סמינר בשיווק

DR. YAEL KARLINSKY,  Columbia Business School

In a world going towards automation, we ask whether salespeople making pricing decisions in a high human interaction environment such as business to business (B2B) retail, could be automated, and under what conditions it would be most beneficial. Using sales transactions data from a B2B aluminum retailer, we create an automated version of each salesperson, that learns the salesperson's pricing policy based on her past pricing decisions. We find that the model of the salesperson performs at least as good as the salesperson when pricing instead of the salesperson, in spite the loss of non-codeable information that is available to the salesperson but not to the model. We show that by adopting a hybrid pricing approach, that follows the model's pricing most of time, but differs to the salesperson's pricing when the model is missing important information, expected profits could be increased by 3%. Conducting a large-scale field experiment, we provide salespeople in the B2B retailer with their own model's price recommendations, and allow them to adjust their original pricing accordingly. We find that providing the model's price increases gross profit and profit margins. Finally, using results from the empirical analysis and the field experiment, we discuss when it is best to direct a new quote to automatic pricing, and when to an expert salesperson.