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All HBS Web
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- Faculty Publications (66)
- 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...
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Keywords:
Customer Management;
Targeting;
Deep Exponential Families;
Probabilistic Machine Learning;
Cold Start Problem;
Customer Relationship Management;
Programs;
Consumer Behavior;
Analysis
Padilla, Nicolas, and Eva Ascarza. "Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach." Journal of Marketing Research (JMR) 58, no. 5 (October 2021): 981–1006.
- August 2021
- Article
Multiple Imputation Using Gaussian Copulas
By: F.M. Hollenbach, I. Bojinov, S. Minhas, N.W. Metternich, M.D. Ward and A. Volfovsky
Missing observations are pervasive throughout empirical research, especially in the social sciences. Despite multiple approaches to dealing adequately with missing data, many scholars still fail to address this vital issue. In this paper, we present a simple-to-use...
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Hollenbach, F.M., I. Bojinov, S. Minhas, N.W. Metternich, M.D. Ward, and A. Volfovsky. "Multiple Imputation Using Gaussian Copulas." Special Issue on New Quantitative Approaches to Studying Social Inequality. Sociological Methods & Research 50, no. 3 (August 2021): 1259–1283. (0049124118799381.)
- 2020
- Working Paper
Is Accounting Useful for Forecasting GDP Growth? A Machine Learning Perspective
We provide a comprehensive examination of whether, to what extent, and which accounting variables are useful for improving the predictive accuracy of GDP growth forecasts. We leverage statistical models that accommodate a broad set of (341) variables—outnumbering the...
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Keywords:
Big Data;
Elastic Net;
GDP Growth;
Machine Learning;
Macro Forecasting;
Short Fat Data;
Accounting;
Economic Growth;
Forecasting and Prediction;
Analytics and Data Science
Datar, Srikant, Apurv Jain, Charles C.Y. Wang, and Siyu Zhang. "Is Accounting Useful for Forecasting GDP Growth? A Machine Learning Perspective." Harvard Business School Working Paper, No. 21-113, December 2020.
- April 2021
- Article
A Model of Multi-Pass Search: Price Search Across Stores and Time
By: Navid Mojir and K. Sudhir
In retail settings with price promotions, consumers often search across stores and time. However, the search literature typically only models one pass search across stores, ignoring revisits to stores; the choice literature using scanner data has modeled search across...
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Keywords:
Consumer Search;
Multi-pass Search;
Price Search;
Store Search;
Spatial Search;
Temporal Search;
Spatiotemporal Search;
Dynamic Structural Models;
MPEC;
Price Promotions;
Store Loyalty;
Consumer Behavior;
Price;
Spending;
Marketing;
Mathematical Methods
Mojir, Navid, and K. Sudhir. "A Model of Multi-Pass Search: Price Search Across Stores and Time." Management Science 67, no. 4 (April 2021): 2126–2150.
- March 2021
- Article
Bayesian Signatures of Confidence and Central Tendency in Perceptual Judgment
By: Yang Xiang, Thomas Graeber, Benjamin Enke and Samuel Gershman
This paper theoretically and empirically investigates the role of Bayesian noisy cognition in perceptual judgment, focusing on the central tendency effect: the well-known empirical regularity that perceptual judgments are biased towards the center of the...
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Xiang, Yang, Thomas Graeber, Benjamin Enke, and Samuel Gershman. "Bayesian Signatures of Confidence and Central Tendency in Perceptual Judgment." Attention, Perception, & Psychophysics (March 2021): 1–11.
- February 2021
- Case
Digital Manufacturing at Amgen
By: Shane Greenstein, Kyle R. Myers and Sarah Mehta
This case discusses efforts made by biotechnology (biotech) company Amgen to introduce digital technologies into its manufacturing processes. Doing so is complicated by the fact that the process for manufacturing biologics—or therapeutics made from living cells—is...
