Disjunctive mapping: Changing the way we understand and predict customer behavior (Part One)

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 argue that these models are generally not good representations of human behavior, 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.