A Beginner’s Guide To AB Testing: An Introduction

If you’re new to AB Testing, then this guide is perfect for you! We’re going to discuss the following:  (1) What is AB Testing? (2) Why AB Testing? (3) How to do AB Testing? (4) Why it takes time? (5) Why AB Testing is difficult? (6) Why is AB Testing effective? (7) What to do after AB Testing?

A/B testing, or split testing, is a method of testing a single variable on your website to see how it impacts the user experience. This can be done by the user, or by software that can extrapolate user behavior from multiple metrics including time on the site, pages viewed, bounce rate, etc. The idea behind this is to see how users’ behavior on your site impacts your marketing and ad campaigns.

To achieve a basic understanding of AB testing, it is imperative to have a basic grasp of how Marketing Automation works. This is where the Advanced Business Automation (AB) testing comes into play. AB testing is the first step in marketing automation. It provides a platform that allows marketers to automate the processes of a business. AB testing, also known as A/B testing, allows marketers to test the success of different marketing tactics to determine which one will yield the greatest results for the business.

Regardless of how thorough your research is, not every marketing effort will provide good results.

As a result, A/B testing is an excellent way to determine the finest online advertising and marketing tactics for your company.

Everything from website content to sales emails may be tested with it. This enables you to discover the most effective version of your campaign before wasting your whole money on ineffective marketing materials. While A/B testing may be time demanding, the benefits outweigh the effort commitment.

Overall, well-designed A/B testing may significantly improve the efficacy of your marketing efforts. Simplifying and merging the most successful parts of a campaign may result in a better return on investment, a reduced chance of failure, and, most importantly, a more effective marketing strategy.

 

A/B Testing: What Is It and Why Does It Matter?

A/B testing is a marketing technique that compares the effectiveness of two alternative versions of a website, advertisement, email, popup, or landing page.

For instance, you might compare two distinct popups (to see which one generates the most webinar sign-ups) or two different Google Ads (to see which drives more purchases). This offers you valuable information into where and how to spend your marketing money, as well as the confidence to make potentially hazardous decisions.

I A/B tested my popup on my own site to see what enticed visitors to interact with my brand.

Neil Patel A/B testing example

Over time, we discovered that providing a free website analysis (which is very valuable to our target audience) was the most successful approach to build expertise and demonstrate our worth to visitors. We were able to analyze clicks and conversion rates to determine which header would have the most effect on retaining visitors on our site.

What Is A/B Testing and How Does It Work?

A/B testing involves displaying two distinct consumers two different versions of the same asset (ad, website, pop-up, offer, etc.). The random component is crucial because it offers more accurate data without skewing the findings.

The “control” group, or the version that is now in use, is one of the versions. A single element is changed in the second version. You may alter several components at once, but this makes it more difficult to determine which one caused the impact. Multivariate testing is the term for this (more on this later).

For example, you might display a blue “purchase now” button to half of your website visitors and a red “buy now” button to the other half. You’d compare conversion rates after a set amount of time (usually at least two weeks) to determine which color button resulted in the most sales.

To build and show the various versions, most marketers utilize a tool—we’ll go over A/B testing tools in a later section.

What Is the Importance of A/B Testing?

Accurate A/B testing may make a significant difference in your ROI. You can find out which marketing tactics perform best for your business and product by conducting controlled experiments and collecting empirical data.

It’s risky to run a promotion without first evaluating whether there’s a chance that one variant is functioning two, three, or even four times better than another without risking a lot of money.

Testing, when done regularly, may significantly enhance your outcomes. It’s simpler to make choices and create more successful marketing plans in the long run if you know what works and what doesn’t (and have proof to back it up).

