Whether you are a marketer, UX designer, data engineer, or project developer, you know that intuition does not work every time. It’s not always the guesswork that can help you drive more revenue especially when the market is saturated. Although it can be enticing to believe in your intuition and create strategies accordingly, the data study says otherwise.
If you want to know what works better for your audience, you can test your ideas randomly in real-time and determine the more effective solutions for your business. A/B testing is a popular testing method that is helping millions of businesses make data-driven decisions.
Are you planning to conduct an A/B test? Or want to see how this data-driven approach is used for optimization? The guide below can help you learn everything about this popular experimental method to drive better results.
What is A/B Testing?
A/B Testing is the process of testing two versions of an online experience at the same time, such as a web page or an app, by presenting each version to different users randomly to analyze which one performs better. So, instead of guessing, you test different scenarios in real-time to determine what is preferred the most by the audience. The efficacy of the new variation is determined by the value it provides and is compared to the original testing variable to evaluate which works better.
In A/B testing, the original variable that is to be tested is called ‘control’ and is referred to as ‘A’ while the new version or variation of the original variable is pointed as ‘B’. The version that drives better results for the business is the winner and based on it, you can make decisions to optimize your website.
In the saturated world of data, you need to analyze what brings more value to your business, what are the high-potential features that you should prioritize, and which features you should not proceed with in the future. For example; you can implement an A/B test on different variants of an email to analyze which performs well. Two different versions of elements such as ads, CTA, image placement, funnels, an application, landing page, etc are tested against each other in real-time to analyze which one brings more value.
History of A/B Testing
While it is complicated to track down the history and initial applications of A/B testing, we can give Claude Hopkins credit for initiating A/B testing in its crude form. He tested the effectiveness of his ad campaigns by offering promotional coupons. Since it was a fundamental form of comparative testing, it did not include the concept of statistical significance which was later defined by Fisher.
In 2000, Google conducted its first A/B test to find out how many results should be displayed on the SERPs but the test didn’t succeed. In 2012, Microsoft tested multiple ways of displaying ad headlines to determine which worked better, and within a few hours, it managed to garner 12% improved revenue by using the alternative variable. Today, Google and Microsoft are conducting more than 10,000 A/B tests every year to determine the most effective solutions.
Benefits of A/B testing
Many of the great platforms we see today go through intense A/B and multivariate testing to make data-driven decisions and improve their products, services, and overall business. The benefits of A/B tests are leveraged in almost every industry.
Maximizing revenue
Conducting A/B tests helps businesses figure out the solutions that work best for them and help generate more revenue by spending their resources only on the right strategies.
Uncovering insights
A/B tests take the guesswork away and allow you to try and test innovative ideas that make you more confident in your experimentations. The test results uncover useful insights on your audience behavior that you are probably missing earlier.
Improved user experience
By conducting A/B tests, you can find out which layouts and styles offer a better user experience and are more engaging. By comparing different results, you can implement the most optimized solution to transform your web presence accordingly and offer users an improved online experience.
Lower risk rate
Instead of changing the entire web page design, you can make minor changes through the A/B test to reduce the risk of losing your current conversion rate. The changes you want to implement on your website may or may not have a positive impact on the audience so it is better to test these changes before launching them to a wider audience to reduce risks.
What can you A/B test?
The marketing teams today establish a healthy culture of A/B testing of different elements to analyze their impact. You can run A/B test experiments on multiple elements and analyze how their results benefit your business.
Titles and headlines on a web page: The titles and headlines on a web page are probably the most prominent elements that grab the visitor’s attention and let him decide if he wants to convert into a customer or not. The shorter, to the point, and compelling titles and headlines are more convincing and appeal large number of visitors. You can try different formats and writing styles of titles and headlines to see which one compels the visitors to convert.
The body content: When a user scrolls through a web page and explores the content written on it, he gradually comes across your offerings and determines if he can relate to them. The body of the webpage should be relevant to the title, well-written, and informative to guide the visitor the right way. You can conduct an A/B test on different content copy styles and formats and analyze the conversion rate for each variant.
CTA (call-to-action): Changing the size, color, and text of CTAs can significantly increase the CTR. You can test multiple style designs and colors for CTA and evaluate which style makes more visitors click on it.
