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The Surprising Breadth of Harbingers of Failure
Abstract
Previous research has shown that there exist “harbinger customers” who systematically purchase new products that fail (and are discontinued by retailers). This article extends this result in two ways. First, the findings document the existence of “harbinger zip codes.” If households in these zip codes adopt a new product, this is a signal that the new product will fail. Second, a series of comparisons reveal that households in harbinger zip codes make other decisions that differ from other households. The first comparison identifies harbinger zip codes using purchases from one retailer and then evaluates purchases at a different retailer. Households in harbinger zip codes purchase products from the second retailer that other households are less likely to purchase. The analysis next compares donations to congressional election candidates; households in harbinger zip codes donate to different candidates than households in neighboring zip codes, and they donate to candidates who are less likely to win. House prices in harbinger zip codes also increase at slower rates than in neighboring zip codes. Investigation of households that change zip codes indicates that the harbinger zip code effect is more due to where customers choose to live, rather than households influencing their neighbors’ tendencies.
... That is, there are customers who tend to avoid buying successful products. Simester et al. (2019) later documented the existence of the harbinger phenomenon in political campaign donations and zip codes. Both of these works study groups of customers who do not influence the decisions of others. ...
... We should acknowledge here the limitations of this work and present potential future research avenues. The main limitation of our research, similar to Anderson et al. (2015) and Simester et al. (2019), is that we do not provide a causal explanation about where the harbingers' preferences are coming from. However, we do provide insights into their personality traits and cognitive biases based on their writing style. ...
We empirically investigate the harbinger of failure phenomenon in the motion picture industry by analyzing the pre-release reviews written on movies by film critics. We find that harbingers of failure do exist. Their positive (negative) pre-release movie reviews provide a strong predictive signal that the movie will turn out to be a flop (success). This signal persists even for the top critic category, which usually consists of professional critics, indicating that having expertise in a professional domain does not necessarily lead to correct predictions. Our findings challenge the current belief that positive reviews always help enhance box office revenue and shed new light on the influencer-predictor hypothesis. We further analyze the writing style of harbingers and provide new insights into their personality traits and cognitive biases.
... Previous research by Yoon and Kwon 6 has demonstrated the viability of revenue-based features as a substitute for internal financial data in predicting merchant bankruptcy. Furthermore, studies such as those by Anderson et al. 46 and Simester et al. 47 have utilized customer-based features to forecast the success and failure of new products. ...
Small and Medium-sized Enterprises play a significant role in most economies by contributing to job creation and economic growth. A majority of such merchants rely on business financing, and thus, financial institutions and investors need to assess their performance before making decisions on business loans. However, current methods of predicting merchants’ future performance involve their private internal information, such as revenue and customer base, which cannot be shared without potentially exposing critical information. To address this problem, we first propose a novel approach to predicting merchants’ future performance using credit card transaction data. Specifically, we construct a merchant network, regarding customers as bridges between merchants, and extract features from the constructed network structure for prediction purposes. Our study results demonstrate that the performance of machine learning models with features extracted from our proposed network is comparable to those with conventional revenue- and customer-based features, while maintaining higher privacy levels when shared with third-party organizations. Our approach offers a practical solution to privacy concerns over data and information required for merchants’ performance prediction, enabling safe data-sharing among financial institutions and investors, helping them make more informed decisions on allocating their financial resources while ensuring that merchants’ sensitive information is kept confidential.
W e present a framework and empirical estimates that quantify the economic impact of increased product variety made available through electronic markets. While efficiency gains from increased competition significantly enhance consumer surplus, for instance, by leading to lower average selling prices, our present research shows that increased product variety made available through electronic markets can be a significantly larger source of consumer surplus gains.. One reason for increased product variety on the Internet is the ability of online retailers to catalog, recommend, and provide a large number of products for sale. For example, the number of book titles available at Amazon.com is more than 23 times larger than the number of books on the shelves of a typical Barnes & Noble superstore, and 57 times greater than the number of books stocked in a typical large independent bookstore. Our analysis indicates that the increased product variety of online bookstores enhanced consumer welfare by $731 million to $1.03 billion in the year 2000, which is between 7 and 10 times as large as the consumer welfare gain from increased competition and lower prices in this market. There may also be large welfare gains in other SKU-intensive consumer goods such as music, movies, consumer electronics, and computer software and hardware.
Many markets have historically been dominated by a small number of best-selling products. The Pareto principle, also known as the 80/20 rule, describes this common pattern of sales concentration. However, information technology in general and Internet markets in particular have the potential to substantially increase the collective share of niche products, thereby creating a longer tail in the distribution of sales. This paper investigates the Internet's “long tail” phenomenon. By analyzing data collected from a multichannel retailer, it provides empirical evidence that the Internet channel exhibits a significantly less concentrated sales distribution when compared with traditional channels. Previous explanations for this result have focused on differences in product availability between channels. However, we demonstrate that the result survives even when the Internet and traditional channels share exactly the same product availability and prices. Instead, we find that consumers' usage of Internet search and discovery tools, such as recommendation engines, are associated with an increase the share of niche products. We conclude that the Internet's long tail is not solely due to the increase in product selection but may also partly reflect lower search costs on the Internet. If the relationships we uncover persist, the underlying trends in technology portend an ongoing shift in the distribution of product sales.
