Test Group vs Control Group: Understanding the Difference

Alex Danchenko

If you are serious about getting the most out of your product promotion strategy, predicting user behavior is one of the biggest challenges. The point is, mobile owners interact with apps in a very personal way, making it difficult to directly correlate your efforts with their actions. 

This unpredictability is often due to a myriad of external factors, from the specific context in which the app is being used to rapidly changing user preferences. In such a complex environment, A/B testing, where test and control groups are compared, is a guiding light for your team. It helps you accurately measure the impact of marketing strategies and provides data-driven insights on how to retain & grow your active customer base.

According to Statista, 70% of top-ranked companies use A/B testing on a regular basis to optimize their mobile apps for better performance and user engagement. In addition, marketing professionals rated the usefulness of A/B testing as a conversion rate optimization (CRO) method at 4.3 out of 5.

Test Group

 Understanding the difference between control and test groups gives you the tools to properly measure, analyze, and optimize your marketing efforts, resulting in more engaging campaigns, better user experiences, and improved return on investment.

In this article, we will delve into the definitions of both groups, explore their differences, and discuss their importance in app promotion. We will also provide insights on how to properly use this powerful solution and identify scenarios where a control group may NOT be necessary.

What is a Test Group

When we talk about a test or experimental group in the context of A/B testing, we mean a pre-selected segment of users who are exposed to a specific variation or event that you want to evaluate. 

This treatment could be anything from a new app feature, a redesigned UI, targeted push notifications, or a special promotional offer. The goal is to observe how the group of users responds to the changes compared to those who continue to experience the app in its original form.

Key characteristics of a test group

  • Representativeness. The test group should be representative of your broader user base to ensure that the results are generalizable. This means it should include users with similar demographics, behaviors, and preferences to those in the control group.
  • Randomization. To minimize bias, users are typically randomly assigned to the test group. This approach ensures that any differences in results are due to the experimental intervention and not to pre-existing differences between the groups.
  • Sample size. The size of the test group should be large enough to detect statistically significant differences in results. This often requires a careful calculation based on expected effect sizes and variability in the data.

By comparing the behavior of the test group to the control group, you can determine whether the new changes result in higher conversion rates, increased user satisfaction, or other desired outcomes.

For example, if you are experimenting with a new loyalty program, the test group will show whether it is effective at keeping users coming back to the app compared to the control group.

So what is a test group? It is an element of the scientific approach that allows your promo team to experiment with in-app changes in a controlled manner and make data-driven decisions that improve the value of your offering to the user.

What is a Control Group

When trying to come up with a proper control group definition, we should start with the point that a control group is a fundamental component of A/B testing. It provides the necessary benchmark against which the performance of the test group is measured.

What is an example of a control group? Imagine a carefully selected segment (or even a sub-segment) of users that is isolated from the experimental changes introduced to the rest of the audience. The primary goal is to provide a baseline for comparison, ensuring that any observed changes in the experiment are due to the new intervention rather than external factors.

Key characteristics of a control group

  • Representativeness. Like its counterpart, the control group should be representative of your entire user base. This ensures that comparisons between the two groups are valid and that the results are applicable to your broader audience.
  • Consistency. Users in the control group continue to experience the app as usual, without any exposure to the new changes being tested. This consistency is critical to establishing a reliable baseline.
  • Randomization. To minimize bias, users are typically randomly assigned to the control group. This approach ensures that any differences in results are due to the experimental intervention and not to pre-existing differences between the groups.

Sample Size for a Control Group

Now here's a tough one. Sample size has a direct impact on the reliability and validity of test results. Here are some factors to consider when determining the appropriate sample size for a control group.

  1. Expected effect size, which is the size of the difference you expect to see between the groups. Smaller expected differences require larger sample sizes to detect statistically significant results, while larger differences can be detected with smaller sample sizes.
  2. Statistical power as the probability of detecting a true effect if it exists. A common power level used in marketing experiments is 95%, meaning there is an 95% chance of detecting a true effect. Higher levels of power require larger sample sizes.
  3. The significance level. A lower significance level reduces the risk of false positives, but requires a larger sample size.
  4. Data variability. The standard deviation of your data also affects the sample size. More variability requires a larger sample to accurately detect differences.

So what is a control group? It is a reference point for evaluating the impact of the new app feature or campaign. It helps you measure the effectiveness of your marketing strategy. For example, if you introduce a new onboarding tutorial to the test group, the performance of the control group will show you what would have happened without the new tutorial, allowing you to quantify the actual benefit.

Use the power of test and control groups to refine your mobile app marketing strategy and stay ahead of market changes.

Test Group vs. Control Group: What's the Difference

Typically, test and control groups work together to give you a clear picture of the improvement’s effectiveness. Without this contrast, it would be difficult to isolate the impact of the change from other variables that may influence user behavior, such as seasonal trends or external events.

What is the purpose of a control group in this tandem?

  • Isolating effects. The control group helps isolate the effects of the intervention by providing a baseline that accounts for external factors affecting both groups equally.
  • Validating results. The comparison ensures that any observed improvements in the test group are not due to random chance, but are statistically significant and directly related to the intervention.
Test group & control group differences

Case in point: A mobile app introduces a new feature to improve user engagement by sending personalized push notifications.

Users in the test group will receive the personalized notifications.

