What is A/B Testing? AB Testing Process You Need To Know

When you design landing pages, write email marketing or design CTA – Call to Action buttons, you often have to use your intuition to predict what will stimulate users to click and optimize conversion rates. – conversion rate optimization.

However, just marketing based on “intuition” does not always bring accurate results!

Instead of making guesses or assumptions, there is a way that can help you know exactly what your users think and behavior – run A/B tests.

In this article, I will specifically explain to you:

  • What is A/B Testing?
  • What are the benefits of using A/B Testing?
  • The process of conducting A/B Testing in SEO you should know
  • How to do A/B Testing
  • 4 common A/B Testing mistakes

Let me explain it to you in detail.

What is A/B Testing?

A/B testing (also known as split testing or bucket testing) is a method to compare two versions of a web page or application to find out which version works better.

What is AB Testing

An A/B test is basically an experiment in which two or more variations of a page are shown to the user at random. And statistical analyzes are used to determine which variation works better for a given conversion goal.

Using AB testing to directly compare a variation with your current experience allows you to question changes to a website or app. And then you can collect data on the effectiveness of those changes.

Testing will take the guesswork out of website optimization and enable data-informed decisions that will move business conversations from “we think” to “we know”.

By measuring changes in the data, you can ensure that every change has a positive outcome.

Why Do We Use A/B Testing?

A/B testing allows individuals, teams, and businesses to make deliberate changes to the user experience while gathering data for results.

Why do we use AB testing

This allows them to formulate hypotheses and better understand why certain factors in their experience influence user behavior.

In other words, they can be proven wrong through A/B testing which is the best experience for a given goal.

More than just answering one-time questions or resolving disagreements, A/B testing can be used consistently to continuously improve individual experiences and goals. For example: conversion rate over time.

For instance, a B2B technology company might want to improve the quality and quantity of leads from campaign websites.

To that end, a team will try A/B Testing changes to the headlines, visuals, opt-in frames (forms), CTAs – calls to action, and the overall layout of the website. Page.

Testing a change at a certain point in time will help them determine exactly if those changes affect user browsing behavior or if there are other changes.

Over time, building on that, they were able to combine the effects of many successful changes from previous tests to demonstrate an improvement in the new experience over the old one.

With this method of reporting changes in UX – User Experience, it allows for an optimized experience towards the desired results. And from there, important steps can be taken in the marketing strategy.

By testing a variety of ads, marketers can learn which version attracts more clicks.

Or by testing the next landing page, they can figure out which layout will best convert users into customers.

The total investment for a marketing campaign can really be reduced if each element in each step works as efficiently as possible to acquire new customers.

Product developers and designers also apply A/B testing to demonstrate that:

New features or changes can also affect the user experience.

All new products, user interactions, methods and in-product experiences can be optimized with AB Testing’s solution. As long as the goals are clearly defined and you set a clear hypothesis.

Test Link

Test link is a web-based test management system that facilitates software quality assurance. It is developed and maintained by Teamtest. The platform provides support for test cases, test suites, test plans, test projects and user management, as well as various reports and statistics.

A/B Testing Process

There are many different ways of implementing a/b testing but what is the most effective way to implement the A/B testing process? Here is a sample A/B Testing process you can use to start testing:

  • Collect data: Your analytics will often provide a sharp, clear view of where you can start optimizing. It helps you get started with high-traffic areas of your website or app. As this will allow you to collect data faster.
  • Finding pages with low conversion rates or high drop-offs can be improved.
  • Define a goal: Your conversion goal is a metric that you’re using to determine if a variation is more successful than the original.
  • A goal can be anything from a button click or a link to a sales website.
  • Hypothesis Generation: Once you have identified your goals, you can start generating AB Testing ideas and hypotheses about why you think they will be better than the current version.
  • Once you have a list of ideas, prioritize them according to expected impact and difficulty of execution.
  • Create Variations: Use your A/B Testing software (such as Optimizely). This helps make desired changes to an element of your website or mobile app experience.
  • This could be as simple as:
  • Change the color of a CTA button
  • Swap the order of elements on the page
  • Hide navigation elements or something completely customizable.
  • Many of the top A/B testing tools have visual editors that will make these changes easier. Make sure your test can work as expected.
  • Run test: Start your test and wait for user access!

