What is A/B Testing? A Practical Guide with Steps and Examples

On average, the Apple App Store sees around 1000 apps released daily. With over 3.5 million apps available on the Android market, the competition is even higher. Such high competition means your app must have a selling point that gives it an edge over others.

So, how do we achieve conversions in such an environment? One successful method is consistent A/B testing. 

In this blog, we’ll understand A/B tests, their importance, types, the processes behind them, and their applications across industries. 

So, let's now move to addressing the elephant in the room.

What are A/B Tests?

Also known as split testing, A/B tests help you compare two versions of an element to see which one performs better. The main goal of a/b tests is to evaluate which version leads to more engagements and conversions.

A/B tests are like science experiments. You form a hypothesis, run a test, and assess the results. Even small tweaks, like changing a button color, can make a world of difference.

This leads you to our next section. Let’s understand why A/B tests are essential to your app.

Why Should You A/B Test?

A/B tests provide a range of advantages to your app, depending on your services. Here are a few reasons why A/B tests are a must for your app:

1. Enhances User Experience by Evaluating Various Design Elements

Testing your app’s design elements, including colors, layouts, and buttons, can significantly impact user engagement and satisfaction. By testing out a variety of elements, you can identify the design that resonates with a particular segment of your app's user base.

2. Increases Conversion Rates by Optimizing Design and Content

Want more people to convert your CTA? A/B tests help you find that sweet combination of words and colors to boost conversions. 

Whether you are improving your content or creating your call-to-actions (CTAs), these tests help ensure that your final design is ready to deliver results.

3. Provides Insight Into User Behavior, Helping in Optimizing Campaigns

A/B tests give you a window into your users' needs. By evaluating versions that perform better, you can learn user needs and design updates accordingly.

Knowing your user needs also implies predicting them. With the evolution of AI, you can predict future user preferences more effectively than ever. Talk about reading minds!

Types of A/B Testing

Now that you have an idea of how these tests can be game-changers, let’s look at the various types of A/B testing. 

The mobile game developer Zynga used standard A/B tests to optimize in-game ads.  

They were able to pinpoint the results by testing a single variable, one ad after every game and one after every three games. 

They concluded that lesser ads increase retention without sacrificing revenue. 

Therefore, it is essential to understand the types of tests to choose the most appropriate one for your app. Here are some commonly used types:

1. Standard A/B Testing: Compares One Variant Against Control

A simple yet effective testing method, standard A/B testing involves comparing a control, which is the original element, with its variant. This testing mode is usually applied when a slight change in your app, such as a headline or button color, is made.

Standard testing has the advantage of helping you identify the impact of a single modification. Therefore, this method is ideal for understanding straightforward experiments.

2. A/B/N Testing: Involves More Than Two Variations

Take the case when you might have to test the efficacy of three different variations of CTA buttons. The A/B/N testing approach can be useful in this case, as it helps you compare the variations with the original simultaneously.

3. Multivariate Testing: Simultaneously Tests Multiple Elements

In multivariate testing, you compare variations of multiple elements like headlines and buttons. 

Multivariate testing assesses and identifies which combinations of elements work best. It helps optimize complex app interfaces with multiple variables.

By using the different types of A/B tests, you can refine user experiences, thus improving conversion rates.

Process of Conducting A/B Testing

Conducting tests in a structured manner promises you the most effective results. 

Streaming services like Netflix conduct regular A/B tests to optimize their user experience. One key area they constantly work on is personalizing thumbnail artwork in the app. This has helped the app prompt users to spend more time on it.

Here are the steps involved in creating the change you are looking for:

1. Create a Hypothesis Based on User Research and Business Insights

Developing a clear hypothesis based on user research before launching any A/B tests. The research should be based on user behaviors, pain points, and interactions with your app. 

Let's take the case of a dataset that shows a drop-off during checkouts in your app. Here, you can strategize and hypothesize to focus on simplifying the payment options to drive revenue.

2. Define the Specific Changes: Focus on a Single Design Element

Once you have identified the pain point and hypothesized its cause, ideate the changes you want. By focusing on a single variable, you can pinpoint the exact impact of a change.  

For example, if you aim to increase conversions, you can change the tone of your CTA copy or the color of your button on your page. Taking an element specifically provides insights into how much improvement it can bring to the hypothesis.

3. Choose the Outcome Metrics: Define Primary and Guardrail Metrics for Evaluation

Identifying the right metrics to measure your A/B test success can impact your results. Ensure you have chosen both your primary and secondary metrics.

Your primary metrics, like user stickiness, should directly relate to your hypothesis. Your guardrail metrics ensure your users don't leave your app soon. 

4. Determine the Time Frame for the Test: Use Tools Like Sample-Size Calculators for Precision

Ensure that you have a set timeframe to plan your tests. Time frames are critical in this testing process to give significant results. A shorter period can lead to skewed conclusions, while a test that’s too long can waste your resources,

Tools like sample-size calculators can help you get the timing right. Based on traffic, the time frame can be several days to weeks. Thus, you can find meaningful data that can provide you with insights for a long time. 

Moreover, adopting these steps ensures a successful testing process, leading to app growth. 

Now, let’s look at a few applications of A/B testing across industries.

Applications of A/B Testing Across Industries

A/B tests have a wide range of applications across industries. They use data to make data-driven decisions to optimize user experience and growth.

Taking a few real-life examples, here's a look at its use across e-commerce, health tech, and fintech apps:

E-commerce: Enhances Purchase Funnels and Optimizes Pricing Strategies

E-commerce applications use A/B tests to refine purchase funnels and pricing decisions. 

Nobody likes to lose sales in the last possible second, right?

A leader in the industry, Amazon uses A/B tests for its product page layouts to find a much simpler checkout flow. This helps the brand cut cart abandonment rates and attain better conversion. 

Fintech: Improves Feature Engagement

Another leading industry that adopts the A/B testing method is fintech.

Apps like Jar improved their onboarding flows through constant testing of mobile adoption elements such as nudges, which helped them increase their monetization by 80%.

In addition, fintech apps can adopt this method to optimize touchpoints for KYC competitions, adopt different features, payment processes, and interface designs, and also improve the entire user experience. 

Healthtech: Optimizing User Onboarding Flow

Taking an example from the health tech industry, the MyFitnessPal app did A/B testing to improve its user onboarding flow, which helped the app improve user activation massively.

Health tech apps can also test various interfaces to ensure user retention and engagement with their augmented content and designs.

Wrapping Up

A/B testing provides concrete evidence of what works best for your app and what doesn't. Making decisions based on the test data eliminates the chances of failure through guesswork.

However, despite their advantages, these tests should be rooted in efficient planning, delivery, and evaluation to obtain unbiased results. 

Moreover, it is important to ensure that these tests aren’t stopped prematurely (before they reach statistical significance), as early results can be misleading. Using only accurate data for your tests is essential, as incomplete data can skew your analysis. 

Want to unlock your app’s full potential? At Plotline, we offer no-code A/B testing for your in-app engagement elements like nudges, stories, and scratch cards across use cases and industries to help you drive long-term success.

Book a demo with us to unlock your app’s full potential.