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Author: Benjamin Reed Shiller
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Benjamin Reed Shiller
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Li, Xi
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Problem definition: Firms heavily invest in big-data technologies to collect consumer data and infer consumer preferences for price discrimination. However, consumers can use technological devices to manipulate their data and fool firms to obtain better deals. We examine how a firm invests in collecting consumer data and makes pricing decisions and whether it should disclose its scope of data collection to consumers who can manipulate their data.Methodology/results: We develop a game-theoretic model to consider a market in which a firm caters to consumers with heterogeneous preferences for a product. The firm collects consumer data to identify their types and issue an individualized price, whereas consumers can incur a cost to manipulate data and mimic the other type. We find that when the firm does not disclose its scope of data collection to consumers, it collects more consumer data. When the firm discloses its scope of data collection, it reduces data collection even when collecting more data is costless. The optimal scope of data collection increases when it is more costly for consumers to manipulate data but decreases when consumer demand becomes more heterogeneous. Moreover, a lower cost for consumers to manipulate data can be detrimental to both the firm and consumers. Lastly, disclosure of data collection scope increases firm profit, consumer surplus, and social welfare. Managerial implications: Our findings suggest that a firm should adjust its scope of data collection and prices based on whether the firm discloses the data collection scope, consumers' manipulation cost, and demand heterogeneity. Public policies should require firms to disclose their data collection scope to increase consumer surplus and social welfare. Even without such a mandatory disclosure policy, firms should voluntarily disclose their data collection scope to increase profit. Moreover, public educational programs that train consumers to manipulate their data or raise their awareness of manipulation tools can ultimately hurt consumers and firms.
Author:
Publisher: GRIN Verlag
ISBN: 334643639X
Category : Business & Economics
Languages : en
Pages : 24
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Academic Paper from the year 2020 in the subject Business economics - General, grade: 72/100 (First Class Honours), Trinity College Dublin, language: English, abstract: This essay is about pricing, a core area of marketing. More specifically, it is about personalised pricing, which must not be confused with dynamic pricing. Personalised pricing describes adjusting the price for every single customer individually, while dynamic pricing describes adjusting the price for all customers subject to external factors like the current demand as of this moment, for example. If an airline company for instance lifts prices on weekends because demand is stronger on weekends in general, this is dynamic pricing. If the airline company however increases the price only for one particular customer, because they find out, for instance, that the customer uses a certain computer type which makes him likely to be wealthier than other customers, this is personalised pricing. The underlying motivation of this essay is to critically assess how personalised pricing is carried out and whether it should be adopted. Therefore, this essay takes the following approach and structure. Firstly, it is examined whether personalised pricing is legally permitted. Only if it is legally permitted to personalise prices it is worthwhile to further investigate this topic. Secondly, the customer’s willingness to pay is analysed. In order to personalise prices, it is necessary to know a customer’s exact willingness to pay. Thirdly, the topic of price elasticity is elaborated. It is necessary to assess whether profit is increased via increasing prices or decreasing prices and therefore higher demand. Fourthly, resulting retaliation as a consequence is explored. It is critically examined whether personalised pricing should be adopted, and empirical evidence is gathered to determine a retribution effect of personalised pricing which might end up making this practice unprofitable.
Author: Anshuman Khare
Publisher: Springer
ISBN: 3319444689
Category : Computers
Languages : en
Pages : 327
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Book Description
This book explores the factors that make digital disruption possible and the effects this has on existing business models. It takes a look at the industries that are most susceptible to disruption and highlights what executives can do to take advantage of disruption to re-invent their business model. It also examines the pivotal role that technology plays in creating new dynamics to business operations and forcing business model changes. Adoption of digital technology has caused process disruptions in a number of industries and led to new business models (e.g., Über, AirBnb) and new products. In addition to covering some of the more popular and well known examples, this book targets not so obvious disruptions in the education sector and in services and changing business models. Phantom Ex Machina: Digital Disruption’s Role in Business Model Transformation is divided into six parts. The book begins with an introduction to digital disruption and why it matters. The next part of the book focuses on business strategy which includes case studies on the impact of social media and how digital disruption changes pricing strategies and price models. For part three, the authors observe technology’s role in digital disruptions. Chapters cover how 3D printing is challenging existing business models and how the automotive industry is innovating with new perspectives. Part four covers higher education, recognizing digital disruption’s transformation in graduate management education. Part five centers upon the service industry with a look at virtual teams and the emergence of virtual think tanks. Finally the book concludes with a look to the future, embracing disruptions.