In this step, visitors to your website or app will be randomly assigned to control or change your experience.

Their interactions with each experience are measured, calculated, and compared to determine how each works.

Analyze the results: Once your experiment is complete, it’s time to analyze the results.

Your A/B Testing software will output the data from the test and show you the difference between how the two versions of your website are performing. And is there a statistically significant difference?

If your variation was successful then congratulations! To see if you can apply the lessons learned from the experiment on other pages of the website, and keep repeating the tests to improve the results.

If your test produces negative results or does not, don’t worry. Think of that experiment as a learning experience and generate new hypotheses that you can test.

Regardless of the results of your experiment, use your experience to apply to other tests in the future. And iterate endlessly in optimizing your app or website.

A/B Testing SEO

Google allows and encourages A/B Testing and has stated that:

Performing A/B testing or multivariate does not pose any problems or risks for website search ranking.

However, it can be detrimental to your search rankings if you misuse the A/B testing tool for purposes like cloaking.

Google has provided some concrete examples to make sure this doesn’t happen:

  • No Cloaking – Cloaking is how your website shows content on search engines that is different from what normal visitors would see. Cloaking can cause your website to drop from the top or even be removed from the search results. To prevent “cloaking”, you should not abuse visitor segments to show different content to Googlebot based on user or agent IP address.
  • Use the rel=”canonical” tag – If you’re testing separately with multiple URLs, it’s a good idea to use the rel=”canonical” attribute to drive variations back to the original version of the page. Doing so will prevent Googlebot from getting confused by multiple versions of the same page.
  • Use 302 redirects instead of 301s – If you are trying to redirect the original URL to the variant URL, use a 302 (temporary) redirect versus a 301 (permanent) redirect.
  • This lets search engines like Google know that the redirect is temporary. And they should keep the original URL indexed instead of the checked URL.
  • Run tests only when needed – Testing for longer than necessary, especially when you’re using a variation of your page for a large percentage of users. This can be seen as an attempt to fool the search engines. Google recommends that you update your site and remove all test variations of your site as soon as the test is over. And especially, avoid running unnecessarily long tests.

How to perform an A/B Testing?

Before performing A/B Testing

#1 Pick a variation to test

As you optimize your websites and email marketing, you may find there are several variations you want to test.
But to gauge how effective a change is, you’ll want to take an “independent variation” and measure its performance.

Let’s say, after testing there is some change from the user, how do you know which factor caused that change? I mean you won’t be able to be sure which variant will be responsible for AB Testing’s changes.

You can test more than one variation for a website or email; just make sure you’re not testing them all at once.

Look at the different elements of your marketing resources and their alternatives for design, wording, and layout. Also you can check the elements:

  • Email subject line
  • Sender’s name
  • Different ways to personalize your email.

Remember that even simple changes, like changing the image in an email or the wording on a CTA, can make a big difference.

In fact, these types of changes are often easier to identify than those that are larger than that.

Note: Sometimes it makes more sense to test multiple variations than just one. The process is called Multivariate Testing.

#2 Define your goals

While you’ll be measuring multiple metrics for each test run, choose a key metric to focus on right before you test. In fact, do this before you even set up the second variant. This is your “dependant variable”.

Think about where you want to place this variation at the end of the test. You can state a key hypothesis and test the results against this prediction.

If you wait until the end to decide whether:

  • What metrics are important to you?
  • What is your goal?
  • Could the changes you suggest affect user behavior?

Then you probably won’t be able to experiment in the most efficient way.

#3 Create ‘control’ and ‘challenge’.

You now have your independent variant, dependent variable, and your desired outcome. Use this information to set the unchanged version of whatever you’re testing as “control”.

If you are testing a website, this is the site that has not been changed since it already exists. If you are experimenting with a landing page, then this will be the design copy of the landing page and the copy you normally use.

From there, build a variation or “challenge” for your website, landing page, or email marketing that you test against that control.

#4 Split your test sample evenly and randomly

For experiments where you have more control – like with email, you need to test with 2 or more equal audiences for the final results.

How you do this will vary depending on the A/B Testing tool you use.

