A report from Forrester titled “Need to Cut Costs? Improve the Web Site Experience” lists a few easy-to-measure indicators that reliably show the benefits of usability testing. These indicators include fewer customer-service calls about products and Web sites and shorter calls regarding complex issues. We can link changes in such indicators to improvements in a Web site’s user interface. However, there are many other factors that can lead to such changes—such as better product promotion, better support documentation, better internal communication, or simply a decrease in sales. As usability professionals, we need a clear way of showing what we’ve learned from usability testing, how our recommended changes have gotten implemented, and what positive outcomes have occurred because of these changes.
Challenges in Implementing Recommendations from Usability Testing
In an ideal situation, after a well-planned usability study, a redesign takes place, and all of a usability professional’s suggested design changes get implemented. However, in real-life situations, there are several challenges that impede such a result.
First, usability testing usually happens when a Web site is already fully functional and, from the client’s? perspective, is in relatively “good shape.” The reason a client wants to invest in usability testing is often to discover quick fixes for the current site. However, frequently, the issues usability testing identifies are neither easy nor quick to fix, which leads to a situation where nothing happens to remedy the usability problems we’ve found.
Second, depending on the amount of time and the budget available for usability testing, studies may involve different numbers of users. Often, the number of users participating in a study is a very small proportion of a larger customer base. Therefore, clients often question the accuracy of the results. “Sure, you heard from two or three people telling you about this particular problem during the testing. What if the thousand other people you didn’t get to talk to disagree with you?” Such thoughts might stop clients from taking full advantage of the test results.
Third, usability testing is qualitative in nature. You may get a finding like “Users are confused by this page and are not sure what to do to move forward.” This tells you that there are problems with the current design, but it’s up to the usability professional to interpret the users’ words and come up with design suggestions, which may vary from person to person. Different usability professionals might agree on the usability problem, but have very different suggestions for improving the design.
A/B Testing and Its Challenges
A/B testing lets you compare the effectiveness of two different versions of the same Web page. Usually a controlled portion of a page’s traffic goes to version A and the rest to version B, so different customers interact with different versions of the page. During A/B testing, you can collect data regarding key performance indicators (KPIs) such as conversion rates, enabling you to compare the results of the two versions. The reason big companies such as Amazon and Google are fond of A/B testing is simple: data talks. Conversion rates don’t lie. Either version A works better or version B does—as the numbers easily demonstrate.
However, achieving really good results with A/B testing involves a whole different set of challenges. First,?you need to develop ideas for alternative design directions for various pages. Is the layout going to be different? Should you try a different picture? Use a different font size? Although ideas may be flying about, there is not necessarily any guidance for how to come up with a winning alternative. What if layout A works better with picture B, but layout B works better with picture A? In cases where there is enough traffic, you can conduct multivariate testing to see which combination of elements works best. However, you can confront a real quandary when all of the alternatives you’ve tested work equally well or badly. It may be that none of the alternatives you’ve tested allow you to discover the factor that would have the most impact on the effectiveness of a page design, so the optimal solution remains undiscovered even after the A/B testing.
Next, in developing the different versions of pages, there may have been a lack of user input and, thus, the designs would have no foundation in user wants and needs. Version B often gets generated by playing around with multiple elements on a page. Without solid user input, such tactics result in generic solutions. Next time, you might run a test, expecting to repeat your successful results in another situation or context and get completely opposite results, because you’re testing with a different user base, who have different motivations, or are accustomed to different user interaction patterns.
Finally, A/B testing is quantitative in nature and, thus, lacks qualitative insights. Although A/B testing usually reaches a large audience, all it provides is a comparison of key performance indicators between two different versions. You might get positive results, but you don’t really know why you got those results or why users prefer one version over another version.