Blog & Cases

In most iGaming products, the recommended games section is generated in one of two ways: either it’s a list of the most popular slots on the platform, or it’s a selection curated manually by the operator’s team. Sometimes it’s a hybrid approach: some games are added manually, while others are selected algorithmically.

This approach is understandable and familiar. But it creates three key problems:

1. All players see the same list, regardless of their preferences.
2. Curated collections require constant manual updates, which is labor-intensive and does not scale well.
3. A static list of games becomes the "default" option rather than a driver of growth.

As a result, the set of recommendations does not consistently help increase retention, LTV, and revenue.

Objective: To increase the operator's revenue and improve the user gaming experience through personalization

To achieve this, MICo (developers of AI and ML solutions for iGaming) integrated the MICo recommendation engine into the product of its client—a gaming operator. The ML model* analyzed each player’s behavior based on a variety of signals and generated personalized game selections.

‍Region:
South Asia

‍Audience:
250,000+ MAU

‍Point of entry:
The “Recommended Games” section, located in the operator’s lobby on the top level; this is one of the key navigation elements that receives a large number of views.
‍*ML (machine learning) — models that analyze data, identify patterns, and make decisions without manual intervention.

The effect was evaluated by comparing two operational scenarios:

1. Manual curation: The list of recommended games is compiled by the operator team.
2. MICo ML Recommendations: The composition and order of games within the same block are determined by an algorithm.

Importantly, the design and layout of the block remained unchanged. No new mechanics were added, and traffic levels remained the same. The only difference was in the logic behind matchmaking.

But to understand exactly what the differences between the two approaches were, it’s worth taking a closer look at how the MICo solution works.

The ML recommender generates a list of personalized recommendations on its own. Here's what happens

• It collects data. The player selects titles, clicks, returns to games, and changes the pace and style—the model analyzes over 200 behavioral parameters for each player.
• It creates a pool of games. Based on the signals, several independent algorithms select relevant games.
• Creates a personalized list. A selection is compiled that reflects the likelihood that the player will be interested in specific games.
• Ranks with a focus on value. The list is reordered to align with the operator’s business objectives—maximizing GGR and ARPU.

Unlike manual curation, which relies on general statistics and past results, ML analyzes the behavior of a specific user and dynamically selects search results, eliminating the need to constantly manually update the list.

Within the first 28 days, the ML-recommendations scenario showed significant growth across key metrics

All metrics were calculated solely for the “Recommended Games” section; in other words, this represents the net effect of the change in the selection logic.

Behavioral metrics:

• Conversion rate from views to clicks: +8 percentage points
• Day-7 retention: +10 p.p.

Financial metrics:

• Average deposit: +40%
• GGR per user in this section: +200%

The results confirm that a recommendation section can be significantly more effective if the selection process is dynamic and based on players' actual behavior and preferences.

What does this mean for operators?

This case study illustrates a simple pattern: recommendations can be more than just a “convenient feature”—they can be a direct driver of revenue if they are adapted to the player’s behavior in real time.

Personalization acts as a real financial lever:

Sustainable growth without increasing your budget. You don’t spend your budget on marketing or hire additional staff—yet your metrics grow because players are getting a more relevant experience.

Reduced manual labor. There’s no need to constantly update lists or allocate resources to do so. The ML solution does this automatically based on real-time data.

Scalability. The more players and events there are, the more accurately the model works. Recommendations can be used everywhere: in the lobby, in search results, in the game list, and even in communications.

The more touchpoints there are, the more noticeable the business impact.

How to Repeat the Result

Test the ML recommender on your own data:

• Duration: 2–4 weeks
• Format: Launch a pilot and evaluate the results
• Result: Measurable uplift and projected growth in metrics

Personalizing recommendations isn’t just a cosmetic improvement. It’s a sustainable growth channel that starts delivering results immediately after launch, without any additional marketing costs. The ML model replaces a static list with dynamic results that adapt to each player, and this has a tangible impact on revenue-related metrics.

VIP players are the small group that drives the platform's unit economics. They typically account for about 80% of any operator's revenue.

That said, the main challenge remains identifying them right from the start. This is critically important, since a high roller’s “lifespan” is estimated at six months, according to various sources; therefore, the sooner such a player comes to the manager’s attention, the more revenue they can generate for the platform.

Why is this opportunity often overlooked? It all comes down to a rule-based approach: the manager opens the CRM, filters the database by deposits, session frequency, and bet sizes—and then relies on their “instinct.”

This model works, but almost always with a delay: a player only comes into focus once most of their life cycle has already passed.

At the same time, simple filters often generate false positives: a player makes deposits for three days in a row, the account manager spends time and bonuses on them, but no conversion occurs.