Images and videos: You may try different image placements of images and videos, compare static images and videos to see their impact, try different color schemes, etc. to see which are more appealing.
Navigation: Try and test different ideas to make your navigation game stronger. Try horizontal navigation bar placement on the top or vertical placement on the left, and put similar features or services in one place so they are easy to navigate and offer a pleasant user experience.
Product pages: You may test different elements on product pages such as product descriptions, images of the products, online payment options, showing a small cart icon on the homepage, displaying the hottest deals, changing the way the shipping charges are displayed, changing the color scheme on the checkout page, etc.
Email subject line: Test email campaigns for their open rates to optimize your email campaigns by finding the best-performing email subject lines. Try different compelling phrases in the email subject line, try emojis, or embed questions and analyze which subject line convinces more people to open the email.
Email copy: Try different versions of email copy to see how they resonate with the audience. Experiment with pre-header text, content inside the email body, CTAs, email lengths, etc to analyze their impact on recipients.
Page layout: The web page layout and design should be optimized to hold the visitors. Not just the web copy but also the images, videos, CTA, testimonials, ads, etc should be placed optimally. Test adding and removing different elements on the webpage to get the optimized page layout that fills all the gaps for the visitors.
Forms: Form is another important element that not only addresses customers who are seeking information about your business but is also used in the purchase journey. You can test multiple form layouts, lengths, fields, etc. to figure out which form works best for your audience for a particular purpose.
Pricing: Try different pricing strategies to determine which are more appealing to your customers. You can experiment with different price points, try discount and promotional strategies, offer customers different pricing plans, and monitor the impact of test results on your audience and revenue.
Mobile apps: Not only on the website, but you can also run A/B tests on mobile apps as well which is considered a bit complicated due to the varied interfaces of Android and iOS. Test different layouts, font sizes, styles, colors, icons, onboarding flows, checkout process, pricing strategies, promotional offers, placement of purchase buttons, impact of new features, push notifications, keywords, app description, etc. In short, you can implement changes in the app’s UI and UX to see their impact on the user experience, engagement, session time, etc. Use the test results to maximize the app downloads, improve the user experience, and target more users.
Social proof: Adding social proof such as testimonials, reviews, use cases, awards, certifications, badges, etc to your website can have a positive impact on your audience. With these social proofs, your targeted audience believes in your claims and consider investing in your business. You can try adding different types of social proofs, their layouts, and placements to see their impact on conversion.
Blog TOC: You can test multiple variations of blog TOC for instance; If your existing TOC displays at the top of the blog, you may create different variants such as displaying sticky TOC that will remain on the page as the reader scrolls down. Another variant comprises the same sticky TOC but with a call-to-action button on the end. You can monitor the impact of each variant to see which option grabs more visitor attention.
Site search bar: Create multiple variants against your search bar such as enlarging the search bar so it is more prominent to the visitor or putting a placeholder on it with the text showing ‘search the website’. Test these variants and analyze their effects on the visitor’s engagements and conversions on a specific page.
How is A/B testing performed?
A/B testing effectively and practically finds out what works and what does not in a specific scenario. While the online competition is soaring high, it is very important to offer your visitors and customers a smooth website experience. A/B testing is not a mythical experimentation but a reliable and practical way to find the best and most prolific solutions among the rest.
A/B test follows an organized framework of following steps and if implemented the right way, it can help you achieve maximum goals.
Analyze and collect data
If you have decided to conduct an A/B test on your website, you should thoroughly examine how your website is performing. Data gathering is the most fundamental step that gives you an idea about which web pages are driving more traffic, the conversion rates of different pages, which page has increased scroll depth and lower bounce rates, etc. The website analytics tool can help you collect data about every web page and you can shortlist them based on the goals that you want to achieve with A/B testing.
The data you collect helps you analyze the areas of improvement or the potentially problematic areas so you can decide where to start optimizing your site. The heatmap tools provide data about the scrolling behavior of the visitors to help you identify the performance of every web page. Surveys can also help uncover helpful insights. You may either start with high-traffic web pages or a page with high bounce rates.
