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EMAC 2024 Annual


Quantitative Marketing 5: Machine Learning & AI
(A2024-119597)

Published: May 28, 2024

AUTHORS

Dominik Papies, University of Tübingen; Alina Ferecatu, Rotterdam School of Management, Erasmus University; Rafael P. Greminger, University College London, School of Management; Daniela Schmitt, Nova School of Business and Economics; Iris Steenkamp, Bocconi University; Sebastian Gabel, Rotterdam School of Management; Shrabastee Banerjee, Tilburg University; Aseem Behl, University of Tübingen; Xinrong Zhu, Imperial College Business School

ABSTRACT

In the data-rich business landscape, effective decision-making hinges not just on data volume, but on the ability of firms and analysts to extract valid and actionable insights. Quantitative marketing, at the intersection of marketing, economics, statistics, and machine learning, therefore, remains pivotal in this era of data-driven decision-making. Against this backdrop, we aim to stimulate the application of advanced empirical methods and innovative quantitative marketing approaches within the EMAC community through a series of 5 special sessions. This Special Session Quantitative Marketing 5: Machine Learning & AI delves into recent advancements in Machine Learning and Artificial Intelligence (AI). The field has garnered increased attention, particularly since the launch of ChatGPT in November 2022. A domain in which Machine Learning has traditionally been known to excel is prediction. In the first paper, titled “Zero-shot learning for product recommendation using deep neural networks”, Gabel and Ringel utilize the predictive power of Machine Learning and more specifically, Neural Networks, to make product recommendations. The key innovation of the paper is that this recommendation is made when no shopping history is available for a given customer, but only the basket’s current composition. Two papers in this session leverage Machine Learning in the context of text data. Karaman, Chakraborty, and Banerjee study the effects of management responses to product reviews, but they go beyond the response / no-response dichotomy and extract different response styles using Machine Learning tools. They then gauge the effects of these response styles both on future reviews as well as demand. Schwarzer and Behl in their paper “Leveraging Large Language Models for Optimized Title Generation in Digital Marketplaces” train large language models to create Airbnb listing titles that—according to human evaluation—are more attractive than those created by humans. Lastly, the newly acquired capabilities of Machine Learning and AI also create concerns that they may replace human labor. In the last paper of this session, titled “Who Is AI Replacing? The Impact of ChatGPT on Online Freelancing Platforms”, Demirci, Hannane, and Zhu study the potential of ChatGPT to replace different types of labor on an online freelancing platform. They observe a significant decline in demand for automation prone jobs both after the introduction of ChatGPT as well as jobs related to image creation after the introduction of image generating AI models such as Stable Diffusion or DALL-E 2. In sum, this session does not only highlight the potential of Machine Learning and AI for quantitative marketing as well as decision makers within firms, but it also showcases the likely challenges and required transitions that it will create on the labor market.