Search

A natural experiment reveals that an ad ban reduces sales of a grocery retailer by 6% but does not affect overall market size. Advertising bans typically target products that deceive consumers in ways that can threaten their physical and mental health. An alternative policy objective might seek environmental protection through a ban on print advertising. Such measures would profoundly affect grocery retailers relying on printed leaflets to communicate weekly promotions. We measure the causal effect of banning advertising on retail performance by studying a temporary advertising ban implemented in a German federal state during the COVID-19 pandemic. The ban resulted in the suspension of all print advertising by grocery retailers, and the exogenous variation in advertising created by this natural experiment serves as our identification strategy. We apply difference-in-differences regressions to data from a national grocery retailer and find that the ban resulted in a 6% sales decrease in the treated state compared with an adjacent state. GfK Household Panel data reveal no effect of the advertising ban on the market level but a negative impact on retailers offering and advertising weekly promotional product assortments. We study the sensitivity of these results to the COVID pandemic and find that neither changes in COVID-19 incidence, vaccination rates, nor customers' mobility moderate the ad ban effect. The findings offer practical insights for regulators and retailers regarding the impact of ad bans and the value of advertising. History: Catherine Tucker served as the senior editor for this article. This paper was accepted through the Marketing Science: Frontiers review process. Conflict of Interest Statement: None of the authors have any financial or non-financial interests in this manuscript's subject matter or materials. Sebastian Gabel consults with a company linked to the retailer that provided the data. Access to the GfK Household Panel data is provided by AiMark (http://www.aimark.org), which is a not-for-profit foundation that supports top academic research with scanner data provided by its data partners GfK and Kantar. Funding: This work was supported by Deutsche Forschungsgemeinschaft (CRC TRR 190 [Project Number 280092119]) and Nederlandse Organisatie voor Wetenschappelijk Onderzoek. S. Gabel acknowledges funding from the Dutch Research Council (NWO) under a Veni grant. M. Spann acknowledges funding from the German Research Foundation. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mksc.2023.0019. [ABSTRACT FROM AUTHOR]

I propose a simple forward difference-in-differences method to estimate causal effects from quasiexperimental data and also develop its inference theory, which is widely applicable. The difference-in-differences (DID) method is the most widely used tool to answer causal questions from quasiexperimental data in marketing and the broader social sciences. Because assignment to treatment in quasiexperiments is not random, the selection of proper control units is critically important for estimating the causal effect. DID requires that the treatment unit's outcomes would have been parallel to the average of the control units' outcomes in the absence of treatment. However, this DID parallel trends assumption is likely to be violated when assignment to the treatment and control groups is not random. We propose a simple forward difference-in-differences (Forward DID) method that uses a forward selection algorithm to flexibly select a relevant subset of control units. The Forward DID has several advantages. First, it can be widely applied and suitable even when DID is too restrictive. Second, Forward DID can accommodate any number of control units. Third, there are no overfitting concerns because Forward DID only needs to estimate one parameter after identifying a subset of control units. Fourth, Forward DID has computational advantages over algorithms that consider all possible subsets of control units. Finally, we establish consistency and develop inference theory, which is applicable to both stationary and nonstationary data. We demonstrate the usefulness of the Forward DID method and compare it with the alternative methods using simulations and an application to store openings. History: Olivier Toubia served as the senior editor. This paper was accepted through the Marketing Science: Frontiers review process. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mksc.2022.0212. [ABSTRACT FROM AUTHOR]

We study the pricing strategy of retailers that promote their sales with a loyalty program. Past research shows that loyalty programs can generate switching costs for consumers and increase their purchase frequency. Theoretical work suggests that if switching costs are significant, firms should charge lower prices in the early periods of a program to boost market share, and increase prices in later periods, to take advantage of the "lock-in" effect. However, it is not clear whether these costs soften or exacerbate price competition. Using a large database of gas stations' prices in the Italian market, we study fuel prices in early and late periods of loyalty programs: the sharp price changes adopted by gas stations affiliated with the program during the introduction and termination dates of the program allow us to establish the causal relationship between the program and the pricing behavior of gas stations. We find evidence that gas stations affiliated with the program increase prices in later periods of the program, as predicted by theory. The higher prices of affiliated stations lead to an increase in prices, on average, across all other stations competing in the local market. We also find that affiliated stations reduce their prices in early periods of the program; however, this evidence is less conclusive, as our data cannot exclude other factors explaining the price reduction. We discuss implications for managers and policy makers. History: Avi Goldfarb served as the senior editor and Tat Chan served as associate editor for this article. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mksc.2022.1416. [ABSTRACT FROM AUTHOR]

This report summarizes the finalists of the ISMS Gary Lilien Marketing Science Practice Prize Competition. This report summarizes the finalists of the INFORMS Society for Marketing Science (ISMS) Gary Lilien Marketing Science Practice Prize Competition, designed to identify, encourage, recognize, and reward the application of impactful marketing science to industry and noncommercial settings. These applications aim to showcase innovative and impactful examples of applications demonstrating the best of rigor and relevance that our profession produces. The 2020 winner described an application of corporate valuation with a customer-driven approach, whereas the 2022 winner reported a large-scale randomized controlled trial of the effect of front-of-pack labels on the nutritional quality of supermarket food purchases. The other four finalists include studies of dynamic structural analysis of salesforce management (2020), promotional campaign duration and word-of-mouth in solar panel adoption (2022), personalization in email marketing (2022), and reducing product returns through green nudges (2022). As the ISMS Vice Presidents of Practice, we draw several conclusions on how to further marketing science applications above and beyond the typical academic settings. History: Olivier Toubia served as the senior editor for this article. [ABSTRACT FROM AUTHOR]

This paper studies the market for fake product reviews on Amazon.com. We study the market for fake product reviews on Amazon.com. Reviews are purchased in large private groups on Facebook and other sites. We hand-collect data on these markets and then collect a panel of data on these products' ratings and reviews on Amazon, as well as their sales rank, advertising, and pricing policies. We find that a wide array of products purchase fake reviews, including products with many reviews and high average ratings. Buying fake reviews on Facebook is associated with a significant but short-term increase in average rating and number of reviews. We exploit a sharp but temporary policy shift by Amazon to show that rating manipulation has a large causal effect on sales. Finally, we examine whether rating manipulation harms consumers or whether it is mainly used by high-quality products in a manner like advertising or by new products trying to solve the cold-start problem. We find that after firms stop buying fake reviews, their average ratings fall and the share of one-star reviews increases significantly, particularly for young products, indicating rating manipulation is mostly used by low-quality products. [ABSTRACT FROM AUTHOR]

In this paper, the authors propose and validate a multiarmed bandit model that dynamically adapts website design to match a user's changing position in the purchase funnel. Websites are created to help visitors take an action, such as making a purchase or a donation. As visitors browse various web pages, they may take rapid steps toward the action or may bounce away. Websites that can adapt to match such consumer dynamics perform better. However, assessing visitors' changing distance to the action, at each click, and adapting to it in real time is challenging because of the sheer number of design elements that are found in websites, that combine exponentially. We solve this problem by matching latent states to web page designs, combining recent advances in multiarmed bandit (MAB), website morphing, and hidden Markov models (HMM) literature. We develop a novel dynamic program to explicitly model the trade-off firms face between nudging a visitor to later states along the funnel, and maximizing immediate reward given current estimates of purchase probabilities. We use an HMM to assess visitors' states in real time, and couple it with an MAB model to learn the effectiveness of each design × state combination. We provide a proof of concept in two applications. First, we conduct a field study on the Master of Business Administration website of a major university. Second, we implement our algorithm on a cloud server and test it on an experimental online store. [ABSTRACT FROM AUTHOR]

In this paper, we analytically prove that the sufficient condition in Proposition 3 of Zhou and Zou (2023) never holds. In this paper, we analytically prove that the sufficient condition in proposition 3 of [Zhou B, Zou T (2023) Competing for recommendations: The strategic impact of personalized product recommendations in online marketplaces. Marketing Sci. 42(2):360–376] never holds. We provide a new sufficient condition that actually holds, and discuss its implications. History: Olivier Toubia served as the senior editor for this article. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mksc.2022.0410. [ABSTRACT FROM AUTHOR]

