Team track record · Revenue
Churn prediction cuts player churn 10% for a global bookmaker
A globally recognised iGaming operator headquartered in the United Kingdom, with a strong presence across Europe, Asia and other regulated markets.
10%reduction in player churn
5%increase in average player value
1churn score per player
The challenge
- High churn soon after onboarding and during early activity.
- Little early visibility into which new players would become valuable.
- CRM and analytics sat in silos, and manual segmentation couldn't scale.
The solution
- Built a churn-prediction framework on gameplay frequency, deposit patterns, session duration, bonus usage, inactivity windows and channel behaviour.
- A supervised machine-learning model gave each player an individual churn probability.
- The same model flagged early VIP potential.
- Predictions fed CRM dashboards and campaign triggers, so retention offers went out automatically.
The outcome
Player churn fell by 10% and early VIP identification raised average player value by 5%. The CRM team now works from a scalable, model-driven framework instead of manual segments.
A project by the BizAcuity team that built GAMWIT. Originally published at bizacuity.com.
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