Web Analytics’ Diversity and Complexity Increase

I’m starting to get my head around a presentation I’ll be giving at the Emetrics Summit in Washington, D.C., next month. What strikes me about the agenda is the vast breadth of material to be covered over three days: 11 different tracks and four workshops. As an Emetrics veteran (this will be my eighth show), I remember being excited when we attendees split up and sat at separate tables for an hour or so in a conference room to discuss different topics. Now we can flock to an entire track on a particular topic and not see one another for three days, excepting networking events.

The Emetrics Summit is a Web analytics industry bellwether. The conference’s growth reflects industry dynamics and signifies the sector’s increased diversity and complexity. Summit tracks include not only marketing optimization and public sector measurement but also Web 2.0 analytics, revealing new disciplines within the field. It won’t be long before Web analytics practitioners and consultants will recognize they can’t cover all the ground. Specialists will crop up in each discipline.

Take Web 2.0 and social media. The Web Analytics Association (WAA) recently established a committee to explore this area because traditional approaches to Web analytics aren’t suited to measuring and understanding this evolving medium’s impact. As social media continues to develop, different measurement tools will likely be born, perhaps requiring different skill sets to analyze and interpret the data. Like the market research industry in which I worked for a few years, there were people skilled in quantitative analysis and others in qualitative analysis. Few could do both well.

Another case in point is the new Statistical Success Track. While the topics sound pretty scary (even to a bunch of Web analysts), it’s yet another indication of the industry’s development and maturity. This track, at which I’m speaking, will zero in on statistical and advanced analytical techniques for evaluating online marketing performance. While relatively new to Web analytics, it’s not new to consumer analytics. The direct marketing industry has predicted likely response for years with advanced analytical techniques such as regression analysis, decision trees, and so on. The market research industry has tapped techniques such as cluster analysis to identify and understand different consumer segments.

Through these techniques, one can better understand different aspects of visitor site behavior and online marketing campaigns’ effectiveness. Techniques such as multivariate testing and behavioral targeting are statistical processes that have been productized and packaged into services by companies such as Optimost, Offermatica, and TouchClarity.

Statistical analysis, data mining, and predictive analytics are being deployed in an ad hoc way by analysts, using tools such as SAS, SPSS, and KXEN. Long-time offline marketing tools, they’ve moved into the online marking analyst’s tool box as well. In my presentation, I’ll examine these advanced analytical techniques in more detail and discuss how they can be applied in online marketing analytics. Because not all of you will make it to Washington, I’ll cover these techniques here in coming weeks.

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