The MICo team discovered that the necessary signals are already present in the data, but they aren't being detected in time

MICo, a developer of AI and ML solutions for iGaming, analyzed data from a number of operators and found that the behavioral patterns of future high rollers become apparent within the first few days of their activity—long before they can be detected by CRM filters.

However, with the standard approach, such signals usually go unnoticed—they manifest themselves in combinations of dozens of parameters and in the dynamics of a player’s behavior—that is, after the fact.

ML algorithms make it possible to identify such patterns much earlier, and these predictions are more accurate.

Based on this approach, MICo developed the VIP Intelligence ML solution, which identifies high-potential players as early as 3–7 days into their activity and flags them for priority follow-up by the VIP team.

In practice, the ML approach has shown improvement across all key metrics

VIP Intelligence was implemented at a CIS-based operator with an audience of over 700,000 MAUs. The impact was assessed by comparing two scenarios for how managers worked:

• Standard filter — a selection of players with whom managers worked under normal conditions, without ML.
• ML prioritization is a selection process in which algorithms identify players as promising starting on their third day of activity. Managers received these players on a priority list but interacted with them according to the standard procedure.

Key point: Managers did not know whether they were working with players who had been selected manually or as a result of ML prioritization.

Results from the product's first month on the market:

• Players on the priority list reached the VIP threshold in an average of 11 days thanks to ML-based prioritization. Without ML, this figure was about 63 days.
• With the implementation of VIP-Intelligence, the retention rate on the 30th day increased from 20% to 31%.
• After the manager's first contact with the high roller, the player's daily NGR increased by ~30%.

VIP Intelligence analyzes hundreds of signals that cannot be processed manually

The MICo product operates as a SaaS service and can be integrated via an API or another format convenient for the operator. The ML solution begins its analysis as soon as a player logs into the platform.

In the early days, the algorithm processes behavioral signals that cannot be analyzed manually: session patterns, betting dynamics, deposit frequency, reactions to bonus mechanics, and dozens of other variables.

As a result, each player is assigned a "Confidence" score—the probability that the user will show rapid growth and become a high roller. Managers receive a prioritized list sorted by this score.

Result: The team focuses its efforts on those who are most likely to deliver real value.

Early identification of high rollers directly increases the operator's profits

Thanks to this model, players are brought into the VIP department’s spotlight three weeks earlier. This provides an additional, controllable monetization period for each high roller—without increasing acquisition costs.

The MICo solution helps identify who is currently most receptive to communications. Managers don’t waste time on those who aren’t likely to convert. In effect, this boosts the VIP department’s productivity without hiring new staff.

NGR is growing. From the moment a manager first engages with a player, daily net gaming revenue from that predicted player increases by ~30%.

For an operator with 700k+ MAU, this single product alone can help generate tens of thousands of dollars in additional revenue each month. Simply because the platform stops missing out on high rollers during their most active first few weeks.

VIP Radar can not only identify high rollers before manual selection but also assess the likelihood of their churn. This allows you to quickly develop preventive retention strategies and minimize the loss of valuable users.

The case studies address the most common questions from operators

"The ML product simply identifies those who would have brought in a lot of money anyway."

Managers did not know whether they were working with players identified by VIP Intelligence. They treated players the same way both before and after the product was implemented. The differences in deposits, conversion rates, and retention are not due to the players themselves, but rather the result of timely engagement.

"Managers can already see large deposits in the CRM"

They realize it, but it’s too late. A major operator has a user base of tens of thousands of active users, and a manager simply cannot effectively handle more than 100 people a day. 63 days to reach VIP status in a group of hand-selected players—that’s not a theoretical benchmark, but a statistical fact.

"Aggressive attention from the very first week will burn a player out faster."

The data suggests otherwise. Retention among players on the priority list reached 31% on day 30, compared to 20% without ML predictions. Early attention from a manager doesn’t burn out a player; rather, it builds their loyalty to the platform before a competitor does.

ML Prediction Is Becoming the New Standard for Working with VIP Players

The case study shows that an ML solution transforms the economics of working with high rollers not through budgets or bonus policies, but through speed. VIP-Intelligence identifies the right player from among thousands within 3–7 days—something that would be impossible to do manually.

Operators who are implementing ML today have a head start—they identify high rollers before their competitors do and earn more from each customer lifecycle.

In a few years, predictive VIP identification will become a core tool for any operator—just as CRM is today.

MICo is a full-cycle development company specializing in ML-based product solutions, with its own team of ML specialists and infrastructure for data processing and analysis. The company is certified to the international ISO 27001 standard, which attests to its high level of data security and protection.