# LTV Cohort Calculator MCP for AI Agents AI Agent Connect

> LTV Cohort Calculator MCP helps you pull historical revenue for specific customer groups and turn that data into 36-month revenue projections. It handles the math for linear and logarithmic growth so you can see where your customer value is actually headed.

## Overview
- **Category:** data-analytics
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_GGCMeMTzL8Bst0yqYODkrw0xT4JdelFGGHgW17vO/ai-agent-connect
- **Tags:** ltv, cohort-analysis, revenue-projection, customer-value, lifetime-value

## Description

Most growth teams spend way too much time in spreadsheets, trying to group customers by signup date and manually summing up their monthly spend. It's a tedious cycle of copy-pasting data just to see if your last marketing campaign actually paid off. This Connector changes that by letting your agent handle the heavy lifting. Instead of you manually aggregating data, you can just ask your agent to pull the history for a specific cohort and tell you the projected lifetime value. It handles the messy parts of growth modeling, like switching between linear and logarithmic curves to see different scenarios. It's a core part of the Vinkius catalog, designed to give you a clear look at your revenue trends without the headache of manual calculation. You get to stop guessing about your growth and start making decisions based on actual projections. You can see exactly where your money is going and which customer groups are actually sticking around for the long haul.

## Tools

### generate_ltv_projections
Uses historical data to project revenue out to 36 months using linear or logarithmic math.

### calculate_cumulative_ltv
Calculates the total value a cohort has reached at specific milestones like 3, 6, or 12 months.

### fetch_cohort_metrics
Pulls raw monthly revenue data for a specific acquisition month. It's your starting point for any cohort analysis.

## Prompt Examples

**Prompt:** 
```
Analyze the January 2024 cohort. Get the history and tell me the LTV at 6 months.
```

**Response:** 
```
I've pulled the data for the January 2024 cohort. Here is the cumulative LTV at your requested milestones:

| Milestone | Cumulative LTV |
| :--- | :--- |
| 3 Months | $450.00 |
| 6 Months | $1,240.50 |
| 12 Months | $3,100.25 |

The cohort shows a steady growth rate of about 12% month-over-month.
```

**Prompt:** 
```
I need a 3-year forecast for the November 2022 group. Show me both linear and log versions.
```

**Response:** 
```
Here are the 36-month projections for the November 2022 cohort based on historical trends:

**Linear Projection:**
* Year 1: $15,000
* Year 2: $30,000
* Year 3: $45,000

**Logarithmic Projection:**
* Year 1: $18,000
* Year 2: $24,000
* Year 3: $28,500

The logarithmic model suggests a slower tail-end growth, which might be more realistic for your current market saturation.
```

**Prompt:** 
```
What's the cumulative value for the April 2023 cohort at the 12-month mark?
```

**Response:** 
```
The cumulative LTV for the April 2023 cohort at the 12-month mark is **$5,820.40**. 

This represents a 15% increase over the 6-month mark, indicating strong retention for this specific group.
```

## Capabilities

### Pull historical revenue for specific signup months
Get a clear view of how much a specific group of customers spent over a set period.

### Calculate total value at specific time milestones
Find the exact cumulative LTV at 3, 6, or 12 months for any given cohort.

### Project long-term revenue using linear methods
See a straight-line forecast of your revenue out to 36 months.

### Forecast growth using logarithmic curves
Model more realistic, decelerating growth patterns for your revenue projections.

### Analyze customer value across different groups
Compare how different acquisition cohorts perform over time.

## Use Cases

### Comparing Channel Performance
A marketer wants to see if users from Facebook have a higher LTV than users from Google. They ask the agent to compare the two cohorts.

### Budget Planning
A finance lead needs a 36-month revenue forecast for the Q1 2023 cohort to plan next year's budget.

### Retention Analysis
An analyst wants to see the cumulative value at the 6-month mark to identify where most customers drop off.

### Growth Modeling
A founder wants to see how revenue looks if growth follows a logarithmic curve instead of a linear one.

## Benefits

- Stop manual data aggregation by using fetch_cohort_metrics to grab historical revenue instantly.
- See the full picture with calculate_cumulative_ltv to find the exact value at 6 or 12 months.
- Model different growth scenarios using generate_ltv_projections to compare linear and logarithmic paths.
- Identify high-value customer groups faster to optimize your marketing spend.
- Get consistent revenue projections that you can share with stakeholders without manual errors.

## How It Works

The bottom line is you get accurate, multi-year revenue forecasts for any customer group in seconds.

1. Tell your agent which acquisition month you want to analyze.
2. The agent pulls the raw revenue data for that specific group.
3. You get a breakdown of cumulative value and future projections.

## Frequently Asked Questions

**What is the LTV Cohort Calculator MCP?**
It's a tool that lets your AI agent pull historical revenue for specific customer groups and calculate their lifetime value. It helps you see which customers are worth the most over time.

**How does it help with revenue forecasting?**
It uses your historical data to project what a customer group will spend over the next 36 months. You can choose different growth models to see various outcomes.

**Can it calculate value for specific months?**
Yes, you can ask your agent to find the cumulative value at any milestone, such as 3 months, 6 months, or a full year after a customer joined.

**What difference is there between linear and logarithmic projections?**
Linear projections assume steady growth, while logarithmic projections model a slowdown in growth over time. Using both helps you see a more realistic range of possibilities.

**How do I use it for marketing analysis?**
You can use it to compare different acquisition cohorts. This helps you see which marketing channels are bringing in customers with the highest long-term value.

**Can it handle multiple cohorts at once?**
Yes, your agent can pull metrics for different signup months so you can compare the performance of various marketing campaigns side by side.

**How do I get the raw historical revenue data needed for LTV calculations?**
Use the `fetch_cohort_metrics` tool. This function retrieves the accumulated monthly revenues for a specific cohort, providing the foundational time-series data required for all subsequent LTV analyses.

**What is the difference between calculating LTV at fixed milestones (e.g., 6 months) and projecting future value?**
First, use `calculate_cumulative_ltv` to determine the exact LTV at fixed points (like 3 or 6 months). Then, if you need a longer-term forecast, feed those historical metrics into `generate_ltv_projections`. The projection tool uses advanced math to estimate value beyond observed data.

**What inputs are required for LTV projections?**
The `generate_ltv_projections` tool requires two key pieces of information: the unique cohort identifier and a structured JSON array containing historical revenue metrics. This data is typically sourced from running `fetch_cohort_metrics` first.