The fuzzy art of decision science

Abstract

Over the past 20 years there has been massive investment in management information systems, data warehouses, enterprise resource planning systems and analysis technology. However, considering these massive changes, customer decisions are still made using similar techniques. Many customer decisions are made using sequential rules embedded within campaign management, collections, new business and other customer processing systems. But customer decisioning is not just an analytical process based on statistics, since it also requires the input of experts and knowledge about other factors that cannot always be based on past experience — for example, the future actions of competitors or changes in business priorities. However, many organisations have found that sequential rules are often difficult to maintain, since they require specialists who change and manage the code and consequently the systems do not always reflect the many different factors that should be considered in making a decision. This paper addresses the role of customer decision making in organisations and how technology supports this task. It examines whether decisioning technology has moved forward to take advantage of the massive quantities of data and enormous computing power now available, and whether a ‘fuzzy decisioning’ approach would provide better decisions in certain situations. A fuzzy decisioning approach would weight the significant factors in the decisioning process to make a particular outcome more likely. By contrast, a rule-based approach would split groups of customers into different populations based on a logical condition. After reading the paper, readers may also wish to reflect on whether customer decision making is actually a science or an art!