A/B testing for Successful Experiments and Data-based Decision-making [Part 1]

Modern business is forced to act in a highly competitive environment with constantly changing customer preferences. It cannot but directly affect conversion and the final choice of buyers of specific solutions. That’s why brands have to apply practical marketing tools that will help with making well-grounded decisions. Research of user sessions on website commercial pages can be a starting point. For this, you can use such tools as Hotjar.

Data analysis and interpretation can be followed by the formation of a hypothesis and consideration of various CRO rules. But how to choose the most effective option, which hypothesis is the most accurate? A/B testing, used to enhance user experience and conversion, will help you confirm or reject all assumptions.

In this article, I want to tell you about A/B testing — what it is, how it is implemented, why companies need to focus on data, and what errors to avoid.

What is A/B testing?

A/B testing is the comparison of two versions of the web page, email, ad placement, subscription or feedback form, text or visual content, navigation. They are identical in all features but for one. An original page is called the control one [version A], and a page with minor changes is the variant [version B].

I will overview A/B testing in the context of web products, but its logic and sequence can be applied in various cases.

It’s better to test a small number of variables at a time, as it can be difficult to determine which option had the greatest impact on success or failure due to the simultaneous testing of several elements.

What’s the point in comparing various website versions? Marketers make decisions based on actual data received from real user interactions with a website, email, etc. A/B testing helps with identifying the elements that have the greatest effect on user behavior and bring desired results. Be it the highest conversion rate, number of clicks, visit frequency, number of subscriptions, or other actions taken by visitors.

A/B testing stages

This experiment is a structured process that guarantees reliable results. The main stages include:

• Data collecting and interpretation. It is the starting point, as a hypothesis is formed based on the information collected. One can rely on analytics data, as well as heat maps, user sessions, support services, etc.
• Formulation of the hypothesis. You need to outline a specific goal or problem you want to solve to increase page conversion rate. Clearly formulate a hypothesis and decide how you will check the result. For example, a brighter button will draw visitors’ attention and force them to click on it. The bigger number of clicks suggests the changes made are successful.
• Creating versions. You need to have a control page and a version for checking the hypothesis, with changes you want to test. Be it another CTA button color, a new title, or another link placement, make sure the changes have a measurable impact. It is better not to use several changes in one version. It results in confusion and can make it difficult to establish which one was the most effective.
• Separating incoming traffic. You need to equally distribute traffic between the control and experimental groups of users. This step guarantees that both groups are representative for your target audience, thus reducing bias in results.
• Starting testing. You need at least 7–10 days to identify the most effective version and get statistically significant test results.
• Determining the winner. Based on the results received, you can conclude which version works best and make changes on the entire platform. If the result is not convincing, revise your hypothesis and conduct another testing, e.g., on a bigger number of pages.

If the result of the A/B testing is unequivocal [page A is better than page B, or page B is better than page A], the most effective version is implemented on the website.

However, if the results received are equal, you can choose one of the versions at your own discretion or keep the data for future testing.

Errors to avoid with A/B testing

To ensure accurate results of the A/B testing, you need to avoid the following mistakes:

• Early conclusions. It takes time to achieve sufficient statistical significance. Avoid making decisions based on premature or inconclusive data.
• Insufficient data. No hypothesis will work if insufficient information is used to formulate it or if it is not based on real data.
• Carry out one-time A/B testing. You need to conduct testing both after failures and successes. It is not clear which hypothesis will work and which will not. Every new experiment must rely on previous data. It is done in order to optimize resources. The more data you have for your new testing, the more efficient result you will get.
• Ignore external factors. The results may be affected by seasonality, holidays, competitors’ advertising campaigns, etc. Therefore, it is advisable to conduct testing either in one period of time or to take into account the maximum number of market factors that can affect the efficiency and accuracy of the data.

Why companies need A/B testing

Today, the business is ruled by data, so you cannot rely on assumptions or guesses. There are several reasons for A/B testing to become an indispensable component of a successful marketing strategy:

• Enhanced user experience. There is a specific purpose for users to visit your website: look through it, get acquainted with the product or service, buy. Everything on the resource must encourage them to stay there as long as possible. That’s why you need intuitive navigation, clear calls to action, high-quality content, etc., all for the best possible user experience.
• Conversion optimization. Even small changes may greatly enhance key metrics, allow for more effective use of the existing traffic without additional expenses, and contribute to an increased number of orders.
• Well-grounded decision-making. Instead of relying on intuition or tendencies, A/B testing offers marketers the possibility to make decisions based on empirical data.
• Competitive advantage. Companies that already use A/B testing stay ahead of the competition and quickly react to changes in customer preferences due to constant improvement of all points of user interaction.

Since A/B testing allows you to make changes gradually, it is almost invisible to the regular target audience.

Conclusion

A/B testing is a powerful tool for improving web resources. Its benefits for every business include more effective advertising campaigns, better user experience, increased profitability, and data-driven decision-making that reduces the risk of implementing unsuccessful changes.

In the following materials, I will show more detail on 9 practical tools for A/B testing. Maybe you will find a working solution that will fit your needs!

And if A/B testing has already become an indispensable part of your working space, share in the comments which elements you test and what solutions you use!