#5 Determine your sample size (if applicable)

How you define your prototype size will also vary depending on your A/B testing tool, as well as the type of A/B test you’re using.

If you’re A/B testing with email, you’ll probably want to send A/B testing to a smaller portion of your list for statistical results.

In the end, you will choose a winning part and send that successful variant to the rest of the list.

You can see the illustration below:

If you’re testing something that doesn’t have a finite audience, like a web page, how long you maintain your test will directly affect your sample size.

You’ll need to let your experiment run long enough to get a significant number of views, otherwise it’s hard to tell if there’s a statistically significant difference between the two variants.

#6 Decide the importance of your results

Where you’ve chosen your target metric, think about how meaningful your results need to be to justify choosing one variation over another.

Statistical significance is an extremely important part of A/B testing and it is often misunderstood. The higher your confidence level percentage, the more certain you will be about your results.

In most cases, you’ll want a confidence level of at least 95% – 98% preferably. Especially if it’s a test that takes a long time to set up.

However, sometimes you should use a lower confidence rate if you don’t need the rigorous testing process.

#7 Make sure you only test at a time on any given campaign

Test more than one thing for a campaign – even if it’s not on the exact same assets. This can complicate your A/B testing results.

During A/B Testing

#8 Use A/B Testing Tool

To perform A/B testing on your website or in email, you will need to use an A/B testing tool.

Options like Google Analytics Tests will let you do A/B Tests on up to 10 full versions of a website and compare its performance using a random sample of users.

#9 Test both variants at the same time

Time plays an important role in the results of your online marketing strategy, whether it’s the time of day, day of the week, or month of the year.

If you ran Instance A for a month and Instance B a month later, how would you know if the performance change was due to a different design or a different month?

When you run A/B testing, you will need to run two variations at the same time, otherwise you may end up with duplicated your results.

The only exception here is if you are testing the time yourself, for example finding the optimal time to send an email.

This is a good thing to check out because depending on what your business offers and who your subscribers are, the optimal time for a subscriber’s engagement process can vary considerably by industry. and target market.

#10 Give enough time for A/B Testing to generate useful data

Again, you’ll want to make sure your test runs long enough to get a significant sample size. On the other hand, it is difficult to know if there is a statistically significant difference between the two variants.

How long is enough?

Depending on your company and how you implement A/B testing, getting statistically significant results can happen in hours…or days…or weeks.

A big part of how long it takes to get meaningful results is the amount of traffic you get – so if your business isn’t getting a lot of traffic to your website, it’s going to take a long time. More time to run A/B Testing.

In theory, you shouldn’t limit the time it takes to collect results.

#11 Ask for feedback from real users

A/B testing has a lot to do with quantitative data… But that won’t necessarily help you understand why people take certain actions towards others.

While you are running A/B Testing, why not collect qualitative feedback from real users? One of the best ways to ask people for their opinions is through a survey or poll.

You can add a survey on your website asking visitors why they didn’t click a certain CTA, or a survey on your thank you page asking why visitors clicked a button. or fill out the form.

After A/B Testing

#12 Focus on your target metrics

Again, although you will have a lot of metrics, focus on the target metrics as you do your analysis.

#13 Measure the importance of your results with an A/B Testing calculator

Now that you’ve determined which variation performs best, it’s time to determine if the results are statistically significant.

In other words, is it sufficient to account for a change?

To learn more, you will need to conduct a statistical significance test. You can do it manually… or you can simply feed the results from your test into the A/B Testing calculator.

For each variation you’ve tested, you’ll be prompted to enter the total number of attempts, like emails sent or impressions seen.

Then enter the number of goals completed – you’ll generally see clicks, but these can also be other types of conversions.

The calculator will give the level of confidence your data makes for the selected variant. Then evaluate that number against your chosen value to determine statistical significance.

#14 Work on your results

If one variation is statistically better than the other, you’ve got a winner. This variant completed your test by disabling the missing variant in the A/B Testing tool.

If no variation is statistically better, you’ve just learned that the variant you tested has no effect on the results and you’ll have to mark the test as null.

In this case, stick with the original variant – or run another test. You can use the failure data to help find a new iteration of your new test.

While A/B tests help you influence results on a case-by-case basis, you can also apply the lessons learned from each test and apply it to future endeavors.

