Peter Fader

Peter Fader
  • Frances and Pei-Yuan Chia Professor
  • Professor of Marketing

Contact Information

  • office Address:

    771 Jon M. Huntsman Hall
    3730 Walnut Street
    University of Pennsylvania
    Philadelphia, PA 19104

Research Interests: lifetime value of the customer, sales forecasting for new products, using behavioral data to understand and forecast shopping/purchasing activities across a wide range of industries. managerial applications focus on topics such as customer relationship management

Links: CV, Twitter (@faderp), Google Scholar page, CoolNumbers.com

Overview

Peter S. Fader is the Frances and Pei-Yuan Chia Professor of Marketing at The Wharton School of the University of Pennsylvania. His expertise centers around the analysis of behavioral data to understand and forecast customer shopping/purchasing activities. He works with firms from a wide range of industries, such as telecommunications, financial services, gaming/entertainment, retailing, and pharmaceuticals. Managerial applications focus on topics such as customer relationship management, lifetime value of the customer, and sales forecasting for new products. Much of his research highlights the consistent (but often surprising) behavioral patterns that exist across these industries and other seemingly different domains.

In addition to his various roles and responsibilities at Wharton, Professor Fader co-founded a predictive analytics firm (Zodiac) in 2015, which was sold to Nike in 2018. He then co-founded (and continues to run) Theta Equity Partners to commercialize his more recent work on “customer-based corporate valuation.”

Fader is the author of Customer Centricity: Focus on the Right Customers for Strategic Advantage and coauthor with Sarah E. Toms of the book The Customer Centricity Playbook. He has been quoted or featured in The New York Times, The Wall Street Journal, The Economist, The Washington Post, and on NPR, among other media. In 2017, Professor Fader was named by Advertising Age as one of its inaugural “25 Marketing Technology Trailblazers,” and was the only academic on the list.

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Research

  • Eva Ascarza, Scott Neslin, Oded Netzer, Zachery Anderson, Peter Fader, Sunil Gupta, Bruce Hardie, Aurelie Lemmens, Barak Libai, David Neal, Foster Provost, Rom Y. Schrift (2018), In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions, Customer Needs and Solutions, 17.

  • Joseph Jiaqi Xu, Peter Fader, Senthil Veeraraghavan (2017), Designing and Evaluating Dynamic Pricing Policies for Major League Baseball Tickets, M&SOM.

    Abstract: Many firms have difficulty evaluating the impact of their pricing policy, which further inhibits their ability to properly design and implement dynamic pricing. We address this issue in the context of single-game ticket pricing for a Major League Baseball franchise. We develop and estimate a comprehensive demand model to help evaluate and design dynamic pricing policies for the franchise. Our model encompasses all relevant aspects of the demand generation process, including ticket quantity and stadium seat section choice. The demand model reveals factors that drive sport ticket revenue such as the effect of home team performance on the overall price sensitivity and the relationship between customers' arrival timing and product choice. We show that by leveraging these insights and allowing sufficient pricing flexibility, the franchise can achieve a potential revenue improvement of 17.2% through daily price re-optimization, which is comparable to that of a clairvoyant policy in which the future evolution of demand is assumed to be known.

  • Necati Tereyagoglu, Peter Fader, Senthil Veeraraghavan (2016), Multi-attribute Loss Aversion and Reference Dependence: Evidence from the Performing Arts Industry, Management Science.

  • Necati Tereyagoglu, Peter Fader, Senthil Veeraraghavan (2016), Pricing Theater Seats: The Value of Price Commitment and Monotone Discounting, Production and Operations Management.

  • Valeria Stourm, Eric Bradlow, Peter Fader (2015), Stockpiling Points in Linear Loyalty Programs, Journal of Marketing Research, 52 (2), pp. 253-267.

    Abstract: Customers often stockpile reward points in linear loyalty programs (i.e., programs that do not explicitly reward stockpiling) despite several economic incentives against it (e.g., the time value of money). The authors develop a mathematical model of redemption choice that unites three explanations for why customers seem to be motivated to stockpile on their own, even though the retailer does not reward them for doing so. These motivations are economic (the value of forgone points), cognitive (nonmonetary transaction costs), and psychological (customers value points differently than cash). The authors capture the psychological motivation by allowing customers to book cash and point transactions in separate mental accounts. They estimate the model on data from an international retailer using Markov chain Monte Carlo methods and accurately forecast redemptions during an 11-month out-of-sample period. The results indicate substantial heterogeneity in how customers are motivated to redeem and suggest that the behavior in the data is driven mostly by cognitive and psychological incentives.      