This paper studies a marketplace design problem with asymmetric information where the platform jointly considers leveraging information revealed in ad auctions and setting sales commissions to maximize the joint profit from ad revenues and sales commissions. E-commerce platforms, such as Amazon and Alibaba, enable hundreds of millions of consumers to search and purchase products offered by millions of independent sellers who can advertise their products on the platforms. In this paper, we study how to design such a marketplace when there are two types of asymmetric information—sellers have some private information about their products that the platform does not have, whereas the platform has private information about consumers that the sellers do not have. Using a game theory model, we formulate the marketplace design problem as maximizing marketplace profit (comprising ad revenues and sales commissions) by jointly considering the following elements: ensuring sellers join the platform, specifying the auction that enables sellers to advertise in the sponsored list, leveraging the information revealed in the ad auction to refine the organic list, and setting the commission rate on sales to consumers who are rational and heterogeneous in their preferences. We show that sellers' bids in ad auctions, through which sponsored slots are allocated, can reveal the sellers' private information to the platform ("information effect"), which it can optimally combine with information that it has about consumers to improve the placement of organic results, a practice we call "strategic listing." However, by introducing an externality between the sponsored and organic sides, strategic listing also leads to more competition in the ad auction ("competition effect"), thus reducing the incentive of sellers to join the platform. The platform can incentivize sellers to join by reducing the commission rate on sales; however, under certain conditions, the platform must also reduce the degree of strategic listing (i.e., commit to limiting the influence of the sponsored ad auction outcomes on the placement of organic results). If the sellers' participation is sufficiently difficult to induce, the platform obtains a larger proportion (under some conditions, all) of its revenue from advertising than from sales commissions. Our results shed light on the variation in practices across different platforms and provide timely guidance for platforms to refine their marketplace design. [ABSTRACT FROM AUTHOR]

This article describes the implementation of a promotion-event forecasting system, PromoCast™, and its performance in several pilot applications and validity studies. Pilot studies involved retail grocery chains with 95 to 185 stores per trading area. The goal was to provide short-term, tactical forecasts useful for planning promotions from a retailer's perspective. Thus, the forecast system must be able to handle any of the over 150,000 UPCs in each store's item master file, and must be scalable to produce approximately 800,000,000 forecasts per year across all the retailers served by efficient marketing services, inc. (ems, inc.). This is a much different task than one that confronts a manufacturer, even one with a broad product line. Manufacturers can benefit from custom modeling in a product line or category. Retailers need a production system that generates forecasts that help promotion planning. Marketing scientists have typically approached promotion analysis from the manufacturer's perspective. One objective of this article is to encourage marketing scientists to rethink promotion analysis from a different perspective. From the retailer's point of view the “planning unit” is the promotion event. Neither weekly store-tracking data nor shopping-trip data from consumer panels are easily aggregated to reflect total sales during a promotion event. We describe the promotion-event databases and the statistical model developed using these databases. The data are the strategic asset. Our goal is to help retailers use their data to increase the profitability of promotions. We have data on the performance of each UPC in each store under a variety of promotion conditions, on each store's adeptness at executing various styles of promotions, as well as on chain-wide historical performance for each UPC. We use many historical averages from these databases to build a 67-variable, regression-style model. The forecast incorporates a simple bias correction needed when using a log-transformed dependent variable (the natural log of total unit sales). We argue that the historical averages matching the planned ad and display conditions provide a benchmark superior to the widely used “base-times-lift” method. When aggregated into case units (the natural unit for product ordering), 69% of the forecasts in our first validation study were within ± one case compared to 39% within ± one case using the appropriate historical averages. We report the results of two over-time validity studies that reflect the value of our model for retailers. The limitations and implications of this planning tool for managerial decision making concerning stocking levels are discussed. Whenever historical data are the strategic asset we face inherent limitations. Our model does not forecast new products. The forecast error increases when an existing product is promoted in a new way. Over 99.5% of the time, we have full data from which to create a forecast. However, with a database for a typical chain market containing over 20 million promotion events in the 30-month time frame we use, 100,000 events have less than ideal data. The breadth of the database (typically 150,000 UPS) makes it impractical to incorporate data on competitive offerings. We find that regression-style modeling is not adept at incorporating information on the 1,200 subcommodities managed in our pilot stores or the 1,000 manufacturers who supply those stores. Despite these limitations we show the value of using promotion-event data, how tactical forecasts based on these data can directly impact the bottom line of grocery retailers, and how store-by-store forecasts can help retailers with problems of running out of stock or overstocking.

We empirically study the effect of Chrysler's Chapter 11 bankruptcy filing on the quantity sold by its competitors in the U.S. auto industry. When financially distressed firms have overwhelming debts, a prominent option for survival is to file for Chapter 11 bankruptcy protection. We empirically study the effect of Chrysler's Chapter 11 bankruptcy filing on the quantity sold by its competitors in the U.S. auto industry. The demand for competitors could increase because they may benefit from the distress of the bankrupt firm (competitive effect). By contrast, competitors could experience lower sales if the bankruptcy increases consumer uncertainty about their own viability (contagion effect). A challenge to measuring the impact of bankruptcies is the coincident decline in economic conditions stemming from the Great Recession and the potential effect of the "cash for clunkers" program (among other confounding factors). To identify the effect of the bankruptcy filing, we employ a regression-discontinuity-in-time design based on a temporal discontinuity in treatment (i.e., bankruptcy filing), along with an extensive set of control variables. Such a design is facilitated by a unique data set at the dealer–model–day level that allows us to compare changes in unit sales in close temporal vicinity of the filing. We find that unit sales for an average competitor decrease by 28% following Chrysler's bankruptcy filing. Several types of evidence suggest that this negative demand spillover effect is driven by a heightened consumer uncertainty about the viability of the bankrupt firm's rivals. For example, we show that the sales of competitors' vehicles that compete within the same segments as the bankrupt firm's vehicles or that provide lower value for money are affected more negatively in response to the Chrysler filing. We also observe more web search activity for Chrysler's competitors after the filing. Our findings are robust to different estimation strategies (global versus local), different functional forms, different estimation windows, the inclusion of various controls (e.g., "cash for clunkers," incentives, advertising, inventory, recalls, price, and consumer confidence), the donut regression discontinuity approach, a potential serial correlation issue, a falsification exercise, and the inclusion of differential trends at various levels. Our study aims to inform policymakers and managers about unintended short-term demand consequences of Chapter 11 bankruptcy. The online appendices and data files are available at https://doi.org/10.1287/mksc.2018.1138. [ABSTRACT FROM AUTHOR]

Geographic price discrimination is generally considered beneficial to firm profitability. However, theoretical results point to conditions under which firms might prefer to price across markets uniformly in oligopolistic settings. This paper provides an empirical analysis of competitive price discrimination and quantitatively assesses the profitability of national pricing relative to store-level pricing policies under different market conditions. Specifically, we construct and estimate a model of retail competition using extensive data from the digital camera market. A series of counterfactuals show that, under reasonable commitment mechanisms, two leading chains would benefit from employing national pricing policies, whereas a discount retailer should target prices in each local market. Additional results explore the boundary conditions of these findings and evaluate hybrid pricing policies. The online appendices are available at https://doi.org/10.1287/mksc.2018.1100. [ABSTRACT FROM AUTHOR]

This paper develops a new rationale for decentralization in distribution channels: providing a one-stop comparison shopping experience for consumers. In our duopoly model, when consumers are knowledgeable about their brand preferences, each manufacturer would distribute through its own vertically integrated retail outlets only. When some consumers are unsure about their brand preferences, however, it may be optimal for one of the manufacturers to also distribute through its competitor's outlets. The resulting equilibrium has several interesting properties. First, only one of the manufacturers chooses to add competitor-outlet distribution, not both--even when the manufacturers are symmetric. Second, the manufacturer distributing through its competitor's outlets also distributes through its own outlets, i.e., its distribution strategy is a hybrid strategy, combining vertical integration and decentralization. Third, when the manufacturers' brands are asymmetric, it is the weaker brand that has a stronger incentive to pursue hybrid distribution. Fourth, the competitor's outlets in question welcome the new brand, even when no consumer would actually buy the new brand--a case of pure showrooming. These results highlight the linkages between distribution strategy, shopping efficiency, and retail formats. Shopping costs and consumers' uncertainty about their own brand preferences create a demand for multibrand retailing, and in pursuing this demand, manufacturers may eschew the efficiency advantages of vertical integration in favor of hybrid distribution. However, the fact that only one of the manufacturers chooses to do so suggests that this strategy also has weaknesses, which we discuss in the paper. [ABSTRACT FROM AUTHOR]

