A Theory-Driven Deep Learning Method for Voice Chat–Based Customer Response Prediction
- 1 December 2023
- journal article
- research article
- Published by Institute for Operations Research and the Management Sciences (INFORMS) in Information Systems Research
- Vol. 34 (4), 1513-1532
- https://doi.org/10.1287/isre.2022.1196
Abstract
As artificial intelligence and digitalization technologies are flourishing real-time, online interaction–based commercial modes, exploiting customers’ purchase intention implied in online interaction processes may foster huge business opportunities. 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 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. History: Yong Tan, Senior Editor; Jingjing Zhang, Associate Editor. Funding: This work was supported in part by the National Natural Science Foundation of China [Grants 71971067 and 72271059] and the China Postdoctoral Science Foundation [Grant 2022M722394]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1196.Keywords
This publication has 56 references indexed in Scilit:
- Direct marketing decision support through predictive customer response modelingDecision Support Systems, 2012
- Monte Carlo analysis of estimation methods for the prediction of customer response patterns in direct marketingEuropean Journal of Operational Research, 2012
- Online Customer Experience: A Review of the Business-to-Consumer Online Purchase ContextInternational Journal of Management Reviews, 2011
- The relationships among service quality, perceived value, customer satisfaction, and post-purchase intention in mobile value-added servicesComputers in Human Behavior, 2009
- Expectation confirmation: An examination of three competing modelsOrganizational Behavior and Human Decision Processes, 2008
- An Exploratory Study of the Impact of e‐Service Process on Online Customer SatisfactionProduction and Operations Management, 2008
- Satisfaction with Online Commercial Group Chat: The Influence of Perceived Technology Attributes, Chat Group Characteristics, and Advisor Communication StyleJournal of Retailing, 2007
- AIMQ: a methodology for information quality assessmentInformation & Management, 2002
- Comparison of different implementations of MFCCJournal of Computer Science and Technology, 2001
- Effect of expectation and disconfirmation on postexposure product evaluations: An alternative interpretation.Journal of Applied Psychology, 1977