  • Vibhanshu Abhishek, Kartik Hosanagar, Peter Fader (2015), Aggregation Bias in Sponsored Search Data: The Curse and The Cure, Marketing Science, 34, pp. 59-77.

    Abstract: There has been significant recent interest in studying consumer behavior in sponsored search advertising (SSA). Researchers have typically used daily data from search engines containing measures such as average bid, average ad position, total impressions, clicks and cost for each keyword in the advertiser's campaign. A variety of random utility models have been estimated using such data and the results have helped researchers explore the factors that drive consumer click and conversion propensities. However, virtually every analysis of this kind has ignored the intra-day variation in ad position. We show that estimating random utility models on aggregated (daily) data without accounting for this variation will lead to systematically biased estimates -- specifically, the impact of ad position on click-through rate (CTR) is attenuated and the predicted CTR is higher than the actual CTR. We demonstrate the existence of the bias analytically and show the effect of the bias on the equilibrium of the SSA auction. Using a large dataset from a major search engine, we measure the magnitude of bias and quantify the losses suffered by the search engine and an advertiser using aggregate data. The search engine revenue loss can be as high as 11% due to aggregation bias. We also present a few data summarization techniques that can be used by search engines to reduce or eliminate the bias.

  • Kinshuk Jerath, Peter Fader, Bruce G.S. Hardie (Under Review), Customer-Base Analysis on a ‘Data Diet’: Model Inference Using Repeated Cross-Sectional Summary (RCSS) Data.

    Abstract: We address a critical question that many firms are facing in this era of "big data'': Can customer data be stored and analyzed in an easy-to-manage and scalable manner without significantly compromising the inferences that can be made about the customers' transaction activity? We address this question in the context of customer-base analysis. A number of researchers have developed customer-base analysis models that perform very well given detailed individual-level data. We explore the possibility of estimating these models using aggregated data summaries alone, namely repeated cross-sectional summaries (RCSS) of the transaction data (e.g., four quarterly histograms). Such summaries are easy to create, visualize, and distribute, irrespective of the size of the customer base. An added advantage of RCSS data is that individual customers cannot be identified, which makes it desirable from a privacy viewpoint as well. We focus on the widely used Pareto/NBD model and carry out a comprehensive simulation study covering a vast spectrum of market scenarios. Our results consistently and convincingly establish that model performance associated with the use of three or four cross-sections of RCSS data (as judged by model fit, parameter recovery, and forward-looking metrics of customer value) can closely match the model performance associated with the use of individual-level data. We confirm the results of the simulations on a real dataset of purchases from an online fashion retailer. The thesis of our approach is that existing statistical models continue to have value in a "big data'' world, but to harness this value one may want to approach estimation of these models in a different manner.

  • Vibhanshu Abhishek, Peter Fader, Kartik Hosanagar (Under Revision), Media Exposure through the Funnel: A Model of Multi-Stage Attribution.

    Abstract: Consumers are exposed to advertisers across a number of channels. As such, a conversion or a sale may be the result of a series of ads that were displayed to the consumer. This raises the key question of attribution: which ads get credit for a conversion and how much credit does each of these ads get? This is one of the most important questions facing the advertising industry today. Although the issue is well documented, current solutions are often simplistic; for e.g., attributing the sale to the most recent ad exposure. In this paper, we address the problem of attribution by developing a Hidden Markov Model (HMM) of an individual consumer's behavior based on the concept of a conversion funnel. We apply the model to a unique data-set from the online campaign for the launch of a car. We observe that different ad formats, e.g. display and search ads, affect consumers differently based on their states in the decision process. Display ads usually have an early impact on the consumer, moving him from a disengaged state to an state in which he interacts with the campaign. On the other hand, search ads have a pronounced effect across all stages. Further, when the consumer interacts with these ads (e.g. by clicking on them), the likelihood of a conversion increases considerably. Finally, we show that attributing conversions based on the HMM provides fundamentally different insights into ad effectiveness relative to the commonly used approaches for attribution. Contrary to the common belief that display ads as are not useful, our results show that display ads affect early stages of the conversion process. Furthermore, we show that only a fraction of online conversions are driven by online ads.