There are a few more advantages to conducting A/B testing on your website and marketing materials on a regular basis:

  • They assist you understand your target audience: You get insight into who your audience is and what they want by seeing what kinds of emails, headlines, and other elements they react to.
  • Increased conversion rates: A/B testing is the most efficient method to boost conversion rates. Knowing what works and what doesn’t may provide you with useful information to help you simplify the conversion process.
  • Keep up with shifting trends: It’s difficult to anticipate how users will react to different types of material, pictures, or other features. Regular testing allows you to remain on top of changing customer behavior.
  • Reduce bounce rates: When visitors enjoy the information on your site, they remain longer. Testing to see what kind of content and marketing materials your visitors like can help you build a better site – one that people will want to remain on.

You’ll reclaim control of your marketing tactics in the end. No more shutting your eyes, hitting the “send” button, and praying for a response from your consumers.

What Is the Best Way to Plan an A/B Test?

When designing an A/B test, the first step is to decide what you want to test. Are you doing an on-site or off-site evaluation?

If you’re doing an on-site test, consider all of your website’s sales-related components before deciding which ones to split test.

You may do the following tests:

  • headlines
  • text with calls to action
  • the placement of the calls to action
  • pop ups
  • pictures in the spotlight
  • copy
  • a form’s total amount of fields

You’re presumably testing an ad or a sales email using off-site tests. Testing ad content to determine which one generates the most conversions may help you concentrate your marketing efforts. It’s simpler to justify investing more money on an ad once you know it’s converting as effectively as it can.

Emails are the same way. Send two copies of your email to your list (choosing which half receives which email at random), and see which one converts better. You may change the format, subject line, pictures utilized, and even the offers in emails.

In the long run, knowing what your audience reacts most to enables you to create more successful emails. Make a list of all the variables once you know what marketing content you want to A/B test. If you want to put your call to action to the test, you might try:

  • the situation
  • the exact wording that was used
  • the color of the button or the surrounding area

A/B testing is a comprehensive procedure, and it’s usual to do several split tests before reaching a final conclusion.

To Get Started, Here’s a Checklist for A/B Testing

Before you begin split testing, be sure you know exactly what you want to achieve. You should already be aware of your baseline result, which is the present state of affairs. You want to compare alternatives A and B, but you also want to make sure that whatever one performs better in the test performs better than your existing results.

Alternatively, you may utilize A as your control (instead of whatever you’re presently using) and then switch to B.

To accommodate for time differences, tests must be performed concurrently. Because you can’t factor in any factors that may have changed between now and then, you can’t test one variant now and the other tomorrow. (For instance, a new Facebook campaign or a new blog article.)

Instead, you should divide the traffic so that everyone may view all of your variants at the same time.

Before you run your first test, go through this A/B testing checklist:

  1. Choose the feature you’d like to try.
  2. Make two copies of the same ad, landing page, software, or other piece of content.
  3. Determine the length of your test. I recommend at least two weeks, but depending on your traffic and business, it may be somewhat longer or significantly shorter.
  4. Select a testing tool to assist you in running your test (more on that later).
  5. Launch!
  6. Take a peek at the outcomes after a few weeks. Which version came out on top?
  7. Rinse and repeat as needed. When A/B testing is done on a regular basis, it is most successful.

The Most Important A/B Testing Elements

In your marketing materials or on your website, you may test almost anything: headlines, CTAs, body text, pictures, navigation bar positioning, and so on. You may try it if you can modify it.

That isn’t to say you should spend months putting everything through its paces. Rather, concentrate on the adjustments that are most likely to have a significant effect on traffic and conversions.

This will most likely appear on your website as:

You may test the headline, pictures, links, CTAs, and segmenting options in one email. You have fewer options to test with a paid ad, particularly a text ad (like a search ad), so you could try the primary headline, offer, picture, or targeting.

It’s critical to try out various offerings. Just make certain that everyone receives the same promotion. For example, if group A receives a free gift and group B receives a discount, you’ll want to ensure that group A and group B always have the same visitors.

You may also try the whole conversion process. You might, for instance, compare newsletter A to landing page A and newsletter B to landing page B. Later on, you may wish to try landing page B with newsletter A, and vice versa.

This may help you figure out what’s working, particularly if your results aren’t consistent or if they’re near. You may also do the following tests.