Identify your goals and audience
To conduct an A/B test, you should be clear about what exactly you want to achieve with the test. The goals you associate with it drive the entire testing process. Probably your goal is to increase conversion, get more sales, increase click-through rates, or maximize engagement. Whatever you plan to achieve with the testing should be specified earlier so you can analyze if the variation is performing better than the original version.
Once you have curated goals, the next important thing is to understand your targeted audience and how you will split them into groups. One of the major drawbacks occurs when the audience is not optimally grouped into different sets which affects the authenticity of the test results. To prevent this, ensure that the audience is randomly divided into similar sizes and does not have pre-existing biases that can impact the test results. The groups you create should not overlap with each other and have similar web experiences except for the variation that you are testing.
Formulate test hypothesis
Now that you have data in hand and goals are specified, you can generate ideas about creating A/B testing variations and formulate a hypothesis about why you think the new variant can be a better alternative to the original version. Once you have created a data-backed hypothesis, you may analyze how confident you are about its success, its implementation, and its effects on your website goals.
A strong hypothesis is an intelligent, data-backed guess about the user impact on a web page or app. For example, if you want to increase the clicks on a CTA button, you may create a hypothesis around changing the color, text, or size of the CTA so it may grab better attention and eventually more clicks. You may create a variant concerning the formulated hypothesis and run it against the original version to see which works better.
Create variations
The next most important step is to create two or more variations of the control variable based on the formulated hypothesis and test them against the control (the existing version). A new version that is created is almost the same as the existing version but with a few changes that you want to test. Different versions are displayed to different sets of audiences which offer the same experience other than the variation that is required to be tested.
The variation is usually developed to offer the audience an improved experience or to maximize conversions. For instance; if your web forms are not performing well, the possible reasons could be the form being too long, asking for personal information, etc. You can create a form variant that is shorter than the original one and has the most relevant fields only.
You can create as many variants as you want with desired changes such as changing the navigation bar, changing the size and color of CTA, trying different headlines, and images, changing the placement of forms, experimenting with different page layouts, trying different pricing formats, etc.
Run experiment
Tools such as Google Analytics allow you to conduct A/B tests by creating variations of a web page, running these variations randomly among different groups of audiences, and comparing their performance.
Once your variants are ready to compete, kickstart your experiment and wait for your audience to respond. The test runs for a specific time and you should not conclude it without completing the specified duration. Each version is randomly assigned to the visitors and their interactions are free from any bias. The performance of every variant is carefully measured and compared to the baseline results to see what is achieved by the new variant or if the performance of the original variable has surpassed it.
Study the results
Since the test runs for a specific time, it can take a considerable amount of time to achieve the results in case of a larger audience. If you don’t wait for the right time before concluding, it can negatively impact the test results. Once the experiment is completed, you can compare the results for both variants to see if there is any significant difference between the performance of both. There should be a statistically significant difference to conclude the test results on success.
If the new variant succeeds, you can implement it and if it does not, you can continue with the original variant. However, you can utilize the insights to conduct subsequent tests in the future.
Analysis of the A/B test results
Every business conducts A/B tests to achieve different results for example, an e-commerce store runs the experiment to grab more purchases while a business website runs A/B tests to get better leads. Once the test is completed and the results are derived, it is time to analyze the results based on your goals.
Check the goal metric
The pre-defined goals you want to achieve with the test are studied to analyze how the results benefit your goals. For example, if you have conducted an A/B test to achieve a better conversion rate, you can evaluate which variant is bringing more conversion.
Statistical significance
As mentioned above, the outcomes of the variants must have a statistically significant difference i.e., the variant you are testing against the control variable should outperform it significantly. When running the test, you are certain that version B has more potential for conversion than version A and if the test concludes with that certainty, it means that version B benefits your marketing metrics. The results in this case are declared statistically significant.
Audience segmentation
The results achieved from A/B tests can be studied based on the audience segments i.e., how each set of audience responded to the versions assigned to them. Based on the segments, you can analyze which version performs better for repeat visitors and which one for new visitors, which version performs well on mobile, and which version performs better on the desktop.
A/B testing metrics
Whatever changes you implement as a part of A/B testing directly impact the user interactions. Tracking these interactions is crucial to figuring out the effectiveness of the A/B test for your business. Before implementing an A/B test, it is important to define what metrics you want to evaluate by performing the test.
