About A/B testing

Digital marketing is constantly evolving, and staying competitive requires continuous improvement. One of the most effective methods for improving digital marketing strategies is through well-designed A/B tests. They can uncover valuable insights, which can help you optimize your campaigns and drive better results.

Preparing for an A/B test

A solid understanding of the key components of an A/B test go a long way to ensuring that your experiments are valid, reliable and useful for improving your digital marketing strategies. The success of an experiment hinges on careful planning and execution. The following explains the key components that contribute to a successful experimentation process.

Clear objective

Every successful experiment begins with a clear and well-defined objective. This is the guiding star that directs all your efforts throughout the experimentation process. In digital marketing, objectives can vary widely. For instance, you may aim to increase the click-through rate (CTR) of an email campaign by 10%, reduce the bounce rate on a landing page by 15%, or improve the conversion rate of a paid ad by 20%. The objective should be specific, measurable, and aligned with your broader business goals. It’s not just about identifying what you want to achieve, but also about making sure that the objective is realistic and attainable within the scope of your resources and timeline.

Well-formulated hypothesis

After defining the objective, the next step is to formulate a hypothesis. Your hypothesis is essentially an educated guess or prediction about the outcome of your experiment. It should be directly related to your objective and grounded in data or previous experience. This hypothesis should be clear and specific, measurable, testable and focused on a particular aspect of your digital marketing campaign. For example, instead of saying, “We want to improve our email click-through rates,” you should say, “We hypothesize that changing the subject line of our email will improve our click-through rates by 10%.”

Key metrics

Key metrics are the quantitative measures you will use to evaluate the success of your experiment. These metrics should be directly tied to your objective and hypothesis. For instance, if your objective is to increase the CTR of an email campaign, the primary key metric would be the CTR itself. However, you might also track secondary metrics such as open rates, unsubscribe rates, and conversion rates to gain a fuller picture of the experiment’s impact. Choosing the right metrics is crucial because they will guide your analysis and determine whether your hypothesis was correct. It’s important to establish these metrics before you start the experiment so that you have clear criteria for success.

The four types of A/B tests in Airship support different metrics.

Experiment design

Designing your experiment is a crucial step that involves planning how you will test your hypothesis. This includes several important components: choosing the A/B test type, determining the sample size, and establishing a control group, if available for your chosen A/B test type. Each of these components plays a vital role in the reliability and validity of your experiment.

A/B testing compares two or more versions of a marketing asset, such as an email, landing page, or ad, to see which one performs better. For example, you might test two different subject lines in an email campaign to see which one generates a higher open rate. Multivariate testing allows you to test multiple elements simultaneously. For instance, you might test different combinations of headlines, images, and CTAs on a landing page to determine which combination leads to the highest conversion rate, or compare personalized content to non-personalized. The design of your experiment should align with your goals and the complexity of the variables you are testing.

• Sample size is a critical factor in experimental design, representing the number of participants or observations. It’s closely tied to your business’s tolerance for error and decision-making agility. Selecting a sample size that’s large enough to yield reliable results, but not so large that it slows down the process, is key. A small sample may produce unreliable findings, while a larger one increases the chance of detecting a true effect. You can use statistical calculators to determine the ideal sample size, taking into account the relevant statistical factors like the expected effect size, desired confidence level, and statistical power.

• A control group is essential for isolating the effect of the changes you are testing. In an experiment, the control group is the group that does not receive the experimental treatment or change. Instead, they are exposed to the original version of the marketing asset or the ‘business as usual’ condition. For example, if you are testing a new landing page design, the control group would see the original landing page, while the test group would see the new design. The purpose of the control group is to provide a baseline against which you can compare the results of the test group. By comparing the outcomes between the test and control groups, you can determine whether the changes made in the test group had a significant impact.

  A holdout group from a project-wide Holdout ExperimentMeasures the effects of excluding a group of audience members from all messages or messages with specific Campaign Categories. You can compare the performance of the two audience groups in reports for selected goal events. can serve as a readily available control group that can be used to establish your initial baseline.

