A/B tests are indispensable for the data-driven marketer, allowing you to make decisions based on real-time user behavior. These tests have a straightforward process that can be applied to any number of marketing campaigns. But like any experiment, A/B tests must be performed with the proper planning and tools for the results to be impactful.
What is an A/B Test?
A/B testing – also known as split testing – compares variations of the same marketing campaign to determine which version is the most successful. As its name suggests, A/B tests compare two distinct variables. This stands in contrast to multivariate testing, used when experimenting with numerous variables at a time.
How Do You Use A/B Testing?
At the most basic level, A/B tests determine the superior version of a given marketing campaign by testing changes to its content or design. A/B tests can compare seemingly minuscule elements of a marketing piece, such as a CTA button color, or larger variables such as the right time of day to send company newsletters.
Beyond learning about the specific variable A/B tests reveal valuable insights about your audience, helping you to hone your messaging and improve customer experiences. A/B test results can be influential over product positioning and other high-level components of your brand strategy if used correctly. Consider the principles below to successfully use A/B testing as an effective marketing tool:
Conduct A/B Tests Regularly
Consistent testing is necessary if A/B tests are to reveal deeper insights about your audience and company strategy. Being “data-driven” is an ongoing process where businesses continually identify areas of improvement, test hypotheses, and optimize campaigns based on user behavior.
One-off tests may help to solve specific problems for a short period of time, but testing won’t be a driving force behind your marketing strategy until it is part of your regular data-gathering efforts. It’s also important to remember that A/B test results are finite. Testing does not lead to one-and-done solutions, since markets and customer needs shift over time.
Cross-Apply A/B Test Results Within Your Marketing Strategy
Rather than thinking of A/B testing in a vacuum, maximize the benefits of your findings by cross-applying results. Consider how test insights can help you understand your audience better and hone your marketing strategy as a whole.
How Do You Run an A/B Test?
A/B tests resemble scientific experiments, with data-driven results that are easily compromised by human error. There are five guidelines we recommend to obtain usable test results.
1. Develop a Hypothesis
If you don’t lay the proper groundwork for your A/B test, you might find yourself in a situation where two campaigns perform differently, yet you lack an accurate understanding of what’s driving the divergence or how to interpret the results.
To run a successful test, marketers need to start with a hypothesis – a theory that changing a particular variable (such as a CTA button) will improve a particular outcome (increased clicks). A/B tests are best built on researched hypotheses with clear goals that have a measurable impact on your marketing plan.
Iterating on an A/B Test Hypothesis
Here’s an example: A business that normally sends an email promotion at 2:00 pm runs a test at 7:00 pm under the hypothesis that communicating with customers outside standard work hours will drive up engagement. The evening campaign sees high enough open rates and click-through rates to confirm the hypothesis. Next, they can try various pre-work hours or late-night hours to find when their audience is most engaged. Since this marketing team had a clear goal starting out, they know the open rate difference is not incidental, but indicative of when their audience has more free time.
2. Select a Measurable Goal
Having a specific, measurable goal for your test is the difference between a true split test and a mere comparison. Choosing a measurable goal requires working with an existing campaign and having access to metrics that track the results. Key performance indicators (KPIs) of an A/B test might include:
- Higher conversion rates
- Increased open rates
- Click-through rate improvement
- Additional landing page traffic
- Lower bounce rates
3. Design the Control vs. Test Campaigns
An A/B test consists of a control campaign and a test campaign. The control campaign is your existing piece — it shouldn’t be changed in any way. The test campaign is the new version containing one experimental variable —and no other changes. Be careful to conduct your control and test campaigns in the exact same way except for the element being tested. Incorporating too many changes is a common A/B testing mistake that will compromise your results, as you won’t know which modifications are affecting campaign performance.
4. Set a Strategic Timeline
A/B tests are run simultaneously to achieve the most accurate results. Email, website, and advertising platforms – for example – make it simple to deliver the two versions to equal portions of your audience. But be careful that your test doesn’t overlap with a holiday, busy season, or any other time that could skew the results – unless those distinct time frames are part of your test.
