The Analyst’s Toolbox: Segmentation (2)

This article, written by Neil Mason, was originally published on Clickz.com and is republished here with permission.

In my last article I started to take a look at the subject of segmentation. Something that is all the rage at the moment in the wacky world of web analytics. I outlined two key considerations in segmentation analyses; the approach you use to segment and the basis on which you segment. The approaches you use to segment may be deterministic or discovery based and I looked at those in the last article. This time we’re going to take a look at the basis on which you might segment your website visitors or customers.

There are three ways that you might choose to segment your users or customers. You might segment them on the basis of their demographics, their behaviour or their attitudes. Attitudinal segmentation is where people are segmented according to what they think about the brand or related issues. Often these attitudinal segments are developed from market research techniques and can be useful for brand development work. However, attitudinal segments are difficult to apply directly back into outbound marketing programmes. How do you recognise a “brand advocate” when they arrive on the site or from what they download or buy?

Demographics such as gender, age, household composition, income and the like may be a useful way to segment customers and visitors. Other lifestyle or geo-demographic classifications such as Mosaic in the UK may add a richer dimension to standard demographic segmentation. Demographic segments may be useful understanding the difference in browsing or shopping behaviour between men and women or between the young or the old. For business to business activities, demographic segmentation translates into business sector classification. This could be through industry standard definitions such as SIC codes or your own customer definitions such as SMEs, strategic accounts etc.

Whilst demographic segmentation can be interesting and sometimes quite useful it does assume similarities in underlying behaviour within the different segments. Do all 18 to 34 men think and behave the same when it comes to buying/using products and services? Probably not.

We often find that some of the most useful approaches to segmenting users and customers is by looking at their behaviour rather than just who they are. Behaviour can be easily observed either in your web analytics tool or in your customer database and those observations can then be used for the basis of outbound marketing programmes.

One of the classic behavioural segmentation approaches is Recency, Frequency, Monetary (RFM) analysis. RFM analysis is a deterministic approach where customers are dividing into segments on the basis of how recently they transacted, how frequently they have transacted in the past and what the value those transactions have been. Typically there are up 5 segments ranked from 1 (low) to 5 (high) on each dimension giving 125 segments in total. The top segment (number 5 on each dimension) is your most valuable customers, they transact a lot, they spend a lot and they have done it recently. They are the ones you don’t want to loose. For more information on RFM analysis you should check out Jim Novo’s site at www.jimnovo.com.

Whilst RFM analysis allows you to segment your customers based on their transactional behaviour in aggregate; it doesn’t give a perspective on what people are buying, downloading, reading and so on. Other segmentation approaches are based upon an analysis of what it that people are buying over time and look for commonalities and patterns in this behaviour. Are there groups of people that tend to buy the same sorts of products?

Discovery based techniques such as cluster analysis (which I discussed last time) are used for this type of segmentation approach. The purchasing behaviour of the individual customers is run through the algorithms to create distinct segments of customers with similar purchasing profiles. The segments are then profiled to understand what those commonalties are in the purchasing behaviour. These behavioural segments should then also be profiled with other data such as demographic and altitudinal data if possible. This additional data may come from the database, lifestyle profiling data or from survey work.

These purchasing segments can be used to improve the effectiveness of direct marketing programmes by adding insight into the type of message that might be relevant for each individual. We had an example of how it can add to a classic RFM approach in a project we worked on with a retail client. The client was using shopping behaviour as a way of segmenting their customer base using measures such as average order value, number of orders in the last year and so on. Using a product purchasing approach we identified two segments with very similar shopping behaviour but who were buying completely different products. Further profiling work showed they also had very different demographic profiles. One segment was older men and the other was younger females. The opportunity was now there to target and communicate to these two different groups in a much more relevant way.

So, segmentation encompasses a wide range of analytical approaches and techniques from the simple to the more complex. The trick is to start gently and build up your understanding of your customers by gradually breaking them down into meaningful and actionable segments, giving a sharper edge to your marketing communications.