# Churn & LTV prediction

The **Churn Prediction** model is a dynamic tool explicitly created for casino & sport operators to identify and categorize players based on their risk of discontinuing play.

This tool is particularly focused on *predicting the likelihood of players not depositing and not betting* (absence of the both events) *within the next 30 days*.  \
The model assigns each player a churn probability score daily. \
This scoring system is integral to categorizing players into risk groups, thereby providing the possibility to enhance player retention.

## Churn definition

In the context of this model, a player is considered to have **churned** if he **has not deposited AND not played (any bet) within the fixed 30 day period**.

## Churn scoring and player ranking.

During the evaluation, the mode assigns a risk score for each player with values between 0 and 1. Where 0 is the lowest probability of churn and 1 is the highest.

All players are assigned to one of **6 churn ranks** for a more straightforward interpretation.

| **Rank name** | **Explanation**                                                                  |
| ------------- | -------------------------------------------------------------------------------- |
| Not set       | Player didn't have any deposit yet                                               |
| Low           | Low probability of chrun, no action required. Risk value 0-0.4                   |
| Medium        | Medium probability of churn, no action required. Risk value 0.4-0.6              |
| High          | There is a high probability of churn; action is recommended. Risk value 0.6-0.85 |
| Critical      | Risk 0.85-1, action is highly recommended.                                       |
| Churned       | Player is churned by definition. Didn't deposit AND bet for 30 days              |

###  **Churn risk ranks**" report - daily, for the last 30 days.

You can find the distribution in dynamics of the players by rank in the "**Churn risk ranks"** report - daily, for the last 30 days.

Go to Reports -> Extended Reports -> *Users’ Segments by Churn Risk*)

<figure><img src="/files/nOoBfiPbjy0sMX9CNqcs" alt=""><figcaption></figcaption></figure>

<figure><img src="https://lh7-qw.googleusercontent.com/docsz/AD_4nXcd8_WPV6Cw2O9hWFBJPcxyqYiEeBuV_1oUfPcK6viuQPhL9Zg3sMSwxkIY9aMwZfTKLNaLwYIb6kgzCtTXH0kRb7OM0bjKVDYfAICM81xHjWB_IoWjl0CjLs0kFed6Rui3WH2VSR_5e9Wm51YKZWias3c?key=bNbm0TfhbFw3AIu1zTww2w" alt=""><figcaption></figcaption></figure>

### **Churn Metrics Snapshot** <br>

Another report for the current day snapshot with metrics is "**Churn Metrics Snapshot"**

Go to Reports -> Extended Reports -> *Churn Metrics Snapshot*<br>

<figure><img src="https://lh7-qw.googleusercontent.com/docsz/AD_4nXfc3PKkoB6uMw4c9sVhVvVsXvxsn1Y4TNDl2zJIamuoIZ44kZD1FbAgX9NmpKHhtn4fXzVxed2SJsbz4Ye1eqtEreaNbYL6dRLilddlqTIQD3J4x47Cj6xM7dI7TqCHGmuobXay36cNs95zE8KWI5-QtL2V?key=bNbm0TfhbFw3AIu1zTww2w" alt=""><figcaption></figcaption></figure>

This report shows the number of players for each rank and the "**Probability to churn**" for this rank, calculated based on historical predictions.

{% hint style="success" %}
Example: A probability of 99% for Critical rank indicates that out of 100 players predicted historically for this rank, 99 players have churned.
{% endhint %}

The "**30-day net-deposit projections**" show the monetary value of the net-deposit for the next 30 days (average, based on user's history) if these players continue to be active.

{% hint style="info" %}
To see the "Probability of churn" metric, the model should be active for at least 30-day period so the model can compare its evaluation with actual churn data and calculate this metric.
{% endhint %}

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