Building a Behavioral Segmentation

Building A Behavioral Segmentation

By Gary Angel
Expert Author
Article Date: 2008-06-26

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 than 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 (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 this post, I'll discuss tackling this problem from the other direction - beginning with a behavioral segmentation and adding demographic and psychographic information.

Behavioral Segmentation

Building a behavioral segmentation is no piece of cake. Unlike traditional segmentation, which is complex but well understood, behavioral segmentation on the web has not been routinized. For traditional segmentations, the mechanics of data collection, the types of variables most likely to be interesting and the tools and techniques to produce a good segmentation are all well understood. The most difficult part of a traditional segmentation tends to be the "art" of naming and describing the resulting segments.

Most of this isn't true for behavioral segmentation. Data collection isn't too big a problem. There are issues with linking online survey data to web behavioral data, but though this is not a slam-dunk it is more of a nuisance than a genuine difficulty. The problems typically begin when we consider the type of variables most likely to be interesting and the tools and techniques to produce a behavioral segmentation.

Companies have struggled to extend their traditional segmentations into the online space, but they've also struggled to build any kind of useful behavioral segmentation - and the problem comes from both variable interpretation and tool limitations.

Web behavioral segmentations tend to use the core web behavioral data points (pages viewed, time on site, visits and, sometimes, poor geo-demographic variables like IP-based DMA).

The problem with web-based geographics isn't accuracy - it's precision. A DMA (or even a Zip-Code) is just too large an area with too diverse a population to be useful in targeting or segmentation. Useful geographics need to be at the census-block or zip+4 level. Down at the block level, you have geographics that are as good - and in some ways better - than the actual personally identifiable information you might get from a customer file or match-back.

If you segment on these core web behavioral variables what you get, almost invariably, is a segmentation scheme that looks like an Egyptian pyramid. At the base you have a big number of visitors who do almost nothing. As you ascend the pyramid, you have a few additional levels that represent groups with middling page views and few visits, a group with middling visits and moderate page views, a group with lots of page views and few visits and, at the apex, a group with lots page views and lots of visits.

This segmentation scheme is about as useful as a real pyramid but much less interesting!

To get a useful behavioral segmentation, you need to look at a different set of variables. We've found two types of variables that generally produce interesting segments.

There are set of variables around each visitors propensity to view content based on your business taxonomy. Most pages on your web site are focused on specific topics. These may be products, investment strategies, health conditions, news areas, etc. The most interesting set of behavioral facts about a visitor is which of these topics they consume and how much of each topic they consume.

So the first, and biggest requirement for good behavioral segmentation, is usually to have a good site taxonomy. This shouldn't be a surprise. Most useful web analysis actually happens at the taxonomy level and there are a host of reasons why you should make sure this information flows through to your web measurement.

The second type of variable we've found interesting are what we call "session-styles." Session-styles are designed to capture two salient behavioral facts about visitors - what type of navigational devices (search, directory, link-drives, images, etc.) they use and what type of sessions they typically have. These two questions are intimately related.

Sessions on sites tend to be a mixture of highly-directed (immediate and specific search) to very unfocused (sideways navigation along the top-navigation). Each site will support a range of session-styles that are quite distinct. Before we begin a visitor segmentation, we typically like to start with a session-based segmentation to identify these styles. The styles then become variables at the visitor-level. It turns out that visitors often split along very interesting fault lines in terms of their types of sessions - even when they share a common topical interest.

Combine visitor profiling based on the depth, frequency, mindshare and time-spent by site area and the mixture of session-styles visitors employ, and you will usually end up with quite a rich set of profiles. It will look nothing like the usage pyramid and, in terms of its ability to support rich descriptives, it will rival (but probably not equal) traditional segmentation.

But the very success of these variables carries the seeds of a serious problem. Most traditional segmentations tend to deliver a relatively small number of profiles - somewhere between 5-10 segments. It's a good number, because it doesn't burden the marketer with too much apparatus. Having 20 or 25 segments is simply too much to hold in your mind.

Unfortunately, good behavioral segmentations tend to spin off quite a few more segments. These can all be combined, of course. The analyst has control over the number of spaces mapped and you can always force segments together. But in my experience, behavioral segmentations tend to produce more very-distinct segments - segments that do not easily collapse without significant loss of information. I'll talk about this problem in more depth later on and show some of the techniques we've used to make large numbers of segments both more palatable and more usable.

The tendency to focus on the wrong variables is only one half of the behavioral segmentation problem. The other half is tool-centric. Web analytic tools simply don't provide the necessary methods to build data-driven segmentations. There is not a single classic web analytic tool - enterprise or otherwise - that has any of the mathematical techniques typically used to build traditional segmentations. As I mentioned in my first post, what web analytics tools have called visitor segmentation is nothing more than primitive rule-based filtering. Even if you had full sql access, you couldn't do real visitor segmentation. And the filtering you can do in the advanced web analytics tools like Discover (even On Premise) or similar enterprise products doesn't even come close to having full sql access.

So one way or another, you'll have to build your segmentations outside the web analytic tool. That's a major drag and can be a deal killer. Fortunately, we are seeing more clients taking data feeds from their WA tool (often for completely different purposes) - so it's becoming easier to get your hands on cleaned-up online behavioral data. But even if you have that data, your problems aren't over. The types of analysis you'll do to build visitor segments are processing intense. Really intense! You probably won't be able to run them against your entire web behavioral stream. So now you have to produce a sample (it must be visitor-based not just n-record) and import it into a true analysis tool.

And, since the variables you care about aren't directly in the data and since most analysis tools will struggle with the general form of the web analytics, you should probably expect to a do a goodly chunk of data transformation and aggregation before you ever get to the segment-building.

It's making me tired just writing about it, so I suppose it's no wonder that this hasn't been a very common undertaking. But with data feeds and access to online data becoming much more common, I expect that the data transformation steps will also get easier. As we do more of these types of projects, the types of variables and the transformations necessary to produce them will become well understood. And once they are well understood, the mechanics will become routinized.

But even though doing behavioral segmentation is still bleeding-edge work, there's a real advantage to be had at the end of your labors; because a behavioral segmentation - particularly when enhanced with survey-based profiling - can provide a rich and fascinating framework for online marketing. And unlike traditional segmentations, it can be blended back into every aspect of your web measurement: from ongoing deep-dive analytics to reporting to CRM.

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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.