A Theory-Driven Deep Learning Method for Voice Chat–Based Customer Response Prediction

Author:

Chen Gang1ORCID,Xiao Shuaiyong2ORCID,Zhang Chenghong3ORCID,Zhao Huimin4ORCID

Affiliation:

1. School of Management, Zhejiang University, Hangzhou 310027 Zhejiang, Peoples Republic of China;

2. School of Economics and Management, Tongji University, Shanghai 200092, Peoples Republic of China;

3. School of Management, Fudan University, Shanghai 200433, Peoples Republic of China;

4. Sheldon B. Lubar College of Business, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin 53211

Abstract

A Theory-Driven Deep Learning Method for Voice Chat–Based Customer Response Prediction In this study, we target the task of voice chat–based customer response prediction in an emerging online interaction–based commercial mode, the invite-online-and-experience-in-store mode. Prior research shows that satisfaction, which can be revealed by the discrepancy between prior expectation and actual experience, is a key factor to disentangle customers’ purchase intention, whereas black box deep learning methods empirically promise us with advantageous capabilities in dealing with complex voice data, for example, text and audio information incorporated in voice chat. To this end, we propose a theory-driven deep learning method that enables us to (1) learn customers’ personalized product preferences and dynamic satisfaction in the absence of their profile information, (2) model customers’ actual experiences based on multiview voice chat information in an interlaced way, and (3) enhance the customer response prediction performance of a black box deep learning model with theory-driven dynamic satisfaction. Empirical evaluation results demonstrate the advantageous prediction performance of our proposed method over state-of-the-art deep learning alternatives. Investigation of cumulative satisfaction reveals the collaborative predictive roles of theory-driven dynamic satisfaction and deep representation features for customer response prediction. Explanatory analysis further renders insights into customers’ personalized preferences and dynamic satisfaction for key product attributes.

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

Subject

Library and Information Sciences,Information Systems and Management,Computer Networks and Communications,Information Systems,Management Information Systems

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