Reverse Segmentation: Some Advantages when Using Both Attitudes/Behaviors and Targeting Variables

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.

A Common Criticism Of Segmentation

To repeat what we said in another article, “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

We’re 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 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, we 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. For more information about how we can help you solve your business problems with reverse segmentation, contact us at contactus@elucidatenow.com.