#15 Plan your next A/B Testing

Your recently completed A/B test may have helped you discover a new way to make content marketing more effective – but don’t stop there.

There will always be a way to be more optimized. You can even try to conduct A/B testing on another feature of the same website or email you just tested.

Benefits of A/B Testing

A/B testing has a multitude of benefits for a marketing team, depending on what you decide to test.

Above all, these trials are valuable to a business because they have low costs but high rewards. Let’s say you use a content creator with a salary of $390/year.

This person publishes 5 articles per week for the company blog, which means a total of 260 articles per year.

If on average each company blog post generates 10 leads, you could say that it costs just over $22 to generate 10 leads for the business ($390 salary 260 posts = $15 / posts).

That’s a big part of the change.

Now, if you asked this content creator to spend 2 days developing A/B testing for an article, instead of writing 2 articles in that time period, you could lose $15 because you are publishing. version less than one article.

But if that A/B testing shows you can increase the conversion rate of each post from 10 to 20 leads, you only need to spend $15 to potentially double the number of customers your business gets. get from your blog.

Of course, if the test failed, you lost $15 – but now you can make your next A/B test even more effective.

If that second experiment was successful in doubling your blog’s conversion rate, you’d end up spending $30 to potentially double the company’s revenue.

Whatever your failure in A/B testing, its ultimate success will almost always outweigh the cost of doing it.

There are many types of split tests that you can run to make the test more valuable. Here are some common goals that marketers have for their business when using A/B testing:

  • Increased Website Traffic: Testing different blog posts or website titles can change the number of people clicking on that hyperlink title to get to your website.
  • This results in increased website traffic.
  • Higher Conversion Rate: Testing different placements, colors, or even anchor text on your CTA can change how many people click these CTAs to get to the landing page.
  • This helps increase the number of people filling out forms on your website, submitting their contact information to you, and “converting” into leads.
  • Lower bounce rate: If your website visitors leave or exit quickly after visiting your website, do experiment on different blog posts, fonts or featured images. This can reduce bounce rates and retain more visitors.
  • Reduce cart abandonment: E-commerce businesses report that between 40% and 75% of customers leave their website while their items are still in the cart. This is called “cart abandonment” or “cart abandonment”. Testing different product images, checkout page design, and shipping display can reduce this abandonment rate.

4 common A/B testing mistakes and how to fix them

#1 Your testing tool is faulty

Fame is a double-edged sword and this is true even with A/B testing software.

The popularity of A/B testing has resulted in a lot of low cost and great software, but with inconsistent quality.

Different tools will of course have different functions, but there are a few differences you need to be aware of. And if you are not aware of those differences, the A/B testing process will be difficult before you even start.

In fact, it has been shown that on average, each page load that lasts 1 second reduces views by 11%, and along with that, the conversion rate also decreases by 7%. This creates a real nightmare when all efforts to improve your website with A/B testing get in the way of your progress.

And even if you think, things couldn’t get any worse, it is your choice of A/B testing software that will impact the outcome of the testing.

Neil Patel is a business owner and influencer. Patel has found that the A/B testing software he currently uses has obvious differences. But when he created a new page, he did not notice a change in the conversion.

The cause actually stems from a faulty testing tool.

So what should you do to ensure the performance of your A/B testing software among the many hidden traps waiting for you?

Workaround – Run A/A test

Before running an A/B test, you should run an A/A test with your software to make sure it still works without impacting the speed and rendering of your page’s content.

For amateurs, an A/A test is the same as an A/B test. The main difference is that in the A/A test, both groups of users see the same web page.

That’s right, the thing to do here is that you need to compare the site to itself.

It sounds a bit ridiculous, but when you run A/A tests, you will realize many problems stem from the testing software.

Particularly for A/A tests, you’ll want your test results to be a bit tasteless.

Because if you see a drop in your conversion rates as soon as you start testing, then the tool you’re using is probably slowing it down. And if you see significant differences between the two, it’s probably your software that’s at fault.

#2 Stop testing as soon as the result reaches the level

Statistically speaking, this is like grabbing a ball and going home. In fact, when conducting A/B testing, stopping testing as soon as you see the desired result is not only unsportsmanlike, but it also makes the results you produce meaningless.

