From Adaptation to Anticipation Identifying Risks Before World Cup Users Leave


Daniil Emelyanov, Head of AI Labs at BETBY, explores how properly designed AI systems model churn as a probability across multiple horizons, detect behavioural drift instead of simple inactivity, and give operators time to respond while recovery is still possible.


In the first article of this mini-series, Danil Emelyanov, Head of AI Labs at BETBY, examines how AI-powered UX must adapt in real time during high-pressure events like the FIFA World Cup. He explained how behavioural signals and derived features allow sportsbooks to personalise the experience moment by moment, responding dynamically to emotional shifts and engagement patterns.


Te second part of this three-article series moves from adaptation to anticipation. If the first challenge during the World Cup is delivering the right experience at the right moment, the next is recognising when engagement is starting to fade before users actually leave.


Te FIFA World Cup attracts attention like no other event. New users arrive in large numbers, casual bettors become active again, and engagement peaks around key matches.


But this surge comes with a structural challenge: much of this activity is irregular. Many users arrive for the tournament, place a handful of bets, and disappear once their team is eliminated or the excitement fades away.


For operators, the difference between short-term traffic and long-term value often comes down to one thing: recognising disengagement early enough to respond.


Tis is where proper AI systems separate themselves from basic rule- based approaches.


WORLD CUP CHURN LOOKS DIFFERENT Under normal conditions, churn is often defined in simple terms: a user


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stops betting for a certain number of days and is marked as inactive. Te World Cup breaks that logic.


A user may bet intensely for two days, go inactive for a week, and return for the knockout stages. Another may browse frequently without placing bets, waiting for a specific match. However, that silence alone does not always translate to loss of interest.


Churn during major tournaments must therefore be understood as a dynamic probability rather than a single threshold.


MODELLING CHURN


Instead of relying on a single inactivity rule, BETBY’s AI models churn across independent time windows: u


Tree-day churn probability u Seven-day churn probability u 14-day churn probability u 30-day churn probability


Each horizon becomes a separate prediction target.


A user trending toward three-day disengagement represents a very different opportunity from someone drifting toward 30-day inactivity. Treating churn as a single event, as a lot of sportsbooks do, removes this nuance and delays operator intervention.


While basic systems typically rely on one inactivity threshold (for example, 14 days) and react only once the user has already disengaged, proper AI identifies risk while recovery is still realistic.


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