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- All HBS Web
(1,778)
- Faculty Publications (603)
- February 6, 2024
- Article
Find the AI Approach That Fits the Problem You’re Trying to Solve
By: George Westerman, Sam Ransbotham and Chiara Farronato
AI moves quickly, but organizations change much more slowly. What works in a lab may be wrong for your company right now. If you know the right questions to ask, you can make better decisions, regardless of how fast technology changes. You can work with your technical... View Details
Keywords: AI and Machine Learning; Organizational Change and Adaptation; Technological Innovation; Analytics and Data Science
Westerman, George, Sam Ransbotham, and Chiara Farronato. "Find the AI Approach That Fits the Problem You’re Trying to Solve." Harvard Business Review (website) (February 6, 2024).
- January 2024
- Supplement
Winning Business at Russell Reynolds
By: Ethan Bernstein and Cara Mazzucco
In an effort to make compensation drive collaboration, Russell Reynolds Associates’ (RRA) CEO Clarke Murphy sought to re-engineer the bonus system for his executive search consultants in 2016. As his HR analytics guru, Kelly Smith, points out, that risks upsetting—and... View Details
Keywords: Restructuring; Talent and Talent Management; Compensation and Benefits; Growth and Development Strategy; Organizational Change and Adaptation; Organizational Culture; Performance Evaluation; Motivation and Incentives; Consulting Industry
Bernstein, Ethan, and Cara Mazzucco. "Winning Business at Russell Reynolds." Harvard Business School Multimedia/Video Supplement 424-704, January 2024.
- 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
Keywords: Customer Relationship Management; Decision Making; Analytics and Data Science; Growth Management; Telecommunications Industry; Technology Industry; Financial Services Industry; Education Industry; Travel Industry
Ascarza, Eva. "Managing Customers for Growth: Course Overview for Students." Harvard Business School Course Overview Note 524-032, January 2024. (Revised February 2024.)
- 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
Keywords: Customer Relationship Management; Analysis; Analytics and Data Science; Marketing Strategy; Segmentation; Consumer Behavior; Travel Industry; United States
Ascarza, Eva, Nicolas Padilla, and Oded Netzer. "Travelogo: Understanding Customer Journeys." Harvard Business School Exercise 524-044, January 2024. (Revised February 2024.)
- January 2024 (Revised May 2024)
- Case
PortageBay and ESG Analytics
By: Vikram S. Gandhi and Radhika Kak
In 2023, sustainable investors faced several challenges. The first was the lack of access to standardized and vetted environmental, social, and governance (ESG) data, and equally, the interpretation of this data into investment-useful insights. Reducing reliance on... View Details
Keywords: ESG Ratings; Investment Funds; Governance; Environmental Sustainability; Corporate Social Responsibility and Impact
Gandhi, Vikram S., and Radhika Kak. "PortageBay and ESG Analytics." Harvard Business School Case 324-065, January 2024. (Revised May 2024.)
- 2024
- Working Paper
The Impact of Culture Consistency on Subunit Outcomes
By: Jasmijn Bol, Robert Grasser, Serena Loftus and Tatiana Sandino
We examine the association between subunit culture consistency—defined as the
congruence between the organizational values espoused by top management and those
perceived and practiced by subunit employees—and subunit outcomes. Using data
from 235 subunits of a North... View Details
Bol, Jasmijn, Robert Grasser, Serena Loftus, and Tatiana Sandino. "The Impact of Culture Consistency on Subunit Outcomes." Working Paper, January 2024.
- 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
Huang, Ta-Wei, and Eva Ascarza. "Debiasing Treatment Effect Estimation for Privacy-Protected Data: A Model Auditing and Calibration Approach." Harvard Business School Working Paper, No. 24-034, December 2023.
- 2023
- Article
Balancing Risk and Reward: An Automated Phased Release Strategy
By: Yufan Li, Jialiang Mao and Iavor Bojinov
Phased releases are a common strategy in the technology industry for gradually releasing new products or updates through a sequence of A/B tests in which the number of treated units gradually grows until full deployment or deprecation. Performing phased releases in a... View Details
Li, Yufan, Jialiang Mao, and Iavor Bojinov. "Balancing Risk and Reward: An Automated Phased Release Strategy." Advances in Neural Information Processing Systems (NeurIPS) (2023).
