Zero to One: Sales Prospecting with Augmented Recommendation

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, interpretable, and flexible. We demonstrate the value of our method in improving sales force productivity.

Keywords: sales force management, deep learning, recommender system, neural network, selection bias.