What is A/B Testing?
A/B testing, also known as split testing or bucket testing, is a method for comparing two versions of a web page or app to see which one performs better. As a key component of Optimizely’s web experimentation platform, A/B testing involves showing different variants of a page to users at random and using statistical analysis to identify which version gets closer to the desired conversion goal. This process helps businesses optimize their websites by understanding which elements most effectively drive user behavior.
Common Mistakes and How to Avoid Them
While 77% of organizations reportedly use A/B testing to optimize their conversion rates on corporate landing pages, it’s easy to make critical mistakes that can skew results and lead to misguided decisions. Here are some of the most frequent missteps and how to avoid them to ensure your tests yield meaningful insights:
Mistake #1: A Flawed Hypothesis
Your hypothesis should be a well-founded prediction based on comprehensive research and data analysis—not just a hunch. A shaky foundation yields unreliable results, and tests based on guesses or hunches often produce inconclusive outcomes.
Ground your hypotheses in user research to address actual pain points. For example, the hypothesis that reducing the number of form fields from 10 to five will increase the conversion rate by 15% could stem from several key insights, including:
- User behavior analysis, such as historical data and session recordings, reveals drop-off points where users abandon lengthy forms.
- User feedback, gathered through surveys, interviews, and usability testing, highlights user frustration with long forms.
- Industry benchmarks from research studies and reports indicate that shorter forms lead to higher conversion rates, reinforcing the hypothesis.
- Competitive analysis, including examining successful competitors and market trends, confirms that simplicity enhances user experience.
Preliminary A/B tests with smaller user groups can also validate the hypothesis on a smaller scale before broader implementation. By grounding your hypothesis in these concrete data points and user insights, you ensure that your A/B tests are data-driven and aimed at achieving measurable improvements.
Mistake #2: Testing Too Many Variables
Overhauling your entire website may be tempting, but testing multiple variables at once obscures the true cause of any changes. Complex tests make it difficult to pinpoint what’s driving a positive or negative outcome. Isolate a single variable per test to establish a clear cause-and effect relationship, providing clarity and precision in your findings.
Imagine an e-commerce shop selling athletic wear wants to improve mobile checkout times, for instance. Instead of testing a combination of changes, such as a streamlined checkout process, a guest checkout option, and Apple Pay integration, they could run separate tests for each variable to see which one has the most significant impact.
Mistake #3: Insufficient Sample Size and Short Test Duration
Rushing to conclusions with limited data is a recipe for disaster. An insufficient sample size or a short test duration can lead to unreliable results and misguided decisions. Use sample size calculators to determine the minimum traffic needed for statistically significant results, and let the test run its course to ensure your decisions are based on robust data rather than premature assumptions.
Mistake #4: Ignoring the Customer Journey
Testing the "About Us" page might make you feel productive, but it won't necessarily move the needle. Prioritize tests on high-impact pages within the conversion funnel where user behavior directly influences your bottom line. Focusing on key areas like product pages, checkout flows, or registration forms can significantly enhance your overall performance.
A travel booking website, for example, might hypothesize that offering users the ability to filter search results by travel style (e.g., adventure, relaxation, luxury) will lead to more bookings. This test would be more impactful than A/B testing a change to the company blog layout, as it directly addresses the user's decision-making process and potential conversion.
The Value of Negative and Neutral Tests
Not every test will be a slam dunk, but that doesn't mean it's a failure. The surprising truth is that negative and neutral tests can be just as valuable as winners.
Negative Tests
Your bold new idea might fall flat—and that's okay. A negative test that disproves an assumption can save you time and resources by preventing the implementation of an ineffective change. It can also refine your understanding of user behavior and guide future test hypotheses.
Let's pretend a B2B cloud storage solution provider hypothesizes that offering a free trial with a shorter signup process (requiring minimal information) will lead to more new customer sign ups. In this example, they implement a streamlined signup process for the free trial in the test group. Meanwhile, the control group experiences the standard signup process requiring more detailed company information. After the test period, the data reveals a negative outcome: the free trial with the shorter signup process had a higher number of signups but a lower conversion rate to paying customers than the control group.
This result, while seemingly negative, is incredibly valuable. It suggests that users taking advantage of the more straightforward signup process were less serious about adopting the cloud storage solution and were signing up for the free trial without a strong intention to convert.
With this insight, the company can refine its free trial offer. They may consider adding a verification step during the shorter signup process to identify genuinely interested users or test offering different types of free trials with varying levels of access to better qualify leads and increase the likelihood of converting them into paying customers.
Neutral Test
A neutral test result might seem inconclusive, but it can be similarly insightful. It might indicate that users are indifferent to the changes you tested. Understanding this can help you prioritize future tests by highlighting areas where user experience craves improvement.
Consider a popular online shoe retailer that hypothesizes adding a 360-degree product view functionality will significantly increase online shoe purchases. They implement the feature in a test group and compare it to the control group with standard product images. After the test period, the data shows no significant difference in conversion rates between the two groups.
This neutral test result suggests that while the 360-degree view might be a nice feature, it's not a significant buying decision factor for their customers. With this understanding, the retailer can focus on testing other areas that have a bigger impact on sales, such as faster checkout processes or improved product filtering options.
Transform Split Testing into a Strategic Advantage
By avoiding these common pitfalls and embracing the value of all test results, you can transform your Optimizely A/B testing from a guessing game into a strategic tool for website optimization. Remember, even negative and neutral results are valuable learnings that can guide you toward a website that truly resonates with your users and drives conversions.
A well-structured hypothesis, focused testing, sufficient sample sizes, and attention to high-impact areas of the customer journey are crucial to successful A/B testing. These best practices help you avoid common mistakes and ensure that your tests provide actionable insights. Whether a test outcome is positive, negative, or neutral, each result is a stepping stone toward better understanding your audience and refining your strategies.
So, get out there, test thoughtfully, and watch your website thrive. Embrace the process, learn from every result, and continually optimize your approach. With careful planning and a strategic mindset, A/B testing can be your most powerful ally in achieving a high-performing website.
Ready to get started? Explore Optimizely's documentation and tutorials, or contact Nansen to learn more.
