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- March 2024 (Revised May 2024)
- Case
Amperity: First-Party Data at a Crossroads
By: Elie Ofek, Hema Yoganarasimhan and Alexis Lefort
In the summer of 2023, Amperity management was facing a critical decision on its future direction. Given the dramatic changes occurring within the digital advertising ecosystem, as concerns over consumer privacy placed limits on the ability to engage in third-party... View Details
- February 2024
- Module Note
Data-Driven Marketing in Retail Markets
By: Ayelet Israeli
This note describes an eight-class sessions module on data-driven marketing in retail markets. The module aims to familiarize students with core concepts of data-driven marketing in retail, including exploring the opportunities and challenges, adopting best practices,... View Details
Keywords: Data; Data Analytics; Retail; Retail Analytics; Data Science; Business Analytics; ＂Marketing Analytics＂; Omnichannel; Omnichannel Retailing; Omnichannel Retail; DTC; Direct To Consumer Marketing; Ethical Decision Making; Algorithmic Bias; Privacy; A/B Testing; Descriptive Analytics; Prescriptive Analytics; Predictive Analytics; Analytics and Data Science; E-commerce; Marketing Channels; Demand and Consumers; Marketing Strategy; Retail Industry
- 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–February 2024
- Article
Shared Service Delivery Can Increase Client Engagement: A Study of Shared Medical Appointments
By: Ryan W. Buell, Kamalini Ramdas, Nazlı Sönmez, Kavitha Srinivasan and Rengaraj Venkatesh
Problem Definition: Clients and service providers alike often consider one-on-one service delivery to be ideal, assuming – perhaps unquestioningly – that devoting individualized attention best improves client outcomes. In contrast, in shared service delivery, clients... 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
- December 2023 (Revised August 2024)
- Case
Monsters in the Machine? Tackling the Challenge of Responsible AI
By: Paul M. Healy and Debora L. Spar
In November of 2022, the small tech company OpenAI released ChatGPT, an artificial intelligence chatbot which quickly captured the public’s imagination—becoming the world’s fastest-growing consumer application within months of its release. Though observers from across... View Details
- 2023
- Article
MoPe: Model Perturbation-based Privacy Attacks on Language Models
By: Marvin Li, Jason Wang, Jeffrey Wang and Seth Neel
Recent work has shown that Large Language Models (LLMs) can unintentionally leak sensitive information present in their training data. In this paper, we present Model Perturbations (MoPe), a new method to identify with high confidence if a given text is in the training... 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
- 2023
- Working Paper
Black-box Training Data Identification in GANs via Detector Networks
By: Lukman Olagoke, Salil Vadhan and Seth Neel
Since their inception Generative Adversarial Networks (GANs) have been popular generative models across images, audio, video, and tabular data. In this paper we study whether given access to a trained GAN, as well as fresh samples from the underlying distribution, if... View Details
- 2023
- Working Paper
In-Context Unlearning: Language Models as Few Shot Unlearners
By: Martin Pawelczyk, Seth Neel and Himabindu Lakkaraju
Machine unlearning, the study of efficiently removing the impact of specific training points on the
trained model, has garnered increased attention of late, driven by the need to comply with privacy
regulations like the Right to be Forgotten. Although unlearning is... View Details
- 2023
- Working Paper
The Customer Journey as a Source of Information
By: Nicolas Padilla, Eva Ascarza and Oded Netzer
In the face of heightened data privacy concerns and diminishing third-party data access,
firms are placing increased emphasis on first-party data (1PD) for marketing decisions.
However, in environments with infrequent purchases, reliance on past purchases 1PD... View Details
- August 2023
- Supplement
Apple: Privacy vs. Safety (C)
By: Henry McGee, Nien-hê Hsieh and Kerry Herman
In September 2021, Apple decided to delay updates to iOS and iPadOS that included features to fight child sexual abuse. While many—including prominent privacy and security experts—praised Apple, others were opposed. They saw Apple introducing features that risked... View Details
- 2023
- Article
Evidence from the First Shared Medical Appointments (SMAs) Randomised Controlled Trial in India: SMAs Increase the Satisfaction, Knowledge, and Medication Compliance of Patients with Glaucoma
By: Nazlı Sönmez, Kavitha Srinivasan, Rengaraj Venkatesh, Ryan W. Buell and Kamalini Ramdas
In Shared Medical Appointments (SMAs), patients with similar conditions meet the physician together and each receives one-on-one attention. SMAs can improve outcomes and physician productivity. Yet privacy concerns have stymied adoption. In physician-deprived nations,... View Details
- 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
- June 2023
- Article
The Salary Taboo: Privacy Norms and the Diffusion of Information
By: Zoë Cullen and Ricardo Perez-Truglia
The limited diffusion of salary information has implications for labor markets, such as wage discrimination policies and collective bargaining. Access to salary information is believed to be limited and unequal, but there is little direct evidence on the sources of... View Details
- May 9, 2023
- Article
8 Questions About Using AI Responsibly, Answered
By: Tsedal Neeley
Generative AI tools are poised to change the way every business operates. As your own organization begins strategizing which to use, and how, operational and ethical considerations are inevitable. This article delves into eight of them, including how your organization... View Details
- May 2023
- Article
Equilibrium Effects of Pay Transparency
By: Zoë B. Cullen and Bobak Pakzad-Hurson
The public discourse around pay transparency has focused on the direct effect: how workers seek
to rectify newly-disclosed pay inequities through renegotiations. The question of how wage-setting
and hiring practices of the firm respond in equilibrium has received... View Details
Keywords: Pay Transparency; Online Labor Market; Privacy; Wage Gap; Corporate Disclosure; Wages; Negotiation
- April 2023
- Article
On the Privacy Risks of Algorithmic Recourse
By: Martin Pawelczyk, Himabindu Lakkaraju and Seth Neel
As predictive models are increasingly being employed to make consequential decisions, there is a growing emphasis on developing techniques that can provide algorithmic recourse to affected individuals. While such recourses can be immensely beneficial to affected... View Details
- January 2023
- Supplement
Apple: Privacy vs. Safety (B)
By: Henry McGee, Nien-hê Hsieh and Christian Godwin
In 2020, as the COVID-19 pandemic swept across the globe, Apple and Google partnered to develop a contact tracing application that would collect information about users infected with the disease and notify those who they had been in contact with. While Apple/Google’s... View Details
Keywords: Iphone; Encryption; Data Privacy; Customers; Customer Focus and Relationships; Decision Making; Ethics; Values and Beliefs; Globalized Firms and Management; Government and Politics; Health; Health Pandemics; Leadership; Markets; Safety; Social Issues; Information Technology; Telecommunications Industry; Technology Industry; Consumer Products Industry; Electronics Industry; Health Industry; United States; Europe
- September 2022
- Case
Deciding When to Engage on Societal Issues
By: Hubert Joly and Amram Migdal
This case provides brief descriptions of 18 examples of corporate leaders confronting questions of whether and how to engage with societal issues, including social, political, and environmental issues. Social issues include COVID-19; social and racial justice;... View Details