Zero to One: Sales Prospecting with Augmented Recommendation

52 Pages Posted: 14 Jan 2022

See all articles by Saiquan Hu

Saiquan Hu

Hunan University

Juanjuan Zhang

Massachusetts Institute of Technology (MIT) - Sloan School of Management

Yuting Zhu

Massachusetts Institute of Technology (MIT)

Date Written: January 12, 2022

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.

Suggested Citation

Hu, Saiquan and Zhang, Juanjuan and Zhu, Yuting, Zero to One: Sales Prospecting with Augmented Recommendation (January 12, 2022). Available at SSRN: https://ssrn.com/abstract=4006841 or http://dx.doi.org/10.2139/ssrn.4006841

Saiquan Hu

Hunan University ( email )

2 Lushan South Rd
Changsha, CA Hunan 410082
China

Juanjuan Zhang

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

Cambridge, MA 02142
United States

HOME PAGE: http://jjzhang.scripts.mit.edu

Yuting Zhu (Contact Author)

Massachusetts Institute of Technology (MIT) ( email )

77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
United States

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