Engineering
AB Testing 101 for Engineers
What I wish I knew about AB testing when I started my career
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TL;DR:
- Eppo's warehouse-native platform offers advanced A/B testing and real-time personalization for mobile apps.
- Mobile experimentation is crucial for understanding user behavior and improving engagement, retention, and conversions.
- Eppo's features include feature flagging, contextual bandits, and privacy-first design.
- The platform is enterprise-ready, scalable, and offers statistical innovation for accurate results.
- Eppo is the best mobile experimentation platform for modern teams, offering flexibility, data management edge, and built-in compliance features.
Creating exceptional mobile experiences means solving for a dynamic pool of devices, user behavior, and evolving business priorities. Mobile apps require constant iteration, nuanced testing, and an infrastructure that scales efficiently. Tracking user interactions with different versions of an app is crucial for understanding user behavior and improving engagement, retention, and conversions.
Eppo's warehouse-native experimentation platform offers a solution. With capabilities ranging from sophisticated A/B testing to real-time personalization through contextual bandits, Eppo empowers teams to iterate confidently and deliver impactful results.
This guide unpacks how Eppo enables best-in-class mobile experimentation, its key features, and technical insights to make onboarding your team seamless.
Why Mobile Experimentation Matters
New devices are released nearly every year, and operating systems are updated constantly. Customers expect high quality, and you'd have to be very lucky to succeed based on assumptions alone. And the cost is high. Say you decide to launch a new onboarding sequence you've been working on for weeks, and your retention metrics tank because it wasn't what people were expecting. You're making expensive guesses about your users without rigorous experimentation to validate decisions.
Here's the thing about mobile experimentation: It takes all of that speculation and turns it into something much more valuable: learning from data. Instead of a single high-risk launch, you get to test features, see how different segments respond, and launch in stages. You get a safety net, but you move at market speed.
Understanding user preferences is crucial to tailoring the app experience effectively and ensuring that different user segments are satisfied. But here's the thing: you can't just use any experimentation tool and expect it to work. You need a platform designed for the complexities of the mobile environment, one that can handle device variations, OS complexities, user behavior, and even the challenge of unreliable internet connections.
What is Mobile App A/B Testing?
Let's talk about A/B testing mobile apps. The basic process is pretty simple: we take all our users and divide them up randomly (you can call them Group A and Group B, hence the name). We assign one version of the app to each group – they might be identical except for a subtle difference we're trying to measure. Easy enough.
What are we testing? Anything that matters to the user experience. Maybe we're testing the position of a button (one of the classic design elements), perhaps we're testing a new feature that engineering has been building for months, or maybe we're testing a new way of sending push notifications.
The point is that we're not just trying to guess what changes will work. Instead, we track how real users interact with the change, collect data, and use that data to make a decision. We ultimately want to make the app work better for the user, and the data shows us how.
Benefits of A/B Testing for Mobile Apps
A/B testing offers a multitude of benefits:
- Hooked Users: We all want users who are engaged with our apps. By testing different features and designs, you'll learn what drives engagement, not just what you think will.
- Converting like Crazy: Conversion funnels aren't a one-shot deal. A/B testing allows you to iterate and play around until you find the version that drives in-app purchases and subscriptions.
- Happy Users: Nobody wants to ship a bad experience. Through testing, you'll identify those friction points that make users want to throw their phones across the room. Instead of guessing, you'll know what works.
- Making decisions based on facts: With an experiment, you have hard data to stand on. You can't say "I think this will work" anymore – you'll know whether it does.
- Reducing risk: Launching a new feature to everyone at once can be risky if something goes wrong. With feature flags, you can test changes with a small group first and gradually roll them out as confidence grows. If a change causes catastrophic side effects, you can quickly disable it without impacting everyone. This approach keeps your users' experience smooth while giving your team the flexibility to experiment safely.
Eppo's Mobile-First Approach
Eppo isn't just mobile-compatible—it's built with mobile complexities in mind from day one. Here's how we stand apart: Eppo also functions as a robust feature management platform, supporting development, DevOps, and product teams in overseeing the entire lifecycle of features, from their initial development to final release.
Warehouse-Native Architecture for Mobile
Eppo's warehouse-native architecture is one of its most compelling differentiators. Unlike legacy platforms dependent on disjointed systems and external pipelines, Eppo leverages your existing data warehouse as the core compute and storage layer.
