The Analyst’s Toolbox: Segmentation (1)

Segmentation. There’s a word. It’s a word that quite often means different things to different people and it’s all the rage in web analytics. Everybody is doing segmentation; all the web analytic tools are offering segmentation. But what it is it what does it mean and how can it be used? In simplest terms segmentation is the process of dividing a group into sub-groups. The idea in marketing segmentation is that there are some meaningful differences between the sub-groups which can be useful for marketing purposes.

I think that there are probably two main things to think about when doing segmentation. The approach you use to segment and the basis upon which you segment. There are two main approaches to segmentation I believe:

  • Deterministic approaches
  • Discovery approaches

The basis on which you segment might be along the lines of:

  • Demographics and lifestyle
  • Behaviour
  • Attitudes

Deterministic approaches are where you create your segments based on some pre-defined or pre-determined classification. It might be a relatively simple classification like “Male vs Female” or they may be more complex like “First time visitors with abandoned shopping carts containing yellow socks”. With deterministic approaches you have some hypothesis that the segment is interesting, important or valuable and you maybe then test that hypothesis. Most web analytic tools now offer what I call this deterministic approach to segmentation. They offer the ability (to varying degrees) to divide or extract visitors into different groups and run reports comparing different groups against each other. In addition you may be able to extract email lists and other details from the segments for outbound marketing purposes.

The ability to segment and analyse different sub-groups of the visitor base is increasingly important. You can’t continue to run the site as a “one size fits all” business. Deterministic approaches are useful to try and identify some meaningful differences or to understand underlying behaviour in more detail. However, you have to go hunting and you may not always go hunting in the right direction. This is where discovery based techniques can come in to play.

By discovery based techniques I mean statistical and other data mining techniques such as cluster analysis and neural networks. Having spent some time in the past in the market research industry, I often think of the use of these techniques when talking about segmentation. Cluster analysis is a statistical technique that segments the population into sub-groups that display some commonality. There are many different cluster analysis algorithms that vary in their application and complexity. The overall objective of cluster analysis though remains the same: to maximise the similarity of the members within each of the sub-groups and to also maximise the differences between the sub-groups. In other words, you want each member of the sub-group to look as similar to each other as possible (all part of the same club) and for each sub-group to have distinct and meaningful differences from each other (all the clubs are different).

Cluster analysis is an iterative statistical process and therein lies the rub. The statistical process can create segments that are distinct but it doesn’t necessarily result in segments that are meaningful! So, the use of these types of techniques is as much an art as it is a science. Just because the analysis software reaches a result that is statistically correct, it’s not necessarily a useful result and these techniques also are dependent on the data that you start with. As the old saying goes “Garbage in, garbage out”.

Neural networks are a more “black box” kind of technique, based on the way that the brain works. They use artificial intelligence algorithms, such as Kohonen Networks, to find relationships or patterns in data. With classical statistical analysis techniques such as cluster analysis, the analyst has more control over the analysis process and can more easily interpret the findings and the outputs. Data mining techniques such as neural networks can be more powerful but also can be more difficult to handle (bit like driving a Ferrari or so I imagine).

In either case, getting some segments out is only half the battle. The other half is about understanding what they mean and what can be done with them. Typically the output of a cluster analysis will tell you that this person belongs to this segment. You then have to work out what it is that characterises the individual segments and what the differences are between the various segments. This is the profiling stage. The way the segments are constructed will be based upon the data that goes into the analysis. So if you use some behavioural data to create the segments, then the differences will be based on those behaviours and that’s the first place to look. However, you will also usually want to pull in other data to help explain what the segments mean. This can be demographic or attitudinal data for example.

So, segmentation can mean different things to different people, from simple classification through to more complex pattern discovery approaches. In this article we’ve looked at some of the approaches and techniques that can be used. In the next article in this series I will take a look at the different types of data that you may want to segment on and how they may be useful to the internet marketer. Till then…

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