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
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. These insights are reflected in his book, “Customer Centricity: Focus on the Right Customers for Strategic Advantage.”
Professor Fader believes that marketing should not be viewed as a “soft” discipline, and he frequently works with different companies and industry associations to improve managerial perspectives in this regard. His work has been published in (and he serves on the editorial boards of) a number of leading journals in marketing, statistics, and the management sciences. He has won many awards for his teaching and research accomplishments.
In addition to his various roles and responsibilities at Wharton, Professor Fader is also co-founder of Zodiac, a predictive analytics firm that aims to make top-notch customer valuation models and insights easily accessible to a broad array of data-driven organizations.
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, Designing and Evaluating Dynamic Pricing Policies for Major League Baseball Tickets.
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.
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.
Managing the Value of Customer Relationships
Applied Probability Models in Marketing
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”
An American Marketing Association conference held in June 2005.
For “Capturing Evolving Visit Behavior in Clickstream Data,” Journal of Interactive Marketing, 18 (winter 2004), 5-19, co-authored with Wendy Moe
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, 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, 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, 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, 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, Frances and Pei-Yuan Chia Professor; Professor of Marketing, was quoted in an article about Amazon’s future marketing strategy.
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, 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, Frances and Pei-Yuan Chia Professor; Professor of Marketing, was interviewed about dual-disc DVD marketing initiatives.
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.
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