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- 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
- 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
- 2024
- Working Paper
Personalization and Targeting: 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
- April 2024
- Article
Detecting Routines: Applications to Ridesharing CRM
By: Ryan Dew, Eva Ascarza, Oded Netzer and Nachum Sicherman
Routines shape many aspects of day-to-day consumption. While prior work has established the importance of habits in consumer behavior, little work has been done to understand the implications of routines—which we define as repeated behaviors with recurring, temporal... View Details
- March 2024
- Supplement
Madrigal: Conducting a Customer-Base Audit
By: Eva Ascarza, Peter Fader, Bruce G.S. Hardie and Michael Ross
This case presents a scenario where Madrigal, a U.S. retailer with a rich 20-year history and a solid loyalty program, faces a turning point with the arrival of a new CEO. This leadership change reveals a critical gap in understanding the customer base, prompting an... View Details
- March 2024
- Supplement
Madrigal: Conducting a Customer-Base Audit
By: Eva Ascarza, Bruce Hardie, Peter S. Fader and Michael Ross
This case presents a scenario where Madrigal, a U.S. retailer with a rich 20-year history and a solid loyalty program, faces a turning point with the arrival of a new CEO. This leadership change reveals a critical gap in understanding the customer base, prompting an... View Details
- March 2024
- Supplement
Madrigal: Conducting a Customer-Base Audit
By: Eva Ascarza, Bruce Hardie, Peter S. Fader and Michael Ross
This case presents a scenario where Madrigal, a U.S. retailer with a rich 20-year history and a solid loyalty program, faces a turning point with the arrival of a new CEO. This leadership change reveals a critical gap in understanding the customer base, prompting an... View Details
- March 2024
- Teaching Note
Madrigal: Conducting a Customer-Base Audit
By: Eva Ascarza, Peter S. Fader, Bruce Hardie and Michael Ross
Teaching Note for HBS Case No. 524-046. This case presents a scenario where Madrigal, a U.S. retailer with a rich 20-year history and a solid loyalty program, faces a turning point with the arrival of a new CEO. This leadership change reveals a critical gap in... View Details
- March 2024
- Case
Madrigal: Conducting a Customer-Base Audit
By: Eva Ascarza, Bruce Hardie, Michael Ross and Peter S. Fader
This case presents a scenario where Madrigal, a U.S. retailer with a rich 20-year history and a solid loyalty program, faces a turning point with the arrival of a new CEO. This leadership change reveals a critical gap in understanding the customer base, prompting an... View Details
- 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
- February 2024
- Course Overview Note
Managing Customers for Growth
By: Eva Ascarza
Keywords: Customer Management
- 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
- February 2024
- Case
Tabby: Winning Consumers' Digital Wallets
By: Eva Ascarza and Fares Khrais
Hosam Arab (MBA 2009), cofounder and CEO of Tabby, a Saudi-based fintech startup, raised its Series D funding round in October 2023, four years after its inception, valuing it as a regional unicorn. Tabby's core product, a buy-now-pay-later (BNPL) service, allowed... View Details
- January 2024 (Revised February 2024)
- Course Overview Note
Managing Customers for Growth: Course Overview for Students
By: Eva Ascarza
Managing Customers for Growth (MCG) is a 14-session elective course for second-year MBA students at Harvard Business School. It is designed for business professionals engaged in roles centered on customer-driven growth activities. The course explores the dynamics of... View Details
- January 2024 (Revised February 2024)
- Exercise
Travelogo: Understanding Customer Journeys
By: Eva Ascarza, Nicolas Padilla and Oded Netzer
In late May 2023, Sarah Merino, the newly appointed manager of the Customer Insights group at Travelogo—an online travel booking platform—initiates a comprehensive analysis of clickstream data to understand the varied behaviors and needs of their users. In preparation... View Details
- 2023
- Working Paper
Debiasing Treatment Effect Estimation for Privacy-Protected Data: A Model Auditing and Calibration Approach
By: Ta-Wei Huang and Eva Ascarza
Data-driven targeted interventions have become a powerful tool for organizations to optimize business outcomes
by utilizing individual-level data from experiments. A key element of this process is the estimation
of Conditional Average Treatment Effects (CATE), which... View Details
- 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
- October 2023 (Revised February 2024)
- Supplement
Managing Customer Retention at Teleko
By: Eva Ascarza and Ta-Wei Huang
- October 2023 (Revised February 2024)
- Teaching Note
Managing Customer Retention at Teleko
By: Eva Ascarza and Ta-Wei Huang
Teaching Note for HBS Case No. 523-005. View Details