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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... View Details
Keywords: Retention/churn; Proactive Churn Management; Field Experiments; Heterogeneous Treatment Effect; Machine Learning; Customer Relationship Management; Risk Management
Ascarza, Eva. "Retention Futility: Targeting High-Risk Customers Might Be Ineffective." Journal of Marketing Research (JMR) 55, no. 1 (February 2018): 80–98.
- 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... View Details
Keywords: Customer Satisfaction; Cost; Standards; Service Delivery; Service Operations; Performance Capacity; Performance Productivity; Financial Services Industry; United States
Campbell, Dennis, and Frances X. Frei. "Market Heterogeneity and Local Capacity Decisions in Services." Manufacturing & Service Operations Management 13, no. 1 (Winter 2011): 2–19. (Lead Article.)
- summer 1991
- Article
Estimating Heterogeneity in Consumers' Purchase Rates
By: Sunil Gupta and Donald G. Morrison
Gupta, Sunil, and Donald G. Morrison. "Estimating Heterogeneity in Consumers' Purchase Rates." Marketing Science 10 (summer 1991): 264–269.
- 24 Feb 2014
- Research & Ideas
Busting Six Myths About Customer Loyalty Programs
categories are far from homogeneous and substantial variation exists between consumers in the same category. So tailor-made loyalty systems go way beyond these heterogeneous customer groupings by offering... View Details
- July–August 2024
- Article
Doing More with Less: Overcoming Ineffective Long-Term Targeting Using Short-Term Signals
By: Ta-Wei Huang and Eva Ascarza
Firms are increasingly interested in developing targeted interventions for customers with the best response,
which requires identifying differences in customer sensitivity, typically through the conditional average treatment
effect (CATE) estimation. In theory, to... View Details
Keywords: Long-run Targeting; Heterogeneous Treatment Effect; Statistical Surrogacy; Customer Churn; Field Experiments; Consumer Behavior; Customer Focus and Relationships; AI and Machine Learning; Marketing Strategy
Huang, Ta-Wei, and Eva Ascarza. "Doing More with Less: Overcoming Ineffective Long-Term Targeting Using Short-Term Signals." Marketing Science 43, no. 4 (July–August 2024): 863–884.
- 2024
- Working Paper
Incrementality Representation Learning: Synergizing Past Experiments for Intervention Personalization
This paper introduces Incrementality Representation Learning (IRL), a novel multitask representation learning framework that predicts heterogeneous causal effects of marketing interventions. By leveraging past experiments, IRL efficiently designs and targets... View Details
Keywords: Heterogeneous Treatment Effect; Multi-task Learning; Representation Learning; Personalization; Promotion; Deep Learning; Field Experiments; Customer Focus and Relationships; Customization and Personalization
Huang, Ta-Wei, Eva Ascarza, and Ayelet Israeli. "Incrementality Representation Learning: Synergizing Past Experiments for Intervention Personalization." Harvard Business School Working Paper, No. 24-076, June 2024.
- June 2023
- Simulation
Artea Dashboard and Targeting Policy Evaluation
By: Ayelet Israeli and Eva Ascarza
Companies deploy A/B experiments to gain valuable insights about their customers in order to answer strategic business problems. In marketing, A/B tests are often used to evaluate marketing interventions intended to generate incremental outcomes for the firm. The Artea... View Details
Keywords: Algorithm Bias; Algorithmic Data; Race And Ethnicity; Experimentation; Promotion; Marketing And Society; Big Data; Privacy; Data-driven Management; Data Analysis; Data Analytics; E-Commerce Strategy; Discrimination; Targeted Advertising; Targeted Policies; 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
- October 2024
- Article
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. Purchases in these categories are a signal that customers are less likely to return to that... View Details
Keywords: Churn; Churn Management; Churn/retention; Assortment Planning; Retail; Retailing; Retailing Industry; Preference Heterogeneity; Assortment Optimization; Customers; Retention; Consumer Behavior; Forecasting and Prediction; Retail Industry
Anderson, Eric, Chaoqun Chen, Ayelet Israeli, and Duncan Simester. "Canary Categories." Journal of Marketing Research (JMR) 61, no. 5 (October 2024): 872–890.
- September 2020 (Revised June 2023)
- 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... View Details
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; Algorithmic Bias; 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
Ascarza, Eva, and Ayelet Israeli. "Artea: Designing Targeting Strategies." Harvard Business School Exercise 521-021, September 2020. (Revised June 2023.)