A few important A/B test metrics that you should track to measure your success are:
Conversion rate
It is the percentage of total users who completed the desired action, i.e., either made a purchase, signed up for an event, etc. The conversion rate that results from a landing page helps you determine how your website data is affecting the user journey. You can test website features, change the page sections, revamp the CTAs, etc to see what brings more conversions.
Bounce rate
The bounce rate is the number of visitors who visit the landing page but leave it without taking any action. Multiple reasons are involved behind the high bounce rate including, difficulty in finding product demos, layout not guiding the visitor, cluttered website design, annoying pop-ups, broken links, or page unresponsiveness. You can implement an A/B test on multiple elements to determine which leads to slower bounce rates for optimizing your online experience.
Click through rate
CTR is the number of times a link is clicked as compared to the number of times it was displayed. A low CTR is most probably associated with poor CTA display, poor ad quality, improper ad placement, low-quality ad format, etc. All your marketing efforts boil down to achieving higher click-through rates.
Scroll depth
Scroll depth is extremely helpful in determining how effective the web page is. It is the percentage of a page that users scroll through on average. It indicates how far down a particular web page is scrolled and how engaging it is for the visitor. A high scroll depth rate with minimum engagement means that the visitor is scrolling the page without any interest or engagement. A low scroll depth with high engagement means that the visitor is only focusing on the top section but is very interested.
Abandonment rate
The abandonment rate is the percentage of users who started taking an action but terminated it midway. If you are running an e-commerce store, the abandonment rate helps determine the number of users who entered items into the shopping cart but left it without purchasing. Multiple factors are involved here including a confused checkout process, unexpected shipping charges, lack of online payment options, etc.
Retention rate
It is the percentage of users who visited your website or landing page again after a specific period. You can compare the retention rate of different page variations to determine the impact of each variant on user retention.
Average time on page
It determines the amount of time a visitor stays on a single page of your website. Tracking the user’s average time on a web page in the A/B test helps determine which variant of your page is garnering more user interest. When a user spends more time on your page, it means that the page is offering more value and appeal to the visitor which might lead to conversion.
Average order value
It tracks how much money a customer is spending to purchase from your website. While conducting an A/B test on AOV, you create two versions of a page displaying different deals to the visitors. One shows discounted prices while the other offers free shipping. The test results help you analyze which version grabs more attention from the visitors and results in maximum sales.
Churn rate
The churn rate is the number of customers who discontinued taking your services or buying your products over a specific period. Conducting an A/B test to determine the churn rate works like remarketing the existing customers who might stop doing business with you anytime in the future and presenting them appealing offers that help them stay engaged and minimize their chances to leave. This way, you can analyze what helps keep your customers engaged and increase your retention rate.
Revenue
It is one of those A/B testing metrics that every business focuses on the most. You can create multiple versions of an app or web page to measure how much revenue is generated by each version and implement the one with the maximum impact.
Goals of A/B test
While startups are trying hard to grab more conversions, large businesses are trying to offer improved user experiences. Whether it is an e-commerce store, a media house, or a SaaS-based platform, every business associates some goals with conducting A/B experiments. For some, it could be to increase conversion rates while for some, it could be to maximize user engagement. Some might want to get over the funnel leaks by implementing testing while some want to reduce cart abandonment rates.
Here are some of the goals that marketers and business owners associate with the implementation of A/B testing.
Reducing cart abandonments
One of the major setbacks for e-commerce business owners is the increased number of customers who add items to the shopping cart but leave it without taking any action. One of the major goals of carrying out an A/B test in an e-commerce industry is to reduce abandonment cart rates and offer customers a smooth shopping experience.
Increasing web traffic
A/B tests are widely conducted to see the traffic impact on your website. You can test web page titles, content placement, blog TOC module, color scheme, and fonts on your website to optimize the website traffic.
Increased ROI
Customer acquisition costs can be significantly higher but with A/B testing, you can test ways to acquire customers without spending a huge amount. It eventually leads to lower acquisition costs and maximum ROI as even a small change can lead to improved conversion.
Higher conversions
Not only does an A/B test increase engagement on your website but it also helps in boosting conversions. Even minor changes such as changing the size and color of the CTA button can encourage visitors to fill out a form and convince potential customers to become paying customers. You may implement an A/B test to optimize your website conversion rates.