  • Eric Schwartz, Eric Bradlow, Peter Fader (2014), Model Selection Using Database Characteristics: Developing a Classification Tree for Longitudinal Incidence Data, Marketing Science , 33 (2), pp. 188-205.

    Abstract: When managers and researchers encounter a data set, they typically ask two key questions: (1) Which model (from a candidate set) should I use? And (2) if I use a particular model, when is it going to likely work well for my business goal? This research addresses those two questions and provides a rule, i.e., a decision tree, for data analysts to portend the “winning model” before having to fit any of them for longitudinal incidence data. We characterize data sets based on managerially relevant (and easy-to-compute) summary statistics, and we use classification techniques from machine learning to provide a decision tree that recommends when to use which model. By doing the “legwork” of obtaining this decision tree for model selection, we provide a time-saving tool to analysts. We illustrate this method for a common marketing problem (i.e., forecasting repeat purchasing incidence for a cohort of new customers) and demonstrate the method’s ability to discriminate among an integrated family of a hidden Markov model (HMM) and its constrained variants. We observe a strong ability for data set characteristics to guide the choice of the most appropriate model, and we observe that some model features (e.g., the “back-and-forth” migration between latent states) are more important to accommodate than are others (e.g., the inclusion of an “off” state with no activity). We also demonstrate the method’s broad potential by providing a general “recipe” for researchers to replicate this kind of model classification task in other managerial contexts (outside of repeat purchasing incidence data and the HMM framework).

  • Arun Gopalakrishnan, Eric Bradlow, Peter Fader (Under Revision), A Cross-Cohort Changepoint Model for Customer-Base Analysis.

    Abstract: We introduce a new methodology that can capture and explain differences across a series of cohorts of new customers in a repeat-transaction setting. More specifically, this new framework, which we call a vector changepoint model, exploits the underlying regime structure in a sequence of acquired customer cohorts, to make predictive statements about new cohorts for which the firm has little or no longitudinal transaction data. To accomplish this, we develop our model within a Hierarchical Bayesian framework to uncover evidence of regime changes for each cohort-level parameter separately, thus disentangling potential explanations for cross-cohort shifts in aggregate transaction patterns.  Calibrating the model using multi-cohort donation data from a non-profit organization, we find that holdout predictions for new cohorts using this model have greater accuracy – and greater diagnostic value – compared to a variety of strong benchmarks. Our modeling approach also highlights the perils of pooling data across cohorts without accounting for cross-cohort shifts, thus enabling managers to quantify their uncertainty about potential regime changes and avoid “old data” aggregation bias.

Teaching

Managing the Value of Customer Relationships

Applied Probability Models in Marketing

Current Courses

  • MKTG776 - Appl Prob Models Mktg

    This course will expose students to the theoretical and empirical "building blocks" that will allow them to develop and implement powerful models of customer behavior. Over the years, researchers and practitioners have used these methods for a wide variety of applications, such as new product sales forecasting, analyses of media usage, customer valuation, and targeted marketing programs. These same techniques are also very useful for other types of business (and non-business) problems. The course will be entirely lecture-based with a strong emphasis on real-time problem solving. Most sessions will feature sophisticated numerical investigations using Microsoft Excel. Much of the material is highly technical.

    MKTG776050

  • WH 398 - Senior Capstone

    Wharton 398 is a for-credit, interactive business simulation that provides Wharton seniors with the opportunity to draw on their business knowledge - finance, management, marketing, leadership, and social responsibility - while formulating and executing business strategy in a competative, team-based environment. Utilizing real-time problem solving within a dynamic simulation environment, teams design and implement strategic plans, integrate feedback from the consequences of those decisions, and interact with other teams to create shareholder and social value. Students must apply to participate in this course. This course is for Wharton students only.

    WH 398002

Past Courses

  • MKTG101 - INTRO TO MARKETING

    The objective of this course is to introduce students to the concepts, analyses, and activities that comprise marketing management, and to provide practice in assessing and solving marketing problems. The course is also a foundation for advanced electives in Marketing as well as other business/social disciplines. Topics include marketing strategy, customer behavior, segmentation, market research, product management, pricing, promotion, sales force management and competitive analysis.

  • MKTG212 - DATA & ANLZ FOR MKTG DEC

    Firms have access to detailed data of customers and past marketing actions. Such data may include in-store and online customer transactions, customer surveys as well as prices and advertising. Using real-world applications from various industries, the goal of the course is to familiarize students with several types of managerial problems as well as data sources and techniques, commonly employed in making effective marketing decisions. The course would involve formulating critical managerial problems, developing relevant hypotheses, analyzing data and, most importantly, drawing inferences and telling convincing narratives, with a view of yielding actionable results.