How long to run your test depends on what length of time allows you to detect true behavior patterns and varies by campaign. A statistically significant timeline is one that gives you enough data to draw accurate conclusions. An advertisement may need to run for a week or more before you start comparing results, while a once-a-month email newsletter may only need 48 hours before you can detect differences between your new campaign and baseline engagement. Observe campaign histories to understand how long it usually takes your audience to interact with content, then you’ll know when to assess results.
Statistical Significance in A/B Tests
Statistical significance in A/B testing means you can prove a direct link between the change that you made and improved campaign performance. Human error or chance are major factors in any user behavior experiment, and your test will need to draw conclusions only from the results you can confidently trace back to a specific variable.
5. Experiment with a Meaningful Sample Size
In order to achieve statistically significant results, your campaign must have enough engagement that the test results are indicative of true patterns in user behavior. If your sample size is too small, you won’t know if performance differences are due to your variable or to chance. The larger your sample size is, the more meaningful the changes in performance metrics become.
A good rule of thumb for email marketing is to wait until your list reaches 1,000 subscribers before testing changes. A lower number may not give you an accurate picture of your target audience. If you are testing changes to your website with a specific goal in mind – such as conversion rate optimization – keep in mind both website traffic numbers as well as current conversion rates. For example, it’s unlikely that a landing page not optimized to convert traffic will have enough conversion data to make an A/B test worthwhile. The page might need an overhaul to align with its purpose before benefitting from A/B tests to hone the messaging.
Common Ways to Use A/B Tests
Split testing is a straightforward method that can be used in any number of marketing efforts. These are some of the most common uses for A/B tests that almost any business owner or marketer can employ with the campaigns they are already running.
Your email list is an owned asset and one of the most regularly used marketing channels in every type of industry. Many companies have established email campaigns with a long history of helpful data analytics and plenty of content to work with. A/B testing for email campaigns could include industry newsletters, announcements, promotions, or any other type of company correspondence that could use higher engagement. Examples of email elements to A/B test include:
- Email subject line
- Preview text
- Sending time
- Visual design (fonts, colors, etc.)
- Call to action
Want to know more about how to A/B test an email? Find details in our guide on split testing email campaigns
Your website is one of your most important sales and marketing channels because it’s the primary means of getting in front of online customers. Buyers in the digital age expect to be able to search for your company and learn all they need to know even before contacting you.
Web pages such as your homepage, pricing page, or checkout page are crucial for getting the attention of website visitors and moving them forward in your sales funnel. A/B testing helps you discover which messages and design elements resonate most with your target audience, allowing you to optimize conversion rates and keep customers engaged with your online brand. Consider A/B testing elements like the following:
- Page length
- Featured lead magnet
- Visual design (fonts, colors, etc.)
- Call to action
Tip: If you’re stuck trying to figure out which parts of your website are worth testing, use heatmap software to identify which sections get the most (or least) interest.
Pay-per-click (PPC) ads on social media or search engines can drive up lead acquisition costs if they are not optimized to perform as efficiently as possible. Split testing helps businesses zero in on exactly the right message and ad placement to bring in qualified leads. There are a wide variety of ad elements that can be tested and refined, such as:
- Ad copy
- Landing page
- Audience targeting
- Time of day
- Call to action
Automate A/B Testing with Marketing Software
Marketing software makes consistent, data-driven testing possible. Email marketing and landing page software enable brands to run automated tests on their assets, and digital advertising platforms include native A/B testing tools. Automation ensures higher quality test results by randomly dividing your campaigns between even numbers of users so the data is accurate and unbiased.
All-in-one marketing software further simplifies the A/B testing process by consolidating marketing tools into one platform. For example, marketers can run tests on website pages by adjusting templates without needing to coordinate with web designers. Plus, all-in-one software integrates data analytics, offering a birds’ eye view of user experience and behavior patterns without having to cross-read data between independent analytics dashboards.