By A/B testing, you check which one of two or more different versions would be more effective and preferred by users. Users see several variations of a web page, advertising campaign, core messages or visuals. Version A is the control version, and version B competes with it. Throughout the test, half of the visitors see version A, and the other half – version B. Depending on the results, you can choose how to proceed with the redesign, with the product launch, with an internet algorithm or your ad campaign.
Why use it:
- Solves visitors’ pain points – The user enters your site for a specific reason but may encounter difficulties. For example, not finding the “buy now” button. Testing helps you see where the obstruction is and helps you deal with it.
- Get better ROI – A / B testing can achieve this. Because sometimes even the slightest change in the website or ad messages in the campaign lead to a significant performance improvement. By releasing different variations, you can decide what to emphasize.
- Reduces the bounce rate – With testing, you can try different variations of a particular element of the site or campaign and choose the most effective version. That leads to a more valuable customer experience, increased time on the site, strengthens the emotional bond of the customer, or improved action response to the CATs (call-to-action).
- Allows you to make changes at low risk – With A/B testing you can focus on maximum performance with minimal modifications. By conducting an A/B test, you analyze the reactions of customers to both variations and know upfront where they’re heading.
- You can achieve statistically significant improvements – There are no guesses and feelings. Outcomes are based on precise data. Which option performs better you can understand by applying your KPIs, like time on the website, cart abandonment rate, frequency of clicks, reactions towards messages or visuals, etc.
- Promotes effective redesign – The decision to implement one version or another in A/B testing is driven by data. Even after the new version goes live, continue with the tests to make sure you provide the most attractive variant to your users.
Industries where you can easily apply websites A/B testing:
# Media and publishing – Among the main goals of this industry is reaching more readers, creating a larger number of subscriptions, making visitors spent more time on the website, generating viral content. To achieve them, you can try two options of the share button, for example, or two different options to register by email, different ways to highlight special offers. If you are a Netflix customer, you can assess the good quality of the streaming. But do you know what stands behind this success? A/B testing enables the provision of a personalized user experience. You get the main page, the display of the titles, the thumbnails, as well as part of the text made especially for you, thanks to the method of different variations.
# E-commerce – In this industry, A/B testing is a tool to optimize the paths to order completion. You can reduce the abandonment rate of the cart, try how and where the product price is displayed, the option for free delivery, manage the display of comments and feedback. Amazon is a leader in this optimization type. Thanks to A/B testing, the company introduced the “1-Click Ordering” back in the 90s. Consumers conduct purchases without ever having to use the shopping cart. Any change in the platform is first tested on the audience and only after that is implemented.
# B2B industry – A/B testing assists with the selection of the specific fields in the potential customers form, the free trial period of registration, the messages on the home page. POSist is a leading restaurant management platform that has implemented A/B testing to increase website traffic. The team creates two versions of the home page, as well as two versions of the “Contact Us” page. Based on the results and consumer reactions, the company chooses the more successful and efficient versions and improves performance.
A/B testing can reduce many of the risks associated with a particular optimization. It is the check of whether it is worth taking a step in a specific direction. As well as making the choice of which way to go.
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