A successful A/B test requires careful planning and execution.
It’s essential to have a team capable of designing effective test scenarios, analyzing results rigorously, and iterating based on learnings. Poorly designed experiments waste time and resources.
In this article, we’ll outline key steps to create successful A/B tests that drive meaningful results.
Pre-testing
Before diving into testing, ensure you have clear criteria for deciding:
- Which elements to test on your website or app
- What external and internal factors could influence the results (e.g., seasonality, marketing campaigns)
- How to create variations for your tests that have a real chance of impacting user behavior
Remember, A/B testing is just one part of the larger conversion optimization process. It should ideally come after foundational work like:
- Developing user personas
- Conducting thorough site analysis
- Refining your design and copy
This ensures your tests target the right areas and are informed by a deep understanding of your audience.
For a more in-depth guide on our CRO process, you might want to check out a more detailed guide on how we conduct conversion optimization projects.
Problem Identification
Before considering elements on the page to test, start by analyzing different problem areas on your website. This helps prioritize your efforts and focus on changes that will have the most significant impact.
There are several conversion optimization methodologies available to guide your analysis. At Invesp, we utilize the Conversion Framework, which systematically examines seven key areas of a webpage:
The Conversion Framework analyzes seven different areas on the page:
- Personas: Are you targeting the right audience?
- Trust & Confidence: Does your page inspire trust and credibility?
- FUDs (Fears, Uncertainties, Doubts): Are there elements that could raise concerns for visitors?
- Incentives: Are you offering compelling incentives for visitors to take action?
- Engagement: Is your content engaging and relevant?
- Buying Stage: Is the page aligned with the visitor’s current stage in the buying journey?
- Sales Complexity: Is the process of purchasing or converting unnecessarily complex?
These seven areas will influence whether visitors stay on your website or leave. Different elements have diverse impacts based on the type of page you are evaluating.
Using the Conversion Framework, a conversion optimization expert can quickly pinpoint 50 to 150 problems on a webpage.
However, attempting to fix all of them at once would be overwhelming and inefficient. Instead, prioritize and focus on the top three to seven problems to start. This targeted approach allows you to make meaningful improvements and gather data for further optimization.
Test Hypothesis
A hypothesis is a predictive statement about the impact of removing or fixing one of the problems identified on a webpage.
For instance, our client selling nursing uniforms experienced high cart abandonment rates. Usability testing revealed visitors were price-conscious and feared overpaying.
Original Design: The initial shopping cart lacked clear assurances about price matching and money-back guarantees. This fueled visitor concerns.
Hypothesis: Adding prominent assurances to the cart page would reduce price concerns and decrease abandonment by 20%.
New Design: We introduced an “assurance center” on the left-hand navigation, highlighting the price match and money-back guarantees.
Results: This change led to a 30% reduction in cart abandonment, validating our hypothesis.
However, hypotheses aren’t universal.
A successful hypothesis for one website doesn’t guarantee success on another. A different client, also aiming to reduce cart abandonment, implemented a similar assurance center.
The image below shows the original design of the cart page:
The following image shows the new design of the cart page with the assurance center added to the left navigation:
Surprisingly, this change decreased conversions by 4%. Several factors could explain this: the design, copy, placement, or even a fundamental difference in their target audience.
The Importance of Validation and Iteration
Validating hypotheses through testing and refining them based on results is core to conversion optimization. In this case, further testing of the assurance center’s elements would be needed to determine its true impact.
Tests that increase conversions are great, but even those that decrease them offer valuable insights into visitor behavior and the accuracy of our hypotheses. The most concerning results are those that show no significant change, as they indicate a need for deeper analysis or a different approach.
Remember, each website and audience is unique. Continuous testing and refinement are crucial for uncovering what truly drives conversions for your specific context.
Create variation based on the test hypothesis.
Once you have the hypothesis, the next step is to create new page designs to validate it.
Be careful when you are creating new designs. Do not go overboard with creating new variations. Most split-testing software allows you to create thousands, if not millions, of variations for a single page. You must remember that validating each new variation requires a certain number of conversions.
We limit page variations to less than seven for high-converting websites. We limit page variations for smaller sites to two or three new variations.
Let visitors be the judge: test the new designs.
How do you judge the quality of the new designs you introduced to test your hypothesis? You let your visitors be the judge through AB or multivariate testing.
Remember the following procedures when conducting your tests:
- Choose the right tools: Select the right AB testing software to speed up the test implementation process. Technology should help you implement the test faster and not slow you down.
- Optimal test duration: Do not run your test for less than two weeks. Several factors could affect your test results, so allow the testing software to collect data long enough before concluding the test.
- Avoid overly long tests: Do not run your test for longer than four weeks. Several external factors could pollute your test results, so limit the impact of these factors by limiting the test length.
Conclusion: A/B Testing for Continuous Improvement
A/B testing isn’t a one-time fix, but an ongoing process of learning and refinement. By systematically identifying problems, testing hypotheses, and creating data-driven variations, you can unlock valuable insights and continuously improve your website’s performance.
Remember, each website and audience is unique. There’s no one-size-fits-all solution. Through rigorous testing and data-driven decisions, you can tailor your website to your specific audience and achieve your conversion goals.
Need expert guidance to optimize your website for maximum conversions? Invesp’s team of conversion optimization specialists is here to help.