9 Best A/B Testing Tools for Mobile App and Game Monetization

A tool that can split users into A and B is not automatically a monetization-testing tool.

The useful question is what it can change safely:

• Mediation sources and waterfall order.
• Floors and auction configuration.
• Ad formats, placements and frequency.
• Reward amounts and game-economy variables.
• Paywalls, prices, trials and subscriptions.
• Entire monetization strategies across stable cohorts.

This list compares nine tools by the decision they are best equipped to test.

Comparison table

1. UndrAds

Best for: publishers testing operational ad-revenue changes without moving the whole stack.

UndrAds runs an autonomous AdOps layer over an existing setup. That makes it suited to tests involving floor strategy, waterfall priority, partner performance and other settings that normally require repeated manual changes.

A well-designed test should retain:

• A concurrent control.
• Stable traffic assignment.
• One declared primary metric.
• Fill, retention, latency and IAP guardrails.
• A record of every configuration change.
• Automatic rollback when a guardrail breaks.

Choose it when the experiment is not one fixed A/B configuration but a bounded policy that adapts to live demand.

Watch for: adaptive systems make analysis harder. Agree on the unit of randomization and evaluation method before the agent acts. See How to Prove AI AdOps Increased Revenue and Static Floors vs AI Floor Pricing.

2. Google AdMob A/B testing

Best for: developers already using AdMob Mediation who want a native, low-friction auction test.

AdMob mediation experiments can test:

• Adding or removing ad sources.
• Manual waterfall eCPMs and order.
• Enabling or disabling source optimization.
• The number of times a source is called.
• Bidding eCPM floors.

Google allows 1% to 50% of users in Variant B, requires at least 10,000 ad requests to determine a result and recommends testing one setting at a time for at least two weeks.

Watch for: the result is designed around mediation performance. Add retention and purchase analysis elsewhere when the change affects player experience.

3. Unity LevelPlay A/B testing

Best for: Unity studios testing both auction mechanics and the player-facing ad experience.

LevelPlay A/B testing covers a wide surface:

• New networks and multiple instances.
• Country-level configuration and instance pricing.
• Payer versus non-payer segments.
• Reward amounts by level.
• Frequency caps by country.
• New formats and banner refresh.
• Retention and cohorted ARPU in results.

That combination makes LevelPlay one of the strongest native options for ad-monetized games.

Watch for: keep a clean distinction between a platform’s experiment result and finalized cross-platform business revenue.

4. Appodeal

Best for: teams wanting mediation, analytics, segmentation and experimentation in one system.

Appodeal supports tests across waterfalls, ad setup, in-app variables and retention-oriented targeting. It can vary floors, frequency, network selection, rewarded amounts and user segments.

Choose it when the team values a broad packaged system and is comfortable centralizing monetization in it.

Watch for: breadth can make platform switching more expensive. Confirm data export and the migration path before committing.

5. Firebase A/B Testing

Best for: testing product and placement logic delivered through Remote Config.

Firebase A/B Testing is useful when the variable lives in the app experience:

• When an interstitial appears.
• Whether a rewarded placement is visible.
• Reward size.
• Paywall timing.
• Introductory offer messaging.
• Onboarding paths that affect payer conversion.

Because Firebase joins Remote Config with Google Analytics outcomes, it is accessible to many small and mid-sized teams.

Watch for: verify that delayed subscription or external revenue events are correctly associated with the experiment variant. A conversion proxy can choose a winner that loses on lifetime value.

6. Statsig

Best for: product teams needing feature flags, experimentation and granular control over exposure.

Statsig can test product logic across mobile and backend systems. It suits studios that want to own the monetization implementation while using a dedicated experimentation platform for assignment, metrics and analysis.

Use it for:

• Placement eligibility.
• Ad-frequency policies.
• Economy and offer logic.
• Feature access by cohort.
• Multi-surface experiments coordinated with backend services.

Watch for: the platform will not configure your mediator or validate your revenue definitions automatically.

7. Optimizely

Best for: larger organizations with mature experimentation teams and cross-platform products.

Optimizely provides feature experimentation and rollout controls across complex product environments. It is relevant when a publisher wants one experiment system across mobile, web and server-side experiences.

Watch for: enterprise flexibility brings implementation and governance overhead. Smaller studios may get faster answers from native mediation or Firebase tests.

8. RevenueCat Experiments

Best for: subscription pricing, packages, trials and paywall tests across stores.

RevenueCat Experiments can compare offerings and follow results across the subscription lifecycle. Variables include:

• Product pricing.
• Trial length and presence.
• Introductory offers.
• Subscription duration.
• Product mix.
• Paywall imagery, copy and layout.

It is especially useful because the decision can reflect renewal behavior rather than only the first purchase.

Watch for: subscription experiments should run long enough to observe trial conversion, refund and renewal differences.

9. Adapty

Best for: mobile teams focused on paywalls, subscription offers and audience-specific monetization.

Adapty combines paywall management, subscription analytics and A/B testing. It belongs on the shortlist when non-engineers need to iterate paywalls without waiting for a full app release.

Watch for: this is a subscription and paywall tool, not an ad-auction experiment platform. Hybrid apps may need separate ad and subscription test systems with coordinated assignment.

Choose the tool by the object being changed

The minimum experiment specification

Write these fields before launching:

• Hypothesis: what action should create what outcome?
• Population: which users, countries, versions and formats qualify?
• Assignment unit: user, device, request, ad unit or time block?
• Primary metric: which single result decides the test?
• Guardrails: what must not get worse?
• Minimum detectable effect: what improvement is commercially meaningful?
• Duration and sample: when is the test eligible for a decision?
• Exclusions: what data will be removed, and why?
• Decision rule: ship, extend, restrict or stop?

Five common monetization-testing mistakes

1. Using eCPM as the only success metric. Higher eCPM can coexist with lower fill and revenue.
2. Changing several things at once. You learn that a bundle moved, not which decision worked.
3. Ending on a favorable day. Choose the decision rule before watching the graph.
4. Ignoring delayed value. Retention, renewals and payer conversion take longer than auction metrics.
5. Letting tests overlap. Two simultaneous monetization changes can contaminate both results.

The broader framework is in UndrAds’ guide to how much mobile games make per ad: format, geography, platform, frequency and mediation all affect the number, so randomization must keep those influences balanced.

FAQ

What is the best A/B testing tool for mobile-game ads?

Unity LevelPlay is a strong native choice for Unity games, while AdMob is the simplest for existing AdMob mediation. UndrAds fits continuous floor and operational-policy tests over an existing setup.

How long should a monetization test run?

Cover at least a full weekday/weekend cycle and the slowest critical metric. Google recommends at least two weeks for AdMob mediation tests; retention and subscription renewals may require longer.

Can I test two mediation platforms against each other?

Yes, but it is difficult. Demand partners may recognize the traffic, implementations may differ, and SDK behavior can contaminate the result. Use stable user assignment, equivalent partner access and independent revenue reconciliation.

Should I optimize for eCPM or ARPDAU?

Use revenue per eligible request for narrow auction tests and total ARPDAU or contribution per user when the treatment affects impression volume, retention or purchases. Never read eCPM without fill.

Can I run ad and paywall tests simultaneously?

Only if cohorts do not overlap or the experiment is explicitly designed as a multi-cell test. Otherwise, one treatment can change the audience reaching the other and make both results hard to interpret.