Peter Fader

• Joseph Jiaqi Xu, Peter Fader, Senthil Veeraraghavan (2018), Designing and Evaluating Dynamic Pricing Policies for Major League Baseball Tickets, M&SOM (Forthcoming). 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.

• 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.
• Necati Tereyagoglu, Peter Fader, Senthil Veeraraghavan (2018), Multi-attribute Loss Aversion and Reference Dependence: Evidence from the Performing Arts Industry, Management Science, 64 (1), pp. 421-436.
• Necati Tereyagoglu, Peter Fader, Senthil Veeraraghavan (2017), Pricing Theater Seats: The Value of Price Commitment and Monotone Discounting, Production and Operations Management, 26 (6), pp. 1056-1065.
• 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.

• All Research from Peter Fader »

Managing the Value of Customer Relationships

Applied Probability Models in Marketing

Past Courses

• ECON4999 - Independent Study

  Individual study and research under the direction of a member of the Economics Department faculty. At a minimum, the student must write a major paper summarizing, unifying, and interpreting the results of the study. This is a one semester, one c.u. course.

• MKTG3990 - Independent Study

• MKTG4760 - 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.

• MKTG7750 - 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.

• MKTG7760 - 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.

• MKTG8950 - Global Business Week

  GLOBAL BUSINESS WEEK: MKTG 895 is one in an array of Global Business Week (GBW) study tour courses offered by various departments across Wharton. Each of the GBW courses offered in a term, will entail travel to a different part of the world and address a different element of economic driver for a country or industry. A faculty member will drive the topic and curriculum associated with a study tour to a region of the world where the study of a topic will provide insights and clarity available only by being in country. In country lectures from the lead faculty and area experts in industry, academia and government will form much of the basis of class time. In addition, students will experience relevant company and cultural settings where they will again hear from industry experts. Each course will require an individual student paper, a participation component, and a pre-travel or in-country set of assignments. See course syllabus for details. CUSTOMER CENTRICITY AT THE LEADING EDGE OF ANALYTICS AND TECHNOLOGY: LEARNING FROM SCANDINAVIA. Instructor: Peter Fader. The concept of "customer centricity," i.e., that not all customers are created equal, is gaining credibility and traction. More and more firms are coming to the realization that understanding and leveraging the behavioral differences across customers can potentially be more sustainably profitable than more conventional product- centric thinking that continues to dominate today's business landscape. At the heart of this transformation are three critical ingredients data, analytics, and technology. Using customer data at a granular level allows firms greater visibility into customer interactions, their use of social media, biometrics, and geolocation as tools to enhance business models and even create new ones. It allows a firm to be deliberate about which customers to go after and what kinds of services to provide them. For many, the key to profitable growth lies in successfully harnessing and developing the tools, organizational structures, and corporate cultures that create and enhance these capabilities.

• MKTG8960 - Global Modular Course

  Open to MBA, Executive MBA and, in some instances, Undergraduate students, these modular courses are intended to provide unique educational experiences to students in a regional context that has particular resonance with the topic. Taught around the globe, the modular courses help us enrich the curriculum and research on our own campuses in Philadelphia and San Francisco.

• MKTG8990 - 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.

• MKTG9950 - Dissertation

  Dissertation

• STAT4760 - 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.

• STAT7760 - 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.

• WH2980 - Topics Intl Bus Developm

  This short-term international business course gives undergraduate students an amazing global opportunity featuring business-site visits, lectures at partner schools, cultural excursions, and networking opportunities with undergraduate students and business contacts from the destination countries. In addition to learning about another country's business environment and culture, students earn 0.5 course units that can be used towards business-breadth or elective credits.

• WH4010 - Integrative Business Sim

  The Wharton Integrative Business Simulation 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 competitive, 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 request registration for this course.

• 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

• 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

• Overwhelmed by Too Many Choices When Shopping? Wharton Retail Expert Weighs In, Knowledge @ Wharton - 12/19/2024
• Michael Lee, Oatly North America’s EVP & Executive Creative Director, Knowledge @ Wharton - 12/19/2024
• Why Anchor Institutions Are Vital to Local Economies, Knowledge @ Wharton - 12/17/2024
• Is the American Dream of Homeownership Still Within Reach?, Knowledge @ Wharton - 12/17/2024
• Even Under Trump, AI Governance Is Here to Stay, Knowledge @ Wharton - 12/17/2024
• 2024 Ripple Rewind (Part 2), Knowledge @ Wharton - 12/17/2024
• What Are the Best Countries of 2024?, Knowledge @ Wharton - 12/16/2024
• Your Pride Could Affect Your Workplace Reputation, Knowledge @ Wharton - 12/12/2024
• Samantha Baker, VP of Commerce at theSkimm, Knowledge @ Wharton - 12/12/2024
• NFL Stats & Analysis with Ben Solak, Knowledge @ Wharton - 12/11/2024