Customer analytics: classification vs. segmentation


In my last post, we looked at a starting point for a classification model for determining the “goodness” of customers, based on some selected dimensions of value, measures, weights, scores and classification levels and thresholds. That being said, these classifications divide your customer based on your criteria.

What might be interesting is to explore similarities of those customers within each of the classes that can be used in two different ways. The first is for segmentation purposes: to identify characteristics of specific variables that can be used proactively for new customers to predict which class they will fall into. An example might be that many of the “good” customers live in an area with a median annual household income between $75,000 and $95,000 and own their own homes. That would suggest that a new customer whose annual household income is $84,000 and lives in her own home is likely to be a “good” customer.

The benefit of this segmentation is that it can guide other decisions in the marketing and customer acquisition process. One case in point: if “good” customers live in an area with a median annual household income between $75,000 and $95,000 and own their own homes, perhaps the best place for media spend is radio ads in areas where people with those salaries own their own homes.

The second is for promotion: to determine whether there are any customers in one class that have the potential to be promoted into a higher customer classification. As an example, if one of the customers in the “fair” class has an annual household income of $82,500 and lives in his own home, there is a case to be made for trying to influence that customer’s behavior to transition that customer into a “good” one. That might mean making the customer an offer in a way that changes the score, such as urging the customer to spend more, or buy more items, or to remain a customer for a longer time. And, as we will discuss next time, one way to influence behavior is through price.


About Author

David Loshin

President, Knowledge Integrity, Inc.

David Loshin, president of Knowledge Integrity, Inc., is a recognized thought leader and expert consultant in the areas of data quality, master data management and business intelligence. David is a prolific author regarding data management best practices, via the expert channel at and numerous books, white papers, and web seminars on a variety of data management best practices. His book, Business Intelligence: The Savvy Manager’s Guide (June 2003) has been hailed as a resource allowing readers to “gain an understanding of business intelligence, business management disciplines, data warehousing and how all of the pieces work together.” His book, Master Data Management, has been endorsed by data management industry leaders, and his valuable MDM insights can be reviewed at . David is also the author of The Practitioner’s Guide to Data Quality Improvement. He can be reached at

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