+14% GGR across the platform: how ML personalization turns game recommendations into revenue

A case study of an operator with ~615K MAU: how to increase the value of an existing audience and drive additional GGR growth through ML personalization and personalized game recommendations, without increasing traffic volume.

Thousands of titles in a catalog do not by themselves guarantee that a player will find a game that matches their interests and gaming intent. A manually curated selection that is the same for everyone may be popular with most players while being completely irrelevant to a particular user.

But when game selection is powered by a machine learning recommendation system, the impact can go far beyond the “Recommended for You” section and affect key business metrics across the entire iGaming platform.

Project starting point: what the operator came with

An operator approached MICo with a specific goal: generate more revenue from its existing audience without increasing traffic.

The platform already had a recommendation section in the game lobby, but its content was generated using predefined rules and did not account for individual player behavior or behavioral signals.

With the operator’s permission, we break down what we changed using an ML-powered game personalization solution and what business impact it delivered.

The problem: what was wrong with the existing recommendations

The operator was expanding its game catalog and acquiring new users, but was looking for a way to generate more value specifically from its existing audience.

Even with a large selection, players did not always find content that matched their interests and playing style. A title that was popular with most users could be completely irrelevant to a particular player.

And manually creating a personalized game selection for every player is impossible.

The goal: increase commercial performance through game lobby personalization and ML recommendations, while increasing the impact of the recommendation section on key business metrics: GGR, retention, deposit activity, and LTV.

Region: CIS
Audience: ~615K MAU
Implementation point: “Recommended for You” section in the game lobby

What we changed and what we achieved

Solution: a personalized recommendation system instead of a rule-based approach

Instead of the standard rule-based recommendation approach, the operator implemented MICo’s Recommendation System — an ML-powered solution for personalized game recommendations in iGaming.

The system creates an individual game selection for each user based on their behavior, session history, preferences, and other parameters.

For four weeks, we compared ML recommendations with the standard rule-based approach: the control group continued receiving the predefined selection, while the test group received personalized MICo recommendations.

Result: growth in global platform business metrics

After implementing the Recommendation System, not only the recommendation section metrics improved — global metrics across the entire platform increased as well.

How the Recommendation System works

1. Evaluation and analysis

The algorithms analyze the current game catalog and available data to identify signals that can be used to personalize game recommendations.

2. Behavioral signal collection

The system collects dozens of player behavior signals: deposits and withdrawals, bets and games played, clicks, returns to games, playing pace and style, visit frequency, interactions with the recommendation section, and other behavioral patterns.

3. Candidate generation and ranking

Several independent algorithms build a pool of relevant games for each user. The final selection is then ranked with a focus on the operator’s business objectives — GGR growth, deposit activity, and retention.

The key difference from a manual approach is that instead of using one list for all players, the system considers each user’s behavior and interests and dynamically selects more relevant games based on those signals.

The operator’s team does not need to manually create and continuously update separate game selections.

Player personal data is not required to train the models.

Where else can the Recommendation System be used?

The Recommendation System can be used beyond the “Recommended for You” section.

ML-powered content personalization can be applied to search, similar-game sections, selections based on saved titles, and other points across the player journey — anywhere personalized content can influence player engagement and interaction with the platform.

This makes a recommendation system for iGaming not just a standalone UI feature, but part of a broader personalization strategy for the player experience.

What this means for the operator

Personalized recommendations are not simply a cosmetic UI improvement. They can become a tool for revenue growth and for increasing the efficiency of existing traffic.

In this case, growth was achieved without increasing traffic — solely by showing existing players more relevant content.

When players receive recommendations based on their individual behavior instead of a single manually curated list, the operator gets:

• Higher engagement. More users interact with recommended games.
• A better player experience. The recommendation section becomes part of the player journey rather than a static default list.
• Improved business metrics. Personalization becomes an additional lever for GGR growth without increasing acquisition costs.
• More value from existing traffic. Audience monetization improves without acquiring additional players.

Scaling the impact of ML personalization

The +14% figure represents a percentage of current GGR, not a fixed amount.

The larger the platform’s GGR, the greater the absolute value of the same uplift:

• At €1M GGR: +14% = an additional €140K.
• At €10M GGR: +14% = an additional €1.4M.

ML personalization scales with the audience: the team does not need to proportionally increase manual effort to create and update game selections.

This is not a one-time uplift, but an automated personalization mechanism that continues to operate as the audience grows.

Conclusion

The results of this case study are an important signal for iGaming operators. Simply having a recommendation section on a platform does not guarantee growth in key business metrics.

The greatest impact comes when recommendations are genuinely personalized with machine learning and take into account both player interests and behavior and the platform’s business objectives.

For an iGaming operator, this means moving from a single rule-based recommendation approach to a dynamic ML recommendation system that helps extract more value from the existing audience and turn personalization into a measurable business outcome.

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