Data-driven decision-making and A/B testing have become increasingly essential in today’s hyper-competitive environment. A/B experimentation is one of the most critical tools in a growth team’s arsenal when it comes to making data-informed decisions. In this article, we will explore how to do A/B testing in marketing and product management and its importance for businesses looking to drive growth.
Drawing insights from our experience, we will provide an overview of the A/B experimentation process, including setting objectives, defining metrics, formulating hypotheses, managing interactions between experiments as well as introducing two practical A/B testing examples.
We will also discuss best practices for running A/B experiments, such as isolating a single change, setting primary metrics, supporting metrics, and monitoring health metrics. By following these best practices, you can make informed decisions that benefit both their users and their bottom line.
Table of Contents
What is A/B Testing
A/B testing, also known as split testing, is a technique used by businesses to compare two versions of a product or website to determine which one performs better. By randomly dividing their audience into two groups, companies can test various elements, such as copy, layout, and design, to see which version produces the desired outcome, such as higher engagement, more conversions, or lower bounce rates.
A/B testing is a crucial tool for businesses looking to drive growth and improve their products. It allows businesses to test and validate their ideas before deploying them to a wider audience, reducing risk, and increasing the likelihood of success.
How to get started
To get started with A/B experimentation, you need to define your objectives and metrics. It’s crucial to isolate a single change in an experiment to accurately assess its impact.
Running multiple changes simultaneously can lead to inaccurate results and make it difficult to determine which change is responsible for the outcome.
Identifying Metrics
When setting objectives, you should identify the primary metric that you want to improve. This metric should be the most important metric that you want to improve while supporting metrics provide additional context and help validate the hypothesis.
Check out our guide on the most important metrics for growth.
Formulating Hypothesis
Formulating a good hypothesis is critical to A/B experimentation. A good hypothesis should be based on evidence, identifying the change, the users it will impact, the expected impact, and how it will benefit the business. A good hypothesis helps protect you from your own biases and makes data-driven decision-making more accessible.
An example of a hypothesis for an A/B testing experiment is: “Adding a live chat feature to the website will result in a higher conversion rate compared to not having a live chat feature.”
An example of a null hypothesis could be: “Adding a live chat feature to the website will not result in a higher conversion rate compared to not having a live chat feature.”
The null hypothesis is simply a statement that there is no difference between the groups in the experiment. If the p-value is low, it means that the result is unlikely to have occurred by chance alone, and thus the null hypothesis is rejected.
Designing the Experiment
Now that you’ve identified the problem and the hypothesis test, it’s time to design the experiment. Consider the randomisation unit or treatment group which is the individual or group of individuals that are subjected to the experiment.
It is important to define that before conducting an experiment so that it can be ensured that the results are representative of the entire population.
Besides the user group, determine also the sample size and duration of the experiment.
Running the Experiment
Once the experiment has been designed, it’s time to run it. Utilise instrumentation to collect the data and track the results.
You should run the experiment for at least one or two full weeks to take into account any seasonality or weekly behaviour patterns. The use of non-inferiority tests for feature rollouts is also discussed. These tests are used when you want to introduce a new feature or fix a bug without necessarily expecting to see an improvement in the primary metric.
Make sure to not peek at the p-value too early. Peeking at it too early during an experiment can lead to bias in the results. This is because the p-value is calculated based on the data, and if the data is examined before the experiment is complete it can give an inaccurate representation of the results. Additionally, if the p-value is examined before the experiment is complete it can lead to false conclusions being drawn.
The p-value is a measure of the probability of obtaining a result at least as extreme as the observed result if the null hypothesis is true. It is commonly used to assess the statistical significance of an experiment.
Health Metrics
Health metrics are used to monitor the overall health of a product during experiments. They are the product’s general performance indicators, such as page load times, app crashes, and errors. They should be monitored in all experiments to detect unexpected changes.
It’s essential to monitor health metrics during the experimentation process to ensure that the changes you make to your product do not impact its overall health.
A/B Testing Examples
Here are two A/B testing examples you could do:
- Changing the font and colour of buttons: A hypothesis behind an experiment like this would be that changing the font and colour of buttons would improve navigation and lead to more purchases on the website. A primary metric would be more purchases, and supporting metrics could include a lower bounce rate and increased engagement. A health metric could be page load time.
- Introducing a payment receipt feature: A hypothesis could be that introducing a payment receipt feature would reduce the number of customer service tickets received. A primary metric could be customer service tickets, and supporting metrics could include a lower bounce rate and increased engagement. A health metric could be app crashes.
It’s important to isolate a single change and test it against a control group so you are able to measure the impact of each change accurately.
A/B Testing Tools
Optimizely is a major A/B testing platform that enables businesses to quickly and easily test different versions of their website or app to see what works best in terms of increasing conversions. They also support multivariate tests.
VWO is another very popular A/B testing platform that helps users optimise their website and increase conversions with its powerful features like heatmaps and visitor recordings.
AB Tasty is an A/B testing platform that allows you to quickly and easily create and run experiments to test different variations of your website and optimise for better results.
A/B Testing Common Pitfalls
We’d like to share with you the 5 most common mistakes that we’ve seen that can ruin any experiment so you won’t.
- Not Having a Hypothesis: When running an A/B test it is important to first define a hypothesis, as this will give a basis for the experiment and help to guide decision-making
- Not Having Enough Traffic: A/B tests require a significant amount of traffic in order to be effective. If the sample size is too small, the results may not be accurate
- Not Allowing Enough Time: A/B tests should be allowed to run for at least a few weeks in order to ensure that the results are accurate and reliable.
- Not Tracking Engagement: It is important to track engagement metrics such as click-through rates and time on page in order to understand how users are interacting with the different variations
- Not Analysing the Results: Once an A/B test has been completed it is important to analyse the results in order to draw the right conclusions and next steps
To avoid these mistakes, it is important to plan the experiment carefully, ensure that there is sufficient traffic, allow enough time for the experiment to run, track engagement metrics, and analyze the results.
Conclusion
In conclusion, A/B experimentation is a powerful tool to help you make data-driven decisions. By setting objectives, defining metrics, and formulating a good hypothesis, you can test and validate your ideas before deploying them.
Remember to isolate a single change in an experiment, set a primary metric and supporting metrics, and monitor health metrics.
By following these best practices, you can make informed decisions that benefit both your users and your business.