Whereas the extant literature on entry-order effects establishes that first entrants often earn higher market shares ('market-share advantage'), the literature on distribution suggests that increased distribution has a positive effect on sales. Can distribution help us better understand entry-order effects on market shares? This paper examines how the first entrant in a geographical market achieves a market-share advantage through distribution. For this purpose, I propose a simple method of decomposing sales into physical distribution and sales performance. The data come from a manually collected panel on six major Japanese convenience-store chains from 47 geographical markets between 1991 and 2007. Using an instrumental variable approach to address the potential endogeneity of entry order, I find first entrants have a positive market-share advantage over later entrants. Specifically, the physical distribution, measured by the number of outlets in a market, drives most of the advantage. Meanwhile, the positive effect on sales performance for the first chain brand becomes nonexistent when I control for the outlet density. This paper further finds that the density of own outlets is nonmonotonically (inverted U) related to sales performance per outlet, suggesting dynamic outlet expansion faces a trade-off between the business-stealing effect in a chain ('cannibalization') and the advertising effect through repetition. Data and the online appendix are available at . [ABSTRACT FROM AUTHOR]

Outlet stores offer attractive prices at locations far from central shopping districts. They form a large and growing component of many firms' retailing strategies, particularly in the fashion industry. I use a structural demand model to show that consumers are segmented according to their sensitivity to travel distance and taste for product newness. I then develop a supply model to predict product development responses to changes in store locations. Through policy simulations, I discover that the firm uses outlet stores to serve lower-value consumers who self-select by traveling to outlet stores from central shopping districts. The firm sells older, less desirable merchandise through outlet stores to prevent cannibalization of regular store revenues by means of exploiting the positive correlation between consumers' travel sensitivity and taste for new products. I find that the rate of new product introduction in regular stores would fall by 16% if outlet stores were closed down, while variable profits would decline by 23%. These results imply that the existence of outlet stores may enable firms to improve quality in their regular channels, thus counteracting brand dilution effects. Data are available at . [ABSTRACT FROM AUTHOR]

In this paper, we propose an automated and scalable garment recommender system using real-time in-store videos that can improve the experiences of garment shoppers and increase product sales. The video-based automated recommender (VAR) system is based on observations that garment shoppers tend to try on garments and evaluate themselves in front of store mirrors. Combining state-of-the-art computer vision techniques with marketing models of consumer preferences, the system automatically identifies shoppers' preferences based on their reactions and uses that information to make meaningful personalized recommendations. First, the system uses a camera to capture a shopper's behavior in front of the mirror to make inferences about her preferences based on her facial expressions and the part of the garment she is examining at each time point. Second, the system identifies shoppers with preferences similar to the focal customer from a database of shoppers whose preferences, purchasing, and/or consideration decisions are known. Finally, recommendations are made to the focal customer based on the preferences, purchasing, and/or consideration decisions of these like-minded shoppers. Each of the three steps can be implemented with several variations, and a retailing chain can choose the specific configuration that best serves its purpose. In this paper, we present an empirical test that compares one specific type of VAR system implementation against two alternative, nonautomated personal recommender systems: self-explicated conjoint (SEC) and self-evaluation after try-on (SET). The results show that VAR consistently outperforms SEC and SET. A second empirical study demonstrates the feasibility of VAR in real-time applications. Participants in the second study enjoyed the VAR experience, and almost all of them tried on the recommended garments. VAR should prove to be a valuable tool for both garment retailers and shoppers. Data, as supplemental material, are available at . [ABSTRACT FROM AUTHOR]

We empirically study the impact of the entry of a new theater on two important product decisions that incumbents in the movie exhibition industry face: (1) whether to invest in screening movies that are expected to be popular, and (2) when to adopt new releases. For theaters, both of these decisions feature a cost-demand trade-off inherent in quality decisions: Although screening popular and recent movies brings more patrons to the theater, distributors take a higher share of the revenue for such movies. The impact of competitive entry on the incumbent's quality decisions is ambiguous, as it may simultaneously increase the competitive pressure to invest more in these dimensions of quality and also change the demand conditions that incumbents face. We find that incumbent theaters do not increase the provision of popular and recent movies in response to rival entry. To identify the role of competitive incentives, we study the differential impact of entry based on whether the entrant belongs to the same parent firm as the incumbent theaters. This comparison reveals that competitive incentives push incumbents to screen movies with high expected success more frequently and to adopt movies sooner. The product responses we document have important implications for the revenue impact of entry and the conclusions that researchers can draw from this impact. Ignoring the provision of these quality dimensions suggests cannibalization to exceed business stealing, a conclusion that is reversed when we account for endogenous product responses. We also show that our findings on popularity and recency cannot be explained by concomitant changes in theaters' other product decisions, such as the variety of movies screened. [ABSTRACT FROM AUTHOR]

This paper addresses the repositioning of Kmart Australia in 2011. It shows how by calibrating emotional as well as cognitive reactions and estimating their impact on purchase intentions, Kmart was able to focus its communications, improving market share. We measure nine key emotions, ranging from surprise to anger. Including these emotions significantly improves our model for likelihood to choose a store. Measuring emotions enabled Kmart's advertising agency to create a television commercial that tapped into the specific emotions that most strongly predict the likelihood to choose a store; that is, the model drove the development of the advertising creative. The resulting television commercial tested well and was effective when launched. At the individual level, cognitions and emotions changed dramatically. At the aggregate level, an econometric model showed that store visits were significantly enhanced. Kmart's EBIT (earnings before interest and tax) increased by 30%, whereas Kmart's main rival had almost no EBIT growth, despite vigorous attempts to counter Kmart's campaign. One of our key contributions is to incorporate emotions into marketing science models of evaluation and purchase intentions. We also provide a new methodology to measure emotions. The approach enables marketing science to participate in the design of marketing stimuli, rather than just testing preexisting ones. [ABSTRACT FROM AUTHOR]

This paper investigates early stage "modern" grocery retail adoption in an emerging market using primary household-level panel data on grocery purchases in India's largest city, Mumbai. Specifically, we seek insight on which socioeconomic class is more likely to adopt, and why. We model adoption as a two-stage process of modern retail choice followed by category expenditures within a shopping trip. We find a nonmonotonic (V-shaped) relationship between socioeconomic class and preferences for modern retail; specifically, modern retail spending and relative preference are greater among the upper and lower middle classes, relative to the middle middle class. Upper middle class preference of modern retail is driven by credit card acceptance, shorter store distance (relative to other segments), and higher vehicle ownership; whereas lower prices and low travel costs drive the preferences of the lower middle class. Modern retail is preferred more for branded and less for perishable categories. Interestingly, the lower middle class share of modern grocery retail's revenues is largest, and this share is projected to grow as prices fall and store density increases. To address concerns of endogeneity and generalizability, we replicate the key results with a "conjoint" type study with exogenous variation in price and distance in two cities—Mumbai and Bangalore. We discuss implications for targeting and public policy in emerging markets. [ABSTRACT FROM AUTHOR]

Firms invest in technology to increase productivity. Yet in emerging markets, where a culture of informality is widespread, information technology (IT) investments leading to greater transparency can impose a cost through higher taxes and the need for regulatory compliance. The tendency of firms to avoid productivityenhancing technologies and remain small to avoid transparency has been dubbed the "Peter Pan Syndrome." We examine whether firms make the trade-off between productivity and transparency by examining IT adoption in the Indian retail sector. We find that computer technology adoption is lower when firms are motivated to avoid transparency. Specifically, technology adoption is lower when there is greater corruption, but higher when there is better enforcement and auditing. So, firms have a higher productivity gain threshold to adopt computers in corrupt business environments that suffer from patchy and variable enforcement of the tax laws. Not accounting for this motivation to hide from the formal sector underestimates productivity gains from computer adoption. Thus, in addition to their direct effects on the economy, enforcement, auditing, and corruption can have indirect effects through their negative impact on adoption of productivity-enhancing technologies that also increase operational transparency. [ABSTRACT FROM AUTHOR]

