EVA - Publications - Faculty & Research - Harvard Business School

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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

13 Apr 2016
Research Event

What Does 'Diversity' Really Mean?

a beauty feature on the needs of women with different types of skin, showcasing a range of actresses of different backgrounds with large close-up shots. Holmes began to throw out some names, including Eva Mendes, Lucy Liu, and Alfre... View Details

Keywords: by Dina Gerdeman

February 2024 (Revised February 2024)
Teaching Note

Travelogo: Understanding Customer Journeys

By: Eva Ascarza and Ta-Wei Huang

Teaching Note for HBS Exercise 524-044. The exercise aims to teach students about 1) Customer Segmentation; and 2) constructing buying personas, 3) Get actionable insights from clickstream data. View Details

Keywords: Customer Relationship Management; Analysis; Analytics and Data Science; Marketing Strategy; Segmentation; Consumer Behavior; Travel Industry; United States

November 2023 (Revised March 2024)
Technical Note

Customer Data Privacy

By: Eva Ascarza and Ta-Wei Huang

This note provides an overview of the evolving landscape of customer data privacy in 2023. It highlights two pivotal aspects that make privacy a central concern for businesses: building and maintaining customer trust and navigating the intricate regulatory... View Details

Keywords: Customer Relationship Management; Governance Compliance; Governing Rules, Regulations, and Reforms; Risk and Uncertainty; Reputation; Trust; Information Management; Retail Industry; Technology Industry; Financial Services Industry; Telecommunications Industry; Europe; United States

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

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July 2021 (Revised January 2022)
Teaching Note

Amazon Shopper Panel: Paying Customers for Their Data

By: Eva Ascarza and Ayelet Israeli

Teaching Note for HBS Case No. 521-058. View Details

September 2020 (Revised June 2023)
Supplement

Spreadsheet Supplement to Artea Teaching Note

By: Eva Ascarza and Ayelet Israeli

Spreadsheet Supplement to Artea Teaching Note 521-041. 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... View Details

Keywords: Targeted Advertising; Algorithmic Data; Bias; Advertising; Race; Gender; Diversity; Marketing; Customer Relationship Management; Prejudice and Bias; Analytics and Data Science; Retail Industry; Apparel and Accessories Industry; Technology Industry; United States

September 2020 (Revised July 2022)
Exercise

Artea (D): Discrimination through Algorithmic Bias in Targeting

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: Targeted Advertising; Discrimination; Algorithmic Data; Bias; Advertising; Race; Gender; Marketing; Diversity; Customer Relationship Management; Prejudice and Bias; Analytics and Data Science; Retail Industry; Apparel and Accessories Industry; Technology Industry; United States

Student-Profile

Ta-Wei "David" Huang

program. He began to look at faculty research interests at HBS and quickly realized that there was a great deal of overlap between his interests and the faculty at Harvard. He found the research of Eva Ascarza, Elie Ofek, and Shunyuan... View Details

2024
Working Paper

Incrementality Representation Learning: Synergizing Past Experiments for Intervention Personalization

By: Ta-Wei Huang, Eva Ascarza and Ayelet Israeli

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

December 2019 (Revised January 2022)
Supplement

Othellonia: Growing a Mobile Game

By: Eva Ascarza, Tomomichi Amano and Sunil Gupta

Article

The Perils of Proactive Churn Prevention Using Plan Recommendations: Evidence from a Field Experiment

By: Eva Ascarza, Raghuram Iyengar and Martin Schleicher

Facing the issue of increasing customer churn, many service firms have begun recommending pricing plans to their customers. One reason behind this type of retention campaign is that customers who subscribe to a plan suitable for them should be less likely to churn... View Details

Keywords: Churn/retention; Field Experiment; Pricing; Tariff/plan Choice; Targeting; Customer Relationship Management; Price; Performance Effectiveness

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01 Apr 2000
News

Time to Vote in University Elections

Sales Operations Manager, BellSouth Business. Atlanta, GA. Eva M. Plaza, AB '80 cum laude; JD '84, Boalt Hall School of Law, University of California, Berkeley. Assistant Secretary for Fair Housing and Equal Opportunity, Department of... View Details

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

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25 Aug 2022
News

Labs Enable Large-scale Research

organizations, or society) by leveraging customer data fairly and transparently? HBS: Ayelet Israeli, Marvin Bower Associate Professor Eva Ascarza, Jakurski Family Associate Professor of Business Administration Digital Emotions Lab How... 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

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March 2024
Case

Unintended Consequences of Algorithmic Personalization

By: Eva Ascarza and Ayelet Israeli

“Unintended Consequences of Algorithmic Personalization” (HBS No. 524-052) investigates algorithmic bias in marketing through four case studies featuring Apple, Uber, Facebook, and Amazon. Each study presents scenarios where these companies faced public criticism for... View Details

Keywords: Race; Gender; Marketing; Diversity; Customer Relationship Management; Prejudice and Bias; Customization and Personalization; Technology Industry; Retail Industry; United States

October 2023 (Revised February 2024)
Supplement

Managing Customer Retention at Teleko

By: Eva Ascarza and Ta-Wei Huang

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

September 2020 (Revised July 2022)
Supplement

Spreadsheet Supplement to Artea (B) and (C)

By: Eva Ascarza and Ayelet Israeli

Spreadsheet Supplement to "Artea (B): Including Customer-level Demographic Data" and "Artea (C): Potential Discrimination through Algorithmic Targeting" View Details

Keywords: Gender; Race; Diversity; Marketing; Customer Relationship Management; Demographics; Prejudice and Bias; Retail Industry; Apparel and Accessories Industry; Technology Industry; United States

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