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08.11.08 Over Time Incorporating Visitor Usage And Trends  By Gary Angel For many years, marketing professionals have relied on a set of analysis techniques designed to help them understand the demographic and psychographic profiles of their customers and prospects. These traditional segmentations are usually derived from complex clustering techniques that map rich primary research data (usually survey based) into common groups or profiles. These groups are then given highly descriptive business names and rich descriptions and provide a framework for a wide range of marketing activities. Though such segmentations can be (and are) applied to online customers, companies that have tried to map these segmentations down to the individual level (for targeting or reporting) in the online world have mostly been disappointed. In Part I of this series, I described the biggest pitfall in extending these segmentations - the near impossibility of mapping demographic and psychographic profiles to visitors about whom we typically know nothing except their online behavior. In Part II, I discussed the advantages and disadvantages of building a behavioral segmentation. In Part III, I covered different strategies for joining survey data to a behavioral segmentation, when each is appropriate, and why the join is necessary at all. In Part IV, I covered basic data transformations for segmentation - focusing on describing visitor-level topic interest. In Part V, I described a Functional approach to building session profiles - and how these session-styles lend a whole new dimension to behavioral segmentation. In this post, I'm going to talk about time-based attributes and how they can be captured either inside or external to the segmentation. One of the interesting problems posed by behavioral segmentation is that behavioral profiles introduce an element of time into the analysis in a way that is fundamentally different from that captured in traditional demographic and psychographic analysis. It's true, of course, that age is one of the most powerful demographic variables and it necessarily evolves over time (as can income, zip, attitudes, etc.). But for marketing purposes, all these variables are treated effectively as a snapshot because the rate of change is too slow to impact any marketing decisions or analysis and because the changes are fundamentally exogenous to the business. Neither is true for the variables in a behavioral segmentation.
When you build a behavioral segmentation, you are necessarily relying on a snapshot in time - and the length of that snapshot will have a profound effect on the nature of the segmentation. A visitor segmentation around 1 visit will be fundamentally different that a segmentation around 1 day, 1 week, 1 month, or for lifetime tracking. Nor is there a single right answer about the best time-frame for a visitor snapshot to drive a segmentation - the most interesting length is heavily dependent on the shape of the business, the goals of the analysis and the infrastructure for tracking. For cookie-based visitor segmentation, there is also a finite duration of time in which enough visitor behavior can be consistently tracked to drive a meaningful analysis - and that time period is usually no more than 2-3 months. What's challenging, from an analytic perspective, is the mix of potential visitor types that any extended time-frame will create in a segmentation. Visitors may be new - arriving at the very end of the segmentation period and having no chance for additional behavior. Visitors may have been new at the beginning of the segmentation period and never have shown additional behavior. They may already have been long-standing visitors at the beginning of the segmentation period and remained constant throughout. Or visitors may have been heavy users at the beginning of the segmentation period but gone inactive as the period progressed. And so on - through an infinite series of possible changes in behavior. Each of these types of visitors are quite different in nature - and different in ways that can shape the analysis and cause it to miss the mark unless at least some time variables are captured in the mix. In particular, the relationship between # of visits (during the period), visit # (lifetime) and start date turns out to be almost universally important. Continue reading this article. About the Author: Gary Angel is the author of the "SEMAngel blog - Web Analytics and Search Engine Marketing practices and perspectives from a 10-year experienced guru. |
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