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A/B testing, also known as split testing, refers to a randomized experimentation process wherein two or more versions of a variable (web page, page element, etc.) are shown to different segments of website visitors at the same time to determine which version leaves the maximum impact and drives business metrics.
If B2B businesses today are unhappy with all the unqualified leads they get per month, eCommerce stores, on the other hand, are struggling with a high cart abandonment rate. Meanwhile, media and publishing houses are also dealing with low viewer engagement. These core conversion metrics are affected by some common problems like leaks in the conversion funnel, drop-offs on the payment page, etc.
As most experienced optimizers have come to realize, the cost of acquiring quality traffic on your website is huge. A/B testing lets you make the most out of your existing traffic and helps you increase conversions without having to spend additional dollars on acquiring new traffic. A/B testing can give you high ROI as sometimes, even the minutest of changes on your website can result in a significant increase in overall business conversions.
A/B testing lets you target your resources for maximum output with minimal modifications, resulting in an increased ROI. An example of that could be product description changes. You can perform an A/B test when you plan to remove or update your product descriptions. You do not know how your visitors are going to react to the change. By running an A/B test, you can analyze their reaction and ascertain which side the weighing scale may tilt.
Redesigning can range from a minor CTA text or color tweak to particular web pages to completely revamping the website. The decision to implement one version or the other should always be data-driven when A/B testing. Do not quit testing with the design being finalized. As the new version goes live, test other web page elements to ensure that the most engaging version is served to the visitors.
A/B testing subject lines can increase your chances of getting people to click. Try questions versus statements, test power words against one another, and consider using subject lines with and without emojis.
Because everything seems so essential, businesses sometimes struggle with finding only the most essential elements to keep on their website. With A/B testing, this problem can be solved once and for all.
For example, as an eCommerce store, your product page is extremely important from a conversion perspective. One thing for sure is that with technological progress in its current stage, customers like to see everything in high definition before buying it. Therefore, your product page must be in its most optimized form in terms of design and layout.
Other important pages whose design needs to be on point are pages like the home page and landing page. Use A/B testing to discover the most optimized version of these critical pages. Test as many ideas as you can, such as adding plenty of white space and high-definition images, featuring product videos instead of images, and testing out different layouts.
Forms are mediums through which prospective customers get in touch with you. They become even more important if they are part of your purchase funnel. Just as no two websites are the same, no two forms addressing different audiences are the same. While a small comprehensive form may work for some businesses, long forms might do wonders for their lead quality for other businesses.
Social proof may take the form of recommendations and reviews from experts in particular fields, from celebrities and customers themselves, or can come as testimonials, media mentions, awards and badges, certificates, and so on. The presence of these proofs validates the claims made by your website. A/B testing can help you determine whether or not adding social proof is a good idea. If it is a good idea, what kinds of social proof should you add, and how many should you add? You can test different types of social proofs, their layouts, and placements to understand which works best in your favor.
Some website visitors prefer reading long-form content pieces that extensively cover even the minutest of details. Meanwhile, many others just like to skim through the page and deep dive only into the topics that are most relevant to them. In which category does your target audience fall?
Understand that content depth impacts SEO and many other business metrics such as the conversion rate, page time spent, and bounce rate. A/B testing enables you to find the ideal balance between the two.
Many people in the testing arena confuse Split URL testing with A/B testing. However, the two are fundamentally very different. Split URL testing refers to an experimentation process wherein an entirely new version of an existing web page URL is tested to analyze which one performs better.
When you run a Split URL test, your website traffic is split between the control (original web page URL) and variations (new web page URL), and each of their respective conversion rates is measured to decide the winner.
When conducted properly, multivariate testing can help eliminate the need to run multiple and sequential A/B tests on a web page with similar goals. Running concurrent tests with a greater number of variations helps you save time, money, and effort and come to a conclusion in the shortest possible time.
There are two ways to conduct a multipage test. One, you can either take all the pages of your sales funnel and create new versions of each, which makes your challenger the sales funnel, and you then test it against the control. This is called Funnel Multipage testing.
Two, you can test how the addition or removal of recurring element(s), such as security badges, testimonials, etc., can impact conversions across an entire funnel. This is called Classic or Conventional Multipage testing.
Ideally, there are two types of statistical approaches used by A/B/n experimenters across the globe: Frequentist and Bayesian. Each of these approaches has its own pros and cons. However, we, at VWO, use, support, and promote the Bayesian approach.
As compared to the Frequentist approach, Bayesian statistics is a theory-based approach that deals with the Bayesian interpretation of probability, where probability is expressed as a degree of belief in an event. In other words, the more you know about an event, the better and faster you can predict the end outcomes. Rather than being a fixed value, probability under Bayesian statistics can change as new information is gathered. This belief may be based on past information such as the results of previous tests or other information about the event.
Unlike the frequentist approach, the Bayesian approach provides actionable results almost 50% faster while focusing on statistical significance. At any given point, provided you have enough data at hand, the Bayesian approach tells you the probability of variation A having a lower conversion rate than variation B or the control. It does not have a defined time limit attached to it, nor does it require you to have an in-depth knowledge of statistics.
In the simplest of terms, the Bayesian approach is akin to how we approach things in everyday life. For instance, you misplaced your mobile phone in your house. As a frequentist, you would only use a GPS tracker to track it and only check the area the tracker is pointing to. While as a Bayesian, you will not only use a GPS tracker but also check all the places in the house you earlier found your misplaced phone. In the former, the event is considered a fixed value, while in the latter, all past and future knowledge are utilized to locate the phone.
A structured A/B testing program can make marketing efforts more profitable by pinpointing the most crucial problem areas that need optimization. A/B testing is now moving away from being a standalone activity that is conducted once in a blue moon to a more structured and continuous activity, which should always be done through a well-defined CRO process. Broadly, it includes the following steps:
Before building an A/B testing plan, one needs to conduct thorough research on how the website is currently performing. You will have to collect data on everything related to how many users are coming onto the site, which pages drive the most traffic, the various conversion goals of different pages, etc. The A/B testing tools used here can include quantitative website analytics tools such as Google Analytics, Omniture, Mixpanel, etc., which can help you figure out your most visited pages, pages with most time spent, or pages with the highest bounce rate. For example, you may want to start by shortlisting pages that have the highest revenue potential or the highest daily traffic. Following this, you may want to dive deeper into the qualitative aspects of this traffic.
Heatmap tools are the leading technology used to determine where users are spending the most time, their scrolling behavior, etc. This can help you identify problem areas on your website. Another popular tool used to do more insightful research is website user surveys. Surveys can act as a direct conduit between your website team and the end user and often highlight issues that may be missed in aggregate data.
Further, qualitative insights can be derived from session recording tools that collect data on visitor behavior, which helps in identifying gaps in the user journey. In fact, session recording tools combined with form analysis surveys can uncover insights on why users may not be filling your form. It may be due to some fields that ask for personal information or users, maybe abandoning your forms for too long.
Get closer to your business goals by logging research observations and creating data-backed hypotheses aimed at increasing conversions. Without these, your test campaign is like a directionless compass. The qualitative and quantitative research tools can only help you with gathering visitor behavior data. It is now your responsibility to analyze and make sense of that data. The best way to utilize every bit of data collated is to analyze it, make keen observations on them, and then draw websites and user insights to formulate data-backed hypotheses. Once you have a hypothesis ready, test it against various parameters such as how much confidence you have of it winning, its impact on macro goals, and how easy it is to set up, and so on.
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