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Understanding customer regional differences from online opinions: a hierarchical Bayesian approach

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Abstract

A large volume of customer reviews is generated from time to time and customer requirements are presented between lines of online opinions. Many studies about online opinions mainly focus on the extraction of customer sentiment, but practical concerns regarding the integration into new product design are far from extensively discussed. To enlighten designers about how consumers differ geographically in terms of their preferences, which is possessing important research significance and practical values, is not well investigated. Specifically, in this study, online reviews are invited to explore market regional heterogeneity. With identified product feature related subjective sentences from online reviews, a straightforward applied approach is to assume the ratio of the number of satisfied customers to the total number of customers as the expected percentage of satisfied customers across different regions. However, such frequency based approach becomes unreliable in case that the number of reviews do not distribute evenly. Accordingly, the Bayesian school of thought is utilized in which statistics of data-rich regions are invited to help to analyze that of data-poor regions. Then, a hierarchical Bayesian model is proposed and it assumes that the expected percentages of customer satisfaction in different regions follow a certain probability distribution. Finally, taking 9541 mobile phone online reviews on Amazon as an example, categories of experiments were conducted. It informs the significance to product designers about the value of online concerns on analyzing market regional heterogeneity and presents the effectiveness of the proposed approach in terms of discovering customer regional differences.

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Acknowledgement

This work was supported by a grant from the National Natural Science Foundation of China (Project No. NSFC 71701019/G0114).

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Authors and Affiliations

  1. School of Economics and Management, Fuzhou University, Fuzhou, 350108, China

    Kejia Chen & Zheng Zhao

  2. Department of Information Management, School of Government, Beijing Normal University, Beijing, 100875, China

    Jian Jin

  3. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, People’s Republic of China

    Ping Ji

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  1. Kejia Chen
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Correspondence to Jian Jin.

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Chen, K., Jin, J., Zhao, Z. et al. Understanding customer regional differences from online opinions: a hierarchical Bayesian approach. Electron Commer Res 22, 377–403 (2022). https://doi.org/10.1007/s10660-020-09420-5

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