Users in the control group continue to receive the standard, non-personalized notifications.

By comparing metrics between the two groups, such as number of app sessions, session duration, and conversion rates, marketers can determine the effectiveness of personalized notifications.

The Importance of Control Group in Marketing

Indeed, why is a control group important in an experiment? The answer is quite simple: it is the foundation upon which effective marketing strategies are built. Let's take a look at a few points that support this statement.

Ensuring accurate measurement by isolating variables. When you conduct experiments, many variables can influence user behavior, such as seasonality, market trends, or external events. The control group helps isolate the impact of the intervention by ensuring that both groups are exposed to the same external conditions.

  • Improving engagement by optimizing the user experience. Understanding what works and what doesn’t through controlled experiments helps you continuously improve the user experience. This is especially essential for mobile apps, where the ability to capture and retain customer attention is critical for long-term success.
  • Boosting conversions with targeted communications. Insights from A/B testing with control groups can guide personalized marketing efforts. Knowing which changes resonate better with different user segments allows you to tailor your campaigns to increase their effectiveness.
  • Validating the effectiveness of interventions by quantifying their impact. Metrics such as Incremental Profit and Incremental Lift can be calculated to see the exact impact of the new strategy or feature on user engagement and conversion.

How to Use Test & Control Groups Properly

Think about it: only 14% of professional marketing tools use A/B testing and similar solutions before launching a campaign.

Yet, using a test or experimental group in a “smart combo” with a control group gives you reliable, actionable insights into how to increase the value of your app for each individual user.

Control Groups in Multivariate and A/B Testing 

Multivariate and A/B testing is not possible without a control benchmark. It must be present throughout the entire experiment, and it doesn't matter what the goals of testing are: whether you want to change the size and color of a button, introduce new in-app features, or add a new communication channel.

Best Practices For Using Both Groups Properly

Start With Clear Objectives

Clearly define what you want to achieve with your test. Whether it's improving user engagement, or increasing in-app purchases/subscriptions, having a specific goal will guide the design of your experiment. For example, 85% of businesses are prioritizing call to action triggers for A/B testing.

Select a Representative Sample

Make sure that both the test and control groups are randomly selected from your user base. This randomness helps eliminate selection bias and ensures that the groups are representative of your entire contact base.

Introduce The Change

Apply the new feature, or campaign to the test group. Ensure that only this group is exposed to the change, while the control one continues with the standard experience.

Track Key Metrics

Collect data on key performance indicators (KPIs) for both groups. Common metrics include conversion rates, and session duration.

Compare Results

Analyze the data collected from both groups. Compare the results and determine whether the differences are significant.

Interpret Results & Take Action

Based on the analysis, draw conclusions about the effectiveness of the changes you made. Determine whether they are consistent with your original goals. If the intervention is successful, consider rolling it out to a larger user base. If not, analyze the data to understand why and refine your approach.

Treat A/B Testing As An Ongoing Process

Continually test new hypotheses, measure their impact, and refine your strategies based on the results. Statistics show that 71% of successful companies run two or more tests per month.

When are Control Groups not Necessary

Now that we know what is a control group used for, it's time to consider when you can do without one. 

Here are some scenarios where control groups may not be necessary or practical that you may face when developing a winning app promotion strategy.

  1. If the intervention is a universal change that affects all users, such as a mandatory app update or a backend improvement that cannot be segmented, a control group is not feasible. In such cases, measuring the overall before-and-after impact can provide insights.
  2. When dealing with a very small user base, splitting users into test and control groups can result in insufficient sample sizes, leading to unreliable results. In such scenarios, it may be better to perform a full rollout and monitor overall performance.
  3. For high-stakes changes where the risk of user churn is significant, exposing only a portion of the user base to an improved feature may not be justifiable. In such cases, it may be more prudent to roll out the improvement to all users to mitigate the risk.
  4. Depending on the change, A/B testing can take anywhere from an hour to two months. When rapid iteration is critical, waiting for control group results can slow the development process. In such cases, continuous monitoring of overall metrics may be a more practical approach.
  5. If there is extensive historical data on user behavior and performance metrics, it may sometimes be possible to make reliable comparisons without a contemporaneous control group. Using historical performance as a benchmark can help evaluate the impact of changes made.

Wrapping It Up

This article has explored the critical roles that test and control groups play in A/B testing, a method widely used by top-ranked companies to optimize app performance and user engagement.

By defining what test and control groups are, we laid the basis for understanding their differences and respective functions in the experimental environment. The test group is exposed to new interventions, such as app features or marketing campaigns. The control group, in turn, provides a baseline by continuing the existing experience. This comparison allows you to isolate the effects of the intervention and make data-driven decisions.

The article also provided a step-by-step guide to the proper use of test and control groups, emphasizing the need for clear objectives, representative samples, careful tracking of key metrics, and continuous iteration.

Finally, we identified scenarios where control groups may not be necessary, such as universal changes, small user bases, high-risk situations, rapid iteration requirements, and reliance on extensive historical data.

For more insights and expert guidance on optimizing your app marketing strategies, address to Reteno. In our niche, we are one of the best Iterative alternatives, offering comprehensive solutions to effectively engage, retain and grow your user base.



October 1, 2022

Alex Danchenko


October 14, 2022

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