Entain & ZingBrain AI: How Personalized Recommendations Improved the Player Experience on Boost Casino

Boost Casino partnered with ZingBrain AI with the aim to move from a manually curated, static lobby to a personalized and adaptive game layout. The goal was to improve key performance metrics while reducing the amount of ongoing manual work.

Working closely together, the two teams introduced two core personalization features: a recommendations section on the main casino page and a “similar games” pop-up shown when a player closes a game.

To accurately measure the impact, the teams ran an A/B test: one group of players continued to see the original lobby, while the other group received the personalized experience.

During the experiment, the brand saw an uplift in both turnover and GGR per player. A key proxy metric — the number of unique games played per player — also showed a slight uplift. After the A/B test ended, both teams continued monitoring performance, and the uplift remained stable over the following months. Over time, players increasingly gravitated toward the recommendation modules, which became the leading source of game discovery on the brand.

Collaboration

Boost Casino’s key objective was to move away from its single, uniform way of sorting games in the lobby and instead deliver more relevant content to each individual player. This approach aimed to broaden players’ game portfolios, strengthen loyalty to the brand, and improve core monetary metrics.

Through close collaboration between both product teams, the companies found an optimal way to introduce personalization into the existing lobby without disrupting the brand’s already strong UX. At the same time, the personalized sections were placed in highly visible positions to maximize impact.

Using the Zing back-office tools, the partner quickly understood the API structure, the personalized outputs, and the required data. Their technical team also provided smooth QA access, enabling thorough testing before launch.

Despite a complex internal architecture and multiple platform dependencies, success was possible due to the strong cooperation between the teams. The integration was completed quickly and rolled out without critical issues that could affect the player experience.

The final stage focused on evaluating results. In-depth analytics — including percentile-based analysis and the CUPED method — clearly demonstrated the uplift and helped the partner make an objective decision to continue the collaboration and further expand personalization across the product.

Solution Overview

Two personalization modules were introduced on the brand:

1. Main Recommended Section

This is the core personalized module that reflects each player’s key preferences. It combines familiar titles the player already enjoys with new games they haven’t discovered yet.

For every player, the system calculates a Discovery Score — a measure of how willing they are to try new games — and adapts the ratio of new vs. known titles accordingly.

Post-processing is also applied to ensure the recommendations stay fresh and do not repeat too often.

2. Similar Games Pop-Up on Game Exit

When a player closes a game, before returning them to the lobby, they see a pop-up with titles similar to the one they just played.

Similarity is calculated using a wide range of parameters, including game type, features, theme, volatility, and collaborative filtering signals.

This allows the system to surface the most relevant alternatives for every game available on the brand.

Results Analysis

Because turnover and GGR are highly volatile metrics, it was important to approach the analysis in a comprehensive way and isolate the real impact of personalization — independent of seasonality, traffic fluctuations, or other external factors.

Several analytical methods were used: trimming groups by percentiles, applying the CUPED method, reviewing median per-player values for long-term players, and checking statistical significance through p-values.

Based on the joint analysis performed by both analytics teams, the conclusion was clear: there was an uplift in both turnover and GGR per player. The key proxy metric — the number of unique games played per player — also showed a slight uplift.

The consistency of this uplift and its stability over an extended period demonstrated that personalization delivers a positive effect, leading the partner to continue and expand the collaboration.

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, Entain Northern and Central Europe, Director of Product Management: “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 proved to be a meaningful step forward for Boost Casino, delivering a clear and sustained uplift in key performance metrics while strengthening the overall player experience. The collaboration between the teams showed that personalization can be integrated quickly, safely, and with measurable impact — even within a complex platform structure.

Looking ahead, both sides plan to expand personalization across additional touchpoints, explore new real-time signals, and test more advanced discovery scenarios. These next steps aim to unlock even deeper engagement and create a continuously evolving, player-centric casino experience.