Ideas for addressing customer classification


Last time we started to look at methods used in setting product prices, and I asked whether knowledge of customer type would contribute to the determination of a “fair” price for an item that might change in relation to customer type.

It might be simple to suggest a specific hierarchy of customer types in relation to some mythical scale of “customer goodness,” and we see this somewhat implicitly applied across the board in the literature surrounding customer centricity and relationship management. Some examples include how to handle your “best customers,” or ways of getting rid of your “worst customers.” The challenge is that without a unit of measure and a scale for goodness, how do organizations classify their customers according to that comparative ranking?

I’d like to suggest two ideas that might help us address customer classification in a way that is easier to manage. The first involves establishing discrete measures and a scale for customer goodness. The second involves having a variety of customer classifications that are not tied to the concept of goodness.

This week, let’s look at the first. Here are some tasks for establishing discrete measures and a scale for customer classification:

  • Selecting some key dimensions of value and a unit of measure (such as annual sales in dollars, number of items purchased or duration of the relationship in months),
  • Selecting a weighting factor for each measured dimension,
  • Deciding on the number of customer goodness levels,
  • Setting thresholds for each level, and
  • Coming up with discrete measures of goodness.

The weighting factors might be initially set in a somewhat arbitrary way. For some of the other decisions, we can presume that the distribution of the customer base is a normal distribution. That means we can start out using the “banding” of six standard deviations to set the customer goodness levels and the thresholds for each level, since more than 99% of the scores should lie within three standard deviations of the mean:

Customer Classification

Threshold Score

Golden > 97.8%
Best 83.6% - 97.8%
Good 50% - 83.6%
Fair 15.8% - 50%
Bad 2.2% - 15.8%
Worst < 2.2%


After evaluating the groupings, you might want to tweak the measures, weights and thresholds – perhaps you did not pull in some expected demographic, or you know a really good customer who didn’t get classified as a good customer. However, this provides at least one starting point for classification. Next time we will look at understanding the characteristics of individuals within each of those classifications.


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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