With a wealth of products and services at our fingertips in the modern marketplace, online reviews have become an invaluable source of information for consumers. This is especially true for “experience goods” such as a haircut or a hotel stay, whose true quality cannot be determined before purchase. Many retailers post user-generated product reviews online to help potential customers judge the quality of their products based on others’ feedback. According to Ying-ju Chen of HKUST and colleagues, online stores selling experience goods can increase their profits simply by choosing the right review-provision strategy.
“Customers often rely on user-generated reviews to evaluate new experience goods,” the researchers tell us. However, some reviews are more informative and relevant than others. Potential buyers who are uncertain about product quality prefer to seek advice from people who share their tastes and preferences. After all, the same product may be evaluated very differently by customers in different “clusters,” say the researchers, “where clusters are segmented by key attributes such as age, gender, geographic region, occupation, and wealth.”
The authors identified two user-generated review provision policies commonly used to sell experience goods to customers in different clusters. Under the association-based policy (AP), a customer in a given cluster can see only the aggregate review (i.e., average rating) generated by users within the same cluster. Under the global-based policy (GP), every customer can see the aggregate review generated by all users across clusters.
“These two policies, AP and GP, provide different information to customers and thus have different impacts on potential buyers’ inferences about product quality and preference,” the authors tell us. A global aggregate review is more informative, as it reflects a wider range of opinions, while an association-based (cluster-specific) aggregate review is more relevant, as it reflects the opinions of customers with similar attributes. “In other words,” say the researchers, “AP provides fewer but more relevant reviews to customers, while GP provides a larger number of less relevant reviews.”
Despite this key difference, both review-provision policies are used widely by online stores. Intrigued by this variation in policy adoption across firms, the authors developed a novel stylized model to evaluate the impact of different review-provision policies on firm revenue and consumer welfare.
“We find that, in general, the firm benefits from a policy that provides a larger number of ‘relevant reviews’ to customers,” the authors report. “When customers are more certain about the product quality and when clusters are more diverse, AP is more profitable than GP because it provides cluster-specific reviews.”
Combining the two policies may be even more beneficial for firms. “We propose a third provision policy that imparts the union of the information by AP and GP,” the researchers explain, “and show that it is more profitable for the firm.” However, they warn that “customers may not always benefit from a more informative review provision policy.”
These novel findings offer important guidance for retailers seeking to capitalize on online reviews to attract customers from different clusters, especially those looking for experience goods. “When customers rely on user-generated reviews to evaluate product quality,” the authors conclude, “a firm can increase its profit significantly by simply changing its review-provision policy.”