Knowledge that Transforms

This paper studies product bundling in a distribution channel where a downstream retailer combines component goods produced by separate manufacturers acting independently. Past literature offers deep insights about bundling by a single firm whose unit costs are not impacted by choice of selling strategy. But when the retailer bundles goods from separate manufacturers, unit costs for the bundler (retailer) are, being the prices set by the manufacturers, no longer exogenous. This alters the economic balance with respect to bundling. I show that channel conflicts weaken the case for bundling. Although bundling is better than component selling for the integrated firm, it is no longer so in the decentralized channel. The culprit is a combination of vertical channel conflict (incentive misalignment with respect to bundle versus component sales) and horizontal conflict (each manufacturer wants a higher share of profits from bundle sales), with the latter playing a dominant role. They cause manufacturers to overprice component goods, weakening the retailer's incentives to bundle. The competitive interplay between firms when one (retailer) merges the prices of several (manufacturers) leads to lower profits for all. Price coordination between the firms could partially restore the role of bundling and improve the firms' profits as well as consumer surplus.

This study develops and estimates a dynamic model of consumer choice behavior in markets for seasonal goods, where products are sold over a finite season and availability is limited. In these markets, retailers often use dynamic markdown policies in which an initial retail price is announced at the beginning of the season and the price is subsequently marked down as the season progresses. Strategic consumers face a trade-off between purchasing early in the season, when prices are higher but goods are available, and purchasing later, when prices are lower but the stockout risk is higher. If the good starts providing utility as soon as it is purchased (e.g., apparel), consumers purchasing earlier in the season can also get more use from the product compared to those purchasing later. Our structural model incorporates three features essential for modeling the demand for seasonal goods: changing prices, limited availability, and possible dependence of total consumption utility on the time of purchase. In this model, heterogeneous consumers have expectations about future prices and product availability, and they strategically time their purchases. We estimate the model using aggregate sales and inventory data from a fashion goods retailer. The results indicate that, in the fashion goods context, ignoring consumers' expectations about future availability or the change in total consumption utility over the season can lead to biased demand estimates. We find that strategic consumers delay their purchases to take advantage of markdowns and that these strategic delays hurt the retailer's revenues. Retailer revenues facing strategic consumers are 9% lower than they would have been facing myopic consumers. Limited availability, on the other hand, reduces the extent of strategic delays by motivating consumers to purchase earlier. We find that the impact of strategic delays on retailer revenues would have been as high as 35% if there were no stockout risk. By means of counterfactual experiments, we show that the highest retailer profits are achieved by offering small markdowns early in the season. On the other hand, given current markdown percentages, the retailer can improve profits by carrying less stock as consumers accelerate purchases and purchase at higher prices when they anticipate scarcity in future periods. As long as the reduction in availability is not great, the profit gain from earlier higher-priced sales can overcome the loss resulting from the reduction in overall sales.

Presidential elections provide both an important context in which to study advertising and a setting that mitigates the challenges of dynamics and endogeneity. We use the 2000 and 2004 general elections to analyze the effect of market-level advertising on county-level vote shares. The results indicate significant positive effects of advertising exposures. Both instrumental variables and fixed effects alter the ad coefficient. Advertising elasticities are smaller than are typical for branded goods yet significant enough to shift election outcomes. For example, if advertising were set to zero and all other factors held constant, three states' electoral votes would have changed parties in 2000. Given the narrow margin of victory in 2000, this shift would have resulted in a different president.

We estimate the joint impact of the frequency reward and customer tier components of a loyalty program on customer behavior and resultant sales. We provide an integrated analysis of a loyalty program incorporating customers' purchase and cash-in decisions, points pressure and rewarded behavior effects, heterogeneity, and forward-looking behavior. We focus on four key research questions: (1) How important is it to combine both components in one model? (2) Does points pressure exist in the context of a two-component loyalty program? (3) How is the market segmented in its response to the combined program? (4) Do the programs complement each other in terms of the incremental sales they produce? Our most basic message is that the frequency reward and customer tier components of loyalty programs should be modeled jointly rather than in separate models. We find strong evidence for points pressure for both the customer tier and frequency reward components using both model-based and model-free evidence. We find a two-segment solution revealing a “service-oriented” segment that highly values cash-ins for room upgrades and staying in “luxury” hotels, and a “price-oriented” segment that is more price sensitive and highly values the frequency reward aspects of the loyalty program. Furthermore, we find that both components generate incremental sales. Also, there was slight synergy between the programs but not a huge amount. Overall, each component contributes to increased revenues and does not interfere with the other.

User-generated content is increasingly created through the collaborative efforts of multiple individuals. In this paper, we argue that the value of collaborative user-generated content is a function both of the direct efforts of its contributors and of its embeddedness in the content–contributor network that creates it. An analysis of Wikipedia's WikiProject Medicine reveals a curvilinear relationship between the number of distinct contributors to user-generated content and viewership. A two-mode social network analysis demonstrates that the embeddedness of the content in the content–contributor network is positively related to viewership. Specifically, locally central content—characterized by greater intensity of work by contributors to multiple content sources—is associated with increased viewership. Globally central content—characterized by shorter paths to the other collaborative content in the overall network—also generates greater viewership. However, within these overall effects, there is considerable heterogeneity in how network characteristics relate to viewership. In addition, network effects are stronger for newer collaborative user-generated content. These findings have implications for fostering collaborative user-generated content.