This paper was accepted by Ramayya Krishnan, information systems.
A key question for Internet commerce is the nature of competition with traditional brick-and-mortar retailers. Although traditional retailers vastly outsell Internet retailers in most product categories, research on Internet retailing has largely neglected this fundamental dimension of competition. Is cross-channel competition significant, and, if so, how and where can Internet retailers win this battle? This paper attempts to answer these questions using a unique combination of data sets. We collect data on local market structures for traditional retailers, and then match these data to a data set on consumer demand via two direct channels: Internet and catalog. Interestingly, our analyses show that Internet retailers face significant competition from brick-and-mortar retailers when selling mainstream products, but are virtually immune from competition when selling niche products. Furthermore, since the Internet channel sells proportionately more niche products than the catalog channel - a phenomenon sometimes called the "Long Tail", the competition between the Internet channel and local stores is less intense than the competition between the catalog channel and local stores. The methods we introduce can be used to analyze cross-channel competition in other product categories, and suggest that managers need to take into account the types of products they sell when assessing competitive strategies.
Theory predicts that in markets with increasing returns, the number of differentiated products -- and resulting consumer satisfaction -- grow in market size. We document this phenomenon across 246 US radio markets. By a mechanism that we term "preference externalities," an increase in the size of the market brings forth additional products valued by others with similar tastes. But who benefits whom? We examine the patterns of -- and mechanisms for -- preference externalities between black and white and between Hispanic and non-Hispanic radio listeners, and among listeners of different age groups. The patterns are striking: while preference externalities are large and positive within groups, they are small and possibly negative across groups. For example, while black-targeted station entry and the black listening share increase in black population, they are unaffected (or possibly reduced) by the size of the white population. Consequently, small groups receive less variety from the market. Forces that increase the size of the market, such as emerging satellite and Internet technologies, may increase the satisfaction of individuals whose preferences do not match their fellow local residents'. I am grateful for much stimulating discussion to Jeff Milyo and Peter Siegelman, who is a coathor on a related project, and to Spencer Glendon, Dennis Yao, and Severin Borenstein for comments. Thanks to seminar participants at Columbia, Wharton, Yale, the University of Pennsylvania Economics Department, MIT, the Federal Reserve Bank of New York, the University of Chicago, Dartmouth, the US Department of Justice, and the Winter 2000 meetings of the NBER IO group at Stanford for helpful comments. Seminar participants at the FCC, the George Washington College of Law, and Cornell provide...
We show that some customers systematically purchase new products that flop. Their early adoption of a new product is a strong signal that a product will fail - the more they buy, the less likely the product will succeed. These customers, whom we call ‘Harbingers’ [of failure], prefer products that other customers do not want. More broadly, we document that distinguishing among the types of customers who adopt a new product can be predictive of whether a new product will succeed or fail. We discuss how these insights can be readily incorporated into the new product development process. Our findings challenge the conventional wisdom that positive customer feedback is always a signal of future success. The possibility that firms are encouraged by Harbingers’ purchases during pilot market tests may help to explain the high failure rate for new products.
Offline retailers face trading area and shelf space constraints, so they offer products tailored to the needs of the majority. Consumers whose preferences are dissimilar to the majority—"preference minorities"—are underserved offline and should be more likely to shop online. The authors use sales data from Diapers.com, the leading U.S. online retailer for baby diapers, to show why geographic variation in preference minority status of target customers explains geographic variation in online sales. They find that, holding the absolute number of the target customers constant, online category sales are more than 50% higher in locations where customers suffer from preference isolation. Because customers in the preference minority face higher offline shopping costs, they are also less price sensitive. Niche brands, compared with popular brands, show even greater offline-to-online sales substitution. This greater sensitivity to preference isolation means that these brands in the tail of the long tail distribution draw a greater proportion of their total sales from high—preference minority regions. The authors conclude with a discussion of implications for online retailing research and practice.
Accurate marketing research depends on accurate user judgments regarding their needs. However, for very novel products or in product categories characterized by rapid change—such as “high technology” products—most potential users will not have the real-world experience needed to problem solve and provide accurate data to inquiring market researchers. In this paper I explore the problem and propose a solution: Marketing research analyses which focus on what I term the “lead users” of a product or process.
Lead users are users whose present strong needs will become general in a marketplace months or years in the future. Since lead users are familiar with conditions which lie in the future for most others, they can serve as a need-forecasting laboratory for marketing research. Moreover, since lead users often attempt to fill the need they experience, they can provide new product concept and design data as well.
In this paper I explore how lead users can be systematically identified, and how lead user perceptions and preferences can be incorporated into industrial and consumer marketing research analyses of emerging needs for new products, processes and services.
The introduction of new products to US supermarket chains represents a strategic area of business conduct with significant economic implications for agribusiness companies, food manufacturers and retailers, and consumers. Development of new products by manufacturers and their subsequent evaluation by retailers absorbs enormous resources in the grocery distribution system. This research examined new product buying practices in the top 200 US supermarket chains. Typical findings show the reasons that nearly 70% of all newly introduced products are rejected by buyers and never make it to store shelves and approximately one-half of newly accepted products are removed from stores within 1 year. This study demonstrates that a food or agribusiness firm must first understand the standard procedures, wants and needs, of the key “gatekeeper” buyer before focusing on the final consumer. �1994 by John Wiley & Sons, Inc.
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