Despite its name, A/B testing doesn’t have to be limited to just two variants. While the term inherently implies a straightforward comparison between version A (the control) and version B (the variant), modern analytical and monetization strategies can employ much broader, multi-variant approaches. To effectively drive app growth and optimize monetization, relying on basic reporting is no longer sufficient. The key lies in building an analytical infrastructure (or taking advantage of an already existing one) that facilitates deep data exploration, uncovers hidden correlations, and enables rapid strategy iteration based on multiple factors. That’s why A/B testing is the perfect engine for continuous yield optimization. Read on to discover how implementing these advanced, sometimes multi-variant testing architectures across your ad stack will help you uncover hidden revenue!
A/B testing architecture for ad monetization
Ad event design
The first foundational pillar of a comprehensive A/B-testing framework lies at the frontend: ad event design. It helps to decide exactly when and where an ad appears within your app’s user flow (e.g., triggering an Interstitial after a level, or placing a native ad in a social feed). Without A/B testing, ad placement is guesswork. You might increase the quantity of ads, hoping for a revenue spike, only to accidentally cannibalize your user retention. By rigorously A/B testing your ad event design, you can precisely identify the optimal friction points in your core loop and determine whether a pre-action or post-action trigger yields the highest completion rates without inducing user fatigue. Furthermore, it enables you to move beyond static ad loads by implementing dynamic audience segmentation. If you want to learn more about A/B testing, game progression and ads, we recommend our article about progression-based monetization.
Ad mediation
Moving beyond the frontend User Experience, the second critical pillar of your testing architecture lies deep within your backend ad mediation strategy.
In a traditional waterfall setup, A/B testing provides a controlled way to optimize different eCPM instances and waterfall configurations. Rather than relying on historical assumptions or gut feelings, it is advised to test different eCPM instance values and waterfall configurations. This controlled comparison helps determine whether higher floor prices can increase eCPMs without causing a significant decline in impressions or fill.
As monetization strategies increasingly incorporate Real-Time Bidding (RTB), the optimization mechanics change. Alongside waterfall A/B testing, it’s highly beneficial to continuously test and optimize minimum eCPM floors and bidding configurations to find price levels that improve yield without significantly reducing impressions.
Finally, for publishers operating in a complex hybrid ecosystem, A/B testing can help optimize both waterfalls and in-app bidding. By continuously evaluating waterfall configurations, eCPM instances, and minimum bid floors alongside real-time bidding performance, publishers can adapt their mediation strategy to changing market conditions and improve overall ad yield.
The Bidlogic testing methodology
Bidlogic’s modus operandi for A/B testing leverages full automation and deep granularity. This approach allows us to manage thousands of eCPM instances across apps and markets, completely offloading the burden from your development resources:
- Iterative testing framework: we treat testing as an ongoing, iterative process. By deploying multiple automated adjustments within a short timeframe, we systematically benchmark the performance of our algorithms against the publisher’s legacy configurations;
- Advanced GEO segmentation: a core component of our strategy. This enables the creation of dedicated mediation groups, effectively bypassing the functional constraints of native ad platforms;
- Algorithmic waterfall & bid floor management: rather than manually managing bid floors and waterfall structures, our system algorithmically tailors monetization strategies for individual markets, driven by historical performance data, impressions, revenue, eCPMs, and network trends;
- Continuous revenue optimization: ultimately, our approach goes far beyond a static, two-variant experiment; it is a proactive, continuous optimization cycle engineered to maximize ad yield.
One of the standard A/B tests we execute during the onboarding of any new application to Bidlogic is a direct, head-to-head performance comparison between the publisher’s legacy optimization settings and the dynamic configurations deployed by our algorithms. We run this controlled experiment over a strict one-to-two-week window to gather statistically significant data free of market noise. To declare the integration a definitive success, the traffic group managed by the Bidlogic engine must demonstrate a measurable, baseline increase ranging from several percentage points up to a double-digit uplift in AD ARPDAU. This controlled experiment gives app publishers quantitative evidence of the revenue impact of moving from manual ad-stack management to an automated optimization approach.
