The Convergence of Traditional Database Marketing and Web Analytics – Tying the Threads Together
Like many a sports fan, I often bemoan the length of the sport’s seasons. Baseball’s interminable 162 game marathon. Hockey’s never ending playoffs after a seemingly never-ending regular season. Basketballs meaningless and difficult to endure regular season. But in the case of this most extended of blog series’ – begun in January and nowhere near completion here in July – who (else) can I possibly blame?
Not only is the series large, but it spans the inevitable interruptions for the hot topic du jour, the Conferences and webinars, the occasional vacation or holiday, and the inevitable flotsam of a blog. It’s hard enough for me to keep the thread intact, so I can only assume that the task is more than I can reasonably ask of any reader.
Yet I have reached another key pivot point in the series. It may not be entirely clear how my extended discussion of Two-Tiered Segmentation fits into the broader theme. In the next posts, I’m going to tackle that question. Before I do, however, I thought it worthwhile to recap the series, refresh the key points, and provide some convenient linkages to the various posts in the series.
The series began with a set of foundational posts on the nature of Web analytics and how it works. In the very first post, I laid out the claim that “EVERY single Web analytics technique depends on some combination of the assumption of intentionality and an understanding of the ‘natural structure’ of the Web site.” By the “assumption of intentionality,” I meant that you could infer what a visitor wanted to do by studying their actual behavior. By the “natural structure” of a Website, I mean that a Website has a set of defined navigational paths that restrict and channel users – sometimes in ways that aren’t suggestive of actual intention.
These two principles are both constantly in-play and often in direct contradiction. Imagine following a person as they walk across NY City. If they go to directly to Point B (a Starbucks) and stop, and, in tracing their route from Point A to Point B we realize it’s the shortest possible route, then we assume that their intention was to go to Starbucks. Suppose, however, that they walk in a more erratic fashion, circle a block once or twice, and then finally go into a Starbucks they passed earlier. Might we assume they were looking for something else and ended up at a Starbucks? The navigational structure of the city imposed limits on their path. The type of intention is inferred from the behavior. That’s how Web analytics works.
In my second post, I showed how the concept of “natural structure” changes analytics methods and makes the direct adaptation of statistical modeling to Web analtyics data mostly fruitless. If you model basic Web behaviors using standard statistical techniques, what you capture is correlations caused by the built in structure of the Website. It’s like concluding that the Holland Tunnel is the favorite drive of NY’ers because they spend the most time there! No one would make that mistake with highways, but statisticians make that type of mistake routinely with Web data. This post also shows how our Functional Analysis techniques are specifically designed to solve some of the problems introduced by Website structure.
In the next couple of posts, I delved into the traditional Database Marketing world – a world with a set of rich and effective analytic techniques being applied to channels that are slowly dying. I showed how Database Marketing uses a few simple techniques to enrich customer data and to make the link between the data we have available and the intentionality we seek. In subsequent posts, I showed how the same techniques could be applied to Web analytics but also discussed some of the challenges. Traditional database marketing didn’t have to deal with the “natural structure” issues inherent in the Web, and they had a direct tie in survey research between the variables they used to target and the variables they collected (Demographics). In digital analytics, we don’t have that direct tie. Nothing we capture in opinion research is a direct tie to the targeting data (web behaviors) that we have available to us in most cases. The task for Web analytics, then, becomes clear. We need to find a way to forge a tie between behavioral data and intentionality.
In “It’s all about the (Meta) Data”, I showed the first step in building that bridge. Meta-Data about page view events provides context to the events in ways that often make intentionality much easier to infer. I described a baker’s dozen meta-data elements – often critical to effective digital analytics – that are mostly ignored by Web analytics implementations. That’s a point I’ve hammered home several times since in showing how an over-reliance on technical expertise in Omniture (and other tool) implementations often leads to analytically impoverished results. People (and companies) who build but don’t use Web analytics implementations for analysis or Targeted Marketing simply don’t understand what types of variables need to be captured no matter how well they understand the workings of the tag or the software.
From there, I jumped into an extended discussion of Two-Tiered Segmentation – a segmentation by Visitor-Type (audience) and Visit-Type (intent). As part of that discussion, I show how our industry has consistently gotten it wrong with our insistence on a “small set of actionable, site-wide KPIs.”
Such KPIs don’t exist and the search for them produces metrics that range between worthless and deceptive. When I walk into an executive’s office and announce that “Traffic is up 5% each month!” I want the executive to ask two questions.: “With whom?” and “What are they trying to accomplish?” Until I can answer those two questions, I haven’t said anything worth hearing. Every metric and every KPI should be placed within the framework of Semphonic’s Two-Tiered Segmentation.
But I haven’t quite brought you up to date. I wanted to show how a two-tiered segmentation can be applied to a wide range of verticals. So I fleshed out examples for Financial Services, Hospitality, Media and .Gov. I also delved into a long discussion of the techniques for actually creating a Two-Tiered Segmentation. In the first of two posts, I showed how behavioral cues including the extensive use of Meta-data and hierarchical segmentation could be used to construct the Visit-Type segmentation essential to a Two-Tiered model. In the second post, I showed how Opinion Research data could be used to both improve and validate that model.
Which brings me, finally, to here. The Two-Tiered Segmentation is the perfect bridge between action and intent (the variables we use to target and the variables we use to understand intent) that I described as the key problem in bringing Database Marketing techniques to Digital. By combining Audience Type and Visit Intent (and incorporating a set of methods for building visit-intent), it creates a bridge between the type of information we use to target (who the customer is and what we think they need) and the behaviors (page views) we measure.
In the next few posts, I’ll show how you can use that bridge to create customer-level aggregations in the warehouse that provide effective targeted marketing – re-uniting the world of Database Marketing with Digital Analytics.
