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Web analystics is helping a new generation of pharma web sites get engaged.
The new generation of pharmaceutical Web sites is richer, more community oriented, more interactive, and more personal than what came before. Today, most branded-site experiences include at least some combination of rich media and community building, routinely featuring extensive video from both healthcare professionals and patients. Assessments, surveys, caregiver support, and communities are becoming the standard, not the exception. Yet as customers begin to shift more attention to the Internet, there remains one vital question: How do we know if this fancier, more expensive experience is actually working?
Web analytics is the study of how visitors behave on a Web site. It's a powerful set of techniques for helping companies optimize their Web sites and understand their customers better. The basic premise of Web analytics is simple: You can relate various site behaviors to outcomes, and figure out which strategies work well and which don't.
Of course, this is all very easy in retail, where outcomes are obvious and measurable. But most industries are not that lucky, and few have as challenging an environment as pharmaceuticals. After all, how do you measure the success—and more importantly, the difference in success—between site designs when you aren't sure what represents "success." The answer: You can't. So the first step in any analytics effort is figuring out how to measure success, then determining what that means for each page being viewed.
The most common measure for what constitutes success on pharmaceutical sites is a concept called "engagement." Engagement is often measured by how much site content a visitor sees. However, that "how much" is often, incorrectly, measured by how long a visitor spends on a site or site tool and how many pages a visitor views. These are useful measures, but they're not always the best measures of engagement, and they rarely tell the whole story.
Here's a common example. A branded pharma site added a set of patient-story videos to their home page. By clicking on a link, visitors were taken to a site that played a patient's story, and were given the option of playing additional stories. The new addition was reasonably popular. Visitors viewed multiple movies, and spent quite a bit of total time watching the patient stories. It looked like a success! Careful measurement, however, showed that it was less of a success than originally perceived. Viewers of the video rarely surfed other places on the site or looked at information about the drug, and they almost never viewed the page on how the drug works. Furthermore, patient-story viewers had extremely low rates of registration or printing information.
The problem resulted from a lack of navigational alternatives within the videos, as well as how they were placed on the site. After viewing a patient story, the only obvious navigational options were more stories. As a result, visitors typically navigated through the stories until they were done, and then left the site—accomplishing nothing for the pharma that owned the site. There was no attempt to match the patient stories to relevant content (such as living with the condition or taking the drug). In addition, the only navigational path to the videos was from the home page. Visitors either viewed the videos or they viewed the rest of the site, but not often both. So even where site content matched a patient story, there was no easy navigational path between the content and the video.
These two factors combined to isolate the patient stories from the rest of the site, making them much less effective than they might otherwise have been. Tighter integration of video and rich-media components on sites like RiseSupport.Com or Tarceva.com illustrates how much more can be attained when these experiences are carefully and thoughtfully embedded within the site.
Web analytics is the perfect tool for pinpointing problems that usability testing and even highly professional design can often miss. Poor integration of rich-media experiences is a common problem on pharmaceutical sites; and the problem, more often than not, is that no one understands how to measure successful integration.
One way to combat this type of parochialism is to use a functional approach to your measurement. The idea behind functional analysis is simple: Each part of your Web site was built for a specific reason, and by tracking how well it serves a function you can measure its success. When you add patient stories to a site, do you only want visitors spending time on that feature, or do you want to influence them to join a community, register for information, or check out how the drug in question works?
With this understanding, look for ways to facilitate those actions by adding links and placing patient stories next to those sections. This will likely boost the performance of the page relative to its function. And by measuring for the outcomes you've targeted, you create a feedback mechanism to drive a cycle of continuous improvement on your site—the ideal for any Web measurement program. Isolate rich media experiences from the rest of your site and risk losing the site's overall effectiveness.
Furthermore, too much focus on measures such as time-on-site or views per visit can simply make the problem worse. This underscores a basic truth about Web analytics—that optimizing to the wrong goals is often worse than no optimization at all. It isn't enough to perform Web analytics, you have to do it right, and doing it right means defining success.
At the opposite end of the spectrum, some sites have focused solely on interactive components like registration or downloads to measure site engagement and success. But "branded drug" sites also have key informational pages that anchor most successful sessions. Remember, if you only focus on interactions, you'll ignore the sessions where visitors seek information about the product but are not motivated to register.
Key pages are somewhat different for every site, but they're usually obvious (like the "how it works" page on most branded sites) both from the standpoint of site design/intention and from a study of actual site behavior. They also have a distinct behavioral signature that will show up in your measurement. Key pages typically have higher exit rates than immediately surrounding pages (a high exit rate is not necessarily bad; it can indicate that the visitor has found what he or she was looking for), a wider range of "next step" behavior than surrounding pages (when visitors finish a task they branch out), and longer average page times than surrounding pages. Most visitors will not be likely, even with the best of sites, to register or self-assess. So it is vital to measure your success by getting visitors to key pages.
Web sites in the pharma industry are becoming more expensive and complicated. Sites that are richer in content, media, and socialization, but still lack the obvious success metrics, are more challenging than ever to measure. But with careful attention to each part of a Web site, its overall structure, and the full range of interesting outcomes, it is possible to judge accurately how well your site works and target ways to improve it. This is the role, and the promise, of Web analytics.
Gary Angel is president of Semiphonic, a web analytics consultancy. He can be reached at firstname.lastname@example.org
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