Data Analysis on Model Outcomes

Data analysis can be conducted on predictive model outcomes, allowing for in-depth evaluation of player behavior, and other trends. Any list of players generated by the predictive model can be further analysed to uncover key insights that drive better decision-making.

Why Analyse Predictive Model Outcomes?

1. Identify Behavioral Patterns

• Understand why certain players are predicted to behave in a specific way (e.g., high-value, at-risk of inactivity, or churn-prone).
• Detect common characteristics among groups, such as preferred games, deposit habits, or acquisition sources.

2. Segment Customers More Precisely

• Use analysis to refine segments beyond the model’s prediction.
• Create more tailored marketing strategies for subgroups within predicted audiences.

3. Uncover Hidden Revenue Opportunities

• Identify cross-sell and upsell opportunities for different customer groups.
• Detect unusual betting patterns that may indicate emerging high-value players.
• Align promotions with player preferences and lifecycle stages.

4. Enhance Customer Experience and Personalisation

• Use insights to create personalised campaigns.
• Offer incentives that match customer motivations, increasing conversion rates.
• Detect early-stage churn indicators and intervene with targeted retention offers.

By conducting data analysis on predictive model outcomes, brands can gain deeper insights into player behavior, refine customer segmentation, and optimize retention and engagement strategies. This ensures proactive, data-driven decision-making, leading to higher customer lifetime value and reduced churn.

Step-by-Step Guide to Conducting Data Analysis on Predictive Model Outcomes

1. Access Prediction Alerts

• Open the prediction alerts related to the desired lifecycle stage.
• Example: If analysing inactive stage predictions, open the Inactive Stage Prediction Alerts.

2. Open Analytics

• Click on the Analytics icon to generate insights based on predictive outcomes.

3. Apply Automatic Filtering

• The analytics interface will automatically filter all reports to include only the predicted players.
• Example: If the model predicts 6,136 players at risk of inactivity, all reports will display insights only for this subset of players.
• A visual indicator will confirm the total number of players filtered in the reports.

4. Analyse Reports

• From this point forward, any report accessed will only include data for the filtered prediction group.
• Users can explore behavioral trends, patterns, and key performance indicators (KPIs) for the predicted audience.

5. Create Targeted Segments for Action

• Using insights from the analytics, users can create targeted segments directly from the reports.
• These segments can be used to trigger:
  • Customer journeys tailored to re-engagement.
  • Retention campaigns designed to prevent inactivity or churn.

✅ Suggested read: Creating a Segment from Analytics

By following these steps, users can efficiently analyse predictive model outcomes, generate data-driven insights, and create high-impact engagement strategies for at-risk players.