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Keywords:
Digital Technologies;
Change;
Change Management;
Decision Making;
Cost vs Benefits;
Decisions;
Information;
Analytics and Data Science;
Innovation and Invention;
Innovation and Management;
Innovation Leadership;
Innovation Strategy;
Technological Innovation;
Jobs and Positions;
Knowledge;
Leadership;
Organizational Culture;
Science;
Strategy;
Information Technology;
Technology Adoption;
Biotechnology Industry;
Pharmaceutical Industry;
United States;
California;
Puerto Rico;
Rhode Island
Greenstein, Shane, Kyle R. Myers, and Sarah Mehta. "Digital Manufacturing at Amgen." Harvard Business School Case 621-008, February 2021.
- January 2021
- Article
Machine Learning for Pattern Discovery in Management Research
By: Prithwiraj Choudhury, Ryan Allen and Michael G. Endres
Supervised machine learning (ML) methods are a powerful toolkit for discovering robust patterns in quantitative data. The patterns identified by ML could be used for exploratory inductive or abductive research, or for post-hoc analysis of regression results to detect...
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Keywords:
Machine Learning;
Supervised Machine Learning;
Induction;
Abduction;
Exploratory Data Analysis;
Pattern Discovery;
Decision Trees;
Random Forests;
Neural Networks;
ROC Curve;
Confusion Matrix;
Partial Dependence Plots;
AI and Machine Learning
Choudhury, Prithwiraj, Ryan Allen, and Michael G. Endres. "Machine Learning for Pattern Discovery in Management Research." Strategic Management Journal 42, no. 1 (January 2021): 30–57.
- November 2020
- Article
Disrupting the Disruptors or Enhancing Them? How Blockchain Re‐Shapes Two‐Sided Platforms
By: Daniel Trabucchi, Antonella Moretto, Tommaso Buganza and Alan MacCormack
The importance of platform‐based businesses in the modern economy is growing continuously and becoming increasingly relevant. Specifically, the deployment of digital technologies has enhanced the applicability of two‐sided business models, enabling companies to act not...
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Keywords:
Blockchain;
Two-Sided Platforms;
Business Model;
Innovation and Invention;
Technological Innovation
Trabucchi, Daniel, Antonella Moretto, Tommaso Buganza, and Alan MacCormack. "Disrupting the Disruptors or Enhancing Them? How Blockchain Re‐Shapes Two‐Sided Platforms." Journal of Product Innovation Management 37, no. 6 (November 2020): 552–574.
- 2023
- Working Paper
When Should Public Programs Be Privately Administered? Theory and Evidence from the Paycheck Protection Program
By: Alexander Bartik, Zoë B. Cullen, Edward L. Glaeser, Michael Luca, Christopher Stanton and Adi Sunderam
What happens when public resources are allocated by private companies whose objectives may be
imperfectly aligned with policy goals? We study this question in the context of the Paycheck
Protection Program (PPP), which relied on private banks to disburse aid to small...
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Keywords:
Paycheck Protection Program;
Targeting;
Impact;
Entrepreneurship;
Health Pandemics;
Small Business;
Financing and Loans;
Outcome or Result;
United States
Bartik, Alexander, Zoë B. Cullen, Edward L. Glaeser, Michael Luca, Christopher Stanton, and Adi Sunderam. "When Should Public Programs Be Privately Administered? Theory and Evidence from the Paycheck Protection Program." Harvard Business School Working Paper, No. 21-021, August 2020. (Revised July 2023.)
- June 2020 (Revised May 2022)
- Case
Vanguard Retail Operations (A)
By: Willy C. Shih and Antonio Moreno
The first two cases in this series are set in the financial services industry, and explore whether it is better for back-office workers to be generalists who provide the flexibility of being able to handle the complete range of transactions that the company faces or...
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Keywords:
Pooling;
Generalist Model;
Specialist Model;
Operations;
Service Operations;
Management;
Job Design and Levels;
Financial Services Industry;
United States
Shih, Willy C., and Antonio Moreno. "Vanguard Retail Operations (A)." Harvard Business School Case 620-104, June 2020. (Revised May 2022.)
- June 2020 (Revised August 2020)
- Supplement
Vanguard Retail Operations (B)
By: Willy C. Shih and Antonio Moreno
The first two cases in this series are set in the financial services industry, and explore whether it is better for back-office workers to be generalists who provide the flexibility of being able to handle the complete range of transactions that the company faces or...
