What’s the ultimate goal when you’re creating your webpages, popups, email campaigns, and ads? To get people to engage and take action.
But figuring out the best way to get them to do that isn’t quite so simple. Even when you make decisions based on past events, there’s still a risk of falling prey to the gambler’s fallacy—a false belief that past events will influence future events.
Nobel laureate Daniel Kahneman’s theory probably says it best: intuitive thinking is faster than a rational approach but more prone to error.
Enter A/B tests, an experiment-driven method of making better marketing decisions.
This article will walk you through everything you need to know about A/B testing, a simple strategy that helped Obama raise an extra $60 million in donations for his nomination campaign. You’ll see exactly how businesses use A/B tests to ace their conversion goals and pick up actionable tips your brand can use for similar results.
What is A/B testing?
A/B testing, also known as split testing, is a method used to compare the performance of two different versions of a variable. It involves showing the two versions to different segments of visitors and then measuring which variant results in a higher conversion rate.
This version is tagged “the winning variant,” and becomes the basis for future tests intended to drive more conversions.
For instance, a company may want to test two versions of a landing page, with version A featuring a red button and version B featuring a blue button. They show version A to half of their target audience and version B to the other half.
Then, they collect data on which version increased conversion rates, improve this winning variant (perhaps through further A/B tests), and use it in future campaigns.
But A/B tests are not limited to web pages. You can also use this methodology to test different versions of a blog post, email, or ad copy. In fact, in Databox’s survey, over 57% of the companies confirmed that they A/B test their Facebook ad campaigns every time.
Similar to A/B testing, multivariate testing allows you to test different variants of a campaign. But in multivariate testing, you’re testing multiple different elements at the same time (e.g. different headlines, images, and calls to action) to determine which combination of components results in the highest conversion rate.
By conducting A/B tests, you can stop relying on intuition and instead base your decisions on reliable data, which can skyrocket conversion rates in unimaginable ways. And while conversion rate optimization is often the desired outcome, there are several other positive results you can expect.
Let’s consider a few reasons why A/B tests should be part of your marketing strategy, regardless of your budget or industry.
Why should you run A/B tests?
The fact that A/B testing is the second most popular CRO method shows just how much good it can do. Here are just some of the benefits you’ll see if you run A/B tests:
1. A better understanding of your target audience
Running A/B tests helps you gain a deeper understanding of what your target audience wants through their behavior on your website. And what you learn about your audience through A/B tests will help you optimize your future marketing campaigns.
By testing different elements on the page, you can also determine which design, copy, and layout elements work best for your unique audience.
2. Data-backed decisions you can feel confident about
Relying on gut feelings might be a risk worth taking when deciding between pizza flavors to try out… but it’s most certainly NOT the best approach when you’re deciding how best to invest a tight marketing budget!
With A/B testing, you can make data-driven decisions based on user behavior, which is just all-around smart.
3. Improved conversion rates
In 2022, Obvi increased conversion rates on a Black Friday popup by 36% in just one week! Pretty incredible that making a simple tweak boosted conversions by that much, right?
By determining statistical significance and analyzing test results, you can make informed decisions on marketing strategies and optimize your pages for more conversions.
4. A higher ROI
When you A/B test your campaigns, you’ll speed up the process of discovering what works best for your audience. Rather than revamping an entire campaign, you may find that you can make one or two small tweaks that will make a huge difference.
You’ll be able to test your hypotheses and prove (or disprove) them, so every change you make is bringing your campaign in the right direction. As a result, you’ll save time and money, increasing the ROI of your campaigns.
What should you A/B test on your website and landing pages?
Convinced that A/B testing is worth your while? Great. Now it’s time to take a look at exactly which elements you should be testing.
Here are a few examples of variables you should test on your landing pages.
1. Main headline and subheadline
Creating and testing two different headlines and subheadlines for a page is a great place to start.
These two elements are found above the fold, meaning they’re almost always what people see first. They can mean the difference between “hooking” your visitor and losing them.
Say you’re running a marketing campaign for a new product on your online store. You create a landing page with the main headline “Introducing the newest, most advanced headphones on the market” and a subheadline “Revolutionize your daily routine with our cutting-edge technology.”
You decide to run a split test and create a variation of the page with the main headline “Upgrade your daily routine with our revolutionary headphones” and the subheadline “Experience the latest technology available on the market.”
After conducting the test, you may find that one variant has a higher conversion rate, and you can then use it for future tests or as part of your marketing strategies.
2. Value proposition
Reviews and user-generated content (UGC) provide insight into your customers’ level of satisfaction with your products, but mining these can be a chore. A/B test results, on the other hand, allow you to directly measure the impact of changes on user behavior and conversion rates.
By sending equal amounts of website traffic to each page and analyzing the results, you can determine which version of the value proposition is more effective at converting visitors.
Continuing with our previous example, maybe the target audience cares more about how the product can enhance their daily routine than they do about it being the trendiest innovation.
