Peter Fader

Peter S. Fader is the Frances and PeiYuan 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 cofounder of Zodiac, a predictive analytics firm that aims to make topnotch customer valuation models and insights easily accessible to a broad array of datadriven organizations.

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

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

Valeria Stourm, Eric Bradlow, Peter Fader (2015), Stockpiling Points in Linear Loyalty Programs, Journal of Marketing Research, 52 (2), pp. 253267.

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 11month outofsample 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. 5977.

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 intraday 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 clickthrough 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), CustomerBase Analysis on a ‘Data Diet’: Model Inference Using Repeated CrossSectional 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 easytomanage 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 customerbase analysis. A number of researchers have developed customerbase analysis models that perform very well given detailed individuallevel data. We explore the possibility of estimating these models using aggregated data summaries alone, namely repeated crosssectional 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 crosssections of RCSS data (as judged by model fit, parameter recovery, and forwardlooking metrics of customer value) can closely match the model performance associated with the use of individuallevel 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 MultiStage 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 dataset 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. 188205.

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 easytocompute) 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 timesaving 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 “backandforth” 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 CrossCohort Changepoint Model for CustomerBase Analysis.

Abstract: We introduce a new methodology that can capture and explain differences across a series of cohorts of new customers in a repeattransaction 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 cohortlevel parameter separately, thus disentangling potential explanations for crosscohort shifts in aggregate transaction patterns.  Calibrating the model using multicohort donation data from a nonprofit 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 crosscohort shifts, thus enabling managers to quantify their uncertainty about potential regime changes and avoid “old data” aggregation bias.

Eric Schwartz, Eric Bradlow, Peter Fader (Under Revision), Customer Acquisition via Display Advertising Using MultiArmed Bandit Experiments.

Abstract: Online advertisers regularly deliver several versions of display ads in a single campaign across many websites in order to acquire customers, but they are uncertain about which ads are most effective. As the campaign progresses, they adapt to intermediate results and allocate more impressions to the better performing ads on each website. But how should they decide what percentage of impressions to allocate to each ad?  This paper answers that question, resolving the classic "explore/exploit" tradeoff using multiarmed bandit (MAB) methods. However, this marketing problem contains challenges, such as hierarchical structure (ads within a website), attributes of actions (creative elements of an ad), and batched decisions (millions of impressions at a time), that are not fully accommodated by existing MAB methods. We address this marketing problem by utilizing a hierarchical generalized linear model with unobserved heterogeneity combined with an algorithm known as Thompson Sampling. Our approach captures how the impact of observable ad attributes on ad effectiveness differs by website in unobserved ways, and our policy generates allocations of impressions that can be used in practice. We implemented this policy in a live field experiment delivering over 700 million ad impressions in an online display campaign with a large retail bank. Over the course of two months, our policy achieved an 8% improvement in the customer acquisition rate, relative to a control policy, without any additional costs to the bank. Beyond the actual experiment, we performed counterfactual simulations to evaluate a range of alternative model specifications and allocation rules in MAB policies.

Elea McDonnell Feit, Pengyuan Wang, Eric Bradlow, Peter Fader (2013), Fusing Aggregate and Disaggregate Data with an Application to Multiplatform Media Consumption , Journal of Marketing Research, 50, pp. 348364.

Abstract: As firms collect greater amounts of data about their customers from an ever broader set of “touchpoints,” a new set of methodological challenges arises. Companies often collect data from these various platforms at differing levels of aggregation, and it is not clear how to merge these data sources to draw meaningful inferences about customerlevel behavior patterns. In this article, the authors provide a method that firms can use, based on readily available data, to gauge and monitor multiplatform media usage. The key innovation in the method is a Bayesian datafusion approach that enables researchers to combine individuallevel usage data (readily available for most digital platforms) with aggregated data on usage over time (typically available for traditional platforms). This method enables the authors to disentangle the intraday correlations between platforms (i.e., the usage of one platform vs. another on a given day) from longerterm correlations across users (i.e., heavy/light usage of multiple platforms over time). The authors conclude with a discussion of how this method can be used in a variety of marketing contexts for which data have become readily available, such as gauging the interplay between online and brickandmortar purchasing behavior.

Managing the Value of Customer Relationships

Applied Probability Models in Marketing

## 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 instore and online customer transactions, customer surveys as well as prices and advertising. Using realworld 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 lecturebased with a strong emphasis on realtime problem solving. Most sessions will feature sophisticated numerical investigations using Microsoft Excel. Much of the material is highly technical.

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 customerlevel 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 longrun customer profitability in both businesstocustomer and businesstobusiness 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 nontrivial 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 nonbusiness) problems. The course will be entirely lecturebased with a strong emphasis on realtime 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

The principal objectives of this course are to provide opportunities for undertaking an indepth 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 librarytype 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 lecturebased with a strong emphasis on realtime problem solving. Most sessions will feature sophisticated numerical investigations using Microsoft Excel. Much of the material is highly technical.

AMA 25year 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), 519, coauthored 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 PeiYuan Chia Professor; Professor of Marketing, was quoted in an article about the role of technologysavvy social leaders in augmenting the publicity of a product

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

Peter Fader, Frances and PeiYuan 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 PeiYuan 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 PeiYuan 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 PeiYuan 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 PeiYuan 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 PeiYuan 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 PeiYuan 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 PeiYuan Chia Professor; Professor of Marketing, was interviewed about dualdisc DVD marketing initiatives.

