Loading solution...
Loading solution...
💬 AI Chat
Click to ask anything
360° player insights. Churn prediction. Personalized engagement. Maximize LTV & retention.
85-95% accuracy forecasting player departure; triggers proactive engagement
AI clustering identifies behavioral cohorts for targeted campaigns
Predict long-term revenue potential per player; optimize acquisition spend
Dynamic game & bonus suggestions based on session history and preferences
Detect at-risk behavior patterns; trigger automated intervention workflows
Real-time analysis of session length, frequency, volatility, and drop-off points
Real-time and batch collection from web, mobile, payments, betting, session systems
Behavioral, transactional, temporal, and contextual feature creation at scale
Unsupervised clustering (K-means, DBSCAN) revealing natural behavioral cohorts
Supervised classification (XGBoost, LightGBM) with 85-95% accuracy forecasting risk
Regression models predicting long-term revenue per player; optimize CAC allocation
Real-time recommendation systems for games, bonuses, content based on preferences
Behavioral risk scoring for problem gambling; automated intervention triggers
Streaming analytics updating player profiles and recommendations within sessions
Probability of departure (0-100); time to churn forecast; primary risk factors
Expected 12-month revenue; CLV by segment; CAC payback period
Session frequency, avg stake, volatility preference, game affinity, time-of-day patterns
Active vs. dormant; session duration trends; bet velocity; promotion responsiveness
Session duration escalation; stake increase patterns; loss-chasing behavior
Recommended game/bonus/promotion; optimal communication channel & timing
Audit player data, analytics gaps, technology stack, business objectives
Design real-time & batch pipelines; select ML models, recommendation engine
Build churn, LTV, segmentation, RG monitoring, personalization models
Deploy on player cohort; validate accuracy, engagement lift, compliance
Full rollout; CRM/marketing automation integration; team training
Real-time personalization. 85-95% churn accuracy. 30-50% LTV improvement. Responsible gaming integration.
Schedule a Demo →Our models achieve 85-95% accuracy identifying players likely to churn within 30-60 days. This accounts for seasonal variations, regional patterns, and player lifecycle stages. Accuracy improves with more historical data.
Yes. Real-time streaming analytics update player profiles and recommendations within active sessions. Sub-second latency enables game suggestions, bonus offers, and content personalization dynamically as players interact.
We continuously monitor session duration escalation, stake increases, loss-chasing, and frequency changes. Behavioral risk scores trigger automated alerts and intervention workflows (deposit limits, self-exclusion options, counseling resources).
We ingest web/mobile sessions, bets, deposits, withdrawals, promotions, and customer profile data. Minimal engineering required—data flows via APIs, event streams, or batch uploads. We map your schema to our feature definitions.
Yes. We segment players by predicted churn/LTV; measure engagement lift and revenue impact per personalization strategy. A/B testing frameworks compare personalized vs. generic offers; calculate ROI per dollar spent.