One way to find out is by carrying out a split test on the landing page, focusing on elements like call-to-action buttons, images, and ad copy to communicate two different value propositions. You’ll gain valuable insights into what resonates with your target audience and improve your marketing strategies.
3. Calls-to-action (CTA)
Conducting A/B tests on your CTAs is one effective way of gathering reliable data on user behavior.
You’ll want to test things like the copy, color, and CTA button position. While these may seem like small changes, they can have a big impact on your click-through rate!
One element you can A/B test with forms is their length. You may want to use a longer form to get more comprehensive information from your visitors, but your users may prefer a shorter, simpler form. By conducting a split test, you can determine which form length is most effective for your website and make adjustments accordingly.
Consider testing the style of your forms, too. For instance, you might try a minimalist design against a more complex one. A/B testing allows you to compare the conversion rates of these two different styles and make a decision based on the results.
This might also be a good place to try multivariate testing, which allows you to test multiple elements of a form simultaneously. This affords you a better understanding of how different combinations of elements affect conversion rates.
A/B testing images is crucial for determining which visual elements are most effective at capturing the attention of potential customers and driving conversions.
You can compare different product images to see what works best in terms of angles, lighting, and styling. A/B testing will reveal which images most effectively showcase the product and entice customers to make a purchase.
Similarly, you can A/B test image layouts. If you’re running an ad campaign, you may want to test layouts such as a single image, a carousel, or even your explainer video.
6. Page structure
With page structure, there are lots of different changes you could make.
You might A/B test the placement of your call-to-action button to see if moving it from the top of the page to the middle of the page increases conversions. You could test a sticky navigation bar against a standard fixed navbar, or see if featuring your social proof directly under the hero section keeps people scrolling down the page.
Since page structure is such a broad area, remember to test only one thing at a time through A/B testing!
7. Product recommendations
When you provide product recommendations, you might want to try testing a grid layout versus a list layout to determine which format is more visually appealing and easy to navigate for your customers. Testing the placement of the recommendations on the page can also show you where customers are most likely to engage with them.
A clothing brand may choose to test these two different offers: “20% off your first purchase” vs. “Free shipping on your first order.” An A/B test can help the business determine which offer is more effective at driving conversions. This winner can then be used as the primary offer in a future campaign.
You can also A/B test different elements of an offer, such as its language, placement, and design. Boosting the sense of urgency by including wording like “limited time” might increase conversions, or simply using a different color scheme might make it more eye-catching. The only way to know for sure? Test!
A step-by-step guide to conducting A/B testing
If you’re worried that A/B testing is too difficult, too much work, or too complex, stay tuned. When you run your A/B testing according to this guide, you’ll be among the 63% of companies who agree that A/B testing is effortless.
Step 1: Analyze your website
You’ll want to start by studying your site’s current state, including its overall design and layout, user flow, and the performance of its existing elements (buttons, forms, and calls-to-action, etc.).
Your website’s performance data, such as traffic and conversion metrics, also gives you insight into underperforming areas so you can prioritize them for testing.
For example, if you discover that a high percentage of visitors leave after viewing only one page, that could indicate that your website’s navigation is not optimal. An improved UX design can boost conversion by up to 400%, but it all starts with keeping visitors engaged and on the site longer.
Google Analytics is a helpful tool for measuring goals. Here are some reports you can check out:
- New vs returning visitors
- Visitors using mobile devices vs desktops
- Source/medium and campaigns
- Landing pages
- E-commerce overview
- Shopping behavior
Step 2: Brainstorm ideas and formulate hypotheses
This step involves generating a list of potential changes you want to test and forming a hypothesis about how each of these changes will affect the desired outcome.
For example, if the goal is to increase website conversions, one idea may be to change the color of the “Shop Now” button from red to green. The corresponding hypothesis would be that the change in color will lead to an increase in conversions.
This step helps to narrow the focus of the testing and guides the next stages in the process.
Step 3: Prioritize ideas
Prioritizing ideas allows you to hone in on the most promising hypotheses and test them first. One effective approach for this is to use the RICE method, which combines four factors (reach, impact, confidence, and effort) to give each idea a score.
Here is a breakdown of the acronym:
- Reach: The number of users or visitors the change will affect.
- Impact: The potential effect of the change on key metrics.
- Confidence: How confident are you that the change will have the desired effect?
- Effort: This refers to the resources required to implement the change.
Considering all four factors helps maximize the return on your testing efforts.
Step 4: Create challenger variants
Next, it’s time to create alternative versions of the website element to test against the original, or “control” version.
For example, if you’re testing the effectiveness of the call-to-action button on your website, the challenger variant of the button might be different in color or size, or it may have different copy.
Creating and testing multiple challenger variants to find the best solution can also be effective. In the call-to-action button example above, you could create three different variants (one with a different color, one with a different size, and one with different copy) and test them all against the control button to see which performs best.