Lower bounce rate
If your website is experiencing high bounce rates, you can conduct an A/B test to determine the causes behind it. Probably it’s the placement of website elements, poor color scheme, improper navigation, or the lack of convincing CTAs that are negatively affecting user engagement on your website. You can test different elements on your website to minimize the bounce rates and encourage visitors to stay and explore.
Mistakes to avoid while A/B Testing
Although it sounds simple, the implementation of A/B testing is not free from mistakes as there are various instances where even minor error leads to producing incorrect test results.
Wrong hypothesis
Since formulating a hypothesis is the main part of the testing and all the next steps depend on it, any error in this stage decreases the chances of successful results.
Testing too many variables together
Marketers and experts do not favor testing multiple elements at the same time. If this is the case, the transparency of the results gets affected as it is difficult to monitor which elements lead to success and which yield unsuccessful results.
Inaccurate audience segmentation
Running an A/B test on the wrong audience is most likely to produce wrong results with no transparency involved. While creating audience segments, it is critical to take into account the demographics, audience behavior and preferences, and mobile users so the experiment leads to accurate outcomes.
Running tests on the wrong pages
Every web page is significant but not every web page is worth the time to conduct an A/B test on. You should not bother running an A/B test on a web page that is only informative and does not directly impact the conversion rate.
Insufficient sample size
An accurate and well-chosen sample size has a significant impact on the test results. If you want to achieve accurate test results, create a sufficient size of the sample. You may utilize a sample size calculator to calculate to determine the appropriate number of participants per variation to avoid creating Insufficient sample sizes.
Implementing too many changes based on test results
If the A/B test generates the desired results, the formulated hypothesis is a success. However, the results are only beneficial on specific pages and not for every web page. Implementing the test results on every web page can create a mess. For instance, if the form sign-up popup increases conversion on one web page, it does not mean that you should place sign-up popups on every page and create annoyance.
Testing for a short time
The tests which run for improper duration never yield accurate results. If you are running an experiment and stop it midway when it starts achieving the desired results, there are high chance that it will not produce accurate results. You have to wait till it runs for the specified duration to achieve statistically significant results.
Prioritizing the test results of others
Two different websites have different goals and different targeted audiences so what worked for one website does not necessarily work for the other too. If you are convinced by the results of A/B testing on someone else’s website and planning on implementing the results on yours too, there is a high chance that the test will not produce a transparent outcome.
Using wrong tools
While A/B testing, if you are using the wrong tools, not only does your site speed get affected but the test also produces inaccurate results. Some of the cheap and low-quality tools that are used for testing come with a high risk of data manipulation.
Not considering repeating the test
Many businesses give up on testing after initial failures which can minimize the chances of test success in the future. Do not just stop testing repetitively even if you encounter success and figure out the best version for your business.
Bottom line
Till now, you are probably convinced that running an A/B test is not only about finding a winner or a loser but it’s more about finding ways to optimize online experiences. Platforms like Kemo SaaS help create multiple variants that can be used for A/B testing purposes. To run an A/B test diligently, you need to plan everything carefully, from formulating a hypothesis to analyzing results, every step should be given enough attention to minimize chances of test failure. The good news about A/B testing is that if the test does not run successfully, you can proceed with the original version without affecting the conversion rates.
FAQs
How is the A/B test conducted?
To conduct an A/B test, you need to create two variations of the same content and present each variant to a different set of audiences. Compare their performance over a specific time duration and draw your judgments.
What are the other names for A/B testing?
A/B testing is also known as split testing or bucket testing.
On which website elements can we perform an A/B test?
A/B test can be performed on multiple website elements to provide a better user experience. The test can be conducted on CTA buttons, headlines, images, and their placement, form field placement, page layout, navigation menu, and many more.
What is the difference between an A/B test and a multivariate test?
An A/B test is performed to compare two variants of a single element while a multivariate test compares multiple variants of a single element at the same time.
Does conducting an A/B test pose any threat to your search ranking?
Google has cleared the misconception that conducting an A/B test affects the website’s search engine ranking. It helps you achieve a better ranking by ensuring a fast page load speed and enhancing user engagement.
