**Winner of the 2023-2024 Gary L. Lilien ISMS Practice Prize Competition**
Authors: Saiquan Hu (Hunan University, [email protected]); Juanjuan Zhang (MIT, [email protected]) ; Yuting Zhu (National University of Singapore, [email protected])
Abstract: Helping new salespeople succeed is critical in sales force management. We develop a deep-learning-based recommender system to help new salespeople recognize suitable customers, leveraging historical sales records of experienced salespeople. One challenge is how to learn from experienced salespeople’s own failures, which are prevalent but often do not show up in sales records. We develop a parsimonious model to capture these “missing by choice” sales records and incorporate the model into a neural network to form an augmented, deep-learning-based recommender system. We validate our method using sales force transaction data from a large insurance company. Our method outperforms common benchmarks in prediction accuracy and recommendation quality, while being simple, explainable, and flexible. We demonstrate the value of our method in improving sales force productivity.
Presented at the 2023-2024 Gary L. Lilien ISMS Practice Prize Competition