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AI-integrated platforms for personalization

Introduction

AI integration in casino platforms opens up new opportunities: automatic selection of games, personalized offers and behavior forecasting. The main tasks are to increase retention and ARPU, avoiding intrusiveness and observing privacy.

1. Data collection and preparation

Events Tracking: Logging clicks, bets, winnings, sessions and rejections at Kafka/ClickHouse.

User Profiles: combining demographics, game history, spending and stock reaction into Customer 360.

Feature Store: feature engineering - average rate, frequency of visits, favorite providers.

2. Recommendation systems

1. Collaborative Filtering:
  • Matrix of players × games, calculation of similarities through ALS/SVD, issuance of "similar players played...."
2. Content-Based:
  • Evaluation of game attributes (RTP, volatility, theme) and matching based on user profile.
3. Hybrid model:
  • Combination of both approaches, ranking taking into account freshness and promotional priorities.
4. Frontend API:
  • 'GET/recommendations/{ playerId}? limit = 10 '→ a list of games with relevance rating.

3. Dynamic bonuses and offers

Bonus personalization module:
  • Generation of individual offers: free spins, match deposits, cashback.
ML model:
  • XGBoost/LightGBM for predicting response probability and LTV, optimization of the offer for KPI.
Automation via Campaign Engine:
  • When creating a campaign, targeting based on 'predicted _ engagement> threshold'.

4. Predictive analytics and churn-prevention

Churn model:
  • Logistic Regression or neural network on a set of features: last session time, average win, bet frequency.
Trigger-actions:
  • Auto-distribution of re-engagement-offers at'churn _ score> 0. 7`.
Performance monitoring:
  • A/B tests with control and test groups, measuring lift in retention.

5. A/B testing and online training

Feature Flags:
  • Experiments at the level of recommendations and offers without code release.
Multi-armed Bandits:
  • UCB/Thompson Sampling algorithms for dynamic distribution of traffic between variants.
Metrics Pipeline:
  • Automatic calculation of p-value and confidence interval in BI.

6. Integration and Infrastructure

Microservices:
  • Separate services for Data Ingestion, Feature Store, Model Serving (TensorFlow Serving, MLflow).
Real-time Inference:
  • gRPC/REST endpoints with latency <50 ms, caching popular recommendations.
Batch Processing:
  • ETL via Airflow for daily retraining and model updates.

7. Privacy and security

GDPR/CCPA:
  • PII anonymization, legal mechanisms for deleting data on request.
Data Governance:
  • Retention, role access, audit models to avoid bias.
Secure ML Pipeline:
  • Data encryption at rest (at rest) and in transit (TLS), isolated environments for experts.

Conclusion

AI personalization turns the casino platform into a smart service, increasing engagement and profitability through recommendation systems, dynamic offers and predictive analytics. The key conditions for success are a clear microservices architecture, reliable models in the production environment and compliance with privacy and security standards.

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