- 2023
- Article
Benchmarking Large Language Models on CMExam—A Comprehensive Chinese Medical Exam Dataset
By: Junling Liu, Peilin Zhou, Yining Hua, Dading Chong, Zhongyu Tian, Andrew Liu, Helin Wang, Chenyu You, Zhenhua Guo, Lei Zhu and Michael Lingzhi Li
Recent advancements in large language models (LLMs) have transformed the field of question answering (QA). However, evaluating LLMs in the medical field is challenging due to the lack of standardized and comprehensive datasets. To address this gap, we introduce CMExam,... View Details
Keywords: Large Language Model; AI and Machine Learning; Analytics and Data Science; Health Industry
Liu, Junling, Peilin Zhou, Yining Hua, Dading Chong, Zhongyu Tian, Andrew Liu, Helin Wang, Chenyu You, Zhenhua Guo, Lei Zhu, and Michael Lingzhi Li. "Benchmarking Large Language Models on CMExam—A Comprehensive Chinese Medical Exam Dataset." Conference on Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track 36 (2023).
- 2023
- Other Article
The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications
By: Mirac Suzgun, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers and Stuart Shieber
Innovation is a major driver of economic and social development, and information about many kinds of innovation is embedded in semi-structured data from patents and patent applications. Though the impact and novelty of innovations expressed in patent data are difficult... View Details
Keywords: USPTO; Natural Language Processing; Classification; Summarization; Patent Novelty; Patent Trolls; Patent Enforceability; Patents; Innovation and Invention; Intellectual Property; AI and Machine Learning; Analytics and Data Science
Suzgun, Mirac, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers, and Stuart Shieber. "The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications." Conference on Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track 36 (2023).
- December 2023
- Article
When Should the Off-Grid Sun Shine at Night? Optimum Renewable Generation and Energy Storage Investments
By: Christian Kaps, Simone Marinesi and Serguei Netessine
Globally, 1.5 billion people live off the grid, their only access to electricity often limited to operationally-expensive fossil fuel generators. Solar power has risen as a sustainable and less costly option, but its generation is variable during the day and... View Details
Kaps, Christian, Simone Marinesi, and Serguei Netessine. "When Should the Off-Grid Sun Shine at Night? Optimum Renewable Generation and Energy Storage Investments." Management Science 69, no. 12 (December 2023): 7633–7650.
- November–December 2023
- Article
Look the Part? The Role of Profile Pictures in Online Labor Markets
By: Isamar Troncoso and Lan Luo
Profile pictures are a key component of many freelancing platforms, a design choice that can impact hiring and matching outcomes. In this paper, we examine how appearance-based perceptions of a freelancer’s fit for the job (i.e., whether a freelancer "looks the part"... View Details
Keywords: Freelancers; Gig Workers; Demographics; Prejudice and Bias; Selection and Staffing; Jobs and Positions; Analytics and Data Science
Troncoso, Isamar, and Lan Luo. "Look the Part? The Role of Profile Pictures in Online Labor Markets." Marketing Science 42, no. 6 (November–December 2023): 1080–1100.
- October–December 2023
- Article
A Practical Guide to Conversation Research: How to Study What People Say to Each Other
By: Michael Yeomans, Katelynn Boland, Hanne K. Collins, Nicole Abi-Esber and Alison Wood Brooks
Conversation—a verbal interaction between two or more people—is a complex, pervasive, and consequential human behavior. Conversations have been studied across many academic disciplines. However, advances in recording and analysis techniques over the last decade have... View Details
Yeomans, Michael, Katelynn Boland, Hanne K. Collins, Nicole Abi-Esber, and Alison Wood Brooks. "A Practical Guide to Conversation Research: How to Study What People Say to Each Other." Advances in Methods and Practices in Psychological Science 6, no. 4 (October–December 2023).
- 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
Olagoke, Lukman, Salil Vadhan, and Seth Neel. "Black-box Training Data Identification in GANs via Detector Networks." Working Paper, October 2023.
