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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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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...
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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...
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- Forthcoming
- 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...
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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 (forthcoming). (Pre-published online March 6, 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...
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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
- Forthcoming
- 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...
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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) (forthcoming). (Pre-published online November 29, 2023.)
- 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...
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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;
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...
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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...
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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...
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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
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...
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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...
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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://pubwww.hbs.edu/faculty/Pages/item.aspx?num=54796 Racial Heterogeneity and Local Government...
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Keywords:
by Sean Silverthorne
- 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...
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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...
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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...
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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.
- 2023
- Working Paper
Personalized Game Design for Improved User Retention and Monetization in Freemium Games
By: Eva Ascarza, Oded Netzer and Julian Runge
One of the most crucial aspects and significant levers that gaming companies possess in designing
digital games is setting the level of difficulty, which essentially regulates the user’s ability to
progress within the game. This aspect is particularly significant in...
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Keywords:
Freemium;
Retention/churn;
Field Experiment;
Field Experiments;
Gaming;
Gaming Industry;
Mobile App;
Mobile App Industry;
Monetization;
Monetization Strategy;
Games, Gaming, and Gambling;
Mobile and Wireless Technology;
Customers;
Retention;
Product Design;
Strategy
Ascarza, Eva, Oded Netzer, and Julian Runge. "Personalized Game Design for Improved User Retention and Monetization in Freemium Games." Harvard Business School Working Paper, No. 21-062, November 2020. (Revised December 2023.)
- 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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Keywords:
Online Learning;
Product Ranking;
Assortment Optimization;
Learning;
Internet and the Web;
Product Marketing;
Consumer Behavior;
E-commerce
Ferreira, Kris, Sunanda Parthasarathy, and Shreyas Sekar. "Learning to Rank an Assortment of Products." Management Science 68, no. 3 (March 2022): 1828–1848.
- 2001
- Article
The Economic Contribution of Information Technology: Towards Comparative and User Studies
By: Timothy F. Bresnahan and Shane Greenstein
By what process does technical change in information technology (IT) increase economic welfare? How does this process result in increases in welfare at different rates in different countries and regions? This paper considers existing literature on measuring the...
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Bresnahan, Timothy F., and Shane Greenstein. "The Economic Contribution of Information Technology: Towards Comparative and User Studies." Journal of Evolutionary Economics 11 (2001): 95–118.