Publications
Publications
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- Faculty Publications (19)
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- 2022
- Working Paper
When Less Is More: Using Short-term Signals to Overcome Systematic Bias in Long-run Targeting
By: Ta-Wei Huang and Eva Ascarza
Firms are increasingly interested in developing targeted interventions for customers with the best response. Doing so requires firms to identify differences in customer sensitivity, which they often obtain using uplift modeling (i.e., heterogeneous treatment effect...
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- Article
The Cross Section of Bank Value
By: Mark Egan, Stefan Lewellen and Adi Sunderam
We study the determinants of value creation in U.S. commercial banks. We develop novel measures of individual banks' productivities at collecting deposits and making loans. We relate these measures to bank market values and find that deposit productivity is responsible...
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- Article
Eliminating Unintended Bias in Personalized Policies Using Bias-Eliminating Adapted Trees (BEAT)
By: Eva Ascarza and Ayelet Israeli
An inherent risk of algorithmic personalization is disproportionate targeting of individuals from certain groups (or demographic characteristics such as gender or race), even when the decision maker does not intend to discriminate based on those “protected”... View Details
- March 2022
- Article
Learning to Rank an Assortment of Products
By: Kris Ferreira, Sunanda Parthasarathy and Shreyas Sekar
We consider the product ranking challenge that online retailers face when their customers typically behave as “window shoppers”: they form an impression of the assortment after browsing products ranked in the initial positions and then decide whether to continue...
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- October 2021
- Article
Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach
By: Nicolas Padilla and Eva Ascarza
The success of Customer Relationship Management (CRM) programs ultimately depends on the firm's ability to understand consumers' preferences and precisely capture how these preferences may differ across customers. Only by understanding customer heterogeneity, firms can...
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- 2022
- Working Paper
Canary Categories
By: Eric Anderson, Chaoqun Chen, Ayelet Israeli and Duncan Simester
Past customer spending in a category is generally a positive signal of future customer spending. We show that there exist “Canary Categories” for which the reverse is true. The more customers purchase in these categories, the less likely these customers are to return...
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- 2020
- Working Paper
The Twofold Effect of Customer Retention in Freemium Settings
By: Eva Ascarza, Oded Netzer and Julian Runge
The main tradeoff in designing freemium services is how much of the product to offer for free. At the heart of such a tradeoff is the balancing act of providing a valuable free product in order to acquire and engage consumers, while making the free product limited...
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- September 2020 (Revised July 2022)
- Technical Note
Algorithmic Bias in Marketing
By: Ayelet Israeli and Eva Ascarza
This note focuses on algorithmic bias in marketing. First, it presents a variety of marketing examples in which algorithmic bias may occur. The examples are organized around the 4 P’s of marketing – promotion, price, place and product—characterizing the marketing...
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Keywords:
Algorithmic Data;
Race And Ethnicity;
Promotion;
＂Marketing Analytics＂;
Marketing And Society;
Big Data;
Privacy;
Data-driven Management;
Data Analysis;
Data Analytics;
E-Commerce Strategy;
Discrimination;
Targeting;
Targeted Advertising;
Pricing Algorithms;
Ethical Decision Making;
Customer Heterogeneity;
Marketing;
Race;
Ethnicity;
Gender;
Diversity;
Prejudice and Bias;
Marketing Communications;
Analytics and Data Science;
Analysis;
Decision Making;
Ethics;
Customer Relationship Management;
E-commerce;
Retail Industry;
Apparel and Accessories Industry;
United States
- September 2020 (Revised April 2021)
- Exercise
Artea: Designing Targeting Strategies
By: Eva Ascarza and Ayelet Israeli
This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and targeting. Parts (B),(C),(D) Introduce algorithmic bias. The...