This approach offers several game-changing advantages:
- Single Source of Truth for Data: Wouldn't it be nice if all of your experimental data were stored next to your business metrics? That's what we do. No more data copies, no more backhauls, just faster, cleaner data in the right place.
- Time to Answer: When experiments live directly on top of the warehouse, you don't have to wait for results to roll in. Mobile teams can analyze what's happening and make decisions in real-time before a dodgy feature reaches production.
- Scalability and Security: Mobile apps generate tons of data. But instead of sending all that sensitive user data to some external server, we keep it right where it belongs: in your systems. Unless, of course, you explicitly want it somewhere else.
For teams running mobile experiments at scale, this approach just makes sense. Your data stays secure, scales naturally with millions of users, and supports all kinds of testing scenarios, whether you're trying to target a segment or run a full-scale experiment. There are no compromises on fairness or bias, just clean, efficient experimentation right where your data lives.
Cross-Platform Compatibility
Mobile development teams come with diverse needs. Some prioritize native languages like Swift or Kotlin, while others lean on hybrid frameworks such as React Native or Flutter. Eppo is designed to accommodate this diversity:
- SDKs for iOS, Android, React Native, and soon Flutter.
- Consistent APIs across platforms for seamless integration.
- Shared learning across platforms via a unified experimentation interface.
With Eppo, product teams don't need to rework functionality whenever a framework changes. It simply integrates into what's already in use.
Real-World Example
Here's an example of Eppo's warehouse native advantage in action. A large e-commerce app optimized their payment flow through an A/B test. By moving the checkout button, they saw a significant lift. With Eppo analyzing metric outcomes for each segment of users directly in their Snowflake warehouse, the team was able to roll out this change globally in record time with full confidence. Optimization also improves app visibility in app stores, so it's a competitive advantage.
The A/B Testing Process
The A/B testing process involves several key steps to get accurate and actionable results:
Define Your Hypothesis and Goal
Before you start an A/B test, you need to define your hypothesis and goal. What change are you testing, and what do you want to achieve? For example, you might hypothesize that changing the 'Subscribe' button color will increase subscriptions by 10%. A clear hypothesis and goal directs your test and helps you measure its success.
Create Your Variations
Once you have your hypothesis and goal, create the different versions of your app that you want to test. With feature flags, you can control application behavior without requiring a deploy. This enables you to test new variations, experiment with changes like interface updates or push notification strategies, and quickly kill a test to revert to default behavior if needed, all without disrupting your users.
Run the Test and Collect the Data
Now that you have your versions run the test and collect the data. This means splitting your user base into 2 groups, with each group getting a different version of the app. The test should run for a statistically significant amount of time so the results are accurate and reliable. To ensure your results are accurate and reliable, use a sample size calculator to determine how many users you need and how long the test should run. By looking at the data, you can see which app version performs better and make informed decisions about future updates.
Analyze the Results and Draw Conclusions
Analyzing the results of your mobile app A/B test is crucial to understanding the impact of the changes you made. When diving into the data, there are several key factors to consider:
- Statistical Significance: First and foremost, ensure that your results are statistically significant. While a textbook definition of statistical significance is beyond the scope of this post, think of this as a measure of how surprised we should be by our experiment results, assuming there was no true difference caused by our experiment. Statistical significance gives you confidence that your changes genuinely affect user behavior.
- Effect Size: It's important to understand the magnitude of a difference beyond knowing that it exists. Calculating the effect size helps you determine how substantial your changes' impact is on user engagement, retention, or other key metrics.
- Confidence Intervals: Use confidence intervals to quantify the uncertainty our experiment leaves us with and estimate the range within which the true effect size is likely to lie. This provides a more nuanced understanding of your results and helps you make more informed decisions.
- Segmentation: Analyzing results by segmenting your users based on demographics, behavior, or other relevant factors can reveal how different groups respond to the changes. This can uncover insights that are not apparent when looking at the overall user base.
- Guardrail Metrics: Guardrail metrics ensure improvements in one area don’t harm others. For example, increasing signups is great, but not if it leads to higher churn. Metrics like retention rates or user satisfaction provide a broader view, helping you track the overall health of your app and avoid unintended consequences.
When drawing conclusions from your analysis, keep the following in mind:
- Avoid Confirmation Bias: Be objective in your interpretation of the results. It's easy to see what you want to see, but true insights come from an unbiased analysis of the data.
