A practical yet meaningful approach to customer segmentation

Claudio Marcus: Vice President of Marketing, TargetSmart, Inc., J.L. Kellogg Graduate School of Management, Northwestern University, Denver, USA

Introduction

There are many analytic methods for market segmentation. Demographic segmentation is the most traditional approach to segmentation. Newer approaches have also taken into consideration buyer attitudes, motivations, patterns of usage and preferences. Companies that capture customer and purchase information use such information to analyze and market to their customer base. This practice has come to be known as database marketing. In the past decade, declining costs of technology along with a desire to better understand customers and to enhance and measure marketing efforts have rapidly expanded the use of database marketing across a variety of industries. Indeed, analysis of customer and purchase information has become the foundation of database marketing practice.

A deeper understanding of customers has validated the value of focussing on them. It is now generally accepted that it costs about five times more to gain a new customer than to keep an existing one, and ten times more to get a dissatisfied customer back (Massnick, 1997). Studies across numerous industries have also shown that a five-point increase in customer retention can increase profits by more than 25 percent (Reichheld, 1996).

With numbers like these, it is no wonder that database marketing is quickly becoming a powerful tool for mainstream businesses. It is expected that the overall market for software and services using data mining technology will grow from approximately $3.3 billion in 1996, to more than $8 billion by 2001 (Meta Group, 1997). Driving such rapid growth are database marketing applications such as:

customer retention;

cross-selling and up-selling;

campaign management;

market, channel, and pricing analysis; and

customer segmentation analysis.

While the availability of customer purchase information has allowed marketers to develop richer, more sophisticated customer segmentation schemes, simplicity has also proven its place. For years, catalog companies and other direct marketers have used RFM (recency, frequency and monetary value) analysis to segment their customer base and optimize the purchase response rates of their marketing efforts (Hughes, 1994). Time and time again, RFM has been challenged by innovative conceptual approaches made possible by new technologies such as neural networks. Yet direct marketers continue to rely on RFM because the lift experienced using alternative methods...