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Keywords:
Algorithmic Data;
Race And Ethnicity;
Experimentation;
Promotion;
＂Marketing Analytics＂;
Marketing And Society;
Big Data;
Privacy;
Data-driven Management;
Data Analytics;
Data Analysis;
E-Commerce Strategy;
Discrimination;
Targeted Advertising;
Targeted Policies;
Targeting;
Pricing Algorithms;
A/B Testing;
Ethical Decision Making;
Customer Base Analysis;
Customer Heterogeneity;
Coupons;
Marketing;
Race;
Gender;
Diversity;
Customer Relationship Management;
Marketing Communications;
Advertising;
Decision Making;
Ethics;
E-commerce;
Analytics and Data Science;
Retail Industry;
Apparel and Accessories Industry;
United States
- 2020
- Working Paper
Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach
By: Eva Ascarza
The success of Customer Relationship Management (CRM) programs ultimately depends on the firm's ability to understand consumers' preferences and precisely capture how these preferences may differ across customers. Only by understanding customer heterogeneity, firms can...
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- February 2019
- Article
The Market for Financial Adviser Misconduct
By: Mark Egan, Gregor Matvos and Amit Seru
We construct a novel database containing the universe of financial advisers in the United States from 2005 to 2015, representing approximately 10% of employment of the finance and insurance sector. We provide the first large-scale study that documents the economy-wide...
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- February 2018
- Article
Retention Futility: Targeting High-Risk Customers Might Be Ineffective.
By: Eva Ascarza
Companies in a variety of sectors are increasingly managing customer churn proactively, generally by detecting customers at the highest risk of churning and targeting retention efforts towards them. While there is a vast literature on developing churn prediction models...
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- September 2014 (Revised June 2016)
- Case
Whole Foods: The Path to 1,000 Stores
By: David F. Drake, Ryan W. Buell, Melissa Barton, Taylor Jones, Katrina Keverian and Jeffrey Stock
The case examines the operations strategy of Whole Foods, one of the largest natural grocery chains in the United States. In late 2013, Whole Foods was expanding rapidly, with a publicly-stated goal of growing from 351 to 1,000 domestic stores by 2022. It was also...
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- July–August 2013
- Article
A Joint Model of Usage and Churn in Contractual Settings
By: Eva Ascarza and Bruce G.S. Hardie
As firms become more customer-centric, concepts such as customer equity come to the fore. Any serious attempt to quantify customer equity requires modeling techniques that can provide accurate multiperiod forecasts of customer behavior. Although a number of researchers...
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- Article
Market Heterogeneity and Local Capacity Decisions in Services
By: Dennis Campbell and Frances X. Frei
We empirically document factors that influence how local operating managers use discretion to balance the tradeoff between service capacity costs and customer sensitivity to service time. Our findings, using data from one of the largest financial services providers in...
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- Article
Product Positioning in a Two-Dimensional Vertical Differentiation Model: The Role of Quality Costs
By: Dominique Lauga and Elie Ofek
We study a duopoly model where consumers are heterogeneous with respect to their willingness to pay for two product characteristics and marginal costs are increasing with the quality level chosen on each attribute. We show that while firms seek to manage competition...
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Keywords:
Duopoly and Oligopoly;
Customers;
Quality;
Product Positioning;
Competition;
Management;
Cost;
Product
- 2009
- Chapter
Nonlinear Pricing
By: Raghuram Iyengar and Sunil Gupta
A nonlinear pricing schedule refers to any pricing structure where the total charges payable by customers are not proportional to the quantity of their consumed services. We begin the chapter with a discussion of the broad applicability of nonlinear pricing schemes. We...
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Keywords:
Price;
Demand and Consumers;
Duopoly and Oligopoly;
Monopoly;
Service Operations;
Research
- summer 1991
- Article
Estimating Heterogeneity in Consumers' Purchase Rates
By: Sunil Gupta and Donald G. Morrison
- Forthcoming
- Article
Which Firms Gain from Digital Advertising? Evidence from a Field Experiment
By: Weijia Dai, Hyunjin Kim and Michael Luca
Measuring the returns of advertising opportunities continues to be a challenge for many
businesses. We design and run a field experiment in collaboration with Yelp across 18,294
firms in the restaurant industry to understand which types of businesses gain more from...
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