Knowledge that Transforms

This paper studies the optimal product and pricing decisions in a crowdfunding mechanism by which a project between a creator and many buyers will be realized only if the total funds committed by the buyers reach a specified goal. When the buyers are sufficiently heterogeneous in their product valuations, the creator should offer a line of products with different levels of product quality. Compared to the traditional situation where orders are placed and fulfilled individually, with the crowdfunding mechanism, a product line is more likely than a single product to be optimal and the quality gap between products is smaller. This paper also shows the effect of the crowdfunding mechanism on pricing dynamics over time. Together, these results underscore the substantial influence of the emerging crowdfunding mechanisms on common marketing decisions.

Firms are increasingly engaging with customers on social media. Despite this heightened interest, guidance for effective engagement is lacking. In this study, we investigate customers’ compliments and complaints and firms’ service interventions on social media. We develop a dynamic choice model that explicitly accounts for the evolutions of both customers’ voicing decisions and their relationships with the firm. Voices are driven by both the customers’ underlying relationships and other factors such as redress seeking. We estimate the model using a unique data set of customer voices and service interventions on Twitter. We find that redress seeking is a major driver of customer complaints, and although service intervention improves relationships, it also encourages more complaints later. Because of this dual effect, firms are likely to underestimate the returns on service intervention if measured using only voices. Furthermore, we find an “error-correction” effect in certain situations, where customers compliment or complain when others voice the opposite opinions. Finally, we characterize the distinct voicing tendencies in different relationship states, and show that uncovering the underlying relationship states enables effective targeting. We are among the first to analyze individual customer level voice dynamics and to evaluate the effects of service intervention on social media.

Digitization of content is changing how consumers and firms use purchase and rental markets. Low transaction costs make accessing content easier for consumers. Digital technology enables firms to create nondurable “rental” versions of their content and to restrict content to the purchasing consumer, effectively shutting down resale markets. To empirically analyze the interaction of purchase and rental markets, I design a preference measurement tool to recover consumers’ intertemporal preferences through current-period choices alone. I then use these preferences to solve for a dynamic equilibrium between consumers and the firm. In the context of the online home video market, I find that when the firm is able to commit to holding prices fixed forever, providing content through the purchase market alone is sufficient. However, when the firm is unable to commit, it should serve both purchase and rental markets. Canonical theory models would predict exclusive rentals, but the purchase option enables indirect price discrimination in practice. I also find that when consumers place a premium on accessing new content, they are less likely to intertemporally substitute, thereby increasing the firm’s pricing power. Consistent with theory, commitment to future prices increases profits considerably. This finding supports the rigid pricing structure of such retailers as Apple, despite studios’ push toward more pricing flexibility.

This paper investigates the economic value of online reviews for consumers and restaurants. We use a data set from Dianping.com , a leading Chinese website providing user-generated reviews, to study how consumers learn, from reading online reviews, the quality and cost of restaurant dining. We propose a learning model with three novel features: (1) different reviews offer different informational value to different types of consumers; (2) consumers learn their own preferences, and not the distribution of preferences among the entire population, for multiple product attributes; and (3) consumers update not only the expectation but also the variance of their preferences. Based on estimation results, we conduct a series of counterfactual experiments and find that the value from Dianping is about 7 CNY for each user, and about 8.6 CNY from each user for the reviewed restaurants in this study. The majority of the value comes from reviews on restaurant quality, and contextual comments are more valuable than numerical ratings in reviews.

Firms track consumers’ shopping behaviors in their online stores to provide individually personalized banners through a method called retargeting. We use data from two large-scale field experiments and two lab experiments to show that, although personalization can substantially enhance banner effectiveness, its impact hinges on its interplay with timing and placement factors. First, personalization increases click-through especially at an early information state of the purchase decision process. Here, banners with a high degree of content personalization (DCP) are most effective when a consumer has just visited the advertiser’s online store, but quickly lose effectiveness as time passes since that last visit. We call this phenomenon overpersonalization. Medium DCP banners, on the other hand, are initially less effective, but more persistent, so that they outperform high DCP banners over time. Second, personalization increases click-through irrespective of whether banners appear on motive congruent or incongruent display websites. In terms of view-through, however, personalization increases ad effectiveness only on motive congruent websites, but decreases it on incongruent websites. We demonstrate in the lab how perceptions of ad informativeness and intrusiveness drive these results depending on consumers’ experiential or goal-directed Web browsing modes.