  • MKTG399 - INDEPENDENT STUDY

  • MKTG476 - APPL PROB MODELS MKTG

    This course will expose students to the theoretical and empirical "building blocks" that will allow them to construct, estimate, and interpret powerful models of consumer behavior. Over the years, researchers and practitioners have used these models for a wide variety of applications, such as new product sales, forecasting, analyses of media usage, and targeted marketing programs. Other disciplines have seen equally broad utilization of these techniques. The course will be entirely lecture-based with a strong emphasis on real-time problem solving. Most sessions will feature sophisticated numerical investigations using Microsoft Excel. Much of the material is highly technical.

  • MKTG768 - CONTAGIOUS

    Why do some products catch on and achieve huge popularity while others fail? Why do some behaviors spread like wildfire while others languish? How do certain ideas seem to stick in memory while others disappear the minute you hear them? More broadly, what factors lead to trends, social contagion, and social epidemics? Interactive media, word of mouth, and viral marketing are important issues for companies, brands, and organizations. This course looks at these and other topics as it examines how products, ideas, and behaviors catch on and become popular. Marketers want their product to be popular, organizations want their social change initiative to catch on and entrepreneurs want their ideas to stick. This course will touch on four main aspects: (1) Characteristics of products, ideas, and behaviors that lead them to be successful. (2) Aspects of individual psychology that influence what things are successful. (3) Interpersonal processes, or how interactions between individuals drive success. (4) Social networks, or how patterns of social ties influence success.

  • MKTG775 - MANAGING CUSTOMER VALUE

    As the concept of CRM becomes common parlance for every marketing executive, it is useful to take a step back to better understand the various different behaviors that underlie the development of successful CRM systems. These "behaviors" include customer-level decisions, firm actions, and the delicate but complex interplay between the two. Accordingly this course is comprised of four main modules. We start with the discussion of customer profitability - focusing on the concepts of "customer lifetime value" and "customer equity". We will examine how to measure long-run customer profitability in both business-to-customer and business-to-business environments, and the uses of these measures as major components assessing overall firm valuation. Second, we move to the value that the firm provides to its customers - better understanding the true nature of customer satisfaction and its non-trivial relationship with firm profitability. Third, we examine each of the three main components of the firm's management of its customer base: customer acquisition, development, and retention - and the complex resource allocation task that must be balanced across them. Finally, we conclude with a discussion of various tactical and organizational aspects of customer relationship management.

  • MKTG776 - APPL PROB MODELS MKTG

    This course will expose students to the theoretical and empirical "building blocks" that will allow them to develop and implement powerful models of customer behavior. Over the years, researchers and practitioners have used these methods for a wide variety of applications, such as new product sales forecasting, analyses of media usage, customer valuation, and targeted marketing programs. These same techniques are also very useful for other types of business (and non-business) problems. The course will be entirely lecture-based with a strong emphasis on real-time problem solving. Most sessions will feature sophisticated numerical investigations using Microsoft Excel. Much of the material is highly technical.

  • MKTG777 - MARKETING STR

    This course views marketing as both a general management responsibility and an orientation of an organization that helps one to create, capture and sustain customer value. The focus is on the business unit and its network of channels, customer relationships, and alliances. Specifically, the course attempts to help develop knowledge and skills in the application of advanced marketing frameworks, concepts, and methods for making strategic choices at the business level.

  • MKTG890 - ADVANCED STUDY PROJECT

    RETAIL ECOSYSTEM ACTION LEARNING PROJECTS: This course offers graduate students from Wharton and other Penn schools an opportunity to work on real-world projects for companies in the retail industry and in the wider retail ecosystem. It requires the exploration and analysis of actual business issues or opportunities identified by sponsoring/client companies, as well as the formulation of recommendations. It combines 1) academic principles, 2) application of prior business knowledge to the project at hand, and 3) a solutions-oriented mentality. In addition to supervised project work and regular updates to the corporate client/project sponsor, the course involves classroom meetings and discussions on topics pertaining to the projects. While this course focuses on "marketing" topics, projects might also incorporate topics from related disciplines such as operations, management of innovation & technology, data analytics, international management, design, and real estate. Indeed, the goal will be to constitute interdisciplinary teams from Wharton and other relevant Penn graduate schools. ADVANCED STUDY PROJECT (GENERAL): The principal objectives of this course are to provide opportunities for undertaking an in-depth study of a marketing problem and to develop the students' skills in evaluating research and designing marketing strategies for a variety of management situations. Selected projects can touch on any aspect of marketing as long as this entails the elements of problem structuring, data collection, data analysis, and report preparation. The course entails a considerable amount of independent work. (Strict library-type research is not appropriate) Class sessions are used to monitor progress on the project and provide suggestions for the research design and data analysis. The last portion of the course often includes an oral presentation by each group to the rest of the class and project sponsors. Along with marketing, the projects integrate other elements of management such as finance, production, research and development, and human resources.