Many tools tolerate this behavior by allowing the user to stop testing as soon as the desired result is achieved.

But if you really want to improve your website, you need to immediately change your mind and want to end the A/B testing process early.

The problem here is called “false positives”: those results erroneously show differences between pages. The more often you check your results, the more likely you are to get a result that is thought to be true but is falsely confirmed.

This won’t be a problem if you stay calm and keep checking further. However, if you end the test as soon as you see a positive result, you’ve probably been tricked by “false positives”.

Analytics firm Heap has released simulations showing how ending a test too early can jeopardize your results.

By the method of checking the data, then checking the results from 1000 users shows that 5% are “false positives”.

If the tester revisits the results from the same user group 10 times, the chance of getting “false positives” increases to 19.55%. And if you test 100 times, the original 5% will be 8 times, up to 40.1%.

These numbers will help wake you up the next time you’re eager to finish early with a positive outcome.

Workaround – Stick to a preset sample size

Understanding what false positives are is one thing, but dealing with false positives is another. To deal with false positives, you have to set rules. You should have a sample set before running an A/B test and resist the temptation to want to finish early.

No matter how promising the results are, don’t wonder how large the sample set must be. There are many tools online to help you calculate the minimum size. Some popular tools include Optimizely and VWO, …

Note: Regarding sample metric sizes, keep in mind that you need an actual sample size for your site.

In fact, everyone wants to have millions of users for testing, but not everyone can do that. I think you should estimate how long it will take you to experiment to reach the sample size set.

#3 You only focus on conversions

When you’re drowning in A/B tests, it’s easy to overlook the big picture. Let me make it easier for you to understand. When it comes to A/B testing, you often focus on each conversion and forget about the long-term business results.

Of course, adding more copy to your site will result in higher conversion rates. And if so, then the users that have converted with lower quality but with a higher conversion rate will not bring good results for the business either.

You will be easily attracted to frivolous things while conducting AB testing. But you have to remember, those things only distract you from the real profitable results.

If you’re testing a call-to-action tactic that leads to a landing page, you shouldn’t just focus on converting to this landing page. Instead you should calculate the path to the page and tie it to the profit generated.

Solution: Hypothesis testing

Before conducting an A/B test, you should make up a hypothesis you want to prove or disprove. And by focusing this hypothesis on business goals that drive business results, you avoid frivolous temptations.

The process of running A/B tests should be evaluated against the impact on business goals, not any other metric. So if you want to increase registrations, focus on the number of subscribers, not the traffic or traffic to the “Sign Up” page or the homepage containing your registration form.

While you are testing to prove or disprove the hypothesis, do not ignore any unimportant results but use them for further testing.

#4: You only pay attention to the little things

In fact, A/B testing is not simply a single element (like testing the color of a CTA button). It includes many other factors as well. It is the fact that you only test the color of the CTA buttons that spoils your A/B testing.

If the big websites will have a spectacular comeback just by changing the CTA button color. Well, for the vast majority of web pages, little things (like the color of the CTA button) won’t produce any meaningful results.

A/B testing will force us to improve little things, but if we do, we will miss out on bigger opportunities.

Workaround – Periodic Basic Check

As a rule of thumb, periodically check for radical changes to your site. That’s why this is called a Periodic Basic Check.

If you’re seeing low conversion rates, then maybe you should spend your time examining fundamental changes rather than minor ones.

Think of testing like a table of cards, sometimes you should bet a little big if you want big profits.

But before you spread the word about the “basic check”, remember that it has many shortcomings in itself.

  • Need more preparation than A/B testing

Basic testing requires you to spend time redesigning the site. Since this will take a long time, I recommend doing it periodically.

  • It’s hard to determine which factors have the biggest impact on your website

And you should keep in mind that basic testing will help you determine if a site redesign is impacting conversion rates, but won’t allow you to pinpoint what exactly drove those results.

Conclusion

Now do you understand the concept of A/B Testing? If you have any questions, feel free to comment below this post!

Good luck!

Reference source:

  1. https://blog.hubspot.com/marketing/how-to-do-a-b-testing
  2. https://en.wikipedia.org/wiki/A/B_testing
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