讲座：Zero to One: Sales Prospecting with Augmented Recommendation 发布时间：2022-01-05
题 目：Zero to One: Sales Prospecting with Augmented Recommendation
嘉 宾：Yuting Zhu, Ph.D. candidate, MIT Sloan School of Management
主持人：张 铄 助理教授 上海交通大学安泰经济与管理学院
Helping new salespeople succeed is critical in sales force management. We develop a recommender system to help new salespeople identify customers with better conversion potential. One challenge is to deal with salesperson-customer combinations that have no historical sales records. These instances are often treated as missing observations in standard recommender systems. We instead highlight the possibility that sales records are absent because previous salespeople did not convert certain types of customers. We develop a parsimonious model to capture these endogenously absent sales records and embed 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 popular industry benchmarks in prediction accuracy, recommendation quality, and sales prospecting efficiency.
Yuting Zhu is a Ph.D. candidate in Management at the MIT Sloan School of Management. Her current research focuses on augmentation of machine learning algorithms for marketing problems using economic theory and marketing domain knowledge. Applications to date include salesforce management, advertising targeting, and promotion personalization. She will join National University of Singapore in July 2022.