In a world advancing towards automation, we ask whether salespeople making pricing decisions in a high human interaction environment such as business-to-business (B2B) retail, can be automated, and when it would be most beneficial. Using sales transactions data from a B2B aluminum retailer, we create an automated version of each salesperson, that learns and automatically reapplies the salesperson’s pricing policy. We conduct a field experiment with the B2B retailer, providing salespeople with their own model’s price recommendations in real-time through the retailer’s CRM system, and allowing them to adjust their original pricing accordingly. We find that despite the loss of non-codeable information available to the salesperson but not to the model, providing the model’s price to the salesperson increases profits for treated quotes by 10% relatively to a control condition. Using a counterfactual analysis, we show that while in most of the cases the model’s pricing leads to higher profitability, the salesperson generates higher profits when pricing for quotes or clients with unique or complex characteristics. Accordingly, we propose a machine learning Random Forest hybrid pricing strategy, that automatically combines the model and the human expert and generates profits significantly higher than either the model or the salespeople.
Karlinsky-Shichor, Yael, and Oded Netzer. "Automating the B2B Salesperson Pricing Decisions: Can Machines Replace Humans and When?" Columbia Business School, April 8, 2019.
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