  • MKTG899 - INDEPENDENT STUDY

    A student contemplating an independent study project must first find a faculty member who agrees to supervise and approve the student's written proposal as an independent study (MKTG 899). If a student wishes the proposed work to be used to meet the ASP requirement, he/she should then submit the approved proposal to the MBA adviser who will determine if it is an appropriate substitute. Such substitutions will only be approved prior to the beginning of the semester.

  • MKTG995 - DISSERTATION

  • MKTG999 - INDEPENDENT STUDY

    Requires written permission of instructor and the department graduate adviser.

  • STAT476 - APPL PROB MODELS MKTG

    This course will expose students to the theoretical and empirical "building blocks" that will allow them to construct, estimate, and interpret powerful models of customer behavior. Over the years, researchers and practitioners have used these models for a wide variety of applications, such as new product sales, forecasting, analyses of media usage, and targeted marketing programs. Other disciplines have seen equally broad utilization of these techinques. The course will be entirely lecture-based with a strong emphasis on real-time problem solving. Most sessions will feature sophisticated numerical investigations using Microsoft Excel. Much of the material is highly technical.

  • STAT776 - APPL PROB MODELS MKTG

    This course will expose students to the theoretical and empirical "building blocks" that will allow them to develop and implement powerful models of customer behavior. Over the years, researchers and practitioners have used these methods for a wide variety of applications, such as new product sales forecasting, analyses of media usage, customer valuation, and targeted marketing programs. These same techniques are also very useful for other types of business (and non-business) problems. The course will be entirely lecture-based with a strong emphasis on real-time problem solving. Most sessions will feature sophisticated numerical investigations using Microsoft Excel. Much of the material is highly technical.

  • WH 398 - SENIOR CAPSTONE

    Wharton 398 is a for-credit, interactive business simulation that provides Wharton seniors with the opportunity to draw on their business knowledge - finance, management, marketing, leadership, and social responsibility - while formulating and executing business strategy in a competative, team-based environment. Utilizing real-time problem solving within a dynamic simulation environment, teams design and implement strategic plans, integrate feedback from the consequences of those decisions, and interact with other teams to create shareholder and social value. Students must apply to participate in this course. This course is for Wharton students only.

Awards and Honors

  • AMA 25-year Consortium Fellow Research Excellence Award, 2009
  • Finalist, O’Dell Award for Best Paper in Journal of Marketing Research, 2009
  • Robert B. Clarke Outstanding Educator Award, given by the Direct Marketing Educational Foundation to honor an academic’s overall achievement in direct/interactive marketing, 2007
  • EXPLOR Award from the American Marketing Association for “the most innovative use of technology that advances marketing research”, 2007
  • David Hardin Award for best paper published in Marketing Research magazine, 2007
  • Paul E. Green Award, 1997, 2006 (given annually by the American Marketing Association for the best article published in the Journal of Marketing Research for its “potential to contribute significantly to the practice of marketing research”), 2006 Description

    Goes to the paper published in the Journal of Marketing Research in the previous year that “shows or demonstrates the most potential to contribute significantly to the practice of marketing research and research in marketing”

  • Best paper award at the Advanced Research Techniques Forum, 2006 Description

    An American Marketing Association conference held in June 2005.