This paper studies optimal product line design when consumers need to incur costly deliberation to uncover their valuations for quality. To induce deliberation, a firm must maintain quality dispersion and cut the price of the high-end product so that consumers are motivated to deliberate in the hope that high-end consumption fits their needs. To prevent deliberation, the firm may have to offer downgraded quality at a low price so that an impulsive purchase will not appear too wasteful. Whether the firm should induce deliberation depends on how much surplus it creates by aligning the supply of quality with heterogeneous demand for quality and how much surplus it captures during this process. Interestingly, equilibrium firm profit, consumer surplus, and social welfare can all increase with the cost of deliberation. We extend the model to accommodate consumers' heterogeneous prior beliefs of their valuations for quality. We also discuss how market research could benefit from taking into account the endogeneity of consumer deliberation.

When a firm can recognize its previous customers, it may use information about their past purchases to price discriminate. We study a model with a monopolist and a continuum of heterogeneous consumers, where consumers have the ability to maintain their anonymity and avoid being identified as past customers, possibly at a cost. When consumers can freely maintain their anonymity, they all individually choose to do so, which results in the highest profit for the monopolist. Increasing the cost of anonymity can benefit consumers but only up to a point, after which the effect is reversed. We show that if the monopolist or an independent third party controls the cost of anonymity, it often works to the detriment of consumers.

User-generated content on social media platforms and product search engines is changing the way consumers shop for goods online. However, current product search engines fail to effectively leverage information created across diverse social media platforms. Moreover, current ranking algorithms in these product search engines tend to induce consumers to focus on one single product characteristic dimension (e.g., price, star rating). This approach largely ignores consumers' multidimensional preferences for products. In this paper, we propose to generate a ranking system that recommends products that provide, on average, the best value for the consumer's money. The key idea is that products that provide a higher surplus should be ranked higher on the screen in response to consumer queries. We use a unique data set of U.S. hotel reservations made over a three-month period through Travelocity, which we supplement with data from various social media sources using techniques from text mining, image classification, social geotagging, human annotations, and geomapping. We propose a random coefficient hybrid structural model, taking into consideration the two sources of consumer heterogeneity the different travel occasions and different hotel characteristics introduce. Based on the estimates from the model, we infer the economic impact of various location and service characteristics of hotels. We then propose a new hotel ranking system based on the average utility gain a consumer receives from staying in a particular hotel. By doing so, we can provide customers with the “best-value” hotels early on. Our user studies, using ranking comparisons from several thousand users, validate the superiority of our ranking system relative to existing systems on several travel search engines. On a broader note, this paper illustrates how social media can be mined and incorporated into a demand estimation model in order to generate a new ranking system in product search engines. We thus highlight the tight linkages between user behavior on social media and search engines. Our interdisciplinary approach provides several insights for using machine learning techniques in economics and marketing research.

Web 2.0 provides gathering places for Internet users in blogs, forums, and chat rooms. These gathering places leave footprints in the form of colossal amounts of data regarding consumers' thoughts, beliefs, experiences, and even interactions. In this paper, we propose an approach for firms to explore online user-generated content and “listen” to what customers write about their and their competitors' products. Our objective is to convert the user-generated content to market structures and competitive landscape insights. The difficulty in obtaining such market-structure insights from online user-generated content is that consumers' postings are often not easy to syndicate. To address these issues, we employ a text-mining approach and combine it with semantic network analysis tools. We demonstrate this approach using two cases—sedan cars and diabetes drugs—generating market-structure perceptual maps and meaningful insights without interviewing a single consumer. We compare a market structure based on user-generated content data with a market structure derived from more traditional sales and survey-based data to establish validity and highlight meaningful differences.

Households incur transaction costs when choosing among off-line stores for grocery purchases. They may incur additional transaction costs when buying groceries online versus off-line. We integrate the various transaction costs into a channel choice framework and empirically quantify the relative transaction costs when households choose between the online and off-line channels of the same grocery chain. The key challenges in quantifying these costs are (i) the complexity of channel choice decision and (ii) that several of the costs depend on the items a household expects to buy in the store, and unobserved factors that influence channel choice also likely influence the items purchased. We use the unique features of our empirical context to address the first issue and the plausibly exogenous approach in a hierarchical Bayesian framework to account for the endogeneity of the channel choice drivers. We find that transaction costs for grocery shopping can be sizable and play an important role in the choice between online and off-line channels. We provide monetary metrics for several types of transaction costs, such as travel time and transportation costs, in-store shopping time, item-picking costs, basket-carrying costs, quality inspection costs, and inconvenience costs. We find considerable household heterogeneity in these costs and characterize their distributions. We discuss the implications of our findings for the retailer's channel strategy.