- Consider Multiple Metrics: To get a comprehensive understanding of the impact, look at a range of metrics, such as engagement, retention, and revenue. Focusing on a single metric can give a skewed view of the results.
- Identify Areas for Improvement: Use the insights gained from your analysis to identify areas for further improvement. Based on these findings, prioritize future testing efforts to continuously enhance the user experience.
By thoroughly analyzing your A/B test results, you can make data-driven decisions that optimize your mobile app and drive user engagement.
Statistical Considerations and Avoiding Bias
When conducting mobile app A/B testing, it's essential to consider statistical significance and avoid bias to ensure reliable results. Here are some key considerations:
- Sample Size: Ensure that your sample size is sufficient to detect statistically significant differences between the control and treatment groups. A small sample size can lead to inconclusive results, while a large sample size makes it easier to distinguish signal from noise.
- Randomization: To minimize bias, users are randomly assigned to the control and treatment groups. Randomization ensures that each group is comparable and that the observed effects are due to the changes made, not other factors.
- Confounding Variables: Randomization helps us control for confounding variables that could affect the test's outcome. Watch for any errors in randomization like Sample Ratio Mismatch, since these could indicate there are external factors that might influence user behavior and skew the results.
- Selection Bias: Avoid selection bias by ensuring that the sample represents the entire user base. This means that the users in your test should reflect the diversity of your overall audience.
- Novelty Effect: Be aware of the novelty effect, where users may respond differently to a new feature or design simply because it's new. This can lead to temporary spikes in engagement that may not be sustained over time.
To avoid bias, consider the following:
- Use a Feature Management Platform: Use a feature management platform to manage the rollout of new features. This ensures that users' assignments to the control and treatment groups are random and unbiased, providing more reliable results.
- Use Objective Metrics: Evaluate the test's success using objective metrics such as engagement and retention. These metrics provide a clear picture of users' interactions with the app, free from subjective interpretation.
- Avoid Cherry-Picking: Resist the temptation to cherry-pick results that support your preconceptions. Instead, consider all the data and be open to findings that may contradict your initial hypotheses.
- Leverage guardrail metrics: Monitor and ensure that other important metrics don't decline, helping to avoid confirmation bias.
By carefully considering these statistical factors and avoiding bias, you can conduct more accurate and reliable A/B tests that provide valuable insights for optimizing your mobile app.
Mobile App Optimization Techniques
Mobile app optimization uses data and testing to improve the user experience and drive engagement. Here are some key techniques to consider:
- A/B Testing: A/B testing is a fundamental technique for comparing different versions of your app to determine which one performs better. You can identify what resonates most with your users by testing variations in design, features, or user flows.
- Heatmaps: Use heatmaps to visualize user behavior within your app. Heatmaps show where users are tapping, scrolling, and spending the most time, helping you identify areas that need improvement or are causing friction.
- Funnel Analysis: Conduct funnel analysis to understand how users navigate through your app and identify drop-off points. This analysis helps you pinpoint where users are abandoning the app and what steps can be taken to improve the flow and increase conversions.
- Push Notifications: Leverage push notifications to re-engage users and drive retention. Personalized and timely push notifications can remind users of your app, encourage them to complete actions and keep them coming back.
When optimizing your mobile app, keep the following in mind:
- Be Data Driven:: Base your optimization efforts on data to ensure that you're making informed decisions. Analyzing user behavior and feedback provides insights into what needs to be improved.
- Test Iteratively: Optimization is an ongoing process. Test iteratively and continuously to ensure that you're always enhancing the user experience. Small, incremental changes can lead to significant improvements over time.
- Focus on User Behavior: Pay close attention to user behavior and use metrics such as engagement and retention to evaluate the success of your optimization efforts. Understanding how users interact with your app is key to making meaningful improvements.
By employing these optimization techniques, you can create a more engaging and user-friendly mobile app that meets the needs and preferences of your users.
Best Practices for Mobile Experimentation
- Start Small: Test one thing at a time. For mobile, simplicity is key to clear results.
- Segment Intelligently: You can use Eppo's audience targeting to reduce noise by segmenting your app users based on app interaction or device capabilities.
- Iterate Fast: Go beyond traditional experiments and use contextual bandits to evolve designs in real time.
