The Internet and Word-of-Mouth (WOM)

Ever since the inception of the Internet, consumers have benefited from extensive opportunities to share their evaluations of products online. Most e-commerce platforms allow consumers to review products, and an increasing number of opinion platforms have been introduced that offer online consumer ratings and reviews. Furthermore, most online retailers are now listing and selling trending products, defined as products that large groups of individuals are currently purchasing or discussing (Kocas and Akkan, 2016). In their article “How trending status and online ratings affect prices of homogeneous products”, Kocas and Akkan explore the pricing implications of these reviews and trending status. The following research questions result:

RQ1: How do standardised average prices vary with product popularity (measured by the trending status)? 

RQ2: When controlled for popularity, how do standardized average prices vary with average consumer ratings?

Related Theory

Research in marketing and economics have shown that it is profitable for retailers to sell popular products at a discount as advertising the low price is an effective and cheap method to inform consumers of the extra surplus they could get by purchasing these products (Elberse, 2008). In the present study, trending is considered an indicator of product popularity as well as a costless form of advertising – trending products signal desirability and potential positive surplus to consumers (Hosken and Reiffen, 2004). Hence, one can assume that trending products are priced lower by retailers, as the resulting increase in demand more than likely compensates for the decrease in marginal revenue per item sold.

Furthermore, several studies have shown that positive ratings and reviews have a positive effect on sales (Baek et al., 2012). Similarly to trending status, high ratings can act as a signal of desirability. Hence one can reasonably assume that highly rated products should be priced lower by retailer for the same reason as aforementioned.

Formally stated,

H1: Retailers randomize prices of products independently. The average and minimum profit-maximizing prices for the trending products are lower than the prices for non-trending products given identical average consumer ratings.

H2: The average and minimum profit-maximizing prices for the product with higher average consumer ratings are lower than the product with lower average consumer ratings given identical trending status.

Results

This study analyses data gathered from 24 of the 28 categories of books available on Amazon.com from May 25 to September 13, 2011 and includes a sample of 466’190 books. Both hypotheses are supported by the experiment, showing that a trending product should be priced lower than other products in order to exploit the higher number of browsers these trending items attract. Similarly, highly rated products lead to a higher conversion rate (from browsing to purchasing) and, hence deserve lower prices.

Strengths & Weaknesses

 Whereas several studies have examined the impact of viral characteristics of products on consumer behaviour and pricing policies, this study is the first to empirically examine the influence of trending status on pricing online in a field experiment with a large dataset. Similarly, whereas several studies have examined the impact of online reviews on consumer behaviour, no prior work has examined how online reviews and ratings affect prices of homogeneous goods. A strong point of this paper is that it acts on these 2 gaps to provide novel findings, and tangible and actionable insights to practitioners.

  Another strength is that this paper provides a detailed methodology, which is complemented by an appendix as well as a detailed explanation of the economic foundations behind the theory (including formulas). This level of details increases the academic relevance of the paper, and allows other researcher to easily replicate the experiments, hence facilitating continuous research on the topic.

    One of the weaknesses of this study is the fact that it only examines one type of products – books. Several studies (e.g. Abdullah-Al-Mamun and Robel, 2014) have shown that price sensitivity varies from one product category to another. Similarly, product reviews are generally more important for certain types of products than others. For instance, for a product such as a microwave, personal taste doesn’t really matter, hence one could expect product reviews to be more important as it provides an objective evaluation. However, for a product such as a science-fiction book, personal taste is important, hence the influence of product reviews is likely lower. Thus, it would be beneficial to replicate this study while taking into account category- and product-specific features as a predictor of prices. This can easily be done by replicating the experiment with more product categories on Amazon, and would validate the robustness of this study’s findings across product categories.

    A second weakness of this paper is the fact that it examines the impact of online ratings by relying only on single-dimensional rating schemes. Online platforms display reviews using a variety of formats, and many platforms provide separate ratings for different product attributes. Research has shown that multi-dimensional and single-dimensional rating schemes in online review platforms have different impact on consumers (Tunc et al., 2017). Similarly, this study only looks at the ratings but not at the content of the review. However, studies have shown that the latter can influence consumer behaviour. Both these factors can influence the conversion rate from browser to buyer (Mudambi and Schuff, 2010) and thus the profitability of retailers. Hence, it would be interesting to replicate the present research in the context of multi-dimensional rating schemes, and take into account the actual content of online reviews.

Implications

We have seen that there are significant advantages to demand-based pricing for popular products with a relatively high market share. Hence, online retailers should monitor signs of trending as they act as a positive desirability signal that increases the demand of price-comparing consumers. By responding to trending signs and adjusting their prices, retailers can optimise their profits. Nevertheless, managers should be cautious of the research findings and conduct further experiments when applying them to products other than books. Finally, managers should be careful about the pace at which they adjust their prices – popularity status can change extremely quickly, but consumers will not react well to frequent price changes.

References

Abdullah-Al-Mamun, M. K. R., & Robel, S. D. (2014). A Critical Review of Consumers’ Sensitivity to Price: Managerial and Theoretical Issues. Journal of International Business and Economics, 2(2), 01-09.

Baek, H., Ahn, J., & Choi, Y. (2012). Helpfulness of online consumer reviews: Readers’ objectives and review cues. International Journal of Electronic Commerce, 17(2), 99-126.

Brynjolfsson, E., Hu, Y., & Smith, M. D. (2010). Research commentary—long tails vs. superstars: The effect of information technology on product variety and sales concentration patterns. Information Systems Research, 21(4), 736-747.

Elberse, A. (2008). Should you invest in the long tail?. Harvard business review, 86(7/8), 88.

Hosken, D., & Reiffen, D. (2004). How retailers determine which products should go on sale: Evidence from store-level data. Journal of Consumer Policy, 27(2), 141-177.

Kocas, C., & Akkan, C. (2016). How Trending Status and Online Ratings Affect Prices of Homogeneous Products. International Journal of Electronic Commerce, 20(3), 384-407.

Mudambi, S. M., & Schuff, D. (2010). Research note: What makes a helpful online review? A study of customer reviews on Amazon. com. MIS quarterly, 185-200.

Tunc, M. M., Cavusoglu, H., & Raghunathan, S. (2017). Single-Dimensional Versus Multi-Dimensional Product Ratings in Online Marketplaces.