This information is useful for people who are tired of segmentation projects that give only unique attitudinal/behavioral segment descriptions or unique demographic/targeting profiles, but not both.

In my last blog post, I gave a general overview of customer segmentation, and I left off with a teaser about reverse segmentation.  Well, here goes.

A Common Criticism of Segmentation

To repeat what I said in my last post, “I got these great-sounding segment names, but they don’t have distinct demographic targeting profiles, so I can’t reach them.”   Traditional attitudinal/behavioral segmentations do a good job of identifying meaningful groups, but targeting the market can be difficult.  Alternatively, demographically-defined segments can be targeted but are often indistinct attitudinally or behaviorally, so they are difficult to “message to”.

Reverse Segmentation Solves These Problems

Reverse segmentation helps the market researcher identify market segments with highly differentiated attitudes and behaviors, while at the same time considering the demographics/ firmographics, media usage, or channel usage information that is needed to reach people and deliver a targeted message.

Before You Begin

I’m not going to spend much time discussing data collection for your segmentation analysis, but of course, you want to have attitudinal and/or behavioral data (probably from a survey, and if you’re lucky, tied to actual behavioral data) and targeting variables (demographics, media habits, etc.) that you hopefully already have in your CRM database.  Ask yourself what attitudinal/behavioral variables would help you describe your customers or prospects – what messages resonate with them, what do they currently own/buy, what are their interests, etc.?  Then, ask yourself how you target, or could target, possible customers – what “selects” do you use when doing media buys, are there specific websites you have in mind?  How would you define the variables used to “reach” your potential customers?

How it Works

Find Your Targeting Variables

Reverse segmentation starts by taking each of your demographic/targeting variables and finding out whether the categories (levels) of these targeting variables show differences on your attitudinal/behavioral variables.  For example, say that gender is one of your targeting variables.  Do men and women show differences on any/some/all of your attitudinal/behavioral variables?  If differences exist on a variety of attitudinal/behavioral variables, then save that targeting variable.

Once you’ve identified some targeting variables that show differences, you build a multi-way table that creates cells with every possible combination of differentiating targeting characteristics. For example, you might have cells made up of gender (2 levels) x age (5 levels) x having children in the home or not (2 levels) x frequency of visiting a certain website (4 levels), etc.  Each of these 2 x 5 x 2 x 4 = 80 targeting units forms the basis for further analysis.  Now group individual cases into the targeting unit where they fit.   Each unit represents people with the same targeting characteristics.  For example, one group might be: women, aged 25-34, with children in the home, who very frequently visit a certain website.

Score Target Targeting Units on Attitudinal/Behavioral Variables

Next, we find the average score for each of these targeting units on the measures of attitudes and behaviors, across all that cases that fall into the unit.  “Average” is a generalized word here, as it could be the mean or a percentage who give a particular response.

Cluster

Finally, we take these average scores and use clustering techniques to combine targeting units based on similarity in attitudes and behaviors.  The resulting segments have distinctive attitudinal/behavioral profiles, which is necessary for constructing a targeted message, while at the same time having clear-cut demographic attributes, which is necessary for reaching the people with the message.  Remember, the things we just clustered were not individuals but were targeting units made up of groups of people with distinct combinations of your targeting variables.

Iterate and Describe Your Segments

Look at a variety of clustering solutions, and consider using ensembling methods to come to a best solution.  Look for reasonable cluster sizes and differentiation.  Consider using Discriminant Function Analysis (DISCRIM) to help pull together themes of attitudinal/behavioral variables that differentiate clusters.

Classifying Future or Other Cases – with NO Misclassification

One of the common goals of a segmentation project is to come up with an algorithm that can be used to classify cases into segments based on a limited amount of information (so you don’t have to give a whole survey and do the whole segmentation analysis every time you want to classify people).

Recall that the segments in reverse segmentation are built on the “targeting units”, that are simply multi-way combinations of targeting variables.  Therefore, our classification “algorithm” simply finds cases that match the targeting unit and we know which segment the case is in.  Using this method, there is NO misclassification into segments.  Let me repeat that point – there is NO misclassification.  If you have a respondent’s data on the variables used to create the targeting units, you can put that respondent into its correct segment.

Maximizing the Utility of Reverse Segmentation

To maximize action that can be taken from the results of reverse segmentation, it can be helpful to focus on targeting variables that are currently in your database.  This approach allows you to flag people in your database as matching a particular behavioral or attitudinal type (segment).  Just because you can’t get survey data from everyone in your database, doesn’t mean you can’t classify them into a segment.

Don’t Forget the Basics

As with any segmentation output, the results from reverse segmentation should be evaluated as follows:

• Are the segments meaningful?  Do they make sense?
• Are the segments large enough to justify targeting them?
• Are the segments reachable (e.g., via ads or direct sales)?
• Are the segments uniquely responsive to marketing efforts? (This characteristic is evaluated over time).
• Is the overall segmentation plan actionable?
• Note that another quality sought in segments is that they are identifiable – reverse segmentation segments are always identifiable, given the way they are formed

Conclusion

The reality with any newer technique that includes a number of steps and is stats-heavy is that it can be difficult to understand your first (or first few) times.  However, if you can get over the hump with this technique, I think you’ll find a very powerful and useful tool that can greatly improve your ability to reach the right people with the right messages.

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.

Why Segmentation?

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.

How?

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.

Multivariate

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 – I’m not an astronomer, so work with me here 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 post, 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 post, that 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?

I once had a client who felt that his market should have 10 or more segments – without looking at the data. I 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:

1. Distinct and Identifiable: Groups have to be different than each other on variables that you can measure now and in the future.
2. Sizable: Groups have to be large enough that they are worth marketing/selling to
3. Reachable: Groups can be identified in the market and targeted (note: this is a big issue that is often not achieved – see below)
4. 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)
5. 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.)
6. 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. I’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 I’ve seen in segmentation is a technique called Reverse Segmentation. It’s important enough that it deserves its own post, so stay tuned. For the moment, I’ll say that it provides a solution for criticism #1 above.