Prove personalization impact before scaling.
Measure how ZingBrain AI changes GGR, turnover, bets, game discovery, and player activity before rolling recommendations out to everyone. The A/B Testing Tool gives casino, product, and analytics teams a clear control vs test view of revenue metrics, player engagement, section usage, game launches, and daily behavior trends.
Teams can track the impact across key casino metrics, including turnover, bets, GGR, unique games, active days, game launches, section usage, and daily behavior trends. The goal is to help operators understand whether personalization creates real uplift, not just random noise.
Before expanding recommendations, operators need to know how ZingBrain AI affects real business results. Raw averages can be misleading: high rollers, large wins, or short test windows can distort the picture. A/B Testing Tool helps teams read results with percentile filters, clean revenue views, and significance checks.
Split players, track performance, reduce noise, and focus on clean revenue impact.
Run a clean A/B test between players who see the standard casino experience and players who receive ZingBrain AI recommendations.
Compare both groups across GGR, turnover, bets, unique games, active days, and game launches. See how performance changes day by day, not only as a final result.
Use percentile filters to reduce the effect of high rollers and extreme results, and check statistical significance to understand whether uplift is real or random.
Send clicks and game-card interactions back to ZingBrain AI to improve recommendations and reporting.
From assumption to clear evidence before you scale.
A/B Testing Tool helps teams move from assuming personalization works to measuring the uplift and deciding what to scale.
It gives product, casino, analytics, and management teams a shared view of results before expanding recommendations to all players.
This is especially useful during the first launch, when teams need to prove value, understand player adoption, and choose the next step.
Four ways teams use the A/B Testing Tool
From validating personalization impact to preparing confident rollout decisions, how casino and product teams use the A/B Testing Tool.
Measure how ZingBrain AI affects revenue, engagement, game discovery, and retention-related behavior during the test.
See how active days, game launches, unique games, and section usage change during the personalization test.
Use clear dashboard results to decide when to scale recommendations to all players with confidence.
Understand the difference between players with and without personalized recommendations across key metrics.
A complete view of personalization impact across all key casino metrics.
Looking beyond GGR to full impact
GGR is important, but it can be volatile, especially with smaller groups or short testing periods. The A/B Testing Tool also helps teams analyze supporting metrics like active days, unique games, bets, and section usage.
Together, these metrics give a more complete view of personalization impact: whether players discover more games, return more often, and interact with the lobby differently.
From assumptions to measurable uplift
A/B Testing Tool helps operators launch personalization with confidence. Instead of relying on intuition or isolated metrics, teams can measure how ZingBrain AI affects real player behavior and business results, then scale recommendations with clear evidence.
The result is a more confident rollout process, with a shared view across casino, product, analytics, and management teams.
