Nudging a Slow‐Moving High‐Margin Product in a Supply Chain with Constrained Capacity

For a slow‐moving high‐margin product, we demonstrate the viability of an information‐based nudging strategy. The motivation to study this problem was because a firm faced availability constraints for one of its slow‐moving high‐margin products, but the available quantities still exceeded the current demand. To identify customers to nudge, we develop a support vector machine (SVM) approach to rank order the customers based on their propensity to purchase the product. The underlying notion in our approach is that Type I errors, to be defined in the paper, in our classifier are not necessarily problematic but are potential nudging targets. Also, as a consequence, traditional ways of evaluating classifiers (with Type I and Type II errors) are not appropriate. Therefore, we conduct a field experiment to evaluate how well the identified customers are nudged through information and/or couponing. We find that in terms of the successful nudges, our SVM‐based approach performed better than other approaches. The experiment also generated insights about when couponing as opposed to information is more effective when nudging.