Boost Casino has teamed up with ZingBrain AI to transform its static, manually curated lobby into a dynamic, personalized game environment. The partnership aimed to enhance player engagement, improve key performance metrics, and reduce ongoing manual management of the lobby.
Working closely, the teams introduced two core personalization features: a Recommendations section on the main casino page and a “Similar Games” pop-up displayed when a player exits a game.
To measure impact, an A/B test was conducted. One group of players continued to see the original lobby, while the other experienced the personalized environment. During the test, Boost Casino observed uplift in both turnover and GGR per player, with a slight increase in the number of unique games played per player. Post-test monitoring confirmed these improvements remained stable over several months, and the recommendation modules gradually became the primary method for players to discover new games.
Collaboration and Implementation
Boost Casino’s main objective was to move away from a uniform lobby layout and provide content tailored to each player. This approach aimed to broaden game exploration, enhance brand loyalty, and improve core revenue metrics.
Through close collaboration, the teams integrated personalization without disrupting the casino’s existing UX. The new sections were strategically positioned to maximize visibility and engagement. Using ZingBrain’s back-office tools, the Boost technical team quickly understood the API structure, personalized outputs, and data requirements. Smooth QA access enabled thorough testing prior to launch.
Despite complex internal architecture and multiple platform dependencies, the integration was completed efficiently and without any critical issues affecting players.
Analytics, including percentile-based analysis and the CUPED method, confirmed the uplift and helped the operator make an objective decision to continue and expand personalization.
Solution Overview
1. Main Recommended Section
This module combines familiar titles players already enjoy with new games tailored to their interests. Each player is assigned a Discovery Score, indicating their willingness to try new games, which adjusts the balance of new vs. known titles. Post-processing ensures recommendations stay fresh and avoid repetition.
2. Similar Games Pop-Up on Game Exit
When players exit a game, a pop-up displays similar titles based on multiple factors: game type, features, theme, volatility, and collaborative filtering. This ensures highly relevant alternatives are presented to each player.
Results Analysis
Due to the volatility of turnover and GGR, results were analyzed using multiple methods: trimming by percentiles, applying CUPED, evaluating median per-player values for long-term players, and checking statistical significance through p-values.
The analysis clearly showed an uplift in turnover and GGR per player, with a modest increase in unique games played. The stability of these results over time validated the positive impact of personalization and supported the decision to expand the initiative.
Expert Commentary
Oleg Smolerov, CPO, ZingBrain AI:
“Personalizing the lobby is a key step for any strong brand. The goals and mindset of the Boost Casino team immediately showed their deep understanding of the topic, which made every stage of the integration fast and seamless — from UX design all the way to evaluating the results. Being able to exchange ideas and test them together with such a strong team is a major driver of success, and I’m confident that we will discover many more effective use cases together in the future.”
Matteo Pacenti, Director of Product Management, Entain Northern and Central Europe:
“Introducing personalization was a major step in evolving the casino experience, and ZingBrain has proved to be the perfect partner. The integration was smooth, the testing rigorous, and the results are clear. The uplift in our core metrics gives us the confidence to expand personalization even further across the product, and we’ll be working closely with ZingBrain to continue to improve the UX for our players.”
Conclusion & Next Steps
The introduction of personalized recommendations has delivered measurable, sustained improvement in Boost Casino’s performance metrics while enhancing the player experience. The success demonstrates that personalization can be implemented quickly, safely, and effectively, even within complex platform architectures.
Looking ahead, both teams plan to expand personalization across additional touchpoints, integrate new real-time signals, and test advanced discovery scenarios, unlocking deeper engagement and creating a continuously evolving, player-centric casino experience.
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