Examples of Practical A/B Testing to Inspire Your Next Campaign

Let’s look at some instances now that we’ve covered what A/B testing is, what you can test, and how to do it. These should demonstrate the value of A/B testing—and what you may be losing out on if you don’t use it.

On Category Pages, GRENE tried out a horizontal layout.

An A/B test was conducted by GRENE, an online store, to see whether there was a method to make it simpler for customers to locate the goods they were searching for. On mobile devices, the product took up the whole page in the original form (left). Users were having trouble scrolling across the many choices.

The variation (right) minimized white space and enabled customers to quickly navigate through the various choices when viewing several items.

Practical A/B Testing Examples to Inspire Your Next Campaign - GRENE

GRENE observed a 15% rise in product box clicks, a 16 percent increase in conversions, and a 10% increase in visits to the thank you page, which signals customers completed a purchase, after altering the style of category pages.

By replacing a slider with a search bar, WallMonkeys increased conversion rates.

WallMonkeys, a company that sells wall decals, sought to boost conversion rates and enhance the user experience. They were able to observe where most consumers glanced first using CrazyEgg’s heatmap.

With this knowledge, they chose to replace the slider’s main picture (top image) with a search bar (bottom image.)

Practical A/B Testing Examples to Inspire Your Next Campaign - WallMonkeys

They were able to improve their conversion rate by 550 percent by switching out featured pictures and relocating the search bar to the center of the page (based on heatmap data).

Unbounce I put a Tweet up against an email opt-in.

Unbounce was searching for methods to boost opt-ins on its landing pages. Rather of requesting an email address, Unbounce opted to test if consumers would choose to tweet about a product instead.

As a result, they contrasted the following opt-in page, which required an email address:

Practical A/B Testing Examples to Inspire Your Next Campaign - Unbounce

Users may get the same course by sending a tweet in this version.

Practical A/B Testing Examples to Inspire Your Next Campaign - Unbounce Tested a Tweet vs. Email Opt-in

What effect did the modifications have on sign-ups?

Users chose to give up an email address to download the course, according to Unbounce. The email version had a conversion rate that was 24% greater than the tweet version. Although the findings aren’t unexpected (after all, most people are accustomed to handing up their email addresses), testing provided Unbounce the assurance that their landing page was on the correct track.

When it comes to A/B testing, how long does it take?

A/B testing is not a job that can be completed in a single day. You may wish to conduct testing for a few days to a few weeks, depending on the amount of traffic you get. Remember that for the best accurate results, you should only perform one test at a time.

Because you don’t obtain a big enough sample of visitors to be statistically accurate, running a test for an inadequate length of time may bias the findings. However, running a test for too long may distort the findings since there are more factors you can’t control over time.

Make sure you’re aware of everything that may have an impact on your test findings so you can account for statistical abnormalities. When in doubt, repeat the exam.

It’s worth devoting a few weeks to properly doing A/B testing, given the effect it may have on your bottom line. One variable at a time should be tested, and each test should be given enough time to execute.

Is it possible to test several things at once?

This question may be approached in two ways. Let’s say you simply want to run some tests on your headline, but you have three options. In such scenario, conducting a single test and dividing your visitors (or receivers in the case of an email) into three groups instead of two is acceptable, and an A/B test would very certainly still be considered.

It is more efficient to do this than to perform three separate tests (A vs. B, B vs. C, and A vs. C). You may wish to extend the time your test runs for a few of days so you can observe what really works.

A multi-variate test is more difficult to perform since it tests several things at once, such as headlines and calls to action. For multi-variate testing, there are many resources available.

You’ll also need to think about how your systems will handle split tests, as well as having people on hand who can evaluate numerous findings and combine the information into manageable chunks.

Multivariate testing adds a lot more to your plate at once, but it isn’t always a bad thing. If you have the necessary processes in place to manage the increased workload, go ahead; if you prefer a more straightforward method, one A/B test at a time will suffice.

