Two pressing needs in digital marketing analytics are estimating ROI and improving conversions through better web metrics. I’ll be talking to you about both in the weeks to come. The first focuses on optimizing the flow of qualified traffic. The second looks at optimizing what you do with that traffic. Both lead to better online profits. Let’s actually start with the second one: conversions. To improve your site’s “close rate,” you first have to acknowledge that it’s a lot like running a bricks-and-mortar store — even if what you’re trying to “sell” is the action of subscribing to an e-newsletter or downloading a whitepaper. You, as a hypothetical shopkeeper, can improve close rates by enhancing your store displays, experimenting with new offers, plus a hundred other tweaks to the “content” of your store. In other words you’d optimize your store’s content the way a web content manager adjusts page content. Both of you would be doing it for the same reason. You’re trying to improve interest levels in order to get higher conversion rates. Measuring consumer interest on a web page For years I’ve been working on a single metric that focuses on visitor interest levels. It scores web content (text, graphics, video and audio) on its ability to cause people to be interested during a key phase in the sales cycle, when consumers are not yet ready to buy. In doing so, I’ve tried to address a galling problem in web analytics: digital window shopping . Today we have a lot of knowledge about what people do once they arrive at a site. Modern web analytics allows us to see exactly what pages someone viewed before they converted. That’s very nice, but this work is driven by a deeply flawed assumption: That everything a visitor needed to know to decide on the purchase was acquired right then, during that singular user session. The problem with optimizing content around conversions is that usually it’s only a small minority of people who convert on their first visit. Instead they visit, look at a page or two, think about it, and then come back once, twice, or even more times — all before they take an action! To add even more complexity, they may use different computers each time, or even a smart-phone visit or two. Say goodbye to tracking people over time via cookie files! People need to become comfortable with your offer, and see how the benefits outweigh the costs. This takes time. Image from Wikimedia.org courtesy of Creative Commons My solution forgets about conversion for the moment. It assumes that before visitors go further into the purchase process, they first become interested. Years ago I was surprised to find that there is little devoted to this important part of the online sales process, so I set about creating a metric of my own. This metric isn’t arrived at by surveying users, but by measuring their behavior. That means standard web analytics systems can come up with this score fairly easily, even the free Google Analytics. (However, it only works for sites with tens of thousands of web visits every month, and extracting the most value from the metric requires a site where content managers can archive and later revisit past versions of the pages they manage.) Paying attention to the nose prints I first published something on this metric, called “content interest index,” on my blog . Back then I struggled to describe what it measures. Now I talk about storefronts and display windows … You, the shop keeper, can look out your storefront and watch prospective customers pass by. If you display merchandise in the window, you can count how many people look in briefly before continuing their walk past. That’s basically what a page view does. It tallies up visitor attention. Which is a good first step; Attention is essential to any sale, and it’s the “be-all and end-all” of traditional advertising. But any bricks-and-mortar retailer will tell you that glancing in a window is one thing. Actually purchasing something is entirely different. Once consumers come into a store and talk to a sales clerk, they’re two stages deeper into the sales cycle. They’re just one step away from a purchase. You might say that they’ve entered a “conversion funnel.” Most sales floor retailers study their in-store conversion funnels as closely as online marketers do. Like us, they try to keep them tuned up and humming along. In both worlds this is the manipulation of consumer “desire” (used in a marketing sense — see my blog post for an elaboration ). But what about that important stage between looking in the shop window (attention) and actually negotiating with a sales clerk (desire)? That crucial middle step is what my content interest index measures. It’s analogous to counting the nose prints on the store window, and comparing how many smudges you find on the glass immediately in front of each product displayed there. It looks for those people who are so interested in a product or offer that they “leave a mark” — something we can measure and compare. So what? This is especially important online because we’ve seen that prospective customers visit our web sites with far more frequency that most people would (or could!) visit a bricks-and-mortar store. To say this another way, online consumers can visit with an interest that is strong, but shy of converting, many times before they finally convert. “Nose prints,” both real and virtual, are behavioral proxies for interest. When reported properly, the virtual kind (expressed through content interest index) can serve as a coaching tool to those content managers responsible for the key web pages surrounding your site’s conversion funnels. My next post will talk about how content interest index is generated by watching behaviors that include sending content to a printer, emailing it to a friend, and social media sharing.
