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List of 25 Best A/B Testing Tools To Improve Conversions

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A/B testing aka split testing or bucket testing is a type of methodology that compares two different variables and determines which one is better. Usually, in this category of experiments, the user is given a demonstration of various versions of any web page or app. After that, it implements statistical tools to analyze the better-performing variant, which is likely to deliver more conversions.

During an AB Test, you compare a variable against existing standards and then implement changes after asking focused questions. In order to understand the impact of the change, you can collect data and then use analytical tools to make result-oriented decisions. A/B testing is a very effective tool since now, you do not need to rely on guesswork anymore for website optimization. You can make informed decisions, based on a real date, thereby ensuring that your changes yield positive results.

The first step of AB testing involves you choosing the targeted web page or app screen and then creating another version of the same, after introducing a slight or huge change. You can alter the headline, a CTA, or redesign the whole layout. Now, half of your customers are shown the original page (which is called control) and the other half is shown the modified page (termed variation).  

The next step is to compare both the performances against one another by collecting the figures in an analytics dashboard and then analyzing the numbers via a statistical engine. You will now know if your change had a positive, negative, or zero impact.

As we already mentioned, in AB testing, we compare two different versions of a webpage, email template, or any other marketing tool by changing just one element. Once you test both versions and then determine which one is the better performer, you can then target to increase your conversion rates. 

In the digital world, making your website or launching your email campaign is just your first step. Once you create the webpage, you need to decide if it actually performs whether it promotes sales. With the help of AB testing, you come to know what are the words, taglines, phrases, clips/videos, testimonials, texts and designs that perform the best. Sometimes, even a slight change can impact the end results of your organization.

There are three similar terms which seem to confuse a lot of people, namely A/B testing, split testing and multivariate testing. Although A/B testing and split testing are more or less similar, multivariate testing comes with many differences. Below we have described the major differences between A/B testing and multivariate testing:

A multivariate test requires you to pinpoint certain sections of a website that needs immediate attention. Then, you have to make different iterations of these specific areas, as opposed to AB testing where you need to create variations of the entire page. As the name suggests, in multivariate testing, you can create several variations for multiple sections. In contrast, in AB testing or split testing, you can check only one variable at a time.

In multivariate testing, the required traffic is much less as compared to that of AB testing. Also, the former is ideal for testing totally distinct ideas for the optimization of conversion rates. On the other hand, AB testing is used to optimize and refine existing pages while not investing too much on the redesign aspects.

In statistical terms, that data gathered from AB testing arrives from champions, challengers, and variations. Each of these elements offers you vital information regarding the amount of traffic on your website.

The champion is the web page, email, Facebook Ad, or any marketing asset that, as per historical trends, has performed well in the past or is expected to yield good results in the future. You can test this champion against a challenger, which is just another iteration of the champion with a slight change.

Once your A/B test is complete and you get the numbers, you discover a new champion or decide that the original version was better. You then create fresh variations and test them against the champion. In most cases, one A/B test is not sufficient to arrive at a final decision. You should continuously implement this method so that you can optimize your marketing and advertising strategies on a regular basis.

Almost all of us think that we know everything about the current business scenario. We assume that we are aware of the latest trends and implementing them would deliver the best results. However, what we forget is that most of these decisions are based on hunches and guesswork. Many of our steps are not data-driven. But with the use of A/B testing, one can take measures based on actual behavioral data and real-time information about customer bases.

As already described above, A/B testing compares two versions of any marketing asset by showing one version to 50% of the audience and the other version to the remaining 50%. Sounds pretty simple, right? But this is not all. 

Several digital marketers use AB testing only as a means to make slight, small, and insignificant changes. However, when you utilize this tool to its maximum potential, you can make a substantial impact on your marketing strategies. With the help of A/B testing, you can gather valuable insights through which you can arrive at result-oriented decisions and suggest better value propositions by testing them on real web users -- by measuring actual interactions with your website. 

When you have enough quantifiable data, you can curate the exact message that resonates with your customers. A/B testing uses data gathered from a huge number of visitors in order to decide which types of marketing campaigns will generate the maximum response. Yet another element that makes A/B testing such a powerful tool is that the findings gathered from this experiment can be used to improve other areas of your marketing.

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Frequently Asked Questions (FAQs)

  • A. There is a myth that A/B testing negatively impacts SEO rankings because it takes the content as duplicate and does not look upon it. This is definitely a false myth. But, if you are still concerned, you can add a 'no-index' tag on your variation page.
  • A. You must test the following parameters for A/B testing

    • Call to Action (CTA)
    • Headline
    • Images
    • Copy length
  • A. A/B testing can be used for emails, PPC campaigns, and CTAs other than web pages.
  • A. Out of a host of A/B testing tools available on the market, here are some top tools that you can use:

    • Google Analytics
    • Optimizely
    • Evergage
    • Leadformly
    • Unbounce

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Shrushti ChawaraBy Shrushti Chawara | Last Updated: October 20, 2020

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