The Basics of A/B Testing

Which color, pink or blue, leads more people to click on the sign-in button of a website? Should they need a Gif rather than a picture on the Homepage? How many menu options, five or seven, will engage the customer? Which version works better in leading the customer to the top of the funnel?

You can take a wild guess and plow ahead with the change Or A/B Test!

An A/B test is employed to work out which version of something will perform more effectively within the market.

From startups to large tech firms, companies of all sizes and industries believe A/B testing to form smarter choices. Even the only tests can help steer big decisions.

What is A/B Testing?

A/B testing may be a process of showing two versions of an equivalent website to different segments of website visitors at an equivalent time to work out which one performs well.

Does it simply affirm which does better — this version(A) or that version(B)?

A/B testing, also referred to as split testing or bucket testing, is an experiment where two or more variants of a billboard, marketing email, or website are shown to users randomly, then different statistical analysis methods are wont to determine which variant drives more conversions.

Typically in A/B testing, the variant that provides higher conversions is that the winning one, which variant can assist you to optimize your site for better results.

Why should you A/B test?

In Email campaigns, two variants of 1 campaign are sent to the users. By doing this, the marketing team will know which email was most effective in terms of encouraging opens or clicks.

But they won’t know what exactly led the user to open the mail. Was it the preheader, the topic line, the visuals, or the e-mail content? This can be determined by the A/B test, which is that the most compelling element of the email?

It can help you examine visitor and customer behavior on your site before committing to major decisions on the changes and help you increase your chances of success. In short, A/B testing helps you avoid unnecessary risks by allowing you to focus on your resources for max effect and efficiency.

When to use the A/B Test?

An Online learning platform wants to form changes to their homepage, where the new, more engaging design will increase the number of users that explore their courses. Or maybe they need a more career-focused description on their course overview page, this might encourage more users to enroll and complete their course — A/B testing can confirm this for them.

Anything on an internet page which will affect the behavior of a visitor while browsing on the location is often tested using A/B Testing.

Here may be a list of variations which will be assessed by A/B Testing — the headline, content, page design, images, advertisements, and offers, Social media mentions, Site navigations and user interface, Call to action (CTA) text and button, Payment options, Delivery Options, etc.

In the A/B test, the comparisons should be kept as simple as possible. For example, don’t compare two completely different versions of your website, because you’ll have no idea what factors made a difference. Similarly, if a replacement module or menu is added to the website it can’t be tested by A/B testing as there’s no point of comparison.

How to conduct an A/B test?

An ideal customer funnel for an e-commerce store is going to be as follows:

Homepage -> Category listing/Search product -> View Product Page -> handcart -> Checkout

The store loses its users as they go down the stages of this funnel. An A/B test is then performed to undertake out changes that will hopefully increase the conversion rate from one stage to subsequent.

A/B testing is done in broadly four steps:

• Determine the change and the metric

First, decide what types of information you’ll be able to collect and analyze. We don’t just guess but use proper analytics tools to be sure you have a problem in the first place and to find where exactly it is.

The analytics will often provide insight into where you’ll begin enhancing. For example, we decide to change the Call to action(CTA) button on the Product page from “Buy Now” to “Shop Now” to increase the number of users that add items to the Shopping cart.

A metric is then chosen to live the extent of engagement from the users. In our example, the metric will be a click-through rate for the “Buy Now” button. Click-through rate (CTR) is the number of clicks by unique users divided by the number of views by unique users. You can select as many metrics as you want, but the more the metric you evaluate, the more likely you are to observe significant differences.

• Define your hypothesis

Hypothesis testing in statistics may be a way for you to check the results of an experiment to ascertain if you’ve got meaningful results. The most important and confusing aspects of Hypothesis testing are determining the Null and Alternate Hypothesis.

To put it simply, the statement that’s true before we collect any data is that the null hypothesis. So, in A/B testing the essential null hypothesis is going to be that the remake is not any better, or maybe worse than the old version. For our example, the new Click-through rate(CTR) is a smaller amount than or adequate to the old CTR.

The alternate is that the competing, non-overlapping hypothesis of null. The statement that we are trying to prove always appear in alternate. So, in A/B testing, the alternate hypothesis is that the remake is best than the old version. For our example, the new CTR is bigger than the old CTR.

• Develop the assessment (sample size, impacting factors, run time, participants.

Typically, users are randomly selected and assigned to either an impact group or a Treatment group. We then run the experiment where the control group sees the old version and therefore the treatment group sees the remake.

Each user sees only one design (A or B), albeit they update the interface. This way, an equivalent number of individuals will view each version and you’ll measure which version achieved the lift that you simply would consider meaningful.

The sample size that you decide on will determine how long you might have to wait until you have collected enough data.

• Analyze the A/B test data

Once your experiment is complete, it’s time to research the results. This is where analysts should have more focus. We calculate the metric values of both the control and treatment groups.

Different statistical techniques like Sampling Distribution using bootstrapping, regression, and various other machine learning algorithms are then applied to gauge the metric and show you the difference between how the two versions of your page performed, and whether there’s a statistically significant difference.

Difficulties in A/B testing

There are many factors to think about when designing an A/B test and drawing conclusions that supported its results. Some common ones are:

• Novelty effect and alter aversion when existing users first experience a change.
• Sufficient traffic and conversions to possess significant and repeatable results.
• Consistency among test subjects within the control and treatment group.
• The best metric choice for making the ultimate decision eg. measuring revenue vs. clicks.
• The practical significance of a conversion rate, the cost of launching a new feature vs. the gain from the increase in conversion.
• Long enough run time for the experiment to account for changes in behavior based on time of day/week or seasonal events.

I have written this blog as simple as possible in order that people with no prior knowledge of A/B testing also can get a basic idea about it. There are many analytical tools that are being used for A/B testing by industries like Google Analytics and Google Optimize, HubSpot’s A/B testing kit, etc.

A/B’s aren’t a luxury for marketers with extra time; they’re the blood of growth hacking.

A precise A/B Testing approach can cause huge benefits-improved user engagement, increased conversion rate, simple analysis, and increased sales — It’s a Win-Win!!