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Personalized game discovery. Cross-sell optimization. Session engagement +25-40%. Real-time AI recommendations.
Personalized slots, table games, live casino suggestions based on play history
Sports events, tournaments, live betting opportunities aligned with interests
Suggest promotions, free bets, cashback most likely to engage each player
Recommend casino to sports players and vice versa; balance category adoption
Real-time "next game" suggestions within active play sessions
Onboard new players with demographic/cohort-based recommendations
Real-time collection of game plays, bets, engagement, demographics, device, location
Game embeddings (theme, volatility, RTP, features), player embeddings (preferences, behavior)
Find similar players; recommend games they enjoyed ("Players who played X also played Y")
Match player characteristics to game features (e.g., high-volatility slots for risk-takers)
Combine collaborative and content approaches for robust, diverse recommendations
Rank recommendations by time-of-day, device, session state, player mood signals
In-session recommendation: "next game" or bonus suggestions within active play
Balance exploitation (known preferences) with exploration (new games) dynamically
Homepage carousel recommendations drive 25-40% more plays per session
Recommend sports to casino players; identify untapped category revenue
Recommend new titles to high-affinity player segments for day-1 engagement
Recommend engaging games + bonuses to at-risk players; re-ignite interest
Recommend high-limit games, exclusive tournaments, premium betting options
Evening recommendations differ from morning; optimize by session time
Analyze game catalog, player interaction data, current discovery capabilities
Build collaborative filtering, content-based, and deep learning recommendation models
Deploy to homepage carousel; measure engagement and revenue lift vs. control
Full deployment across all surfaces; integrate with CRM and marketing
A/B testing, diversity optimization, model retraining, new surfaces
25-40% session lift. 15-25% revenue per session boost. Real-time AI recommendations. Sub-100ms latency.
Schedule a Demo →Sub-100ms latency from API call to ranked recommendations. Feature lookups cached in memory; model inference optimized for real-time serving. Multiple strategies (pre-compute, edge serving) ensure consistent speed.
For new players, we bootstrap with demographic/geographic cohort models, trending games, and new player onboarding recommendations. As they interact, models quickly personalize. Contextual bandits accelerate learning.
Yes. Identify sports players likely to enjoy casino and vice versa; recommend tailored games from adjacent category. Measure cross-category adoption; optimize recommendations for portfolio balance.
Multi-armed bandits balance exploitation (known preferences) with exploration (new games). Diversity metrics ensure recommendations span game types. A/B tests validate optimal mix.
Typical operators see 25-40% engagement lift, 15-25% revenue increase per session. Payback within 2-3 months through higher player spend and retention. ROI compounds as personalization improves.