In introductory college microeconomics classes students are exposed to the concept of price elasticity – that is, the predicted response of a buyer in terms of quantity demanded when a seller raises or lowers the price of a certain good or service. In the real world, companies in competitive industries are continually trying to extend insights about elasticity and other behavior-response metrics across thousands of customers and products. The problem with this is that real-world business problems bear very little resemblance to the theoretical examples contained in Microeconomics 101 textbooks. First of all, customer behavior is very hard to pin down. Numerous variables affect every translation. How can we say with a high degree of confidence that a price change was the main cause of a change in demand? Why not something else – perhaps an especially strong and persuasive effort by the salesperson to make the sale? Or something completely outside our control, like the weather that day?
Modern business intelligence systems are rising to meet this challenge by encompassing more explanatory variables into their algorithms. But even so there is still a problem. These models are still confined to looking backwards, to past events, to formulate guidance about what to do in the present and future. Sales & marketing decision makers need to complement their insights from historical data with an approach that can work in the constantly changing environment of their markets in real time. That approach has to draw on the processing capabilities of a system uniquely suited to the ambiguities and constant flux of dynamic markets: the human brain. Continue reading