How to Interpret A/B Testing Results

You’ll have a lot of data after your A/B test. What method do you use to determine which version won? Occasionally, the winner will be obvious. Without studying much data, you can tell who won if one version of a landing page leads in 50% more email sign-ups.

Sometimes it’s not so obvious. Here’s how to be sure you’re looking at the correct version:

  1. Make sure you have enough data: Having approximately two weeks of data is the best method to determine which variant will perform in the long run (at least 30 conversions).
  2. Use a significance calculator for A/B testing: Some have an A/B testing significance calculation built in, but you can also use my free tool here. Simply multiply your visitor and conversion statistics to discover how much the change boosted revenue.

Examine measurements beyond the obvious: not all metrics are created equal. I recommend that you look at conversion rates and traffic. Other data, such as average order size, may be more important to certain companies. Changing your “purchase” button to blue, for example, increases conversions, but those consumers spend considerably less each order, so you’ll want to keep exploring.

To Try the Best A/B Testing Tools

You’re not alone if you think A/B testing is difficult. Many marketers and company owners avoid A/B testing because it seems to be too time consuming or they are concerned that they may make a mistake. Hopefully, the advice provided above will give you confidence in your ability to do the task properly. Let’s move on to the tools you may utilize to do A/B testing.

The tool you choose will be determined by the characteristics you want to evaluate. If you wish to test email headlines, for example, your email provider most certainly has this feature (both MailChimp and Constant Contact offer this). This functionality is also available in Facebook advertisements.

There are also a number of low-cost or no-cost technologies that may be used to test website components and determine which version is the most successful.

A/B Testing Significance Calculator for Free

If you’ve ever wondered how a change in design or site text affected your sales, I’ve created a tool to assist you. My calculator lets you input your visitor and conversion statistics, and it calculates whether or not a change boosted your sales and by how much.

Neil Patel's A/B testing significance calculator.

Frequently Asked Questions about A/B Testing

A/B Testing: What Is It and Why Does It Matter?

A/B testing is a marketing technique that compares the effectiveness of two alternative versions of a website, ad, email, popup, or landing page. It’s one of the most efficient methods for boosting conversion rates.

What Is the Best Way to Plan an A/B Test?

Decide what to test, make two versions, set a time limit for the test, choose a tool, and discover what works!

What should I run an A/B test on?

Pop-ups, emails, landing pages, and featured pictures are all examples of parts of a sponsored ad, website, or marketing material.

When it comes to A/B testing, how long does it take?

Most tests should last at least two weeks, although A/B testing should be done on a regular basis.

Is it possible to test several things at once?

In certain instances, yes. It’s preferable to keep two copies of the same item in general.

What tools should I use for A/B testing?

Optimize by Google is a free and effective A/B testing tool. This functionality may be available via your email platform, landing page tools, or website plugins. Consider Optimizely if you’re looking for a premium tool.

Conclusion of A/B Testing

A marketer’s best buddy is A/B testing. It enables you to observe which advertisements generate the most sales, which offers your audience reacts to, and which blog headlines generate the most traffic, for example.

You can get started with a number of tools, like Google Optimize (which is free!) and Optimizely.

If you want to learn how to A/B test with Google Analytics, that’s a good place to start. Remember, A/B testing is a great tool that every marketer should utilize.

Have you experimented with A/B testing? If not, what is preventing you from doing so?

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AB testing is a testing technique that’s quickly growing in popularity and use. An AB test is a form of A/B testing that uses a visual interface instead of the old A/B testing methods such as A/B testing through the actual backend of the site.. Read more about a/b test plan template and let us know what you think.

Frequently Asked Questions

How do I start my AB test?

You need to sign up for an AB test. Once you have done that, you will be able to start your AB test.

How do you explain AB testing?

AB testing is a way to test the effect of one variable on another. For example, you might want to know what would happen if you increased your ad spend by $10. You could then run an AB test and see what happens.

What test is used for AB testing?

The A/B testing is a statistical method used to determine which version of a website or app is better.

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