Many firms operate customer communities online. This is motivated by the belief that customers who join the community become more engaged with the firm and/or its products, and as a result, increase their economic activity with the firm. We describe this potential economic benefit as "social dollars." This paper contributes evidence for the existence and source of social dollars using data from a multichannel entertainment products retailer that launched a customer community online. We find a significant increase in customer expenditures attributable to customers joining the firm's community. While self-selection is a concern with field data, we rule out multiple alternative explanations. Social dollars persist over the time period observed and arose primarily in the online channel. To assess the source of the social dollar, we hypothesize and test whether it is moderated by participation behaviors conceptually linked to common attributes of customer communities. Our results reveal that posters (versus lurkers) of community content and those with more (versus fewer) social ties in the community generated more (fewer) social dollars. We found a null effect for our measure of the informational advantage expected to accrue to products that differentially benefit from content posted by like-minded community members. This overall pattern of results suggests a stronger social than informational source of economic benefits for firm operators of customer communities. Several implications for firms considering investments in and/or managing online customer communities are discussed. [ABSTRACT FROM AUTHOR]

We study how peers impact worker productivity growth among salespeople in the cosmetics department of a department store. We first exploit a shift assignment policy that creates exogenous variation in sales-persons' peers each week to identify and quantify sources of worker learning. We find that peer-based learning is more important than learning-by-doing for individuals, and there is no evidence of forgetting. Working with high-ability peers substantially increases the long-term productivity growth of new salespeople. We then examine possible mechanisms behind peer-based learning by exploiting the multiple colocated firms in our setting that sell products with different task difficulties and compensate their sales forces using either team-based or individual-based compensation systems. The variation in incentives to compete and cooperate within and across firm boundaries, combined with variation in sales difficulty for different product classes, allows us to suggest two mechanisms behind peer-based learning: observing successful sales techniques of peers and direct teaching. Our paper advocates the importance of learning from one another in the workplace and suggests that individual peer-based learning is a foundation of both organizational learning curves and knowledge spillovers across firms. [ABSTRACT FROM AUTHOR]

This study examines the strategic implications of retailer shelf layout decisions in a market characterized by consumer fit uncertainty. A retailer can display competing products in the same location, allowing consumers to inspect various products all at once or in distant locations, which induces consumers to inspect one product first and then decide whether to incur the travel cost to inspect another product. We consider a model in which two competing manufacturers distribute two horizontally differentiated products through a common retailer. Our analysis shows when the two manufacturers offer products of the same fit probabilities, the retailer obtains a greater profit by displaying competing products in distant locations if the products' fit probabilities are not too high; otherwise, the retailer is better off displaying competing products in the same location. When manufacturers offer products of differentiated fit probabilities, a retailer is more likely to benefit from displaying competing products in distant locations with an increased fit difference between products. Finally, a retailer is more likely to benefit from displaying competing products in distant locations when facing less competition from other retailers. [ABSTRACT FROM AUTHOR]

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 tradeoff 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 [ABSTRACT FROM AUTHOR]

While millions of products are sold on its retail platform, Amazon.com itself stocks and sells only a very W small fraction of them. Most of these products are sold by third-party sellers who pay Amazon a fee for each unit sold. Empirical evidence clearly suggests that Amazon tends to sell high-demand products and leave long-tail products for independent sellers to offer. We investigate how a platform owner such as Amazon, facing ex ante demand uncertainty, may strategically learn from these sellers' early sales which of the "mid-tail" products are worthwhile for its direct selling and which are best left for others to sell. The platform owner's "cherry-picking" of the successful products, however, gives an independent seller the incentive to mask any high demand by lowering his sales with a reduced service level (unobserved by the platform owner). We analyze this strategic interaction between a platform owner and an independent seller using a gametheoretic model with two types of sellers--one with high demand and one with low demand. We show that it may not always be optimal for the platform owner to identify the seller's demand. Interestingly, the platform owner may be worse off by retaining its option to sell the independent seller's product, whereas both types of sellers may benefit from the platform owner's threat of entry. The platform owner's entry option may reduce consumer surplus in the early period, although it increases consumer surplus in the later period. We also investigate how consumer reviews influence the market outcome. [ABSTRACT FROM AUTHOR]

The Internet has increased the flexibility of retailers, allowing them to operate an online arm in addition to their physical stores. The online channel offers potential benefits in selling to customer segments that value the convenience of online shopping, but it also raises new challenges. These include the higher likelihood of costly product returns when customers' ability to "touch and feel" products is important in determining fit. We study competing retailers that can operate dual channels ("bricks and clicks") and examine how pricing strategies and physical store assistance levels change as a result of the additional Internet outlet. A central result we obtain is that when differentiation among competing retailers is not too high, having an online channel can actually increase investment in store assistance levels (e.g., greater shelf display, more-qualified sales staff, floor samples) and decrease profits. Consequently, when the decision to open an Internet channel is endogenized, there can exist an asymmetric equilibrium where only one retailer elects to operate an online arm but earns lower profits than its bricks-only rival. We also characterize equilibria where firms open an online channel, even though consumers only use it for research and learning purposes but buy in stores. A number of extensions are discussed, including retail settings where firms carry multiple product categories, shipping and handling costs, and the role of store assistance in impacting consumer perceived benefits. [ABSTRACT FROM AUTHOR]

his paper examines pairwise assortment similarities at U.S. supermarkets to understand how assortment composition and size are related to underlying factors that describe local store clientele, local competitive structure, and the retail outlets' characteristics. The top-selling items, which cumulatively make up 50% of sales, are sold at nearly every store, but other items are viewed as optional. We find that, within states, supermarkets owned by the same chain carry similar assortments and that the composition of their clientele and the presence of competing stores have effects on assortment similarity that are an order of magnitude smaller than ownership structure. In contrast, we find that, across states, supermarkets owned by the same chain do version their assortment. We explain this difference using extant work on the minimal efficient scale of supermarkets and on local demand effects. Furthermore, we investigate the distribution and role of regional brands. We find that regional brands are primarily distributed by small regional chains or independent stores. "Value" regional brands are primarily distributed by supermarket firms without store brands, whereas the distribution of "premium" regional brands is unrelated to the presence of store brands. We discuss our findings in the context of modeling assortment decisions and manufacturers designing distribution policies. [ABSTRACT FROM AUTHOR]

Many consumer durable retailers often do not advertise their prices and instead ask consumers to call them for prices. It is easy to see that this practice increases the consumers' cost of learning the prices of products they are considering, yet firms commonly use such practices. Not advertising prices may reduce the firm's advertising costs, but the strategic effects of doing so are not clear. Our objective is to examine the strategic effects of this practice. In particular, how does making price discovery more difficult for consumers affect competing retailers' price, service decisions, and profits? We develop a model in which a manufacturer sells its product through a high-service retailer and a low-service retailer. Consumers can purchase the retail service at the high-end retailer and purchase the product at the competing low-end retailer. Therefore, the high-end retailer faces a free-riding problem. A retailer first chooses its optimal service levels. Then, it chooses its optimal price levels. Finally, a retailer decides whether to advertise its prices. The model results in four structures: (1) both retailers advertise prices, (2) only the low-service retailer advertises price, (3) only the high-service retailer advertises price, and (4) neither retailer advertises price. We find that when a retailer does not advertise its price and makes price discovery more difficult for consumers, the competition between the retailers is less intense. However, the retailer is forced to charge a lower price. In addition, if the competing retailer does advertise its prices, then the competing retailer enjoys higher profit margins. We identify conditions under which each of the above four structures is an equilibrium and show that a low-service retailer not advertising its price is a more likely outcome than a high-service retailer doing so. We then solve the manufacturer's problem and find that there are several instances when a retailer's advertising decisions are different from what the manufacturer would want. We describe the nature of this channel coordination problem and identify some solutions. [ABSTRACT FROM AUTHOR]

We study the effects of retailer in-store media on distribution channel relationships. Retailers open in-store media (ISM) and allow manufacturers to advertise to shoppers. Our results suggest that ISM has an important role in coordinating a distribution channel on advertising volume and product sales, and on mitigating supplier competition. Improved channel coordination is achieved through the internalization of advertising decisions from commercial forms of media (e.g., radio, TV, newspaper). A retailer may strategically subsidize manufacturers for their advertising on ISM. This subsidy is optimal even if ISM is more effective than commercial media. With manufacturer competition, a retailer can strategically use a "competitive premium" to ration excessive advertising between competing suppliers in a category. When manufacturers are asymmetric with preadvertising brand awareness, a retailer has an incentive to price discriminate by charging lower prices to manufacturers whose brand awareness is higher. [ABSTRACT FROM AUTHOR]