Author: Jean-Pierre Dubé
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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We study the welfare implications of personalized pricing, an extreme form of third-degree price discrimination implemented with machine learning for a large, digital firm. Using data from a unique randomized controlled pricing field experiment we train a demand model and conduct inference about the effects of personalized pricing on firm and consumer surplus. In a second experiment, we validate our predictions in the field. The initial experiment reveals unexercised market power that allows the firm to raise its price optimally, generating a 55% increase in profits. Personalized pricing improves the firm's expected posterior profits by an additional 19%, relative to the optimized uniform price, and by 86%, relative to the firm's unoptimized status quo price. Turning to welfare effects on the demand side, total consumer surplus declines 23% under personalized pricing relative to uniform pricing, and 47% relative to the firm's unoptimized status quo price. However, over 60% of consumers benefit from lower prices under personalization and total welfare can increase under standard inequity-averse welfare functions. Simulations with our demand estimates reveal a non-monotonic relationship between the granularity of the segmentation data and the total consumer surplus under personalization. These findings indicate a need for caution in the current public policy debate regarding data privacy and personalized pricing insofar as some data restrictions may not per se improve consumer welfare.
Author: Executive Office of the President
Publisher: CreateSpace
ISBN: 9781503016446
Category : Political Science
Languages : en
Pages : 84
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Book Description
Since the first censuses were taken and crop yields recorded in ancient times, data collection and analysis have been essential to improving the functioning of society. Foundational work in calculus, probability theory, and statistics in the 17th and 18th centuries provided an array of new tools used by scientists to more precisely predict the movements of the sun and stars and determine population-wide rates of crime, marriage, and suicide. These tools often led to stunning advances. In the 1800s, Dr. John Snow used early modern data science to map cholera “clusters” in London. By tracing to a contaminated public well a disease that was widely thought to be caused by “miasmatic” air, Snow helped lay the foundation for the germ theory of disease.Gleaning insights from data to boost economic activity also took hold in American industry. Frederick Winslow Taylor's use of a stopwatch and a clipboard to analyze productivity at Midvale Steel Works in Pennsylvania increased output on the shop floor and fueled his belief that data science could revolutionize every aspect of life.2 In 1911, Taylor wrote The Principles of Scientific Management to answer President Theodore Roosevelt's call for increasing “national efficiency”: Today, data is more deeply woven into the fabric of our lives than ever before. We aspire to use data to solve problems, improve well-being, and generate economic prosperity. The collection, storage, and analysis of data is on an upward and seemingly unbounded trajectory, fueled by increases in processing power, the cratering costs of computation and storage, and the growing number of sensor technologies embedded in devices of all kinds. In 2011, some estimated the amount of information created and replicated would surpass 1.8 zettabytes. In 2013, estimates reached 4 zettabytes of data generated worldwide.
Author: David Birget
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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In a context where more and more personal data is gathered by companies thanks to the rise of social networks, the internet of things, e-commerce and many other online services, it is interesting to explore how this data can be used profitably by different businesses. One potential application is to use online data to estimate the willingness to pay of consumers in order to implement personalised pricing. The goal of this thesis is threefold. First of all, I investigate to what extend price discrimination is already being used online. Secondly, I analyse the future economic potential of personalised pricing in the light of the recent progress in data collection and analytics. Thirdly, I examine the economic implications for different stakeholders resulting from a hypothetical large-scale use of personalised pricing by online shops. In this context, I also review current regulation and suggest potential improvements.
