Player churn
Users fail to discover content that matches their interests and quickly lose engagement with the platform, reducing retention.
- API
- Delivery 3 days
- Integration
Recommendation System
Personalized ML recommendations that help operators grow retention and revenue across gaming platforms. Delivered via API.
B2B software for licensed operators. MICo does not operate gaming services, does not accept wagers and does not offer games or content to players.
Discover
Create a more relevant game experience that increases engagement and monetization in every session
Problems We Solve
Up to 90–95% of new players churn within the first month after registration when relevant content is missing.
Revenue loss
Acquired users do not stay on the platform long enough, leaving their LTV unrealized due to weak personalization of the gaming experience.
Static content feeds
Identical lobbies and game collections ignore user behavior and preferences, reducing engagement.
Manual segmentation
Teams spend resources creating and updating game lists that quickly become outdated and do not scale efficiently.
What the ML Recommendation Does
The system constantly analyzes user behavior and can adapt game delivery for each player.
Analyzes behavior
Processes behavioral patterns on the platform and adapts to each user's gaming preferences.
Predicts interests
Identifies the games and content most relevant to each player based on behavior patterns and activity history, and also takes into account the platform's interests.
Personalizes content
Generates dynamic game recommendations for various scenarios, tailored to user preferences, to increase engagement and reduce the risk of churn.
Constantly adapts
Updates recommendations based on changing player behavior to maximize retention and revenue.
Results
Impact is measured on the operator's own metrics during a 2-4 week pilot.
- Average Session Time
- Total Betting Volume
- Retention
- Engagement
- Revenue
- NGR Per User
- Betting Activity
- Deposits
- Visit Frequency
×1.5
Deposits per player
+200%
GGR in the recommendation section
+12%
Global GGR
Results above were measured at a South Asian operator with 250,000+ MAU over 28 days, within the recommended-games section only. Block design, placement and traffic volume were unchanged — the only variable was selection logic. Outcomes depend on data quality, product structure and audience.
Built for operator CRM, retention and analytics teams
Behind every relevant recommendation
Collect user behavior signals
Build recommendation logic
Suggest the best choice
Features
Main Features
API Integration
Connect the system via API to process player activity, engagement, and behavioral data.
Player & Operator Analysis
Collect behavioral signals, game activity, deposits, retention, and operator data.
Secure ML Infrastructure
Uses depersonalized data and encryption.
Personalized Recommendations
Results appear across the entire platform.
Ready to see it on your data?
A technical demo with our ML team: how the model works, what data it needs, and how we evaluate the result on a 2-4 week pilot.