We propose a dynamic programming framework for retailers of frequently purchased consumer goods in which the prices affect both the profit per visit in the current period and the number of visitors (i.e., store traffic) in future periods. We show that optimal category prices in the infinite-horizon problem also maximize the closed form sum of a geometric series, allowing us to derive meaningful analytical results. Modeling the linkage between category prices and future store traffic fundamentally changes optimal pricing policy. Optimal pricing must balance current profits against future traffic; under general conditions, optimal long-run prices are uniformly lower across all categories than those that maximize current profits. This result explains the empirical generalization that category demand in grocery stores is inelastic. Parameterizing profit per visit and store traffic reveals that, as future traffic becomes more sensitive to price, retailers should increasingly lower current prices and sacrifice current profits. We also determine how the burden of drawing future traffic to the store should be distributed across categories; this is the foundation for a new taxonomy of category roles. [ABSTRACT FROM AUTHOR]

Standardizing performance expectations across different outlets within a chain, differing in their individual features, their consumers, and the nature of competition they face, can be an onerous task. We develop an integrated, nonlinear, block group-level market share model of store expectations that draws upon the existing trade area as well as store performance literatures. By incorporating and normalizing a large number of external and internal factors impacting performance, we are able to offer a means for the retailer to determine equitable standards. The model is estimated using a variation of the maximum-likelihood estimation, on a data set fashioned from several sources and aggregated at the block group and store levels. Finally, we propose a set of indices that allows us to evaluate relative performances of stores and regions given the competitive environments they face. We find that a block group-level model offers a better fit, as well as significantly richer implications, than a traditional store-level model. Results show that a significant number of stores operate well below their expected levels, an insight not obvious from the raw numbers used to report store statistics to upper management. [ABSTRACT FROM AUTHOR]

In certain product categories, large discount retailers are known to offer shallower assortments than traditional retailers. In this paper, we investigate the competitive incentives for such assortment decisions and the implications for manufacturers' distribution strategies. Our results show that if one retailer has the channel power to determine its assortment first, then it can strategically reduce its assortment by carrying only the popular variety while simultaneously inducing the rival retailer to carry both the specialty and popular varieties. The rival retailer then bears higher assortment costs, which leads to relaxed price competition for the commonly carried popular variety. We also show that when the manufacturer has relative channel power, it chooses alternatively to distribute both product varieties through both retailers. Our analysis suggests, therefore, that when a retailer becomes dominant in the distribution channel, it facilitates retail segmentation into discount shops, carrying limited product lines, and specialty shops carrying wider assortments. We also illustrate how retailer power leading to strategic assortment reduction can lead to lower consumer surplus. [ABSTRACT FROM AUTHOR]

This paper develops a model that extends the traditional aggregate discrete-choice-based demand model (e.g. Berry et al. 1995) to account for varying levels of product availability. In cases where not all products are available at every consumer shopping trip, the observed market share is a convolution of two factors: consumer preferences and the availability of the product in stores. Failing to account for the varying degree of availability would produce incorrect estimates of the demand parameters. The proposed model uses information on aggregate availability to simulate the potential assortments that consumers may face in a given shopping trip. The model parameters are estimated by simulating potential product assortment vectors by drawing multivariate Bernoulli vectors consistent with the observed aggregate level of availability. The model is applied to the UK chocolate confectionery market, focusing on the convenience store channel. We compare the parameter estimates to those obtained from not accounting for varying availability and analyze some of the substantive implications. [ABSTRACT FROM AUTHOR]

While retailers have sales data to forecast demand, manufacturers have a broad understanding of the market and the coming trends. It is well known that pooling such demand information within a distribution channel improves supply chain logistics. However, little is known about how information-sharing affects whole-sale pricing incentives. In this paper, we investigate a channel structure where a manufacturer and two retailers have private signals of the state of the demand. Our model identifies the presence of a pricing distortion, which we term the inference effect, when a manufacturer sets price to an uninformed retailer. Because of this inference effect, the manufacturer would like to set a low wholesale price to signal to the retailer that the demand is low. On the other hand, the manufacturer would like to set a high wholesale price so that he earns the optimal margin on each unit sold. Vertical information sharing benefits the manufacturer by eliminating the distortion caused by the inference effect, which is more profound in a channel whose retailer has a noisier signal. This result implies that when there is a cost associated with transmitting information, the manufacturer may choose to share information with only the less-informed retailer rather than with both. [ABSTRACT FROM AUTHOR]

We investigate the sensitivity of cross-brand pass-through estimates to two types of pooling: across stores, and across regular price and promotional price weeks. Using the category data from Besanko, Dubé, and Gupta (2005), hereafter BDG, we find consistent support across all 11 categories for the predictive power of the wholesale prices of substitute products for retail shelf prices. A Bayesian procedure is used to address the small sample issues that arise in the absence of pooling. Even though the unpooled results render our inferences for specific cross-brand pass-through magnitudes reported in BDG as imprecise, consistent with McAlister (2007), we do find significant empirical support for cross-brand pass-through. We next assess the sensitivity of cross-brand pass-through estimates to pooling. This requires us to construct a much longer time series of 224 weeks for the refrigerated orange juice category, in contrast with the 52- week samples used in BDG and McAlister (2007). We find strong empirical support for the predictive power of wholesale prices of substitute products for retail shelf prices. In addition, we find evidence of nonzero own- and cross-brand pass-through elasticities for which our inferences are much more precise. These findings are robust to the separation of regular and promotional price weeks. However, the magnitudes of own-brand and cross-brand pass-through are quite different during promotional and regular price weeks. Our results clearly show that with longer data series and more robust models that can handle small sample sizes, there is evidence of cross-brand pass-through, substantiating the findings in BDG. Finally, we comment on why our results are entirely consistent with both the theoretical and empirical literatures on category pricing and retailer behavior. [ABSTRACT FROM AUTHOR]

We consider the optimal two-part tariff contract between a manufacturer and a retailer. We show that retail competition (in the presence of either fixed costs or bargaining power) may lead to slotting allowances in an optimal contract, even with a monopoly manufacturer and no information asymmetry. On the other hand, slotting allowances do not arise with a monopoly retailer and no information asymmetry, whether fire manufacturer is a monopoly or not. We also show that more intense retail competition, higher retail bargaining power, larger retailer fixed costs, lower marginal costs of retailing, as well as larger relative retailer size (whether coming from a location or operating advantage), have a positive impact on the incidence and the magnitude of slotting allowances. The opposing effects of the fixed and marginal operating costs on slotting allowances, as well as the impact of competition and bargaining power on profits, underscore the importance of careful definitions of these variables in empirical research. [ABSTRACT FROM AUTHOR]

The growing dominance of large retailers has altered traditional channel incentives for manufacturers. In this paper, we present a theoretical model to illustrate a strategic manufacturer response to a dominant retailer. In our model, a dominant and a weak retailer compete for the sale of a single product supplied by a single manufacturer The dominant retailer has the power to dictate the wholesale price, but the manufacturer sets the wholesale price for the weak retailer. The manufacturer also has partial ability to transfer demand between retailers. In the strategic manufacturer response, the manufacturer begins by raising the wholesale price for the weak retailer over that for the dominant retailer This makes the weak retailer the high-margin channel. The manufacturer then transfers demand to the weak retailer by engaging in joint promotions and advertising. We then use this strategic response model to derive a testable hypothesis that may guide future research in determining the source of dominant retailers' low prices. [ABSTRACT FROM AUTHOR]

How can supermarkets use the vast data they have to design strategies to compete for large-basket shoppers, potentially their most profitable customers? We say, analyze the data to glean basket composition of heterogeneous consumers. Of theoretical and practical interest is the question, will our suggestion improve supermarket profits, and if so, by what pricing strategies? By answering this we can also answer the question, do results of extant research on single-product marketing that using such information intensifies competition and lowers profits, generalize to multiproduct marketing by supermarkets that carry several products, and whose patrons purchase multiple items? We derive equilibrium data-based intelligent pricing strategies that also produce increased supermarket profits. What is more interesting is our result that data analysis is profitable whether or not a competitor undertakes data analysis. If not too costly, implementing data analysis is a dominant strategy. Moreover, with comprehensive data analysis connecting basket information and household data, stores can increase profits further by targeting rewards at individual households, thereby segmenting the market more completely. In contrast to past research findings, we show that for supermarkets, even under competition, use of information on past purchases enables intelligent pricing, better segmentation, and higher profits. [ABSTRACT FROM AUTHOR]

