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Identify at-risk players 30-60 days early. Prevent churn. Maximize LTV. 85-95% prediction accuracy.
Real-time prediction of likelihood player abandons in next 30-60 days (0-100 scale)
Identify critical churn signals (engagement drop, spending decline, session frequency decrease)
Group at-risk players by churn drivers (boredom, losing streaks, engagement gap)
Personalized offers tailored to player preferences and churn drivers
Identify critical window when player most likely to respond to retention offer
Measure impact of retention campaigns; optimize future spend allocation
Session frequency, duration, game type preferences, bet patterns, engagement trends
Deposit amounts, withdrawal frequency, betting velocity, win/loss ratios, spending volatility
Time-of-day patterns, day-of-week preferences, seasonal trends, inactivity windows
Login frequency, session length decay, bonus responsiveness, promotional engagement
Time-to-event modeling predicting remaining player lifetime; competing risk models
Identify true churn drivers vs. correlation; predict intervention responsiveness
Cluster high-risk players by churn driver (e.g., boredom vs. loss aversion vs. engagement gap)
Continuous churn risk updates as player behavior evolves within sessions and across days
Identify high-value players at risk; prioritize retention budget on whales
Detect new account abandonment patterns; improve onboarding to combat early churn
Identify recently churned players most likely to respond to re-engagement offers
Predict churn spikes during low-season; proactive retention campaigns
Understand churn drivers by acquisition channel, geography, game preference
Identify game/feature gaps causing churn; guide product roadmap
Audit historical churn patterns, player data, retention campaigns, business objectives
Develop churn prediction models (survival analysis, XGBoost); validate on historical data
Deploy on player cohort; run retention campaign; measure engagement and LTV lift
Full deployment; integrate with CRM/marketing; configure automated triggers
Monitor accuracy, measure ROI, refine interventions, expand to new segments
85-95% accuracy. 30-60 day early warning. Personalized interventions. 25-40% LTV improvement.
Schedule a Demo →Our models achieve 85-95% accuracy forecasting player churn within 30-60 days. Accuracy depends on data quality, historical churn window, and player behavior stability. We validate on holdout test sets; measure precision/recall for your risk tolerance.
We forecast churn 30-60 days in advance, giving operators critical window for retention intervention. Longer horizons (90+ days) reduce accuracy; shorter windows (<30 days) capture more recent signals but leave less time for action.
Top predictors: session frequency decline, session duration contraction, game diversity loss, betting velocity drop, deposit frequency cessation. Financial volatility and inactivity windows are also strong signals. Explainability shows driver ranking per player.
Yes. Causal inference identifies which offer types (bonus amount, game recommendation, channel) resonate with each player segment. Bandit algorithms optimize timing and offer mix. Measure response lift via holdout A/B tests.
Implementation ROI typically positive within 3-6 months. Typical operators reduce churn 5-10% (25-40% LTV increase) with personalized retention campaigns. Retention spend ($2-5 per high-risk player) recovers quickly via extended player lifespan.