Author: Federal Trade Commission
Publisher: Createspace Independent Pub
ISBN: 9781508815129
Category : Political Science
Languages : en
Pages : 110
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Book Description
In this report, the Federal Trade Commission discusses the results of an in-depth study of nine data brokers. These data brokers collect personal information about consumers from a wide range of sources and provide it for a variety of purposes, including verifying an individual's identity, marketing products, and detecting fraud. Because these companies generally never interact with consumers, consumers are often unaware of their existence, much less the variety of practices in which they engage. By reporting on the data collection and use practices of these nine data brokers, which represent a cross-section of the industry, this report attempts to shed light on the data broker industry and its practices. For decades, policymakers have expressed concerns about the lack of transparency of companies that buy and sell consumer data without direct consumer interaction. Indeed, the lack of transparency among companies providing consumer data for credit and other eligibility determinations led to the adoption of the Fair Credit Reporting Act ("FCRA"), a statute the Commission has enforced since its enactment in 1970. The FCRA covers the provision of consumer data by consumer reporting agencies where it is used or expected to be used for decisions about credit, employment, insurance, housing, and similar eligibility determinations; it generally does not cover the sale of consumer data for marketing and other purposes. While the Commission has vigorously enforced the FCRA, 1 since the late 1990s it has also been active in examining the practices of data brokers that fall outside the FCRA.
Author: Yihong Hu
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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With the rise of big data technology, an online platform can easily gather customer information to engage in price discrimination and obtain additional profits. Whether to share information remains an unsolved strategy decision for the platform. We employ a game-theoretic model to characterize the interplay of information sharing by the platform and the pricing strategies of two firms. We find that the seller always has incentives to acquire information, while the platform is not always willing to share information. With different combinations of the commission rate and the new customer ratio, the equilibrium of the overall system has three possible results where the information is always used for price discrimination by either one party or two parties. With a high commission rate and a low new customer ratio, the platform no longer pursues a demand for its own product and lets the seller occupy the whole market, which leads to the lowest consumer surplus and social welfare. We finally show that in most cases, prohibiting information sharing increases consumer surplus and social welfare, verifying the necessity of regulation. These results could provide useful guidelines for platform managers and regulators to better design information sharing and price discrimination policies.
Author: Joseph Turow
Publisher: Yale University Press
ISBN: 0300225075
Category : Business & Economics
Languages : en
Pages : 265
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Book Description
The author of Media Today offers “a trenchant, timely, and troubling account of [retailers’] data-mining, in-store tracking, and predictive analytics” (The Philadelphia Inquirer). By one expert’s prediction, within twenty years half of Americans will have body implants that tell retailers how they feel about specific products as they browse their local stores. The notion may be outlandish, but it reflects executives’ drive to understand shoppers in the aisles with the same obsessive detail that they track us online. In fact, a hidden surveillance revolution is already taking place inside brick-and-mortar stores, where Americans still do most of their buying. Drawing on his interviews with retail executives, analysis of trade publications, and experiences at insider industry meetings, advertising and digital studies expert Joseph Turow pulls back the curtain on these trends, showing how a new hyper-competitive generation of merchants—including Macy’s, Target, and Walmart—is already using data mining, in-store tracking, and predictive analytics to change the way we buy, undermine our privacy, and define our reputations. Eye-opening and timely, Turow’s book is essential reading to understand the future of shopping. “Turow shows shopping today to be an exercise in unwitting self-revelation—and not only online.”—The Wall Street Journal “Thoroughly researched and clearly presented with detailed evidence and fascinating peeks inside the retail industry. Much of this information is startling and even chilling, particularly when Turow shows how retail data-tracking can enable discrimination and societal stratification.”—Publishers Weekly “Revealing . . . Valuable reading for shoppers and retailers alike.”—Kirkus Reviews