- 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... View Details
Keywords: Customer Management; Targeting; Deep Exponential Families; Probabilistic Machine Learning; Cold Start Problem; Customer Relationship Management; Programs; Consumer Behavior; Analysis
Padilla, Nicolas, and Eva Ascarza. "Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach." Journal of Marketing Research (JMR) 58, no. 5 (October 2021): 981–1006.
- 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... View Details
Keywords: Customer Management; Targeting; Deep Exponential Families; Probabilistic Machine Learning; Cold Start Problem; Customer Relationship Management; Customer Value and Value Chain; Consumer Behavior; Analytics and Data Science; Mathematical Methods; Retail Industry
Padilla, Nicolas, and Eva Ascarza. "Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach." Harvard Business School Working Paper, No. 19-091, February 2019. (Revised May 2020. Accepted at the Journal of Marketing Research.)
- 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... View Details
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
Israeli, Ayelet, and Eva Ascarza. "Algorithmic Bias in Marketing." Harvard Business School Technical Note 521-020, September 2020. (Revised July 2022.)
- 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... View Details
Keywords: Price; Demand and Consumers; Duopoly and Oligopoly; Monopoly; Service Operations; Research
- 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... View Details
Keywords: Churn; Retention; Contractual Settings; Access Services; Hidden Markov Models; RFM; Latent Variable Models; Customer Value and Value Chain; Consumer Behavior
Ascarza, Eva, and Bruce G.S. Hardie. "A Joint Model of Usage and Churn in Contractual Settings." Marketing Science 32, no. 4 (July–August 2013): 570–590.
- 14 Aug 2018
- First Look
First Look at New Research and Ideas, August 14, 2018
employment—and provide evidence that none of them can account for a quantitatively relevant fraction of our results. Download working paper: https://www.hbs.edu/faculty/Pages/item.aspx?num=54796 Racial Heterogeneity and Local Government... View Details
Keywords: by Sean Silverthorne
- 2024
- Working Paper
Advancing Personalization: How to Experiment, Learn & Optimize
By: Aurelie Lemmens, Jason M.T. Roos, Sebastian Gabel, Eva Ascarza, Hernan Bruno, Elea McDonnell Feit, Brett Gordon, Ayelet Israeli, Carl F. Mela and Oded Netzer
Personalization has become the heartbeat of modern marketing. Advances in causal inference and machine learning enable companies to understand how the same marketing action can impact the choices of individual customers differently. This article provides an academic... View Details
Keywords: Personalization; Targeting; Experiments; Observational Studies; Policy Implementation; Policy Evaluation; Customization and Personalization; Marketing Strategy; AI and Machine Learning
Lemmens, Aurelie, Jason M.T. Roos, Sebastian Gabel, Eva Ascarza, Hernan Bruno, Elea McDonnell Feit, Brett Gordon, Ayelet Israeli, Carl F. Mela, and Oded Netzer. "Advancing Personalization: How to Experiment, Learn & Optimize." Working Paper, July 2024. (Revised March 2025.)
Zhongming Jiang
Zhongming Jiang is a first-year Ph.D. student in Marketing (Quantitative) at Harvard Business School. His research focuses on developing methodologies for Customer Relationship Management (CRM) that enable personalized interventions, dynamic customer... View Details
- May–June 2023
- 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... View Details
Dai, Weijia, Hyunjin Kim, and Michael Luca. "Which Firms Gain from Digital Advertising? Evidence from a Field Experiment." Marketing Science 42, no. 3 (May–June 2023): 429–439.
- 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... View Details
Keywords: Human Capital; Food; Expansion; Market Entry and Exit; Operations; Strategy; Retail Industry; Food and Beverage Industry; United States
Drake, David F., Ryan W. Buell, Melissa Barton, Taylor Jones, Katrina Keverian, and Jeffrey Stock. "Whole Foods: The Path to 1,000 Stores." Harvard Business School Case 615-019, September 2014. (Revised June 2016.)
- 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... View Details
Keywords: Productivity; Banks and Banking; Valuation; Performance Productivity; Value Creation; United States
Egan, Mark, Stefan Lewellen, and Adi Sunderam. "The Cross Section of Bank Value." Review of Financial Studies 35, no. 5 (May 2022): 2101–2143.