Segmentation and Ranking of Online Reviewer Community: The Role of Reviewers' Frequency, Helpfulness, and Recency

Segmentation and Ranking of Online Reviewer Community: The Role of Reviewers' Frequency, Helpfulness, and Recency

Aakash Aakash (Department of Operational Research, University of Delhi, India) and Ajay Jaiswal (Shaheed Sukhdev College of Business Studies, India)
Copyright: © 2020 |Pages: 21
DOI: 10.4018/IJEA.2020010106

Abstract

Online reviewer societies flourish on contributions from different reviewers, who display a wavering engagement behavior. Effort has been made in the e-marketing literature for segmenting individuals with the help of their engagement behavior. In this study, the authors segment the reviewers of a popular travel website (TripAdvisor) through k-means clustering based on three dimensions (F-frequency, H-helpfulness, R-recency), resulting in four different reviewer segments-valuable, trustworthy, new and valueless. The authors calculate the reviewer value using fuzzy AHP and then rank the reviewer segment accordingly. The authors find that the valuable reviewers, who post eWOM regularly and get greater helpful votes by eWOM readers, are the most important. Surprisingly, the trustworthy, who also get more helpful votes with higher eWOM volume, but not posting any review recently, are the second most important. This research is a novel effort on reviewer segmentation and gives valuable insights to e-marketers.
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1. Introduction

The electronic commerce has gained immense popularity in the modern digital era. The prompt nature of the electronic-word-of-mouth (eWOM) available on electronic commerce websites enable the sharing and seeking of consumers' experiences. The eWOM can be defined as “any negative or positive text posted by actual, former, or potential customers related to service/product, which is available online at any e-commerce or social websites through internet” (Cheng & Zhou, 2010). According to Spiegel Research Center, almost 95% customers read eWOM before making a buying decision (Spiegel, 2017). However, 93% consumers say eWOM influence their buying decisions (Podium, 2017). In the recent times, eWOM has emerged as an important communication channel between the marketer and customers. Customers and managers can also connect with other customers through eWOM (Aggarwal & Aakash, 2018a; Heinonen, 2011). Therefore, eWOM has the potential to be powerful medium for aiding online businesses, as it allows customers to openly share their views and personal experiences related to product/service they have used.

Consumers pay attention to eWOMs as well as to the reputation of reviewers (Hu et al., 2008). Customers usually respond to those eWOMs which are posted by the highly reputed reviewers (Hu et al., 2008). The online reviewer communities have been reported to enhance significantly to the revenues of local businesses (Luca, 2016). Banerjee et al. (2017) highlighted the importance of reviewers in online businesses by identifying the indirect impact of reviewer trustworthiness on sales. They also noted that the interaction of reviewer trustworthiness and eWOM-based online reputation significantly enhance the business patronages (Banerjee et al., 2017). Therefore, it can be concluded that opinions of online reviewers matters for online businesses. Another interesting observation which may be made with respect to reviewers is that usually there is not much difference among the opinions of different reviewers related to a product/service (Forman et al., 2008). Due to homogeneity in the reviewers’ comments, it is very difficult for readers to understand that which reviewers are valuable and trustworthy and which are not. Therefore, there is a growing need for marketers to assign a numerical value to each type of reviewer. Assigning a particular value to each type of reviewer can helps readers to find the valuable and trustworthy reviewers.

In our daily lives, we trust opinions of persons we value, much more than those of persons we do not. Customers usually find eWOMs useful when making their decisions, but they may doubt the reputation of the reviewer (O'Mahony & Smyth, 2009). Many past studies measured the reviewers' reputation by total number of helpful votes they received from the people who read the eWOM (O'Mahony & Smyth, 2009; Otterbacher, 2009). Hu et al. (2008) showed that customers not only rely on eWOM ratings but also on reviewer reputation. They noted that eWOMs posted by the reputed reviewers are perceived to be more favorable by the markets (Hu et al., 2008). Otterbacher (2009) proposed two important characteristics of online reviewers such that total helpfulness votes they received and total eWOMs they written. Hu et al. (2008) measured the reviewers' exposure through the total eWOMs written by the reviewer. They also found that reviewers' exposure impact the product sales. Wang et al. (2013) measured reviewers' credibility by jointly considering their expertise and trustworthiness through total eWOMs published by the reviewer and total helpful votes they got.

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