Key Features for Mobile Experimentation
Advanced A/B Testing
Eppo's statistical powerhouse simplifies the rigors of mobile experimentation:
- CUPED++ (Controlled Experiments using Pre-Experiment Data): Particularly useful for mobile apps with smaller user pools, CUPED++ reduces variance by incorporating pre-test baseline data into the results. This dynamic ensures tests reveal actionable insights faster.
- Real-Time Sequential Testing: Instead of running fixed-duration tests, Eppo can enable the live monitoring of statistical significance via sequential testing, adjusting p-values to control for error rates as samples are collected.
With tools like these, Eppo provides sharper analysis than many competitors relying on traditional means.
Feature Flagging for Mobile Apps
Modern app teams use feature flags to roll out changes iteratively. Eppo adds to this foundation:
- Dynamic Updates Across Devices: Deploy changes instantly without waiting for app store review cycles.
- Fast Rollbacks: Turn off bad features if something breaks.
- Granular Targeting: Roll out features to specific user groups, like app versions, devices, or regions.
- Cached Configurations: Ensure seamless performance by storing configurations locally, making it perfect for mobile apps.
This gives you capabilities that are not possible with basic tools so you can move forward without fear of disruption.
Personalized Experiences and User Engagement Through Contextual Bandits
Today's users expect personalized experiences that match their preferences, but static A/B testing can't deliver that. Eppo's Contextual Bandits gives you near real-time optimization, learning from the user journey to give them tailored responses.
Examples:
- Targeted push notification delivery times
- Custom carousels for product discovery
- Adaptive onboarding sequences based on usage patterns
Unlike traditional A/B tools, contextual bandits accelerate time-to-value by rapidly testing multiple variations and dynamically matching the right variation to the right audience, especially when there’s no clear winner.
Privacy-First Design
Eppo knows compliance isn't optional – it's required, especially for mobile users. Our architecture gives you peace of mind with:
- No Data Transmission Options: Keep all user-level data local; never send data to your warehouse unless explicitly configured.
- Compliance Guarantees: Built-in features ensure you comply with evolving regulations like GDPR or CCPA.
Eppo doesn't make the trade-offs others do by making compliance integral without sacrificing functionality.
Enterprise Ready Scaling
Whether you have thousands or millions of users, Eppo's infrastructure can handle global experimentation. By avoiding the bottlenecks common in competitor pipelines, Eppo gives enterprise products the reliability they need. Plus, Eppo allows you to add custom push notifications to increase user engagement and retention.
Why Eppo is the Best Mobile Experimentation Platform
Eppo goes beyond being "just another testing platform." Its unique combination of advanced features, technical depth, and practicality makes your mobile experiments work.
- Data Management Edge over Competitors: Unlike Optimizely or Mixpanel, Eppo keeps your experimentation data in your main analytics system. Competitors introduce complexity with separate data streams and sync issues.
- Statistical Innovation: Basic tools like Firebase fail in complex, rigorous scenarios. Eppo's CUPED-backed testing gives you more insight into smaller datasets and more precise recommendations.
- Flexibility for Modern Teams: Other SDKs struggle to work across mobile ecosystems. Eppo works seamlessly in blended environments with reliable integrations across iOS, Android, and hybrid frameworks.
- Built for Enterprise: Whether it's dynamic user bases, fragmented geographies, or data privacy concerns, Eppo tackles each challenge head-on with an architecture built to scale and secure. Eppo also supports mobile A/B testing, so you can optimize user engagement and app functionality with server-side testing and feature flags in experimentation.
The Future of Mobile Experimentation with Eppo
Eppo is pushing the boundaries:
- Flutter SDK's Launch: Experimentation for hybrid framework users, especially in the context of mobile app development.
- More Mobile Metrics: More segmentation options are available via geography, engagement level, and app version.
- Flexible Deployment: Configure mobile SDKs with persistent caches, polling for updates, and other customizable options.
Conclusion
Eppo is more than an experimentation platform—it's a competitive advantage. Whether through dynamic feature flag rollouts, real-time stats, or vast flexibility for managing privacy and data, Eppo is built for today's mobile teams.
Get control of your mobile experimentation today. Eppo is your platform for scale, whether you're fine-tuning a critical UI update or personalizing user journeys at scale.
Optimize smarter with Eppo and request a demo today.
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