Sales Automation
**Winner of the 2023-2024 Gary L. Lilien ISMS Practice Prize Competition**
Authors: Saiquan Hu (Hunan University, husaiquan@hnu.edu.cn); Juanjuan Zhang (MIT, jjzhang@mit.edu) ; Yuting Zhu (National University of Singapore, y.zhu@nus.edu.sg)
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
Business Policy Experiments using Fractional Factorial Designs: Consumer Retention on DoorDash
Authors: Yixin Tang (DoorDash, yixin@doordash.com; Yicong Lin (DoorDash, Nicole.lin@doordash.com; Navdeep S. Sahni (Stanford University, navdeep.sahni@stanford.edu)
Abstract: This paper investigates an approach to both speed up business decision-making and lower the cost of learning through experimentation by factorizing business policies and employing fractional factorial experimental designs for their evaluation. We illustrate how this method integrates with advances in the estimation of heterogeneous treatment effects, elaborating on its advantages and foundational assumptions. We empirically demonstrate the implementation and benefits of our approach and assess its validity in evaluating consumer promotion policies at DoorDash, which is one of the largest delivery platforms in the US. Our approach discovers a policy with 5% incremental profit at 67% lower implementation cost.
Presented at the 2023-2024 Gary L. Lilien ISMS Practice Prize Competition
Modelling Customer Lifetime Value in the Retail Banking Industry
Authors: Greig Cowan (NatWest Group Greig.Cowan@natwest.com), Salvatore Mercuri (Univ. of Edinburgh smercuri@ed.ac.uk), and Raad Khraishi (NatWest Group Raad.Khraishi@natwest.com)
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Motivating Sustainable Energy Consumption Within Organizations: The Role of Artificial Intelligence and Behavioral Insights
Authors: Christopher Amaral (University of Bath, ca786@bath.ac.uk); Ceren Kolsarici (Queen’s University, ceren.kolsarici@queensu.ca); Iina Ikonen (University of Bath, imhi21@bath.ac.uk); Nicole Robitaille (Queen’s University, nicole.robitaille@queensu.ca)
Introduction
Effect of front-of-pack labels on the nutritional quality of supermarket food purchases
**Winner of the 2022 Gary L. Lilien ISMS Practice Prize Competition**
“Effect of front-of-pack labels on the nutritional quality of supermarket food purchases” by Pierre Dubois (Toulouse School of Economics, France; pierre.dubois@tse-fr.eu); Paulo Albuquerque (INSEAD, France; paulo.albuquerque@insead.edu); Olivier Allais (INRA, France; oallais@gmail.com), Céline Bonnet (Toulouse School of Economics, France; celine.bonnet@tse-fr.eu); Patrice Bertail (University Paris Nanterre, France; patrice.bertail@gmail.com); Pierre Combris (INRA, France;pierre.combris@gmail.com); Saadi Lahlou (London School of Economics and Political Science, UK;s.lahlou@lse.ac.uk); Natalie Rigal (University Paris Nanterre, France; rigal.n@free.fr); Bernard Ruffieux (Grenoble Applied Economics Lab, France; bernard.ruffieux@grenoble-inp.fr).
Reducing Net Product Returns through Green Nudges and Causal Machine Learning
“Reducing Net Product Returns through Green Nudges and Causal Machine Learning” by Moritz von Zahn (Goethe University Frankfurt, vzahn@wiwi.uni-frankfurt.de); Kevin Bauer, (Leibniz SAFE Frankfurt, bauer@safe-frankfurt.de); Cristina Mihale-Wilson (Goethe University Frankfurt, mihale-wilson@wiwi.uni-frankfurt.de); Maximilian Speicher (Jagow Speicher Consulting, max@maxspeicher.com ); Johanna Jagow (Jagow Speicher Consulting, johanna@maxspeicher.com); Oliver Hinz (Goethe University Frankfurt,ohinz@wiwi.uni-frankfurt.de).
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Personalization in Email Marketing: The Role of Non-informative Advertising Content
“Personalization in Email Marketing: The Role of Non-informative Advertising Content” by Navdeep Sahni (Stanford University; navdeep.sahni@stanford.edu), Chirstian Wheeler (Stanford University; christian.wheeler@stanford.edu), Pradeep Chintagunta (University of Chicago; pradeep.chintagunta@chicagobooth.edu).
Promotional Campaign Duration and Word-of-Mouth in Solar Panel Adoption
“Promotional Campaign Duration and Word-of-Mouth in Solar Panel Adoption” by Bryan Bollinger (New York University, bryan.bollinger@stern.nyu.edu), Kenneth Gillingham (Yale University and National Bureau of Economic Research, kenneth.gillingham@yale.edu), Stefan Lamp (Toulouse School of Economics, stefan.lamp@tse-fr.eu), Tsevtan Tsevatov (University of Kansas, tsvetanov@ku.edu).
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