• PROJECTION
  • ANALYSIS
  • INSIGHT
  • HUMAN

How to calculate user lifetime with your AI

Connect your cohort data once. Your AI handles the math to project future user activity.

Ask AI about this page

Short answer

How do I calculate user lifetime with my AI?

Feed your cohort decay data to your AI client. It uses that decay rate to project the expected number of months a user will remain active. You'll get a specific lifetime estimate based on your actual retention patterns.

The outcomes

What you get from this task

Once you provide the data, your AI delivers these specific results.

PROJECTION

Expected user lifetime

The AI calculates the average number of months a user stays active. This gives you a concrete number for your long-term planning.

ANALYSIS

Cohort decay trends

Your agent identifies how quickly different user groups drop off. This highlights which cohorts are sticking around longer.

INSIGHT

Retention health check

The AI compares current decay rates against historical benchmarks. You'll see if your retention is improving or sliding.

HUMAN

Strategic decisions

The AI provides the math, but you decide how to adjust your product roadmap or marketing spend based on the numbers.

The workflow

What your AI does when the data arrives.

The AI processes your raw retention numbers through a specific mathematical sequence.

  1. Ingest decay data

    Your agent reads the cohort retention percentages you provide from your database or spreadsheet.

    calculate_average_lifetime

  2. Model the decay

    The AI analyzes the rate at which users drop out of each cohort over time.

    calculate_average_lifetime

  3. Project lifetime

    Using the decay curve, the AI calculates the mathematical expected value for user longevity.

    calculate_average_lifetime

  4. Summarize results

    The AI presents the final lifetime estimate and the underlying decay trends in plain text.

    calculate_average_lifetime

Try it

Copy these to start.

Use these prompts to trigger the calculation immediately.

Starting points

Replace the bracketed text with your actual data sources like a CSV file or a specific database table.

Cohort Retention Analytics Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_0clZooXkip3nj1SVREyeX8jIZAq7iA6bQySCHT9q/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 — Cohort Retention Analytics capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "cohort-retention-analytics-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_0clZooXkip3nj1SVREyeX8jIZAq7iA6bQySCHT9q/mcp"
    }
  }
}
Copy into chat04

  • Calculate the expected user lifetime using the cohort decay data in my 'retention_stats.csv' file.

  • Based on the user activity in my production database, what is the projected lifetime for the January cohort?

  • Analyze the decay rates in this spreadsheet and tell me the average number of months a user stays active.

  • Using the current retention trends from my marketing dashboard, project the lifetime for new users acquired last month.

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

Start here

Connect Cohort Retention Analytics once, then ask.

Just click the link to connect your client. Your credentials stay encrypted, and you can start asking questions immediately.

Connect Cohort Retention Analytics to your AI

FAQ

How this task behaves

  • 01

    Can the AI change my retention data in my database?

    No. The AI only reads the data to perform calculations. It cannot write to or modify your source files.

  • 02

    Does the AI create new user accounts?

    No. This connector is strictly for analyzing existing cohort data and projecting lifetime values.

  • 03

    What kind of data do I need to provide?

    You need to provide cohort-based retention percentages or decay rates, typically from a CSV or a database connection.

  • 04

    Can the AI predict future cohorts that haven't joined yet?

    No. It can only project the lifetime of users based on the decay patterns found in your existing data.

  • 05

    How accurate are these lifetime projections?

    The accuracy depends entirely on the quality and volume of your cohort decay data. The AI follows the mathematical patterns present in your data.

  • More questions about Cohort Retention Analytics? The Connector page answers them. See everything the Cohort Retention Analytics Connector can do

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