What is A/B testing?
A/B tests also called split tests, allow users to compare two versions of something to determine which one is more efficient. Simply say, do your customers prefer version A or B more?
The idea is like that of the scientific method. If you want to know what happens when you alter something, you must create a scenario in which only one thing is changed.
Take a look at the experiments you did in elementary school. For example, if you plant two seeds in two cups of dirt and place one inside the cupboard and another near window, you’ll get different outcomes. This type of experiment is called A/B testing.
Why should you consider A/B testing?
Businesses in B2B today are not happy with the countless lead generation they receive every month; eCommerce stores, in contrast, struggle with a high cart abandonment rate. In the meantime, publishing and media companies are also struggling with the low engagement of their viewers. In addition, this fundamental measurement of conversion is impacted by typical issues, such as gaps in this funnel of conversions, drops on the payment pages, and so on.
Let’s take a look at the reasons why you should conduct A/B tests:
1. Help to address the visitor’s pain points
Visitors visit your website to accomplish a particular objective they are aiming for. For example, it could be to find out more about your service or product, purchase a specific product or service, learn more about a certain subject, or just browse. Whatever the visitor’s purpose, they could be confronted with common issues when achieving their goal. For example, it might be difficult to understand the copy or locate the CTA button, such as purchasing now or requesting a demonstration or a demo, etc.
The inability to meet their goals results in an unsatisfactory user experience. This creates friction and ultimately reduces conversion rates. Instead, utilize data collected through analysis of visitor behavior tools like heatmaps, Google Analytics, or web surveys to resolve your visitors’ issues. This is true for every business, such as travel, eCommerce, SaaS, education, publishing, media, and more.
2. Increase the ROI from existing traffic
As many experienced optimizers have recognized, the expense of acquiring high-quality traffic to your website is enormous. A/B testing helps you get the most out of your current traffic and allows you to increase conversions without spending extra money on the acquisition of new visitors. In addition, a/B testing can provide you with a high return on investment since, sometimes, the smallest changes to your site could increase business conversions.
3. Reduce bounce rate
A crucial metric you should be tracking to evaluate your website’s performance is the bounce rate. There could be several reasons for your website’s bounce rate being high, like having numerous options to select from, mismatching expectations with reality, a confusing navigation system, the overuse of technical terminology, and so on.
Because different websites have different objectives and cater to different groups of users, There isn’t a universal solution for reducing bounce rates. However, conducting an A/B test could prove useful. Using A/B testing, you can experiment with different versions of an element within your site until you have the most effective version. This will help you identify any friction or problems and improve the overall experience, making them spend more time on your website and possibly become paying customers.
4. Make minor adjustments
Make small, incremental changes to your site’s content using A/B tests instead of reviving the entire page. This reduces the chance of degrading the existing conversion rates.
A/B testing allows you to target your resources to produce the highest output, with only minor modifications, and to result in a greater ROI. One example could be changed to the description of a product. Conducting an A/B test if you intend to eliminate or modify your product descriptions is possible. However, knowing how your customers will likely react to the changes is difficult. If you run an A/B test, it is possible to examine the reaction of your guests and identify which way the weighing scale will be tilted.
5. Make statistically significant improvements
Because data completely drive A/B testing, and there is no room for guesswork, gut feeling, or intuition, It is easy to discern the difference between a “winner” and a “loser” by analyzing statistically significant improvements in measures like the amount of time you spend on the page, the number request for demonstrations abandonment rates of carts or click-through rate and more.
6. Design a website to boost the future profits of businesses
Redesigning can be anything from a small CTA and color change to specific web pages to completely overhauling the site. The decision to go with either one or the other must be based on data during A/B testing. Don’t stop testing until the design is being developed. When the new version goes live, you can test the other elements of the page to ensure it is the one that will be the most interesting available to your visitors.
How to conduct A/B Testing
1. Choose one variable to test.
If you’re trying to optimize your website pages and email messages, you may have many variables you’d like to try to measure. However, to determine how effective a modification is, you must identify the one “independent variable” and determine the performance of that variable. In the absence of this, you won’t determine which variable is the cause of changes in performance.
It’s possible to test multiple variables for a single website page or email, but ensure that you’re testing them all simultaneously.
Identifying your variable examines the components within your marketing tools and their potential alternatives for wording, design, and layout. Other items you could try are email subject lines, sender names, and other methods to customize your email messages.
Be aware that small changes will significantly improve, such as changing the image you use in your emails or the text on your call-to-action button. These kinds of changes are typically more easily measured than more significant ones.
2. Identify your goals.
Even though you’ll test multiple aspects in a single test, you should choose one primary measure to concentrate on before you begin the test. You should do it before you set up the second variant. The “dependent variable” can change based on how you modify your independent variable.
Consider where you would like the dependent variable to be when you finish the test. It is possible to even make unofficial hypotheses and then examine the results in accordance with this idea.