Asymmetric pricing or asymmetric price adjustment is the phenomenon where prices rise more readily than they fall. We offer and provide empirical support for a new theory of asymmetric pricing in wholesale prices. Wholesale prices may adjust asymmetrically in the small but symmetrically in the large, when retailers face cost of price adjustment. Such retailers will not adjust prices for small changes in their costs. Manufacturers then see a region of inelastic demand where small wholesale price changes do not translate into commensurate retail price changes. The implication is asymmetric--a small wholesale price increase is more profitable because manufacturers will not lose customers from higher retail prices; yet, a small decrease is less profitable, because it will not lower retail prices; hence, there is no extra revenue from greater sales. For larger changes, this asymmetry in the behavior of wholesale price vanishes as the price adjustment cost is compensated by the increase in retailers' revenue resulting from correspondingly large retail price changes. We present a formal economic model of a channel with forward-looking retailers and cost of price adjustment, test the derived propositions on the behavior of manufacturer prices using a large supermarket scanner data set, and find that the results are consistent with the predictions of our theory. We then discuss the implications for asymmetric pricing, channels, and cost of price adjustment literatures, as well as public policy. [ABSTRACT FROM AUTHOR]

In this paper we describe the pass-through behavior of a major U.S. supermarket chain for 78 products across 11 categories. Our data set includes retail prices and wholesale prices for stores in 15 retail price zones for a one-year period. For the empirical model, we use a reduced-form approach that focuses directly on equilibrium prices as a function of exogenous supply-and demand-shifting variables. The reduced-form approach enables us to identify the theoretical pass-through rate without specific assumptions about the form of consumer demand or the conduct of a category-pricing manager. Thus, our measurements of pass-through are not constrained by specific structure on the underlying economic model. The empirical pricing model includes costs of all competing products in the category on the right-hand side (not only the cost of the focal brand) and yields estimates of both own-brand and cross-brand pass-through rates. Our results provide a rich picture of the retailer's pass-through behavior. We find that pass-through varies substantially across products and across categories. Own-brand pass-through rates are, on average, more than 60% for 9 of 11 categories, a finding that is at odds with the claims of manufacturers about retailers in general. Importantly, we find substantial evidence of cross-brand pass-through effects, indicating that retail prices of competing products are adjusted in response to a change in the wholesale price of any given product in the category. We find that cross-brand pass-through rates are both positive and negative. We explore determinants of own-brand and cross-brand pass-through rates and find strong evidence in multiple categories of asymmetric retailer response to trade promotions on large versus small brands. For example, brands with larger market shares, and brands that contribute more to retailer profits in the category, receive higher pass-through. We also find that trade promotions on large brands are less likely than small... [ABSTRACT FROM AUTHOR]

I provide a general formulation of the channel pass-through problem as a comparative static of the retail price equilibrium, and I analyze the impact of category management and retail competition on pass-through, focusing on brand and retailer differences, and the nature of the cost change being passed through--whether it is brand specific, retailer specific, both, or neither. With category management, a retailer's response to a brand-specific cost change is not limited to that brand; in general, a retailer will also change the prices of other brands. The cross-brand effect can be positive or negative, and, depending on its sign, it either enhances or attenuates pass-through. I explain the cross-brand effect as an interaction between two forces: a demand-substitution force that pushes for a negative cross-brand effect, and a strategic-complementarity force that pushes for a positive cross-brand effect. Retail competition adds another layer of strategic complementarity, causing other retailers to respond even for retailer-specific cost changes and increasing pass-through of categorywide cost changes. But its effect for brand-specific cost changes is ambiguous. I apply the theory to two commonly used demand functions--linear demand and nested logit--and show that they have significantly different pass-through properties. The paper concludes with a discussion of how the theory relates to the empirical literature, including the companion piece by Besanko et al. (Besanko, D., J-P. Dub&ecute;, S. Gupta. 2005. Own-brand and cross-brand retail pass-through. [ABSTRACT FROM AUTHOR]

This paper empirically investigates the determinants of retailers' pricing decisions. It finds that competitor factors explain the most variance in retailer pricing strategy. Only in the cases of price-promotion coordination and relative brand price do category and chain factors explain much variance in retailer pricing. These findings are derived from a simultaneous equation model of how underlying dimensions of retailers' pricing strategies are influenced by variables representing the market, chain, store, category, brand, customer, and competition. The optical scanner data base describes 1,364 brand-store combinations from six categories of consumer packaged goods in five U.S. markets over a two-year time period. Our study classifies retailers' pricing strategies based on four underlying dimensions: price consistency, price-promotion intensity, price-promotion coordination, and relative brand price. These four pricing dimensions are statistically related to: (1) competitor price and deal frequency (competitor factors), (2) storability and necessity (category factors), (3) chain positioning and size (chain factors), (4) store size and assortment (store factors), (5) brand preference and advertising (brand factors), and (6) own-price and deal elasticities (customer factors). These findings are useful to retailers profiling alternative pricing strategies, and to manufacturers customizing the levels of marketing support spending for different retailers. [ABSTRACT FROM AUTHOR]

We examine the retailer's store brand positioning problem. Our game-theoretic model helps us identify a set of conditions under which the optimal strategy for the retailer is to position the store brand as close as possible to the stronger national brand. In three empirical studies, we examined whether market data are consistent with some of the implications of our model. In the first study, using observational data from two US supermarket chains, we found that store brands are more likely to target stronger national brands. Our second study estimated cross-price effects in 19 product categories, and found that only in categories with high-quality store brands, store brand and the leading national brand compete more intensely with each other than with the secondary national brand. In a third product perception study, we found that although explicit targeting by store brands influenced consumer perceptions of physical similarity, it had no influence on consumers' perceptions of overall or product quality similarity. While it appears that retailers do follow a positioning strategy consistent with our model, it changes buying behavior in the intended fashion only if the store brand offers quality comparable to the leading national brands. [ABSTRACT FROM AUTHOR]

In recent years, interest in category management has surged, and as a consequence, large retailers now systematically review their product assortments. Variety is a key property of assortments. Assortment variety can determine consumers' store choice and is only gaining in importance with today's increasing numbers of product offerings. To support retailers in managing their assortments, insight is needed into the influence of assortment composition on consumers' variety perceptions, and appropriate measures of assortment variety are required. This paper aims to extend the assortment variety model recently proposed by Hoch et al. (1999) in Marketing Science. It conceptualizes assortment variety from an attribute-based perspective and compares this with the product- based approach of Hoch, Bradlow, and Wansink (HBW). The attribute-based approach offers an alternative viewpoint for assortment variety Attribute- and product-based approaches reflect basic conceptualizations of assortment variety that assume substantially different perception processes: a consumer comparing products one-by-one versus a consumer examining attributes across products in the assortment. While the product-based approach focuses on the dissimilarity between product pairs in an assortment, the attribute-based approach that we propose focuses on the marginal and joint distributions of the attributes. We conjecture and aim to show that an attribute-based approach suffices to predict consumers' perceptions of assortment variety. In operationalizing the attribute-based approach, two measures of assortment variety are described and compared to product- based measures. These two measures relate to the dispersion of attribute levels, e.g., if all products have the same color or different colors, and the dissociation between attributes, e.g., if product color and size are unrelated. The ability of product-based and attributed-based measures to predict consumers' perceptions of assortment variety is assessed, The product-based measures (Hamming) tap the dissimilarity of products in an assortment across attributes. The attribute-based measures tap the dispersion of attribute levels across products (Entropy) and the dissociation between product attributes (1 -Lambda) in an assortment. In two studies, we examine the correlations between these measures in a well-behaved environment (study 1) and the predictive validity of the measures for perceived variety in a consumer experiment (study 2). Study 1, using synthetic data, shows that the attribute-based measures tap specific aspects of assortment variety and that the attribute-based measures are less sensitive to the size of assortments than product-based measures are. Whereas HBW focus on assortments of equal size, study 1 indicates that an extension to assortments of unequal size results in summed Hamming measures that correlate highly with assortment size. The latter is important when assortments of different size are compared. Next, we examine how well the measures capture consumers' perception of variety. Study 2, a consumer experiment, shows that the attribute-based measures account best for consumers' perceptions of variety. Attribute-based measures significantly add to the prediction of consumers' perceptions of variety, over and above the product-based measures, while the reverse is not the case. Interestingly, this study also indicates that assortment size may not be a good proxy for perceived assortment variety. The findings illustrate the value of an attribute-based conceptualization of assortment variety, since these measures (1) correlate only moderately with assortment size and (2) suffice to predict consumers' perceptions of assortment variety. In the final section we briefly discuss how attribute-based and product-based measures can be used in assortment management, and when product- and attribute-based approaches may predict consumers' variety perceptions. We discuss how an attribute-based approach can identify which attribute levels and attribute combinations influence consumers' perceptions of variety most, while a product- based approach can identify influential products. Both approaches have applications in specific situations. For instance, an attribute-based approach can identify influential attributes in an ordered, simultaneous presentation of products, while a product-based approach can assess the impact of sequential presentations of products better. In addition, we indicate how the random-intercept model estimated in study 2 can be further extended to capture the influence of, e.g., consumer characteristics. [ABSTRACT FROM AUTHOR]

