Correspondence: Michael Raskin, Veritec Solutions, 824 Miramar Terrace, Belmont, California 94002, USA. E-mail: warren@veritecsolutions.com

¹is Vice President, Research at Veritec Solutions, a consulting and software development company specializing in revenue management, pricing and forecasting customer behavior. His expertise includes organizational behavior and data analysis aimed at practical problem solving. Dr Raskin holds a PhD in Administrative Sciences from Yale University. He is writing a book whose working title is The Arithmetic of Human Behavior.

²is President of Veritec Solutions. Warren has served as Chair of the Revenue Management and Pricing Section of the Institute for Operations Research and the Management Sciences (INFORMS) and currently serves on the editorial board for the Journal of Revenue and Pricing Management as well as the Board of Directors for INFORMS as Vice President, Information Technology. Dr Lieberman began his career in yield management at American Airlines in 1984. Warren pioneered the application of revenue management techniques in the cruise, timeshare exchange and equipment leasing industries, providing both design and technical leadership. He holds a PhD in Operations Research from Yale University.

³is a Vice President at Veritec Solutions. Mr Mullin has over 20 years experience in designing and implementing decision support systems. He has served as VP of Supply Chain Operations at Blackhawk Network, a subsidiary of Safeway that is a pioneer in third-party stored value programs such as prepaid financial and gift cards. In this role, he was responsible for the groups managing all forecasting, allocation, and distribution services in North America during a period of 300 per cent growth. Before Blackhawk, Mr Mullin held operational management positions at Amazon.com and Sun Microsystems.

Received 1 May 2009; Revised 1 May 2009; Published online 25 September 2009.

Abstract

Relative to the traditional statistical techniques that we have come to rely on, this article presents a fundamentally different way to analyze and predict customer behavior. In addition, new analytical tools are described that highlight where and how opportunities exist to modify customer behavior to better achieve desired outcomes. Many commonly used techniques to understand and predict consumer behavior presume an underlying functional relationship – a model – buried in confusing data. We take the position that these models are generally not good representations of human behavior. Furthermore, with desktop computing having become so powerful, it is now practical to challenge whether the modeling approaches that we have come to rely on represent the best paradigm for understanding and predicting consumer behavior. Underlying our approach is the notion that there are generally multiple routes (sets of influences and decisions) leading to any outcome and their effects can be measured in terms of change in an outcome's probabilities. Rather than attempt to capture central tendencies or capitalize on dominant patterns, Disjunctive Mapping (DM) obtains its power by focusing on the multiple ways events occur. DM metrics enable users to measure the change in probability of an outcome due to the influence of any factor or set of factors in the data, without building models. A structured inquiry process allows it to offer direct, accessible, comprehensive, and prioritized measures in answer to practical questions.