Why Segmentation is a Strategic, Not Analytic, Exercise
Marketing Analytics Author Mike Grigsby Explains Why Segmentation Does Not Start with Data
So I was in a meeting the other day and a retail client said they wanted to do segmentation. Now, those who know me know that that is what I LOVE to do. Segmentation is often a good first step: it is the foundation of much analytics that follow. Remember the 4Ps of strategic marketing? Partition (segmentation), probe (marketing research), prioritize (rank financially) and position (compelling messaging). Strategy starts with segmentation.
They began talking about what data they have available. But that is NOT the right place to start. Segmentation is a strategic, not an analytic, exercise. Surprised to hear me say that? Note that while segmentation is the first step in strategic marketing (see above), it is PART of strategic marketing. That is, it starts with strategy.
Where does strategy start? It starts with clearly defined objectives. For segmentation to work it must start with strategy, and strategy starts with clearly defined objectives.
So I asked the client what it is that they wanted to do.
'Sell more stuff, man! Make money.' Duh.
'Yeah, I get that. Have you thought about HOW you are going to sell more stuff? How are you going to make more money?'
Sure, I can take all their data (demographics, transactions, attitudes/lifestyle, loyalty/satisfaction, marcomm responses, etc.) and throw it into some algorithm--I like latent class myself--and out will pop a statistically valid (within the confines of the algorithm) segmentation solution. That will be acceptable analytically, but IT WON’T WORK. It does not solve anything, it does not (necessarily) give marketers levers to pull for a solution because the solution was not inherent in the design.
For example, a recent telecom client had a problem with churn (attrition). They needed a list of who is most likely to churn in the next 60 days so they could intervene and try to slow down/stop the churn.
The solution was to segment based on reasons to churn. We brainstormed about what causes churn: high bills, high uses of data/minutes, dropped calls, etc. Then we collected data on the causes of churn and segmented based on that data. We came up with a segment that was sensitive to price and churned because of high bills. Another segment was sensitive to dropped calls and churned because of an increase in dropped calls. Then survival modeling was applied to each segment and we could produce a list of those most likely to churn and WHY they would churn. This WHY gave the client a marketing lever to use in combating churn. (The details of this example are in my book Marketing Analtyics.) For the “sensitive to high bill” segment, those most at risk could be offered a discount. (If a $5 discount keeps a subscriber on the system for 60 more days, it’s worth it.) Note that the solution had marketing actions in the design. That’s why it worked.
Note that the analytics were embedded in the strategy. As it should be. Analytics without strategy is like a science fiction movie without a plot: a lot of lasers and explosions and cool costumes and spaceships but it goes nowhere, there is no point.
Even though the telecom client had demographic data, we did not segment based on demographics. We did not segment based on attitudes. But we could have. The algorithm does not know (or care) what the data is. The mathematics around the solution have nothing to do with what variables are used. Analytically, a solution is a solution. But without marketing strategy as part of the design it will not work.
So for the retail client, there was a conversation. Segmentation is NOT a magic bullet that will solve all marketing problems. But thinking will help.
So the retail client admitted they were probably discounting too much (all retailers discount too much) and they did not know how to target their discounting. Clearly some of their customers would not buy without a discount, but some were more loyal and did not really need a discount to buy, etc. So one way to make more money is to not give such high discounts. Thus that is a marketing strategy. If we could find groups that differ on price sensitivity, we could segment based on that. One segment needs a discount and another segment does not.
Another way to make more money is to save on direct mail. Some customers preferred a catalog and others did not care and were happy with email. Direct mail is expensive so if segmentation could be done to find a group that required direct mail and to find a group that did not, clearly send a catalog to the DM group but send the email group an email. See?
Note again that demographics, attitudes, loyalty metrics, etc. were not part of the solution because they were not part of the problem. There could be a strategy that needs a segmentation based on loyalty, etc. but not in the current example.
So the key take away is that segmentation does NOT start with data, it starts with thinking about objectives, what marketing levers can be pulled, what problem is (specifically) being solved. Without that you have nothing and “He who aims at nothing will hit it.” Segmentation is a strategic, not an analytic, exercise.
About the author: Mike Grigsby has been involved in marketing science for over 25 years. He was marketing research director at Millward Brown and has held leadership positions at Hewlett-Packard and the Gap. With a wealth of practitioner experience at the forefront of marketing science and data analytics, he now heads up the strategic retail analysis practice at Targetbase. Mike is also known for academic work, having written articles for academic and trade journals and taught at both the graduate and undergraduate levels. He is a regular speaker at trade conventions and seminars.
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