ML in iGaming: How to Identify a VIP Player Before They Become One

ML in iGaming: How to Identify a VIP Player Before They Become One

In iGaming, there are players on whom the entire platform economy is built. These are high rollers — and the sooner they come into the focus of the VIP department, the more revenue the platform can generate. The problem is that most operators find them too late. This means losing the opportunity to monetize them at their peak activity.

How does this usually happen?

• The manager opens the CRM, filters the database based on what has already happened: deposit amounts, session frequency, bet sizes — in other words, they work with data retrospectively.
• A future high roller only comes into the focus of the VIP department after they have already shown their potential — typically 30, and often 60+ days after joining the platform.
• Standard analytical filters do not predict; they record information. Therefore, they give false positive signals: a player makes deposits for three days, the manager spends time and bonuses — but there is no conversion.
• A large operator has tens of thousands of active users, and a manager physically cannot handle more than 100 people a day.

What is the problem?

• A potential high roller is typically active on one platform for about six months. Every day without attention to them is a missed monetization opportunity.
• Behavioral patterns of a future high roller manifest within the first days of activity — but standard tools do not capture them.

How to fix this is demonstrated by the MICo case.

MICo — developers of ML solutions for iGaming — implemented an ML product for early identification of high rollers and predicting their churn for a CIS operator with over 700,000 MAU.

What their case showed:

• Players prioritized by ML reached VIP status in 11 days compared to 63 days without ML.
• Retention on day 30: with ML prediction — 31% (compared to 20% without it).
• After the first contact with the manager, the player’s daily NGR increased by approximately 30%.

Importantly, managers worked with both groups of players under the same conditions — the same processes, the same mechanics. The only difference was the selection principle, and the difference in results was the effect of timely engagement.

Why the ML approach solves this differently:

• The algorithm analyzes hundreds of behavioral signals within the first 3-7 days: session patterns, betting dynamics, deposit rhythms, and reactions to bonuses.
• Each player receives a Confidence parameter — the likelihood of becoming a high roller. The manager works with a prioritized list rather than intuition.
• The product also predicts churn 30 days before the event — providing time for proactive retention.

Conclusion

The MICo case shows that the speed of identifying a high roller directly affects additional profit. What takes 30+ days manually, an ML solution accomplishes in 3-7 — and this is not just a speed-up of the process, but additional weeks of monetization from each valuable player.

ML prediction of player behavior is a competitive advantage. It allows for the early identification of promising players, effective management of their engagement, and predicting churn before it happens. In the near future, such technologies will become standard for iGaming operators.