Help CenterProactively identifying predicted inactive customers allows brands to intervene before disengagement occurs. Using custom predictive models, the system analyses behavioral trends, engagement signals, and usage patterns to detect customers who show early signs of inactivity.
These models are trained on brand-specific data, ensuring that inactivity risk is based on real customer behavior patterns. The goal is to enable timely, personalised interventions, such as:
- Retention incentives (e.g., personalised bonus offers or cashback).
- Proactive reactivation campaigns targeting potential lapsing customers.
- Engagement strategies that encourage continued play and interaction.
By leveraging predictive insights, brands can preempt churn, retain valuable customers, and optimise long-term engagement before inactivity fully develops.
ℹ️ Predictive models are add-ons and require a customised setup. Contact the product team for further information.
Accessing Inactive Customer Predictions
Inactive customer alerts, generated by the predictive model, are available within the Active lifecycle stage. These alerts identify players who may be at risk of becoming inactive, enabling targeted engagement strategies.
- Navigate to Active in the lifecycle stage section.
- Click on the menu icon in the right slider.
- Select View Predictions to access the predictive insights.
Understanding the Predictive Model Statistics
- The displayed statistics provide aggregated Key Performance Indicators (KPIs) of customers included in the predictive model.
- To the right of the statistics box, next to the people icon, the displayed number represents players who may be at risk of becoming inactive.
- Example: If the number shown is 12, the model predicts that 12 customers are likely to become inactive.
Engagement Options for At Risk Customers
Users can engage with predicted inactive customers using two approaches:
1. One-Off Engagement
- A single campaign is launched, targeting the specific list of at risk players provided by the model.
- This is suitable for one-time incentives such as personalised offers or exclusive promotions.
2. Dynamic Campaigns
- A continuous campaign is triggered automatically whenever a new customer is flagged as at risk customers by the model.
- This ensures ongoing engagement with high-value players as they are identified.
Step-by-Step Guide to Creating a Campaign
Select Campaign Type
- Click on Target and choose One-Off Campaign or Dynamic Campaign.
Configure Campaign Details
- Assign a name to the campaign.
- Add tags for easy searchability (tagged objects can be found using the search bar at the top).
⚠️ Dynamic campaigns require both a start date and an end date.
Define Target Audience
Option 1: Target All Predicted Customers
- By default, the campaign will include all players identified in the prediction model.
Option 2: Target Customers with Consecutive Predictions
- Toggle the Consecutive Days switch to narrow the audience to customers who have been flagged as high-value for multiple consecutive days.
- Since the predictive model updates daily, this option allows users to focus on customers with consistent at risk potential.
- Pros: Increases targeting accuracy.
- Cons: If too much time passes, preemptive engagement becomes less effective.
- The dark box on the right dynamically updates to display the number of players currently being targeted.
✅ Recommendation: while the system allows for a higher number, it is not recommended to use a value higher than 3 days.
Option 3: Apply Additional Filters
- Toggle Activate Filters to refine the segment further.
- Apply additional attributes to create a more granular and customised campaign.
Choose Campaign Execution Type
- Proceed with a standard campaign setup, or
- Set up an AB/n test to test multiple variations before finalising the campaign.
By following these steps, users can efficiently create highly targeted one-off campaigns, ensuring strategic engagement with high-value customers while maintaining flexibility in audience selection.
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