- 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
Keywords: Customer Journey; Privacy; Consumer Behavior; Analytics and Data Science; AI and Machine Learning; Customer Focus and Relationships
Padilla, Nicolas, Eva Ascarza, and Oded Netzer. "The Customer Journey as a Source of Information." Harvard Business School Working Paper, No. 24-035, October 2023. (Revised October 2023.)
- September 2023 (Revised January 2024)
- Case
AB InBev: Brewing Up Forecasts during COVID-19
By: Mark Egan, C. Fritz Foley, Esel Cekin and Emilie Billaud
In July 2021, the CEO of AB InBev's European operations and his team strategized to position the company for success post-pandemic. As the world's largest beer company, boasting over 500 brands, revenue of $46 billion, and a workforce of 160,000 in 2020, AB InBev... View Details
Keywords: Beer; Forecasting; COVID-19; Decision; Forecasting and Prediction; Analytics and Data Science; Crisis Management; Decisions; Financing and Loans; Investment Return; Resource Allocation; Distribution; Production; Business Processes; Strategic Planning; Health Pandemics; Digital Transformation; Markets; Food and Beverage Industry; Belgium; Europe; Latin America; North and Central America
Egan, Mark, C. Fritz Foley, Esel Cekin, and Emilie Billaud. "AB InBev: Brewing Up Forecasts during COVID-19." Harvard Business School Case 224-020, September 2023. (Revised January 2024.)
- September 2023 (Revised December 2023)
- Case
TetraScience: Noise and Signal
By: Thomas R. Eisenmann and Tom Quinn
In 2019, TetraScience CEO “Spin” Wang needed advice. Five years earlier, he had cofounded a startup that saw early success with a hardware product designed to help laboratory scientists in the biotechnology and pharmaceutical spaces more easily collect data from... View Details
Keywords: Entrepreneurship; Business Growth and Maturation; Business Organization; Restructuring; Forecasting and Prediction; Digital Platforms; Analytics and Data Science; AI and Machine Learning; Organizational Structure; Network Effects; Competitive Strategy; Biotechnology Industry; Pharmaceutical Industry; United States; Boston
Eisenmann, Thomas R., and Tom Quinn. "TetraScience: Noise and Signal." Harvard Business School Case 824-024, September 2023. (Revised December 2023.)
- September 13, 2023
- Article
How the Best Chief Data Officers Create Value
By: Suraj Srinivasan and Robin Seibert
Despite the rapidly increasing prominence of data and analytics functions, the majority of chief data officers (CDOs) fail to value and price the business outcomes created by their data and analytics capabilities. It comes as no surprise then that many CDOs fall behind... View Details
Srinivasan, Suraj, and Robin Seibert. "How the Best Chief Data Officers Create Value." Harvard Business Review (website) (September 13, 2023).
- September 2023 (Revised October 2024)
- Case
Forecasting Climate Risks: Aviva’s Climate Calculus
By: Mark Egan and Peter Tufano
In late 2021, Ben Carr, Director of Analytics and Capital Modeling at Aviva Plc (Aviva)—a leading insurer with core operations in the UK, Ireland and Canada,—was preparing for an upcoming presentation before the company's board which included its CEO, Amanda Blanc,... View Details
Keywords: Climate Risk; Climate Finance; Forecasting; Insurance; Risk Measurement; Climate Change; Risk Management; Forecasting and Prediction; Insurance Industry; United States
Egan, Mark, and Peter Tufano. "Forecasting Climate Risks: Aviva’s Climate Calculus." Harvard Business School Case 224-025, September 2023. (Revised October 2024.)
- 2023
- Article
On Minimizing the Impact of Dataset Shifts on Actionable Explanations
By: Anna P. Meyer, Dan Ley, Suraj Srinivas and Himabindu Lakkaraju
The Right to Explanation is an important regulatory principle that allows individuals to request actionable explanations for algorithmic decisions. However, several technical challenges arise when providing such actionable explanations in practice. For instance, models... View Details
Meyer, Anna P., Dan Ley, Suraj Srinivas, and Himabindu Lakkaraju. "On Minimizing the Impact of Dataset Shifts on Actionable Explanations." Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI) 39th (2023): 1434–1444.