Firms often search enthusiastically for distinguishing traits that they may use to price discriminate between segments. Yet there are occasions in which firms forgo the opportunity to price discriminate and instead charge a single price. Traditional explanations for why retailers forgo the opportunity to price discriminate focus on the cost of discriminating, including operational costs, explicit discrimination costs, and implicit discrimination costs. In this paper we identify an additional reason for why firms may forgo an opportunity to price discriminate. By revealing that a product is being sold to a broad range of segments, a retailer implicitly claims that the product is suitable for each segment. However, claiming that a premium-quality product is suitable for price-sensitive consumers undermines the credibility of a retailer's quality claim. The signaling explanation was motivated by extensive discussions over more than a year with a major catalog retailer that sells premium-quality jewelry and gifts. Discussions with managers revealed that they were reluctant to use any price-discrimination mechanism that signals their products are targeted at price-sensitive customers. For example, the catalog does not include sale or clearance sections and does not target more price-sensitive customers by using separate items. However, management was under some pressure to consider installment-billing offers, which allow customers to pay over a series of periods rather than in a lump sum. Management feared that offering installment billing may adversely affect customers' quality perceptions and demand. To investigate this issue, we develop a general game-theoretic model, illustrate how the model extends to installment billing, and conduct a large-scale field test. The general model illustrates how selling to multiple segments may lead to an adverse quality signal. We illustrate how the model extends to... [ABSTRACT FROM AUTHOR]

This paper is concerned with how retailers, supermarkets in particular, communicate price discounts and use unadvertised specials. A common practice for supermarkets is to communicate price deals on some products through newspaper advertisements, while communicating discounts on other products through in-store mechanisms such as shelf-talkers. This raises the question: So far as store choice is concerned, how might consumers take into account not only advertised prices at competing stores, but also expected prices of unadvertised goods? It also begs the question of why stores have unadvertised specials since their effect on store choice is not quite the same as the advertised discounts. Further, competing supermarkets advertise the same products part of the time, and different products at other times. They also tend to sometimes advertise a product in consecutive weeks, but sometimes not. Can these actions be part of a strategy? We formulate a game-theoretic model of retail competition by first extending the work of Lal and Matutes (1994) and then developing an alternative framework to answer these questions. Our model has two retailers, each of whom carries two goods. To simplify exposition, we assume that the stores are symmetric, the two goods are symmetric in their reservation prices, and are neither substitutes nor complements. Consumers are identical in their preferences and consumer heterogeneity is in the convenience that each store presents to a representative consumer The stores may advertise the price of one good, reflecting the reality that stores do not advertise theft whole assortment. They compete through advertising and prices to maximize profits. We thus recognize the strategic role of advertised prices and furthermore, we investigate the strategic role of unadvertised prices in retail competition. for this model, we derive a Rational Expectations Nash equilibrium in which each store randomly advertises the price of one good following a mixed strategy. Consumer expectations of the prices of the unadvertised goods are rational. We obtain three kinds of results. First, unadvertised specials occur in equilibrium, and induce temporal and cross-sectional variation in the identity of advertised goods, consistent with casual observation. In this equilibrium, the two stores advertise the same good part of the time and different goods at other times. When they advertise the same good they do not offer any unadvertised discount on the other good. However, when they advertise different goods, they offer an unadvertised discount on the good that they do not advertise. Intuitively, unadvertised discounts come about because stores randomize the identity of the advertised good in the mixed strategy equilibrium. If retailers were to advertise the same good at all times, they would have to compete intensely for store traffic and therefore discount the advertised good very deeply. And, having done so, they would find it optimal to set the unadvertised good at the reservation price and offer no discount on it. However, if stores randomize the advertised good as shown in this paper, both stores advertise the same good some of the time and at other times they advertise different goods. Because they advertise different goods some of the time, they do not fight intensely for store traffic on just one good, but rather they find it optimal to offer a discount on the unadvertised good also. As a result, an implication of our equilibrium for consumer choice is that unadvertised discounts affect store choice, and in equilibrium some consumers may shop around. Second, we obtain managerial insights into the role of unadvertised specials. They affect store choice, prevent consumer shopping around either fully or partly, and reduce head-to-head competition on the price of the advertised good. The most salient strategic implication of retailers' offering unadvertised discounts is to reduce competition among stores, and this is again due to the randomization strategy of the stores. In fact, stores can reduce head-to-head competition further by increasing the number of products in their assortment and randomizing on the advertised good from this assortment. Third, we provide a resolution of the Diamond (1971) paradox, which says that prices at competing stores approach the monopoly price. In our equilibrium, expected prices of both advertised and unadvertised goods are always below the monopoly price. [ABSTRACT FROM AUTHOR]

In a merchandise depth test, a retail chain introduces new products at a small sample of selected stores for a short pe- Hod prior to the primary selling season and uses the observed sales to forecast demand for the entire chain. We describe a method for resolving two key questions in merchandise testing: (1) which stores to use for the test and (2) how to extrapolate from test sales to create a forecast of total season demand for each product for the chain. Our method uses sales history of products sold in a prior season, similar to those to be tested, to devise a testing program that would have been optimal if it had been applied to this historical sample. Optimality is defined as minimizing the cost of conducting the test, plus the cost of over- and understocking of the products whose supply is to be guided by the test. To determine the best set of test stores, we apply a k- median model to cluster the stores of the chain based on a store similarity measure defined by sales history, and then choose one test store from each duster. A linear programming model is used to fit a formula that is then used to predict total sales from test sales. We applied our method at a large retailer that specializes in women's apparel and at two major shoe retailers, comparing results in each case to the existing process used by the apparel retailer and to some standard statistical approaches such as forward selection and backward elimination. We also tested a version of our method in which clustering was based on a combination of several store descriptors such as location, type of store, ethnicity of the neighborhood of location, total store sales, and average temperature of the store location. We found that relative to these other methods, our approach could significantly improve forecasts and reduce markdowns that result from excessive inventory, and lost margins resulting from stockouts. At the apparel retailer the improvement was enough to increase profits by more than 100%. We believe that one reason our method outperforms the forward selection and backward elimination methods is that these methods seek to minimize squared errors, while our method optimizes the true cost of forecast errors. In addition, our approach, which is based purely on sales, outperforms descriptor variables because it is not always clear which are the best store descriptors and how best to combine them. However, the sales-based process is completely objective and directly corresponds to the retailer's objective of minimizing the understock and overstock costs of forecast error. We examined the stores within each of the dusters formed by our method to identify common factors that might explain their similar sales patterns. The main factor was the similarity in climate within a cluster. This was followed by the ethnicity of the neighborhood where the store is located, and the type of store. We also found that, contrary to popular belief, store size and location had little impact on sales patterns. In addition, this technique could also be used to determine the inventory allocation to individual stores within a cluster and to minimize lost demand resulting from inaccurate distribution across size. Finally, our method provides a logical framework for implementing micromerchandising, a practice followed by a significant number of retailers in which a unique assortment of merchandise is offered in each store (or a group of similar stores) tuned to maximize the appeal to customers of that store. Each cluster formed by our algorithm could be treated as a "virtual chain" within the larger chain, which is managed separately and in a consistent manner in terms of product mix, timing of delivery, advertising message, and store layout. [ABSTRACT FROM AUTHOR]