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

Peter Fader, Frances and PeiYuan Chia Professor; Professor of Marketing, was quoted in an article about CDDVD dual discs and how they will promote music sales.

Knowledge @ Wharton

Can Instore ‘Experiences’ Save Retail?, Knowledge @ Wharton 07/07/2017 Can Retailers Escape the Scourge of Free Shipping?, Knowledge @ Wharton 06/06/2017 How Customer Behavior Can Be Used to Value Your Company, Knowledge @ Wharton 04/13/2017 Are Retailers Facing a Coming ‘Tsunami’?, Knowledge @ Wharton 04/06/2017 How Facebook’s Big Bet on Video Could Change TV, Knowledge @ Wharton 03/21/2017 How Whole Foods Can Emerge from a Slump, Knowledge @ Wharton 02/20/2017 Omnichannel 2.0: Delivering a Tailored Experience to Customers, Knowledge @ Wharton 12/14/2016 Has Black Friday Lost Its Magic?, Knowledge @ Wharton 11/25/2016 The End of Digital Advertising as We Know It, Knowledge @ Wharton 11/17/2016 Leveraging Customer Analytics: Hotels and Travel Agencies, Knowledge @ Wharton 11/08/2016 AT&T’s Time Warner Deal: Big Risk or Big Reward?, Knowledge @ Wharton 10/28/2016 Leveraging Customer Analytics: The Airline Industry, Knowledge @ Wharton 10/25/2016 Why Samsung Could Get Burned in the Android Market, Knowledge @ Wharton 10/18/2016 How Target and Amazon Are Changing the Rules of Retailing, Knowledge @ Wharton 10/14/2016 Leveraging Customer Analytics: The Insurance Industry, Knowledge @ Wharton 10/11/2016 Leveraging Customer Analytics for Business Success, Knowledge @ Wharton 09/28/2016 Is Dynamic Pricing a Hit?, Knowledge @ Wharton 07/27/2016 Why Apple Must Move Beyond the ‘Wow’ Moment, Knowledge @ Wharton 05/06/2016 Will Unlocked TV Settop Boxes Turn on the Competition?, Knowledge @ Wharton 05/02/2016 How Smart Homes Can Unlock the Mainstream Market, Knowledge @ Wharton 03/29/2016 The Promise — and Perils — of Dynamic Pricing, Knowledge @ Wharton 02/23/2016 Can Twitter Find a Reliable Growth Model?, Knowledge @ Wharton 02/17/2016 Will Slowing Growth Take a Bite out of Apple?, Knowledge @ Wharton 02/01/2016 Why Your Business Is Only as Valuable as Your Customers, Knowledge @ Wharton 01/26/2016 How Can Barnes & Noble Avoid Borders’ Fate?, Knowledge @ Wharton 12/18/2015 Why YouTube Red’s Launch Is a ‘Watershed Moment’ for Google, Knowledge @ Wharton 11/04/2015 Why Ad Blockers Are Spurring a New Technology Arms Race, Knowledge @ Wharton 10/14/2015 The Mobile Arms Race: Why Privacy Is the Next Battleground, Knowledge @ Wharton 07/06/2015 How Brands Can Build Better Loyalty Programs, Knowledge @ Wharton 06/30/2015 The Promising Tidal Wave of Streaming Music, Knowledge @ Wharton 05/07/2015 Who Will Survive the Great Mall Shakeout?, Knowledge @ Wharton 03/31/2015 What Can Apple Watch Learn from Google Glass?, Knowledge @ Wharton 03/24/2015 When Does the ‘Human Touch’ Matter in Retail?, Knowledge @ Wharton 03/04/2015 Virtual Reality: Real at Last?, Knowledge @ Wharton 02/10/2015 How Barnes & Noble Can Recover from the Nook’s Downward Spiral, Knowledge @ Wharton 12/11/2014 Amazon’s Future: Looking Beyond the Balance Sheet, Knowledge @ Wharton 10/28/2014 Can Ello — or Any Social Network — Take on Facebook?, Knowledge @ Wharton 10/08/2014 Will Consumers Be Sold on an eBaySotheby’s Collaboration?, Knowledge @ Wharton 07/23/2014 How to Fix Google+: Accentuate the Positive, Knowledge @ Wharton 06/11/2014 Apple’s Beats Buy: Desperation or Opportunity?, Knowledge @ Wharton 06/04/2014 Domain Name Land Rush: More Room for Companies, Competition and Scam Artists, Knowledge @ Wharton 05/21/2014 The Feedback Loop: More Data Doesn’t Always Mean Better Customer Service, Knowledge @ Wharton 04/23/2014 How the Music Industry Could Use Streaming to Reinvent Itself, Knowledge @ Wharton 03/21/2014 Delta’s New Frequent Flyer Policy: Reward Your Best Customers, Knowledge @ Wharton 03/05/2014 Finding a Place for Market Research in a Big Data, Techenabled World, Knowledge @ Wharton 01/29/2014 Nike FuelBand: Did the Brand Score a Goal?, Knowledge @ Wharton 11/25/2013 ‘Small Box’ Retail: Passing Fad or ‘Eureka Moment’ for eCommerce?, Knowledge @ Wharton 10/10/2013 In a Dysfunctional Industry, Pandora Seeks an Algorithm for Profitability, Knowledge @ Wharton 07/31/2013 Yahoo Continues Its Search for a New Identity, Knowledge @ Wharton 06/19/2013 The ‘Social’ Credit Score: Separating the Data from the Noise, Knowledge @ Wharton 06/05/2013