  • Journal of Interactive Marketing Best Paper Award, 2005 Description

    For “Capturing Evolving Visit Behavior in Clickstream Data,” Journal of Interactive Marketing, 18 (winter 2004), 5-19, co-authored with Wendy Moe

In the News

  • Looking at Life as One Big Subscription, New York TImes - 10/11/2009
  • Free For All? Profits Can Be Elusive Online, NPR - 08/19/2009
  • Microsoft and Yahoo Are Linked Up. Now What?, New York TImes - 07/29/2009
  • The Cookie Crumbles: By banning online sales, are the Girl Scouts failing our daughters?, Newsweek - 03/11/2009
  • Professors to Watch, Financial Times - 01/26/2009
  • Marketing in a Downturn (video), Financial Times - 01/22/2009
  • Why Napster Was the Best Thing To Happen to the Music Industry (and They Killed It), EMTM Newsletter - 10/15/2007
  • Dr. Peter S. Fader to Receive DMEF’s 2007 Robert B. Clarke Outstanding Educator Award, DMA - 07/10/2007
  • What Data Mining Can and Can’t Do, CIO Insight - 06/13/2007
  • The Link Between Ants, Actuaries, and Customers’ Actions, 1to1 magazine - 06/11/2007
  • The Traveling Salesman and the Grocery Shopper, RetailWire - 12/06/2006
  • Peter Fader News, Entrepreneur Magazine - 09/05/2005 Description

    Peter Fader, Frances and Pei-Yuan Chia Professor; Professor of Marketing, was quoted in an article about the role of technology-savvy social leaders in augmenting the publicity of a product

  • Peter Fader News, Progressive Grocer - 09/01/2005 Description

    Peter Fader, Frances and Pei-Yuan professor of marketing, and Eric T. Bradlow, professor of marketing and statistics and academic director of the Wharton Small Business Development Center, were featured in an article about their research on supermarket shopping patterns.

  • Peter Fader News, Philadelphia Inquirer - 08/08/2005 Description

    Peter Fader, Frances and Pei-Yuan Chia Professor; Professor of Marketing, was quoted in an article about how the music industry has changed in the past several decades.

  • Peter Fader News, The Economic Times (India) - 08/03/2005 Description

    Peter Fader, Frances and Pei-Yuan Chia Professor; Professor of Marketing, was quoted in an article about the role of technology in the general marketing of products.

  • Peter Fader News, Pioneer Press - 07/20/2005 Description

    Peter Fader, Frances and Pei-Yuan Chia Professor; Professor of Marketing, Eric Bradlow, associate professor of marketing and statistics, and Jeffrey Larson, doctoral student in the Marketing Department, were quoted in an article about the time consumers spend in a supermarket and how this impacts future shopping trends.

  • Peter Fader News, The New York Times - 07/10/2005 Description

    Peter Fader, Frances and Pei-Yuan Chia Professor; Professor of Marketing, was quoted in an article about Amazon’s future marketing strategy.

  • Peter Fader News, The Washington Post - 06/08/2005 Description

    Peter Fader, Frances and Pei-Yuan Chia Professor; Professor of Marketing, Eric Bradlow, associate professor of marketing and statistics, and Jeffrey Larson, doctoral student in the Marketing Department, were quoted in an article about the time consumers spend in a supermarket and how this impacts future shopping trends. ( A similar article appeared in The Globe & Mail, 6/8/05 )

  • Peter Fader News, National Public Radio: Marketplace - 05/20/2005 Description

    Peter Fader, Frances and Pei-Yuan Chia Professor; Professor of Marketing, was interviewed about Mexican panaderias and starting hybrid chains using Starbucks as a business model.

  • Peter Fader News, National Public Radio - 04/25/2005 Description

    Peter Fader, Frances and Pei-Yuan Chia Professor; Professor of Marketing, was interviewed about dual-disc DVD marketing initiatives.

  • Peter Fader News, The Seattle Times - 03/09/2005 Description

    Peter Fader, Frances and Pei-Yuan Chia Professor; Professor of Marketing, was quoted in an article about CD-DVD dual discs and how they will promote music sales.

Knowledge @ Wharton

Activity

Latest Research

Eva Ascarza, Scott Neslin, Oded Netzer, Zachery Anderson, Peter Fader, Sunil Gupta, Bruce Hardie, Aurelie Lemmens, Barak Libai, David Neal, Foster Provost, Rom Y. Schrift (2018), In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions, Customer Needs and Solutions, 17.
All Research

In the News

How Will Targeted Ads Fare in an Era of Data Protection?

Although many companies are uncertain how the General Data Protection Regulation that went into effect last month will impact them, one thing is clear: They will not be able to target their advertising as freely as in the past.

Knowledge @ Wharton - 2018/06/22
All News

Awards and Honors

AMA 25-year Consortium Fellow Research Excellence Award 2009
All Awards