a given store, and search cost for comparing across two competing electronic wine stores. Consumers spent their own money purchasing wines from two competing electronic merchants selling some overlapping and some unique wines. We show four primary empirical results. First, for differentiated products like wines, lowering the cost of search for quality information reduced price sensitivity. Second, price sensitivity for wines common to both stores increased when cross-store comparison was made easy, as many analysts have assumed. However, easy cross-store comparison had no effect on price sensitivity for unique wines. Third, making information environments more transparent by lowering all three search costs produced welfare gains for consumers. They liked the shopping experience more, selected wines they liked more in subsequent tasting, and their retention probability was higher when they were contacted two months later and invited to continue using the electronic shopping service from home. Fourth, we examined the implications of these results for manufacturers and examined how market shares of wines sold by two stores or one were affected by search costs. When store comparison was difficult, results showed that the market share of common wines was proportional to share of distribution; but when store comparison was made easy, the market share returns to distribution decreased significantly. All these results suggest incentives for retailers carrying differentiated goods to make information environments maximally transparent, but to avoid price competition by carrying more unique merchandise. A fundamental dilemma confronts retailers with stand-alone sites on the World Wide Web and those attempting to build electronic malls for delivery via the Internet, online services, or interactive television (Alba et al. 1997). For consumers, the main potential advantage of electronic shopping over other channels is a reduction in search costs for products and product-related information. Retailers, however, fear that such lowering of consumers' search costs will intensify com- petition and lower margins by expanding the scope of competition from local to national and international. Some retailers' electronic offerings have been constructed to thwart comparison shopping and to ward off price competition, dimming the appeal of many initial electronic shopping services. Ceteris paribus, if electronic shopping lowers the cost of acquiring price information, it should increase price sensitivity, just as is the case for price advertising. In a similar vein, though, electronic shopping can lower the cost of search for quality information. Most analyses ignore the offsetting potential of the latter effect to lower price sensitivity in the current period. They also ignore the potential of maximally transparent shopping systems to produce welfare gains that give consumers a long-term reason to give repeat business to electronic merchants (cf. Alba et al. 1997, Bakos 1997). We test conditions under which lowered search costs should increase or decrease price sensitivity. We conducted an experiment in which we varied independently three different search costs via electronic shopping: search cost for price information, search cost for quality information within [ABSTRACT FROM AUTHOR]

Managing the allocation of shelf space for new products is a problem of significant importance for retailers. The problem is particularly complex for exhibitors-the retailers in the motion picture supply chain-because they face dynamic challenges, given the short life cycles of movies, the changing level of demand over time, the scarcity of shelf space, and the complex revenue sharing contract between the exhibitor and the distributor. In the face of this complexity, the aim of current research is to provide a structure for analyzing management problems of exhibitors in the movie industry. Using a mathematical programming approach and a fast, but readily accessible algorithm, we propose a decision support model, SilverScreener, whose aim is to help exhibitors make effective and timely decisions regarding theater screens management. The major objective is to help select and schedule movies for a multiple-screens theater over a fixed planning horizon in such a way that the exhibitor's cumulative profit is maximized. By treating the multiple screens as parallel machines and the movies as jobs, we provide an analogy of the current problem to the parallel machine scheduling problem. We formulate the resulting problem as an integer program. We depart from the typical parallel machine scheduling problems by introducing the time-indexed formulation that is particularly useful for solving the current problem. An important distinction between the current problem and typical machine scheduling problems is that the present approach allows for the choice of which movies to play; typically, in machine scheduling, all jobs have to be scheduled. We provide various analyses of normative versus actual decision making, based on publicly available data. The developed model is readily implementable and appears to lead to improved profitability in different comparative cases. Through sensitivity analysis, we demonstrate that the above results are robust to variations in various parameters of the problem. The main findings and insights from the normative policy suggest the following: Based on SilverScreener's recommendations, the exhibitor can achieve substantially higher cumulative profit. The improvement over actual decisions in terms of profitability appears to result from a combination of better selection and scheduling of the movies. The general structure of the exhibitor's normative decision is: choose fewer right movies and run them longer. We propose a two-tier integrated application of the model to show how the model can be applied to realistic decision making. The first tier involves development of a movie selection plan to help the manager plan an entire season and bid for movies before the start of that season. An ex ante revenue prediction scheme is developed, based intuitively on a matching of the forthcoming movies with similar movies played in this theater previously. If the forthcoming season's scheduling plan can be visualized as a two-dimensional (week-by- screen) matrix, then that matrix contains only "empty cells" before the first tier. After a bid plan is developed, the exhibitor can "fill" some of those empty cells. The remaining empty cells represent slots, which can be decided during the season by either extending movies the exhibitor booked before the season or by scheduling other movies which may become available later in the season. This motivates the second tier-adaptive scheduling approach-of the integrated approach. The second tier helps the exhibitor in weekly decision making during the season. This application involves "rolling," and updating data, from one time window to another. The approaches followed in the two tiers of the integrated application are quite general in that they can incorporate a sophisticated demand prediction model, managerial judgments, or a combination of both. We also propose an alternative behavioral decision rule (heuristic), which exemplifies relationship dilemmas in the movie industry. This heuristic shows that the exhibitors need to be selective in their choice of movies and may suffer a substantial loss in profitability if they place too much emphasis on accommodating distributors. [ABSTRACT FROM AUTHOR]

This article describes the implementation of a promotion- event forecasting system, PromoCast, and its performance in several pilot applications and validity studies. l'ilot studies involved retail grocery chains with 95 to 185 stores per trading area. The goal was to provide short-term, tactical forecasts useful for planning promotions from a retailer's perspective. Thus, the forecast system must he able to handle any of the over 150,000 UPCs in each store's item master file, and must be scalable to produce approximately 800,000,000 forecasts per year across all the retailers served by efficient marketing services, inc. (ems, inc.). This is a much different task than one that confronts a manufacturer, even one with a broad product line. Manufacturers can benefit from custom modeling in a product line or category. Retailers need a production system that generates forecasts that help promotion planning. Marketing scientists have typically approached promotion analysis from the manufacturer's perspective, One objective of this article is to encourage marketing scientists to rethink promotion analysis from a different perspective. From the retailer's point of view the "planning unit" is the promotion event. Neither weekly store-tracking data nor shopping-trip data from consumer panels are easily aggregated to reflect total sales during a promotion event. We describe the promotion-event databases and the statistical model developed using these databases. The data are the strategic asset. Our goal is to help retailers use their data to increase the profitability of promotions We have data on the performance of each UPC in cacti store under a variety of promotion conditions, on each store's adeptness at executing various styles of promotions, as well as on chain-wide historical performance for each UPC'. We use many historical averages from these databases to build a 67-variable, regression-style model. The forecast incorporates a simple bias correction needed when using a log-transformed dependent variable (the natural log of total unit sales). We argue that the historical averages matching the planned ad and display conditions provide a benchmark superior to the widely used "base-times-lift" method. When aggregated into case units the nature I unit for product ordering), 69% of the forecasts in our first validation study were within ± one case compared to 39% within ± one case using the appropriate historical averages. We report the results of two over-time validity studies that reflect the value of our model for retailers. The limitations and implications of this planning tool for managerial decision making concerning stocking levels are discussed. Whenever historical data are the strategic asset we face inherent limitations. Our model does not forecast new products. The forecast error increases when an existing product is promoted in a new way. Over 99.5% of the time, we have full data from which to create a forecast. However, with a database for a typical chain market containing over 20 mullion promotion events in the 30-month time frame we use, 100,000 events have less than ideal data. The breadth of the database (typically 150,000 UPS) makes it impractical to incorporate data on competitive offerings. We find that regression-style modeling is not adept at incorporating information on the 1,200 subcommodities managed in our pilot stores or the 1,000 manufacturers who supply those stores. Despite these limitations we show the value of using promotion-event data, how tactical forecasts based on these data can directly impact the bottom line of grocery retailers, and how store-by-store forecasts can help retailers with problems if running out of stock or overstocking. [ABSTRACT FROM AUTHOR]