Enhancing Player Value and Rewards Prediction with Machine Learning
Business Impacts
Facilitated predictive analytics using historical data
Provided a 360° view into player metrics and analytics
Enabled data-driven actions to mitigate churn and retain high-value players
Customer Key Facts
- Country : US
- Industry : Entertainment and Gaming
Problem Context
The client, an American gaming company specializing in manufacturing and distributing slot machines and gambling technology, aimed to gain insights into historical player engagement data. To achieve this, they sought to develop machine learning engines to:
- Assess the Player’s Lifetime Value
- Identify player churn risk during gameplay
- Determine suitable rewards for players exhibiting high churn risk
Challenges
- Visualization Gap: Inability to visualize model insights and relevant KPIs/metrics for player analytics
- Player Segmentation Hurdle: Absence of a 360-degree view of players hindered effective player segmentation for targeted strategies
Technologies Used
Google BigQuery
Cloud Storage
AppEngine
Vertex Ai
Cloud IAM
Solution
- Quantiphi developed ML models for Player Lifetime Value, Player Churn, and Next Best Offer, covering key aspects of player analytics
- Quantiphi built dashboards offering insights into player demographics, behavior classification, churn rate, and geographical distribution. Additionally, revenue predictions for top players were provided
- Quantiphi developed dashboards enabling visualization of individual player profiles, conducting segment analysis, and categorizing players based on churn percentage and player lifetime value
Results
- Leveraged historical data for predictive analytics, enhancing strategic decision-making
- Provided a 360° view of player metrics and analytics, offering a holistic understanding of player behavior and engagement
- Enabled data-driven actions to proactively mitigate churn and retain high-value players