Step 5: Run test
This is the phase where you execute the experiment and collect results. Run the test long enough to gather sufficient data to make informed decisions about the versions being tested.
Your average daily and monthly visitors are vital factors here. If your website sees a high volume of daily visitors, you can probably run the test for a short period. In contrast, you’ll need to run the test longer if you have a lower volume of visitors so that you can gather enough data.
The number of variants you’re testing may also impact the duration of the test. The more variants you have, the more time you’ll need to gather data on each one.
Step 6: Evaluate test results and optimize
The final step for conducting A/B testing is to evaluate the results and optimize them. Here, you analyze the data collected during the test to determine which variant performed better. You can do this by comparing metrics such as conversion rate, bounce rate, and click-through rate between the control version and the challenger version.
If the results show that one variant performed significantly better than the other, this version becomes the winner. You can then optimize the campaign using the winning variant to improve performance.
However, if the results are inconclusive or do not support the initial hypothesis, further optimization is necessary. This usually involves implementing new ideas or conducting additional tests to better understand the results.
For example, if you run the test on an email campaign and the results showed no significant difference in open rates, optimize the campaign by testing new subject lines or changing the email design.
3 real-life A/B testing examples
Ok, we’re done singing the praises of A/B tests and their magic! Take a look at a few real-world examples of top brands that have used split tests:
1. A/B test the design of your messages
In this example, DTC brand Obvi wanted to see if their hypothesis that adding a countdown timer to their discount popup would increase the sense of urgency and result in higher conversion and coupon redemption rates.
They created two variations of the popup, one with a timer and the other without, and tested them with a sample size of their target audience. They were right!
The variant with a countdown timer converted 7.97% better than the one without, indicating that the timer was effective at increasing urgency and conversions.
2. A/B test the effectiveness of teasers
In this second example from Obvi, they tested two versions of their Black Friday popup: one with a teaser (a small preview of the popup) and one without.
The variant with the teaser resulted in 36% more SMS subscribers and a higher conversion rate for the campaign. So they learned that including a teaser in their popup was an effective strategy for increasing engagement and driving more sales.
3. A/B test different types of campaigns
A/B testing different types of campaigns, like in the example below from the team at Christopher Cloos, is a way to discover which version resonates better with your visitors.
In this case, the team tested a classic welcome popup against a more personalized conversational popup and found that the conversational popup converted at a higher rate (15.38% higher, to be exact).
This test was run for a duration of one month, which was ideal based on the store’s traffic. If they’d run the test for a shorter period, it may not have given the conversational popup a chance to fully perform.
Also, note that longer-duration tests can be impacted by external factors such as seasonality, trends, or changes in consumer behavior, which could influence results.
3 A/B testing mistakes to avoid
The last thing you want is to dedicate all that effort and marketing budget into split testing, only to get a false positive or an inaccurate test result. Here’s how to avoid the most common (and costly!) mistakes:
Mistake 1: Changing more than one element
When conducting an A/B test, you should only change one element at a time so that you can accurately determine the impact of that specific change.
Are you testing the effect of changing a button color? Then change only the color of the button in the challenger variant and nothing else. If you also change the text on the button or the layout of the page, you’ll find it difficult to determine which change had the greatest impact on the results.
Changing multiple elements at once can also lead to inaccurate results as the changes may interact with one another in unexpected ways.
Mistake 2: Ignoring the statistical significance
In A/B testing, it’s possible that the results of a test come from chance rather than a true difference in the effectiveness of the variants. This can lead to false conclusions about which variant is better, resulting in poor decisions based on inaccurate data.
Here’s an example: your test shows that variation A has a slightly higher conversion rate than variation B, but you don’t take into account how significant the results are. So you end up concluding that variation A is the better option. However, considering the statistical significance would have made it clear there wasn’t enough evidence to conclude that variant A was indeed better.
Ignoring statistical significance in A/B testing leads to a false sense of confidence in the results, causing you to implement changes that may not have any real impact on performance.
Mistake 3: Not running tests for long enough
This next mistake goes hand in hand with mistake #2: ending a split test before it has had enough time to collect sufficient data to produce a statistically significant. You’ll end up with inaccurate conclusions about the element you’re testing.
Imagine an A/B test runs for only a week and you declare a particular variant the winner. In reality, the results were only due to chance. Make sure you’re running tests long enough to accurately capture the differences between the versions.
Hopefully, this article has shown you just how critical A/B testing can be for optimizing your online store. Once you understand all the different ways A/B testing can help you improve, it’s hard to believe that only 44% of companies use split testing software!
If your business is not currently running A/B tests, it’s not too late to give your conversion rate the TLC it deserves. By split testing different variants, you can identify which elements of your website or marketing campaigns are working (or not) and make strategic changes in line with your goals.
Remember that it’s as easy as creating different versions and comparing the results to determine the best-performing version. Whether you’re a small business owner or a marketing professional, A/B testing is an essential tool to have in your arsenal!