Publications

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

2021
Working Paper

Deep Learning for Two-Sided Matching

By: Sai Srivatsa Ravindranatha, Zhe Feng, Shira Li, Jonathan Ma, Scott Duke Kominers and David Parkes

We initiate the use of a multi-layer neural network to model two-sided matching and to explore the design space between strategy-proofness and stability. It is well known that both properties cannot be achieved simultaneously but the efficient frontier in this design... View Details

Keywords: Strategy-proofness; Deep Learning; Two-Sided Platforms; Marketplace Matching; Balance and Stability

June 2021
Case

Bozoma Saint John: Leading with Authenticity and Urgency

By: Francesca Gino and Frances X. Frei

In this multimedia case, Bozoma Saint John recounts numerous defining moments from her childhood and work experiences. We learn what empowered and inspired her to be her authentic self, to be vulnerable and open to new experiences, to find commonality with others, to... View Details

Keywords: Biases; Personal Development and Career; Identity; Interests; Ethics; Values and Beliefs; Opportunities; Leadership Style; Diversity

2021
Working Paper

First Law of Motion: Influencer Video Advertising on TikTok

By: Jeremy Yang, Juanjuan Zhang and Yuhan Zhang

This paper engineers an intuitive feature that is predictive of the causal effect of influencer video advertising on product sales. We propose the concept of m-score, a summary statistic that captures the extent to which a product is advertised in the most engaging... View Details

Keywords: Influencer Advertising; Video Advertising; Computer Vision; Machine Learning; Advertising; Online Technology

SSRN

February 2021
Case

Emma Dench: Leadership and Ancient Rome

By: Francesca Gino and Frances X. Frei

In this multimedia case, classics scholar Emma Dench guides us in understanding leadership insights that can be captured from historical figures and works dating back to Ancient Rome. We learn the language, ideas, and patterns of behavior that are relevant to... View Details

Keywords: Empowerment Leadership; Leadership; History; Management Skills

Article

Development of a Deep Learning Algorithm for Periapical Disease Detection in Dental Radiographs

By: Michael G. Endres, Florian Hillen, Marios Salloumis, Ahmad R. Sedaghat, Stefan M. Niehues, Olivia Quatela, Henning Hanken, Ralf Smeets, Benedicta Beck-Broichsitter, Carsten Rendenbach, Karim R. Lakhani, Max Helland and Robert A. Gaudin

Periapical radiolucencies, which can be detected on panoramic radiographs, are one of the most common radiographic findings in dentistry and have a differential diagnosis including infections, granuloma, cysts, and tumors. In this study, we seek to investigate the... View Details

Keywords: Artificial Intelligence; Diagnosis; Computer-assisted; Image Interpretation; Machine Learning; Radiography; Panoramic Radiograph; AI and Machine Learning

Article

Detecting Adversarial Attacks via Subset Scanning of Autoencoder Activations and Reconstruction Error

By: Celia Cintas, Skyler Speakman, Victor Akinwande, William Ogallo, Komminist Weldemariam, Srihari Sridharan and Edward McFowland III

Reliably detecting attacks in a given set of inputs is of high practical relevance because of the vulnerability of neural networks to adversarial examples. These altered inputs create a security risk in applications with real-world consequences, such as self-driving... View Details

Keywords: Autoencoder Networks; Pattern Detection; Subset Scanning; Computer Vision; Statistical Methods And Machine Learning; Machine Learning; Deep Learning; Data Mining; Big Data; Large-scale Systems; Mathematical Methods; Analytics and Data Science

July 2019
Teaching Note

AT&T, Retraining, and the Workforce of Tomorrow

By: William R. Kerr and Carl Kreitzberg

A Teaching Note for the "AT&T, Retraining, and the Workforce of Tomorrow" case study (HBS#820-017). The case describes how AT&T designed and implemented a program to retrain 100,000 of its workers. The case first reviews the technological forces that compelled AT&T to... View Details

July 2019 (Revised November 2019)
Case

Osaro: Picking the Best Path

By: William R. Kerr, James Palano and Bastiane Huang

The founder of Osaro saw the potential of deep reinforcement learning to allow robots to be applied to new applications. Osaro targeted warehousing, already a dynamic industry for robotics and automation, for its initial product—a system which would allow robotic arms... View Details

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

SSRN

September 2018 (Revised December 2019)
Case

Zebra Medical Vision

By: Shane Greenstein and Sarah Gulick

An Israeli startup founded in 2014, Zebra Medical Vision developed algorithms that produced diagnoses from X-rays, mammograms, and CT-scans. The algorithms used deep learning and digitized radiology scans to create software that could assist doctors in making... View Details

2017
Mimeo

Science for Society: Science and Technology Based Social Entrepreneurship

By: Tarun Khanna, Shashank Shah and Kundan Madireddy

This publication is an outcome of the team's research, engagement and interactions with over 25 science and technology-based social enterprises in India. It provides details on the research process, insightful outcomes and innovative impact.
Throughout the... View Details

Keywords: Social Entrepreneurship; Science-Based Business; Information Technology; Business and Community Relations; India

February 2015 (Revised September 2016)
Teaching Note

Making stickK Stick: The Business of Behavioral Economics

By: Leslie K. John and Michael Norton

Email mking@hbs.edu for a courtesy copy.

This Teaching Note explains the theory of the case and teaching plan for the case: Making sticK Stick: The Business of Behavioral Economics (514019). The case focuses on a... View Details

Keywords: Behavioral Economics; Behavior Change; B2B Vs. B2C; Human Resource Management; Marketing Of Innovations; Health ＆ Wellness; Weight Loss; Charitable Giving; Marketing; Consumer Behavior; Entrepreneurship; Internet and the Web; Health; Business Model; Sales; Human Resources; Health Industry; United States

12 Dec 2014
Conference Presentation

Aspect Specific Sentiment Analysis Using Hierarchical Deep Learning

By: Himabindu Lakkaraju, Richard Socher and Chris Manning

2014
Book

Managerial Accounting: Making Decisions and Motivating Performance

By: Srikant M. Datar and Madhav Rajan

Managerial Accounting: Making Decisions and Motivating Performance enables future managers and business owners to attain the core skills they need to become integral members of their company’s decision-making teams. This new program from established authors... View Details

April 2011
Article

Why Leaders Don't Learn from Success

By: Francesca Gino and Gary P. Pisano

We argue that for a variety of psychological reasons, it is often much harder for leaders and organizations to learn from success than to learn from failure. Success creates three kinds of traps that often impede deep learning. The first is attribution error or the... View Details

Keywords: Learning; Innovation and Management; Leadership; Failure; Success; Performance Evaluation; Prejudice and Bias