A segmentation primer

One of the things that you hear being talked about a lot more about these days in the wacky world of web analytics is “segmentation”. But I sometimes wonder what people mean when they talk about segmentation. I think it’s one of those words that is used more often than it is necessarily understood. Understood in the marketing sense of the word anyway.

I’ll take one example. One of the largest and most successful web analytics systems vendors has a section in their report menu called “Segmentation”. What we actually find there are reports on the most popular pages and sections of the site. I’m not too sure what that has to do with segmentation. Other vendors talk about segmentation as well but mean different things. Sometimes they talk about the ability to filter along different dimensions or the ability to analyse the data by combining different variables. So, segmentation could mean reporting particular data, filtering data or analysing data. All of these things are good things, and potentially even useful things, but are they segmentation?

I dug out some of my marketing text books to see if there was a consensus view in them about what segmentation actually is. I found that what they tend to talk about is that segmentation is a means of identifying different groups of people in order to develop different strategies for each group. So, segmentation is a purpose rather than an outcome and I think that’s the difference between classification (which is what a lot of analysis tools do) and segmentation which is what marketers or marketing analysts do.

The point of segmentation is that you do something as a result of having it. For example:

  • You target different groups of people with different messages in your acquisition campaigns
  • You present a different site experience dependent on your understanding of who that person is
  • You interact with different people differently dependent on where they are in a customer lifecycle

In one of the books that I looked at that was actually written 20 years ago, the authors described three conditions of a good segmentation*. They are:

Homogeneity - the degree to which people in the segment are similar in ways that is interesting to you

Parsimony - the degree to which the segmentation would make every person a unique target

Accessibility - the degree to which you can describe the segments in ways that help you deploy differentiated marketing strategies

That all sounds pretty theoretical (well, it was a text book), so what does this mean in practice?

My interpretation of this is that a good segmentation has to be robust, useful and actionable. There are many ways that you might segment say a site’s visitors or your customer base from simple classification approaches through to complex statistical techniques but they have to pass the sense check of being robust, useful and actionable.

You might simply classify according to some demographic or geographic variables. For example classifying the customer base between male vs female is a form of segmentation but it is only robust and useful if men and women exhibits differences that are potentially useful to you and only actionable if you can realistically target them in different ways.

Alternatively, you might develop a segmentation based on some attitudinal variables. Many years ago I was involved in a project where we segmented the visitors across the number of different sites we had in Europe according to their attitudes to online shopping and their motivations for visiting the site. Whilst the results were certainly interesting and highlighted some interesting differences in the visitor profile of the different sites, we had to question how useful it was to us. How were we going to action the insight? We couldn’t identify and classify people arriving on the site by their attitudes nor could we easily use it in our retention marketing activities as we didn’t have people’s attitudes stored on our customer database.

So, I think that there is always a balancing act in satisfying those three conditions of homogeneity, parsimony and accessibility in a good segmentation. In our own work, we tend to use behavioural segmentation approaches as it makes it easier to act on the outcomes. This may often involve using statistical methods such as cluster analysis to segment customers into groups that are distinct from each other in a meaningful way like their browsing behaviour or their purchasing behaviour.

However, we are also mindful of the ability to the client to be able to act on the results. There is no point in developing a sophisticated methodology that identifies some really meaningful segments if there is neither the skills nor the tools available to realise the opportunity. For example if your email tool is not easily integrated into your customer database then it’s going to be difficult to execute improved target marketing initiatives. It is best to start with something simple and develop the capabilities to act in line with the development of the insight itself.

As it’s getting to that time of the year, in my next article I will be taking a personal look back at 2005 and reflecting of the some of the key events from my perspective and trying to get a sense of where we may be heading in 2006.

* “Marketing Decision Making - A model-building approach” by Gary Lilien and Philip Kotler

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