The purpose of this paper is to identify electronic word-of-mouth (eWOM) customers from customer reviews. Thus, firms can precisely leverage eWOM customers to increase their product sales.
This research proposed a framework to analyze the content of consumer-generated product reviews. Specific algorithms were used to identify potential eWOM reviewers, and then an evaluation method was used to validate the relationship between product sales and the eWOM reviewers identified by the authors’ proposed method.
The results corroborate that online product reviews that are made by the eWOM customers identified by the authors’ proposed method are more related to product sales than customer reviews that are made by non-eWOM customers and that the predictive power of the reviews generated by eWOM customers are significantly higher than the reviews generated by non-eWOM customers.
The proposed method is useful in the data set, which is based on one type of products. However, for other products, the validity must be tested. Previous eWOM customers may have no significant influence on product sales in the future. Therefore, the proposed method should be tested in the new market environment.
By combining the method with the previous customer segmentation method, a new framework of customer segmentation is proposed to help firms understand customers’ value specifically.
This study is the first to identify eWOM customers from online reviews and to evaluate the relationship between reviewers and product sales.
This work was supported by grants from the National Science Foundation of China (nos 71601190, 71771223, 71471157) and the Hong Kong GRF Grant No. 11504515.
Zhao, P., Wu, J., Hua, Z. and Fang, S. (2019), "Finding eWOM customers from customer reviews", Industrial Management & Data Systems, Vol. 119 No. 1, pp. 129-147. https://doi.org/10.1108/IMDS-09-2017-0418Download as .RIS
Emerald Publishing Limited
Copyright © 2018, Emerald Publishing Limited