If you don’t wait until later to decide what metrics matter to your priorities and what changes you’re proposing could affect the behaviour of users. If you don’t, you may not be able to set up your tests in the most efficient method.
3. Select the page you’ll be testing
Begin with your primary page. The page could serve as your home page or a high-traffic landing page. In any case, it will be a major factor in your business’s bottom line.
4. Deciding on Splitting and Evaluation Metrics
Two things to consider are: where and how we will divide our users into different groups of experimenters when they visit the website, as well as what metrics we’ll employ to measure the effectiveness or failure of our experimental modification. The type of unit we choose for diversion (the moment we split observation into different groups) could affect the metrics for evaluation we use.
The control group, also known as the “A” group, will see the original homepage, and the experimental group, also known as ‘B, will get the new site that focuses on the trial of 7 days.
5. Create your control group and test group.
After you have determined your null and alternative hypotheses, the next step is to design the control group and test the (variant) class. Two key ideas are to be considered during this process: sampling and sample size.
Random sampling is one of the most popular sampling methods. This is because every population sample has a chance of being selected. Random sampling is crucial in hypothesis testing as it reduces bias in sampling, and it’s crucial to avoid bias since you’d like the outcomes of a test to be representative of the whole population, not just the test sample itself.
It’s crucial to establish the minimum size sample for your A/B test before conducting the test so that you remove bias due to under-coverage and bias due to sampling too many observations.
6. Accumulate data
This is the waiting-and-see phase. With A/B testing tools like Crazy Egg, data gets collected in a way that is automatic. You can track the progression during your tests at any point, and once the test ends, you’ll receive information about how many people attended every variation, what devices they utilized, and other details.
7. Review the A/B testing statistics
Take a look at which variant was the winner, either the champion or one who was a challenger. Once you know what your audience prefers more — and how much — you are able to begin this process of 10 steps over by introducing a new variation.
6 Best A/B testing software
1. AB Tasty
Ab Tasty is a reasonably-priced and easy-to-use tool that serves as a solid start for businesses that are just beginning their journey to optimization of conversion.
AB Tasty offers A/B testing and split testing. Multivariate Testing funnel testing and funnel-testing capabilities. Utilizing AB Tasty’s editor in the visual format allows you to create quick and easy variations and tests and get real-time reports that provide confidence levels on your objectives.
2. Google Optimize 360
Google Optimize 360 is the premium or paid edition of Google Optimize. It includes all the essential functions of its free version, like A/B testing and the native Google Analytics integration, URL targeting, Geo-targeting, and more, but without the different caps that are available with the free edition. Like all tests tools we’ve discussed so far, using Optimize 360, you have the ability to:
- Try up to 36 combinations performing an exam that is multivariate
- More than 100 experiments can be conducted simultaneously
- More than 100 personalizations can be created at once
Remember, Google Optimize 360 is a more expensive alternative without additional benefits or advantages in comparison to VWO, Optimizely, or AB Tasty.
3. Adobe Target
Adobe Target is an enterprise tool of great popularity that offers targeted testing as well as personalization.
Adobe Target walks you through the three steps of a workflow in which you create the variation and then select the variation based on user segmentation. Finally, you’ll be able to set your own goals and preferences to test. With this type of targeted advertising, it should be no surprise that the most interesting feature is automatically personalized.
4. Google Analytics
Google Optimize is a great choice for experienced optimizers who are just beginning their exploration journey. A Google Product, this software allows you to build and test different versions of websites and then analyze which one is better.
Because Google Analytics powers it, you get the benefits of having a comprehensive research tool. Utilizing Google Optimize, you can conduct simple A/B tests, split URL tests, and multivariate tests.
Conductrics is a cutting-edge tool that provides methods from A/B testing and machine learning to provide the best experience for each user in an automated way.
It can be used as either a client-side server or server-side tool, and you are able to almost customize it in any way you wish to utilize it.
6. Wasabi Platform for A/B testing
Wasabi A/B Testing Services is a live, 100 percent API-driven enterprise-grade product that allows you to utilize your own data to run tests across the internet, mobile, and desktop. It’s quick, simple to use, with lots of features, and the instrumentation is very minimal. Wasabi is the platform for experimentation that supports TurboTax, QuickBooks, Mint.com, and other Intuit products.
How Much Time Does A/B Testing Take?
A/B testing isn’t an overnight task. Based on the volume of traffic you receive, it is possible to conduct tests for anything from a few days up to several weeks. Keep in mind that you should only conduct one test at a given time to get the most precise results.
A test that is run with a small duration can cause distortion of the results since there isn’t an adequate number of participants to make the test statistically reliable. A test that is too long could result in unbalanced results because the test is influenced by more elements that you cannot control over an extended period of time.
Be sure to stay informed of any changes that may impact your test results to be able to account for any statistical irregularities in your test results. If you are unsure, take the test once more.
When you consider the effect that A/B testing has on your bottom line, it’s well worth investing a few weeks to be sure to conduct the tests properly. Try one test at a given time, and give each test the proper time to be run.
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