Implementing A/B tests, outcomes, and compliance

Once your experiment design is in place, it’s time to implement. The key steps for implementation are sample randomization, data collection, and continuous monitoring. Proper implementation is critical to ensuring that your experiment runs smoothly and yields accurate results.

• Randomization is a critical factor in a good experiment. The test and control groups should be selected randomly to ensure that any differences observed are due to the treatment and not other factors helping to eliminate bias and increase the reliability of your results. For example, if you’re testing a new email campaign, you want to randomly select a portion of your email list to receive the new campaign while the remaining portion receives the old campaign.

• Accurate data collection is vital for meaningful analysis and informed decision-making. During the experiment, you’ll need to track a variety of metrics that are relevant to your objective and hypothesis.

  Continuous monitoring is the process of tracking the progress of your experiment in real time and making adjustments as needed. Continuous monitoring is important because it allows you to identify and respond to any issues that arise during the experiment. For instance, if you notice a significant drop in engagement during an ad campaign, you might need to investigate and address the issue before the experiment concludes. Additionally, continuous monitoring allows you to gather initial insights and make data-driven decisions during the experiment. This can be especially useful in longer experiments, where ongoing monitoring can help you optimize the experiment’s performance and ensure that it stays on track.

Airship takes care of these for you, generating randomized experiment groups and collecting data for your chosen metrics. You can monitor the data continuously from the start of your experiment. While you’re not responsible for setting up the experiment infrastructure, understanding these concepts will better equip you to design and run successful experiments.

Rigorous analysis

As results come in, it’s essential to evaluate whether the data provides clear insights before making decisions. Raw data can provide useful insights, and it’s important to assess whether the observed differences are meaningful or simply due to chance. If statistical significance is provided with your experiment results, use it to understand whether the differences are likely due to the changes you tested rather than random variation. If not, consider a few key factors:

• Is the difference between test groups large enough to be meaningful?
• Was the sample size big enough to ensure reliable results?
• Have the results remained consistent over time, or are they fluctuating unpredictably?

If you’re unsure, online calculators can help assess statistical significance by comparing sample sizes and conversion rates. Taking the time to review results thoughtfully ensures you make informed, data-driven decisions that lead to real improvements.

Applying experiment findings

Once you’ve reviewed your results, the next step is deciding how to act on them. If your experiment produced a clear winner, you may choose to roll out that experience to a broader audience. If results were inconclusive, consider whether you need more data, a longer test duration, or a refined hypothesis. Sometimes, experiments reveal unexpected insights that lead to new questions. Use these learnings to shape future tests and continuously refine your approach. Experimentation isn’t just about finding immediate wins.It’s about building a culture of learning and iteration that drives long-term success.

Documentation and learning

Documentation and learning are crucial steps in the experimentation process, as they ensure that the insights gained from your experiment are captured and shared across your organization. After analyzing the results, it’s important to document your process and findings in a detailed report. This report should include your objective, hypothesis, experiment design, key metrics, results, and any conclusions or recommendations. For instance, if your experiment showed that personalized email subject lines consistently improve open rates, you should document this finding and share it with your team so that it can inform future email campaigns. Additionally, documenting any challenges or unexpected results can help you learn from the experience and improve future experiments. The ultimate goal of documentation is to create a knowledge base that helps your organization continuously improve its marketing strategies and decision-making processes.

Ethical and legal compliance

It’s essential to ensure that your experiments comply with ethical standards and legal regulations. In digital marketing, this might involve adhering to data privacy laws like the General Data Protection Regulation (GDPR)  and the California Consumer Privacy Act (CCPA) , ensuring that you have the necessary consent to use customer data and being transparent about how data will be used. For example, if you’re conducting an experiment that involves collecting personal data from users, you need to ensure that you have obtained their consent and that you are storing and using the data in accordance with legal requirements. Ethical considerations might also include ensuring that your experiments do not mislead or harm participants. For instance, if you’re testing different pricing strategies, it’s important to be transparent about pricing changes and to avoid practices that could be considered deceptive or unfair.

By prioritizing ethical and legal compliance, you can protect your organization’s reputation and build trust with your customers.