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Starting conversion rate optimization with website A/B testing

Measure the value of your marketing campaigns by experimenting, comparing and creating actionable data with A/B testing to start conversion rate optimization. Nabler is a pure play digital analytics consulting company which provides a marade of conversion rate optimization services that also includes A/B testing services.

In this article we discuss what is website A/B testing and Multivariate testing and the difference between the two. The best A/B testing tools available in the market and how does all this plays out for enhancing the conversion rate for Conversion Rate 0ptimization.

What is A/B testing?

Website A/B testing is the practice of showing two variants of an online or an offline campaign such as the landing page, ad text, a headline, call-to-action or any other element of a marketing campaign for your website. And then analyzing the responses of the different segments of website visitors simultaneously and comparing which variation drives more conversions. The one that gives higher conversions wins! Do remember that different websites will have different ways to measure. For some it maybe transformation, while for others it may be lead generation or data science around visitors.


So why do you even need to conduct an A/B testing?

When you exit from the “cart” page of an online ordering portal without placing the actual order, it leads the website or product developer to think what led you to do so. Was it loss of interest? Did a time lapse occur? Was the product costlier than what you had budgeted for? Or was it just the whole ordering process? Unless a test is conducted, you will never be able to gather information about and analyze customer behavior to assess the conversion rate on your website. If you’re still looking for more information on as to why your website needs this test, you can find it here.

Let’s look at some of benefits of how A/B testing assists you towards Conversion Rate Optimization

You can:

  • Solve visitor pain points: Use data gathered through visitor behaviors, analysis tools such as heatmaps Google analytics, and surveys to solve your visitors’ pain points. This stands true for all businesses, be it e-commerce, travel, SaaS, education, or media, and publishing.
  • Get more conversion by investing less: The cost of acquiring paid traffic can be huge. A/B testing lets you make the most out of your existing traffic and helps you increase the conversion rate without having to spend on acquiring new traffic. Additionally, the ROI from A/B testing can be significant with minor changes resulting in a substantial increase in the website conversion rate.
  • Reduce bounce rate: With A/B testing, you can test multiple variations of an element of your website till you find the best possible version. Because of this, your content quality improves, making visitors spend more time on your site, reduces bounce rates and accelerates conversion rate due to conversion rate optimization.
  • Make low-risk modifications: Make minor, incremental changes to your website with A/B testing instead of getting the entire site redesigned. This can reduce the risk of jeopardizing your current conversion rate. A/B testing lets you target your resources for maximum output with minimal modifications, resulting in increased ROI.
  • Data-driven: Because A/B testing is completely data driven with no room for guesswork, gut feelings, or instincts, you can quickly determine a “winner” and a “loser” based on metrics like time spent on the page, number of demo requests, cart abandonment rate, click-through rate, and so on.
  • Redesigning your website: Redesigning can range from a minor CTA text or color tweak to a complete revamping of the site. The decision to implement one version or the other should always be backed by data-driven A/B testing.
  • Changing the product pricing: Perform the test when you plan to remove or update your product prices. You do not know if your visitors are going to react positively to the change or not. This testing is one way to ascertain which side the scale will tilt.
  • Feature change: If you decide to change something, never change a feature or service on your website without testing, especially if the changes affect customer data or purchase funnel. Changes without testing may or may not pay off but making changes after testing can make the outcome certain.

The A/B testing tools that help you achieve the conversion rate you want

Google Analytics -If data collection and testing solutions are your priority, look no further than Google Analytics. Even it’s free to use option has adequate information for you to derive conclusions. The only drawback here is you should know how to navigate this A/B testing tool and unless you are clear about it, the data search and derivations from it appear vague.

Optimizely– What if you had a testing tool that could deliver personalized messages to different audience segments and suggest content recommendations. Easy to build, easy to navigate, the Optimizely tool has it all. This helps in identifying audience trends in simple ways and has a dedicated mobile testing platform. Considering how cellphones have taken over, this makes it even more appealing.

VWO– This A/B testing tool is close to what Optimizely does but has that one advantage which can be considered an X factor- it allows users to give their feedback directly, this can be considered a game changer considering this is directly coming up from the people you are testing this on and trying to get a conclusion.

Adobe Target– Adobe gives the opportunity to get automated personalization, mobile app optimization and recommendations based on your customers’ behavior and data. Now add in customer optimization and this is one of the best tools around to increase Conversion Rate Optimization.

An important point to note here is that A/B testing may give you a winner but to finally influence your customers, it is important for you to go a little deeper and analyze the results accurately to uncover the real truth about your customer behavior.

Another question to think about here is does one set of variables given to two sets of people help you arrive at a conclusion? Wouldn’t an option of handling multiple variants help? This is where Multivariate Testing steps in.

What is Multivariate testing

Multivariate testing uses the same core mechanism as A/B testing, but compares a higher number of variables, and reveals more information about how these variables interact with one another.


In this type of testing, a Web page is treated as a combination of elements (including headlines, images, buttons and text) that affect the conversion rate. Essentially, you decompose a Web page into distinct units and create variations of those units. For example, if your page is composed of a headline, an image and accompanying text, then you would create variations for each of them. Now how long does anyone spend on each of these subunits will determine what is driving traffic on the website and what is not.

In a nutshell, the objective of Multivariate testing is to determine which combination of these versions achieves the highest conversion rate.

A/B testing vs Multivariate testing

A/B testing– Simple in concept and design, it is a powerful and widely used testing method. Keeping the number of tracked variables small, means these tests can deliver reliable data very quickly, as they don’t require a large amount of traffic to run. This is especially helpful if your site has a small number of daily visitors. Splitting traffic into more than three or four segments would make it hard to finish a test.

Limitations– A/B testing is best used to measure the impact of two to four variables on interactions with the page. Tests with more variables take longer to run, and in itself, the testing will not reveal any information about the interaction between variables on a single page.

Limitations– Too much or too little traffic means too many changing elements which could result in more time taken for completion.

Multivariate testing– Although the methodology is the same as in A/B testing, this testing compares a higher number of variables, and reveals more information about how these variables interact with one another. Once a site has received enough traffic to run the test, the data from each variation is compared to find not only the most successful design but also to potentially reveal which elements have the most significant positive or negative impact on a visitor’s interaction.

The Bottom Line

User behavior or a trend analysis tends to offer real insights into web analytics. This analysis will hold good if tested and what better than testing it with the best options available.

User behavior or a trend analysis tends to offer real insights into web analytics. This analysis will hold good if tested and what better than testing it with the best options available.

But don’t let the differences between A/B testing and Multivariate testing make you think of them as opposites. Instead, think of them as two powerful optimization methods that complement one another. Pick either or use them both together to help you get the most out of your site and boost your website conversion rate. Because in the end, it is Conversion Rate Optimization that adds value to your enterprise. Be it Google Tag Manager or any other Tag management system to manage your website analytics or your mobile marketing, a Tag Management Solution with its tag manager is necessary to prevent the chaos around digital marketing, increase your website performance, save costs, reduce tags- and most importantly deliver consistent, personalized and real time experiences to customers across devices.

Drive better results by understanding customer data