# Cohort Retention Analytics MCP for AI Agents AI Agent Connect

> Cohort Retention Analytics MCP helps you turn raw user data into clear retention curves and lifetime projections. It lets your AI client calculate how long users stick around, evaluate specific milestones like Day 30 retention, and benchmark your progress against industry standards for SaaS, Gaming, or E-Commerce.

## Overview
- **Category:** product-management
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_0clZooXkip3nj1SVREyeX8jIZAq7iA6bQySCHT9q/ai-agent-connect
- **Tags:** retention, cohorts, analytics, saas, benchmarking

## Description

This Connector gives your AI agent the ability to handle the heavy lifting of product growth analytics. Instead of manually pulling numbers from a spreadsheet to see where users are dropping off, you can have your agent analyze month-over-month cohort data directly. It handles the math for calculating average user lifetimes and building out retention decay curves so you can see the actual shape of your growth. You can also check how your specific numbers stack up against established benchmarks for categories like FinTech or E-Commerce. By connecting this to your workflow through Vinkius, you move from staring at raw arrays to getting a clear picture of product health. It's about getting answers on whether your latest feature actually improved stickiness without having to build a custom dashboard for every single query.

## Tools

### calculate_retention_curve
Generates a sequence of data points representing the decay of a cohort over time. This helps visualize the shape of your growth.

### calculate_average_lifetime
Determines the expected number of months a user stays active. This helps you project long-term value and churn trends.

### compare_performance_to_benchmark
Compares your specific metric against the hardcoded industry standard for a chosen category. It gives your data immediate context.

### evaluate_retention_milestone
Checks the specific retention percentage for a user at a requested point in time. Use this to find exactly where users drop off.

## Prompt Examples

**Prompt:** 
```
Our retention for the last 4 months was 1.0, 0.8, 0.6, and 0.4. Can you give me the curve points?
```

**Response:** 
```
Here is the retention curve for your cohort:

| Month | Retention Rate |
| :--- | :--- |
| **Month 0** | 100% |
| **Month 1** | 80% |
| **Month 2** | 60% |
| **Month 3** | 40% |
```

**Prompt:** 
```
Is a 25% retention rate for E-Commerce considered good?
```

**Response:** 
```
The status for your metric is **At Risk**. 

*   **Your Rate:** 25% 
*   **Industry Benchmark:** 30% 

Since your current retention is below the standard for E-Commerce, you may want to investigate where users are dropping off in the checkout flow.
```

**Prompt:** 
```
What's the expected lifetime if our rates are 1.0, 0.5, and 0.2?
```

**Response:** 
```
Based on the retention rates provided, the **calculated average lifetime is 1.7 months**. 

This suggests that while you have a strong start, there is a significant drop-off after the first month.
```

## Capabilities

### Generate retention decay curves
Turn a sequence of monthly rates into a clear data set showing how your cohort shrinks over time.

### Project average user lifetimes
Get a concrete number for how many months a user typically stays active based on your current data.

### Check specific retention milestones
Find the exact retention percentage for any specific point in time to identify where churn peaks.

### Compare against industry standards
See how your metrics stack up against hardcoded benchmarks for SaaS, Gaming, E-Commerce, and FinTech.

### Analyze cohort health
Transform raw retention arrays into actionable insights about your product's stickiness.

## Use Cases

### Evaluating a new onboarding flow
A Growth Manager wants to see if a new flow improved Day 7 retention. They ask the agent to evaluate_retention_milestone for the latest cohort.

### Benchmarking E-Commerce performance
A Product Lead needs to know if their 25% retention is actually good. They use compare_performance_to_benchmark to get an industry status.

### Projecting user lifetime
A founder wants to project revenue based on stickiness. They use calculate_average_lifetime to get a realistic month count for their model.

### Visualizing cohort decay
A data analyst needs to present a visual decay of a 2023 cohort. They use calculate_retention_curve to get the specific data points.

## Benefits

- Stop guessing about stickiness by using calculate_retention_curve to see the exact decay of your cohorts over time.
- Get immediate context on your growth by using compare_performance_to_benchmark to see how you stack up against SaaS or Gaming standards.
- Quickly identify churn points by using evaluate_retention_milestone to check specific percentages at key intervals like Day 30.
- Make better roadmap decisions with calculate_average_lifetime to see the actual expected duration of a user's journey.
- Save hours of manual spreadsheet math by letting your agent handle all the cohort calculations automatically.
- Gain a clearer picture of product health by transforming raw retention arrays into actionable growth insights.

## How It Works

The bottom line is you get instant answers on user stickiness without doing any manual math.

1. Connect the Cohort Retention Analytics MCP to your AI client via Vinkius.
2. Provide your agent with your raw cohort retention data, such as a list of monthly percentages.
3. Ask specific questions about your retention curves, lifetime projections, or industry benchmarks.

## Frequently Asked Questions

**Can Cohort Retention Analytics help me see where users drop off?**
Yes. It allows you to check specific retention percentages at any point in time, making it easy to identify exactly which month or milestone sees the biggest churn.

**Does Cohort Retention Analytics work for my gaming app?**
Yes. The Connector includes hardcoded benchmarks specifically for the Gaming industry, so you can see how your stickiness compares to other titles.

**How do I use Cohort Retention Analytics to see my user lifetime?**
You just provide your agent with your retention rates, and it will calculate the expected number of months a user typically stays active for you.

**Can Cohort Retention Analytics compare my SaaS to others?**
Yes, it has a specific benchmark for SaaS that helps you determine if your growth metrics are hitting industry standards.

**Can I use Cohort Retention Analytics with Cursor?**
Yes, you can connect this Connector to Cursor or any other compatible client through the Vinkius platform to start analyzing your data.

**What kind of data do I need to give Cohort Retention Analytics?**
You need to provide a sequence of retention rates (like monthly percentages) for a specific cohort of users.

**How do I calculate the retention curve?**
Use the `calculate_retention_curve` tool by providing an array of retention rates where the first element is 1.0 (representing 100% retention at Month 0).

**How can I check if my SaaS retention is good?**
Use the `compare_performance_to_benchmark` tool. Pass 'SaaS' as the product category and your observed metric as the actual value to see if it is 'Above Benchmark' or 'At Risk'.

**What does average lifetime represent in this tool?**
The `calculate_average_lifetime` tool calculates the expected number of months a user remains active by summing all retention rates provided in your input array.