. Pattern recognition

    The AI looks for correlations between specific program stages and the timing of company departures.

    analyze_attrition_drivers

  3. Driver isolation

    The agent separates noise from real causes, like a lack of technical support or poor mentor matching.

    analyze_attrition_drivers

  4. Risk reporting

    The AI summarizes the findings into a list of specific reasons why companies are leaving.

    analyze_attrition_drivers

Try it

Copy these to start.

Use these prompts to begin your analysis.

Starting points

Swap the bracketed text for your actual data sources like a specific Google Sheet or CRM link.

Accelerator Metrics Service Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_v5RiOHzHplcpbABx1uMTKTedSHZUAJqsedikFb3r/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — Accelerator Metrics Service capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "accelerator-graduation-metrics-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_v5RiOHzHplcpbABx1uMTKTedSHZUAJqsedikFb3r/mcp"
    }
  }
}
Copy into chat04

  • Analyze the attrition data in my [Sheet Name] and tell me why the last three companies dropped out.

  • Look at the engagement logs in [CRM Name] and find the common denominator for companies that didn't graduate.

  • Based on the exit interview notes in [Folder Name], what are the top three reasons for attrition?

  • Identify which cohort in my [Program Tool] is most at risk of dropping out based on current activity levels.

  • Claude
  • ChatGPT
  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Start here

Connect Accelerator Graduation Metrics once, then ask.

Connect your data source once. Your credentials stay encrypted on our side, and you can start asking questions immediately.

Connect Accelerator Graduation Metrics to your AI

FAQ

How this task behaves.

  • 01

    Can the AI reach out to companies to ask why they are leaving?

    No. The AI identifies the reasons for attrition, but you must handle all direct communication with the companies yourself.

  • 02

    Does the AI change my data in my CRM or Google Sheets?

    No. The AI only reads your data to perform analysis. It cannot edit, delete, or modify your original records.

  • 03

    Can it predict which companies will drop out next month?

    Yes. By analyzing current engagement patterns against historical dropout data, your agent can flag companies that match the profile of previous dropouts.

  • 04

    What kind of data does it need to work?

    It needs access to engagement metrics, milestone completion rates, and exit interview text or survey results.

  • 05

    Will it tell me which mentors are causing issues?

    If your data includes mentor-to-company matching and engagement logs, the AI can identify correlations between specific mentorship pairings and attrition.

  • More questions about Accelerator Graduation Metrics? The Connector page answers them. See everything the Accelerator Graduation Metrics Connector can do

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