Research

This article studies the question and answer (Q&A) technology of electronic commerce platforms, an increasingly common form of user-generated content that allows consumers to publicly ask product-specific questions and receive responses, either from the platform or from other customers. Using data from a major online retailer, the authors show that Q&As complement consumer reviews: unlike reviews, questions are primarily asked prepurchase and focus on clarification of product attributes rather than discussion of quality; answers convey fit-specific information in a predominantly sentiment-free way. Drawing on these observations, the authors hypothesize that Q&As mitigate product fit uncertainty, leading to better matches between products and consumers and, therefore, improved product ratings. Indeed, when products suffering from fit mismatch start receiving Q&As, their subsequent ratings improve by approximately .1 to .5 stars, and the fraction of negative reviews that discuss fit-related issues declines. The extent of the rating increase due to Q&As is proportional to the probability that purchasers will experience fit mismatch without Q&A. These findings suggest that, by resolving product fit uncertainty in an e-commerce setting, the addition of Q&As can be a viable way for retailers to improve ratings of products that have incurred low ratings due to customer–product fit mismatch. 

In this paper, we aim to provide a comprehensive overview of customer feedback literature, highlighting the burgeoning role of AI. Customer feedback has long been a valuable source of customer insights for businesses and market researchers. While previously survey focused, customer feedback in the digital age has evolved to be rich, interactive, multi-modal and virtually real-time. Such explosion in feedback content has also been accompanied by a rapid development of artificial intelligence and machine learning technologies that enable firms to understand and take advantage of these high-velocity data sources. Yet, some of the challenges with traditional surveys remain, such as self-selection concerns of who chooses to participate and what attributes they give feedback on. In addition, these new feedback channels face other unique challenges like review manipulation and herding effects due to their public and democratic nature. Thus, while the AI toolkit has revolutionized the area of customer feedback, extracting meaningful insights requires complementing it with the appropriate social science toolkit. We begin by touching upon conventional customer feedback research and chart its evolution through the years as the nature of available data and analysis tools develop. We conclude by providing recommendations for future questions that remain to be explored in this field. 

Online reviews have become more interactive, with most businesses responding to reviewers on review platforms. These public responses can influence both future ratings and sales. While past literature has studied the impact of the decision to respond on subsequent ratings, the impact on sales has not been

investigated, nor the important question of how to respond. In this paper, we leverage a proprietary dataset comprising of online reviews, corresponding responses, and subsequent sales over three years from a major international hotel group to study the impact of both these decisions on subsequent ratings

and sales. We use advanced text analysis to classify response text beyond simple metrics into elements such as problem acceptance, responsibility, regret, and action, and analyze their effects on future ratings and sales using causal inference techniques. Our findings indicate that responding generally benefits

both ratings and sales, but the impact varies significantly depending on response elements. First, we find that personalizing responses based on review content boosts ratings and sales, whereas mimicking the reviewer’s style has adverse effects on both outcomes. Second, contrary to previous findings, responding

to positive reviews can be advantageous for specific strategies, and the effects of response elements vary between sales and ratings. Finally, for negative reviews, we find that an accommodative strategy is less effective than a defensive one for both outcomes, in contrast to earlier findings in offline complaint man-

agement. Thus, our work offers new evidence and insights into the boundary conditions for management response effectiveness in an online context. 

How do consumers respond to "Starting From'' (floor) prices advertised by firms that differ from the actual purchase price to be paid? While a low "From'' price is likely to draw consumers in, a high one is likely to be closer to the true price and thus be perceived as more fair. Although floor prices are ubiquitous, they have been studied much less compared to other forms of advertised reference prices (e.g.,"Was-Now"). In this paper, we conduct pre-registered field experiments on an online travel marketplace (Holidu.com) to investigate how consumers respond when floor prices are raised. We find that high floor prices lead to decreased user engagement (as measured by listing clicks, number of searches, and time spent on the website), and noisy but negative effects on booking related outcomes. These effects occur despite higher floor prices providing users with an estimate closer to actual prices on average. Our findings indicate that less accurate up-front prices can actually lead to more customer engagement, and dominates the countervailing sticker-shock or anchoring effect, wherein consumers would be deterred if offered a low initial price estimate and a higher price further down the purchase funnel. Overall, this result has implications for platform design and regulation by demonstrating the tension between customer engagement and providing accurate price estimates up front.

How can a platform capture the value it creates for its users without damaging its ecosystem? In this study, we leverage a natural experiment on Goodreads.com to examine the potential intended and unintended consequences of monetizing a popular promotional program run by the platform: Goodreads Giveaways.

Participating in this program was free for authors and publishers till January 2018, after which Goodreads enacted a policy change and began to charge a fixed participation fee. We collect large-scale data to analyze both the supply side (i.e., authors and publishers) and demand side (i.e., consumers) response to this monetization policy. We document several novel insights about the consequences of monetization that are above and beyond the traditional concern of network effects. Specifically, we find that Goodreads’ monetization policy (i) increases supply concentration by increasing the representation of Big 5 publishers in the Giveaways marketplace, (ii) decreases product diversity by reducing participation from niche genres, and (iii) results in worse matches between consumers and products as measured by book ratings. Our findings highlight a more subtle and complex view of evaluating monetization and suggest that platforms need to counterbalance these effects by offering more flexible and nuanced incentive structures for different players in its ecosystem.