So what, better than what, and what type of results?
This snippet from the Economist’s review of Brian Christian’s book about artificial intelligence – The Most Human Human: What Talking With Computers Teaches Us About What It Means To Be Alive – reminds me that web analytics is about people, not data:
“People produce timely answers, correctly if possible, whereas computers produce correct answers, quickly if possible. Chatbots are also extraordinarily tenacious: such a machine has nothing better to do and it never gets bored.”
This spring the nine hardy students who took my USC Annenberg web analytics class came up with wonderful insights that could never have come just from reading a report straight from Google Analytics, Omniture or any other chatbot. Part of their grade was based on whether the (equally hardy) participating news and nonprofit organizations were actually going to use their analyses for decision-making. This meant each student had to really understand the organization’s strategies, goals and personalities before he/she dug into the data. Here are some of the things we learned.
Content is indeed king, but only if it’s coded
None of the organizations coded site content with enough detail to make decisions about what to do with their sites. Data coming straight out of Google Analytics or Omniture was coded only by date published and by broad categories such as “News.” This is the equivalent of marking a box of books “MISC” – or putting in “stuff” in any search engine.
For example, let’s say an organization believes it can build and engage audiences by adding “more local politics and government coverage.” To track whether it did indeed produce “more,” and what coverage did result in increased visits and engagement, the org needs to track how many politics stories it has, by topic and local geographic area, and how much traffic each topic and/or area gets.
Each student developed a taxonomy of codes the organization could use to classify its content, and then manually (I told you they were hardy!) coded sample data pulled out from chatbots, er, Google Analytics or Omniture.
Track traffic by day, week and by topic, to determine if the site is getting the traffic it should
Many organizations look at monthly data, and celebrate traffic spikes. Hidden in monthly data, however, are clues to where to build targeted audiences and advertisers. Health/fitness section traffic, for example, should increase the second week in January, perhaps fall off after Valentine’s Day (!), and increase before swimsuit season.
More content = more traffic
Looking at visits by day of week, we saw radical drops in visits on the weekends. This seemed to be due to little unique local content being posted on Saturdays or Sundays. In this age of the 24/7 newsroom and increased Internet access through mobile, can news orgs afford to make resource decisions based on non-audience-based, chicken-and-egg logic (“We don’t get much traffic on the weekends so we can’t justify adding weekend staff.”)?
Sometimes you should have separate sites for each audience segment….
Josh Podell, an MBA student, focused on analyzing the e-commerce donation functions on the nonprofit sites. He observed that it’s hard to understand what works and what doesn’t when donors are coming to the site to find out more about the organization but residents are coming to find out about programs and services. Josh’s suggestion: Have a completely separate site – and Google Analytics account – for donors. An org could have much more focused content for each audience, and metrics such as visits per unique visitor, page views per visit and the percent of people who left the site after looking at just the home page (home page bounce rate) would give much more clear indicators for both sites.
….but sometimes you shouldn’t.
One of the organizations had its main site on one Google Analytics account, and its blog on another. Dan Lee, a graduate Strategic Public Relations student, noticed extremely high home page bounce rates from returning visitors compared to new visitors.
With the question of why burning in his head, Dan looked in detail at the site content and structure, and hypothesized that returning visitors were most likely to go to the home page, see the teaser about the latest blog entry, and immediately “leave” the site to go to the blog. The Google Analytics account for the blog did indeed show that its top referring site was the main site.
Google Analytics was “correct,” but only a human could have produced the right answer for the organization.
So here’s to Brian Christian for explaining how to be “the most convincingly human human,” and how the competition for the Loebner prizes continues to result in what the Economist calls “a resounding victory for humanity.” And here’s to the most adventurous and tenacious students at USC Annenberg – the journalism, public relations and business majors who took an experimental elective class with “web analytics” in the title!