# Churn Prediction Model (AI-Powered) :paintbrush: **Redefining Churn Prevention** Predictive models help redefine how we approach churn strategies. The Churn Prediction Model no longer focuses solely on deposits but instead evaluates bets and wins aggregated in sessions. The model assesses factors such as session frequency, game engagement, and indicators of a positive or negative experience. Every 24 hours, players receive a score that determines how likely they are to churn.\ \ The focus is no longer just on reactivating customers who are already churned or close to churning, but also on preventing these scenarios, allowing you to react swiftly and prevent churn while avoiding unnecessary and intrusive outreach. {% hint style="info" %} More information on the basic Churn Prevention Model can be found here: * [Churn Prevention Model (Basic)](https://www.fasttrack.ai/en/resources/knowledge-base/ft-crm/churn-prevention/churn-prediction-model-basic) {% endhint %} ## 📩 How Does the Model Work First, the model is trained with at least six months of historical data, allowing it to understand player behaviour and adapt to their habits. Once activated, here’s an example of how it works: * A player frequently plays slots and makes deposits * The last session showed signs of frustration (e.g., consecutive losses, early session exits). * The model detects this negative experience and segments the player accordingly. Based on your strategy, you can then trigger a tailored re-engagement offer—such as free spins on their favourite game or a personalised bonus—to bring them back. #### 📌 Definition We define a player as churned when they have at least **30 days** with no real bets or deposits. The model is applied **once a day** on every inactive player between **1-29 days** to detect potential churn risks early and allow for timely intervention. #### 📈 Segmentation Based on Churn Rate and Usage The predictive model assigns each player a **Churn % Rate** (e.g., 20% churn rate, 30% churn rate) based on their risk level. This segmentation enables a more targeted and structured churn prevention strategy, allowing, for example, for: * **Offer Generosity Scaling (recommended use)** – The churn rate can determine the generosity of incentives, where higher-risk players receive more compelling offers. This allows for dynamic campaign structures where promotions trigger at strategic intervals to encourage re-engagement at critical moments.

Overview of how model can work

* **Tailored Churn Prevention Campaigns** – Players can be placed into specific retention campaigns based on their churn probability, ensuring messaging and incentives are relevant to their risk level. By leveraging these churn segments, operators can fine-tune their engagement strategies, optimising retention efforts while minimising unnecessary costs on incentives for lower-risk players. {% hint style="success" %} Incorporating [**Control Groups**](https://www.fasttrack-solutions.com/en/resources/knowledge-base/ft-crm/actions/control-groups) in Churn Prevention campaigns is essential to maintaining the model's integrity, ensuring it accurately predicts each player's churn rate over time. {% endhint %} ## 📊 Data Requirements For the Churn Prediction Model to function optimally, the following data points should be available: * [**Casino events**](https://www.fasttrack-solutions.com/en/resources/integration/real-time-data/casino) – Tracking overall player engagement and success in gaming sessions.
Mandatory fields for the model to work round\_id user\_id currency exchange\_rate timestamp is\_round\_end type
Optional fields to improve performance real\_amount bonus\_amount locked\_amount user\_bonus\_id
* [**Payment events**](https://www.fasttrack-solutions.com/en/resources/integration/real-time-data/payments) – Monitoring deposits and withdrawals to understand financial behaviour.
Mandatory fields for the model to work amount exchange\_rate payment\_id status timestamp user\_id type external\_user\_id
Optional fields to improve performance user\_bonus\_id bonus\_code currency vendor\_id
* [**Blocked events**](https://www.fasttrack-solutions.com/en/resources/integration/operator-api/player-blocks) – Identifying account restrictions that might contribute to churn. (Not mandatory, but recommended)
Mandatory fields for the model to work with Blocked events timestamp blocked excluded user\_id deposit\_limit valid\_until
* **Historical data** – To ensure the model is reliable and produces accurate results, the following data thresholds must be met: * A minimum of **6 months** of player activity * At least **65,000 player sessions\*** in total (used for training the model) ➡️ As a general rule of thumb, if you have **3,000 unique active players (UAPs) per month**, you’re likely to meet the required number of sessions.

📌 Why does this matter?
The model needs enough behavioural history to recognise patterns that indicate churn. Insufficient data will limit its ability to identify trends and reduce accuracy.

*What is a Player Session?

A player session is defined as a player’s game activity (e.g. betting on a game), separated by 30 minutes of no activity.

What if you don’t meet the UAP threshold?

Please note: the 65,000 player sessions are the true requirement for the model to operate effectively.

Even if you have fewer than 3,000 UAPs per month, your model may still work — provided you’re generating enough player sessions overall.

On the other hand, if you have 3,000+ UAPs but don’t hit the session count, the model will not have sufficient data to perform accurately.

#### 🚧 Restrictions To ensure the accuracy and effectiveness of the model, the following restrictions apply: * **Casino data only** – The model currently applies exclusively to casino gaming data. * **Minimum player activity** – A player must have at least **4 gaming sessions** recorded. * **Minimum account duration** – The player must have been registered for at least **30 days** before they can be assessed. For further information on the data points highlighted above, kindly refer to our Integration [Knowledge Base](https://www.fasttrack-solutions.com/en/resources/integration). ## :checkered\_flag: Getting Started To run the requirement checks and enable the model in your Fast Track environment, go to **FT Singularity Model → Player Features**. Under **Available Player Features**, select **Churn Prediction Model**.

Player Features

Click **Run Requirements Check** to start an automated validation of your data for the model. If any issues are found, a summary of errors will be displayed.

Results of the check

Once your data is validated, the system will generate a new [Player Feature](https://www.fasttrack.ai/en/resources/knowledge-base/the-singularity-model/player-features) called **Churn Prediction Model**. This feature comes with pre-built logic and will automatically assign each player to a classification.

Possible Segmentation

Once the process is complete, a new [**Segment** **field**](https://www.fasttrack.ai/en/resources/knowledge-base/ft-crm/segments/segment-fields) will be available in the **Player Features** section. This allows you to accurately Segment players based on their likelihood of churn. The Segment options are listed below.
Available Segmentation | Label | Description | | ---------------- | ------------------------------------------------------------------------------- | | Active | Player was active in the last 24 hours | | Very Low Risk | Probability of churn between 0% and 20% | | Low Risk | Probability of churn between 20% and 30% | | Medium Risk | Probability of churn between 30% and 40% | | Medium High Risk | Probability of churn between 40% and 50% | | High Risk | Probability of churn between 50% and 60% | | Very High Risk | Probability of churn between 60% and 70% | | Critical | Probability of churn between 70% and 80% | | Very Critical | Probability of churn between 80% and 100% | | Inactive | Player has reached 30 days inactivity | | Initialised | When a player is eligible to the player feature but has not been evaluated yet. |