This information is useful for people who are interested in matching the best messages or products possible to those customers or prospects most likely to buy them.
If you make a product, have a service, market or sell, customer segmentation is important to you. The days of one-size-fits-all approaches are long over, and especially in tough economic times, it is essential to be as efficient and effective as possible in getting the best version of your product or service in front of the people most likely to need it and buy it.
There are many ways to break your customers into unique groups. Methods range from simple to complex to implement, and result in a variety of valuable information. All these methods should have the same goal – how can your company better create need for your products or services by targeting the people most likely to buy them with appropriate messaging or offerings.
Basic Demographic Splits
As simple as it gets, cutting your customers by a single demographic variable is done all the time. Think, “the pink one for the baby girls and the blue one for the baby boys”. This method is very common in B2B situations where the cut is company size. How many companies do you know that base sales and marketing programs around “small”, “medium” and “large” business targets? This method is primarily useful because of its intuitive nature, and is so commonly used because it takes nothing more than a customer database or access to Dunn and Bradstreet data. Of course, this method has its limitations. Are all your “small” customers really the same? Do they all behave the same way and want the same things? Probably not.
One common way to present these basic splits is in banner tables. A banner table highlights differences between segments on a number of other variables – be they other fields from a database or variables from a survey.
Let’s Take it to Two Levels
If a single split doesn’t cut it (forgive the pun :), how about first cutting by something like company size and then by, say, industry? Fine enough. Here, you start to isolate more and more unique groups, and by better understanding their uniqueness, you can better reach them with appropriate and effective marketing messages and provide them the product types and features they most need.
And So On…
One could, in theory, continue to cut by more and more variables – company size, then industry, then job function, etc. (or for B2C: gender, then age, then income, etc.). However, the human brain can only handle looking at so many segments at once, let alone find differences, so the output becomes unwieldy. To get this greater level of detail, we need to take the stats up a notch.
Let’s start by making sure we all understand the meaning of this word: multivariate. Multi = many. Now, how about variate? Think “variable”. A variable is something that varies – the opposite of a constant. So, multivariate = many variables. That’s it – we’re looking at a number of variables, all at the same time.
Cluster/Latent Class Analysis
These analyses identify relatively homogeneous groups of cases (let’s say customers) based on selected characteristics (these are the variables). If you can imagine multidimensional space, you can wrap your head around these analyses. Imagine a star in space. Now imagine a cluster of stars. You can look in the sky and see one group of stars that clump together over in one part of the sky, and another group that clump together in a different part of the sky. These analyses basically put your customers into homogeneous groups based on how “close” the customers are to each other. “Close”, in this case, takes into account how a customer looks on a number of variables (remember, multivariate). You end of with a group of customers that are like each other and are different than another group (cluster). Now you can see why we can only go so far with crosstabs – we just can’t capture the multivariate space without some better analytics.
Note that there are other analytic methods that can be used to segment customers (e.g., CHAID), however, for the purposes of this article, let’s focus on clustering methods.
Exploring Segmentation Solutions
These analyses are what we call “exploratory” techniques – in other words, to paraphrase Forrest Gump, “it’s like a box of chocolates – you never know what you’re going to get”. Here’s where the science and art of statistics merge (yes, don’t laugh, statistics can be artful). Between “many” and “one” cluster lies a potentially useful number of clusters where similarities exist within the clusters and differences exist between clusters. A quick note, beyond the scope of this article, ensembling techniques (in essence, looking at multiple cluster solutions together to find one that is more stable) can help you come to a better solution. Also, here’s where you put on your business hat (or call in the business people if you don’t own that hat) to figure out how actionable the segmentation solution is.
What Makes a Good Segmentation?
We once had a client who felt that his market should have 10 or more segments – without looking at the data. We brought him 4 and 5 segment solutions – based on the data. Who was correct? Here are a few things to keep in mind when choosing a final segmentation solution:
- Distinct and Identifiable: Groups have to be different than each other on variables that you can measure now and in the future
- Sizable: Groups have to be large enough that they are worth marketing/selling to
- Reachable: Groups can be identified in the market and targeted (note: this is a big issue that is often not achieved – see below)
- Stable: Groups need to look tomorrow like they look today (note: customers change over time, and segmentation solutions do get “stale” – when that happens, it’s time to run a new study)
- Profitable/Valuable: Groups that are reached act on the messages/products that they receive by purchasing (note: not all groups will fit into this category – you will identify some groups that will likely not buy – that’s good to know, so you don’t reach out to them.)
- Relevant: Groups are integrated with your larger marketing plan and make sense in the context of your strategic direction.
Criticisms of Segmentation
“I got these great-sounding segment names, but they don’t have distinct demographic targeting profiles, so I can’t reach them.” Making up cool names based on attitudinal and/or behavioral clustering can be a lot of fun, but if your segments aren’t unique on the variables you use to target them, then that doesn’t help. Note that this is more of a methodological issue than anything else (see below).
“My segmentation report just sits on the shelf”. Sad story – all too common. Sometimes, this outcome can’t be helped. We’ve seen VPs torpedo a good-looking segmentation solution because it didn’t match their preconceived notions. To give yourself the best chance of a solution being used in your organization, make sure you have clear objectives and buy-in from key stakeholders, and involve key people over the life of the project.
But Wait, There’s More…
One of the most exciting developments we’ve seen in segmentation is a technique called Reverse Segmentation. It’s important enough that it deserves its own article (see our Resources page). For the moment, we’ll say that it provides a solution for criticism #1 above.
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