In my last column, I started looking at segmentation, something that’s the current rage 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 may be deterministic or discovery based. Today, we’ll look at the basis on which you might segment site visitors or customers: by attitudes, demographics, or behavior.
With attitudinal segmentation, people are segmented according to what they think about the brand or related issues. Often, 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 to outbound marketing programs. How can you recognize brand advocates when they arrive on the site or from what they download or buy?
Demographics such as gender, age, household composition, and income may be a useful way to segment customers and visitors. Other lifestyle or geo-demographic classifications, such as Experian’s Mosaic UK, may add a richer dimension to standard demographic segmentation. Demographic segments may be useful to understanding the difference in browsing or shopping behavior between men and women, or between the young and old. For business-to-business activities, demographic segmentation translates into business sector classification. This could be through industry standard definitions, such as SIC codes (define), or your own customer definitions, such as SMBs (define), strategic accounts, and so on.
Though demographic segmentation can be interesting and sometimes quite useful, it does assume similarities in underlying behavior within the different segments. Do all 18-34 men think and behave the same way when it comes to buying products and services? Probably not.
We often find some of the most useful approaches to segmenting users and customers is by observing their behavior rather than based on who they are. Behavior can be easily observed in either your Web analytics tool or your customer database. Those observations can then be used for the basis of outbound marketing programs.
One classic behavioral segmentation approach is recency, frequency, monetary (RFM) analysis. RFM analysis is a deterministic approach in which customers are divided into segments on the basis of how recently they transacted, how frequently they’ve transacted in the past, and the value of those transactions.
Typically, there are up five segments ranked from one (low) to five (high) on each dimension, giving 125 segments in total. The top segment (number five on each dimension) is your most valuable customers: they transact a lot, they spend a lot, and they’ve done so recently. They’re the ones you don’t want to loose. For more information on RFM analysis, check out Bryan Eisenberg’s “Betting the Farm on RFM, Part 1” and Jim Novo’s article on RFM.
Although RFM analysis allows you to segment your customers based on their transactional behavior in aggregate, it doesn’t provide a perspective on what people are buying, downloading, reading, and so on. Other segmentation approaches are based on an analysis of what people are buying over time, and they look for commonalities and patterns in this behavior. Are there groups of people who tend to buy the same sorts of products?
Discovery-based techniques such as cluster analysis (discussed last time) are used for this type of segmentation approach. Individual customers’ purchasing behavior 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 behavior. These behavioral segments should then be profiled with other data, such as demographic and attitudinal data, if possible. This additional data may come from the database, lifestyle profiling data, or survey work.
These purchasing segments can be used to improve direct marketing programs’ effectiveness 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 behavior to segment its customer base using such measures as average order value and number of orders in the last year. Using a product-purchasing approach, we identified two segments with very similar shopping behaviors but who were buying completely different products. Further profiling work showed they also had very different demographic profiles: one segment was older men, the other was younger females. An opportunity was now there to target and communicate to these two different groups in a much more relevant way.
Segmentation encompasses a wide range of analytical approaches and techniques, from simple to complex. The trick is to start gently and build up your understanding of your customers by gradually breaking them down into meaningful, actionable segments, thus sharpening the edge of your marketing communications.
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