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Keywords:
Pooling;
Generalist Model;
Specialist Model;
Service Operations;
Management;
Financial Services Industry;
United States
Shih, Willy C., and Antonio Moreno. "Vanguard Retail Operations (B)." Harvard Business School Supplement 620-105, June 2020. (Revised August 2020.)
- 26 Apr 2020
- Other Presentation
Towards Modeling the Variability of Human Attention
By: Kuno Kim, Megumi Sano, Julian De Freitas, Daniel Yamins and Nick Haber
Children exhibit extraordinary exploratory behaviors hypothesized to contribute to the building of models of their world. Harnessing this capacity in artificial systems promises not only more flexible technology but also cognitive models of the developmental processes...
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Keywords:
Exploratory Learning Behaviors;
Modeling;
Artificial Intelligence;
AI and Machine Learning
Kim, Kuno, Megumi Sano, Julian De Freitas, Daniel Yamins, and Nick Haber. "Towards Modeling the Variability of Human Attention." In Bridging AI and Cognitive Science (BAICS) Workshop. 8th International Conference on Learning Representations (ICLR), April 26, 2020.
- 2020
- Conference Presentation
Towards Modeling the Developmental Variability of Human Attention
By: K-H Kim, M. Sano, J. De Freitas, N. Haber and D. L. K. Yamins
- March 2020
- Article
Diagnosing Missing Always at Random in Multivariate Data
By: Iavor I. Bojinov, Natesh S. Pillai and Donald B. Rubin
Models for analyzing multivariate data sets with missing values require strong, often assessable, assumptions. The most common of these is that the mechanism that created the missing data is ignorable—a twofold assumption dependent on the mode of inference. The first...
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Keywords:
Missing Data;
Diagnostic Tools;
Sensitivity Analysis;
Hypothesis Testing;
Missing At Random;
Row Exchangeability;
Analytics and Data Science;
Mathematical Methods
Bojinov, Iavor I., Natesh S. Pillai, and Donald B. Rubin. "Diagnosing Missing Always at Random in Multivariate Data." Biometrika 107, no. 1 (March 2020): 246–253.
- 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
Padilla, Nicolas, and Eva Ascarza. "Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach." Harvard Business School Working Paper, No. 19-091, February 2019. (Revised May 2020. Accepted at the Journal of Marketing Research.)
- 2024
- Working Paper
Estimating Models of Supply and Demand: Instruments and Covariance Restrictions
By: Alexander MacKay and Nathan H. Miller
We consider the identification of empirical models of supply and demand with imperfect
competition. We show that a restriction on the covariance between unobserved demand and
cost shocks can resolve endogeneity and identify the price parameter. We demonstrate how
to...
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Keywords:
Demand Estimation;
Identification;
Endogeneity Bias;
Covariance Restrictions;
Ordinary Least Squares;
Instrumental Variables;
Price;
Demand and Consumers;
Competition
MacKay, Alexander, and Nathan H. Miller. "Estimating Models of Supply and Demand: Instruments and Covariance Restrictions." Harvard Business School Working Paper, No. 19-051, October 2018. (Revised January 2024. Direct download.)
- 2020
- Working Paper
Machine Learning for Pattern Discovery in Management Research
Supervised machine learning (ML) methods are a powerful toolkit for discovering robust patterns in quantitative data. The patterns identified by ML could be used as an observation for further inductive or abductive research, but should not be treated as the result of a...
View Details
Keywords:
Machine Learning;
Theory Building;
Induction;
Decision Trees;
Random Forests;
K-nearest Neighbors;
Neural Network;
P-hacking;
Analytics and Data Science;
Analysis
Choudhury, Prithwiraj, Ryan Allen, and Michael G. Endres. "Machine Learning for Pattern Discovery in Management Research." Harvard Business School Working Paper, No. 19-032, September 2018. (Revised June 2020.)
- September 2018
- Article
Assembling the Sales Team
Data and analytical tasks have lengthened productivity ramp-up times in many sales contexts, making each hire a bigger sunk cost for a longer time. Most companies adopt two common practices: They hire on the basis of “experience” and/or look at their best reps and try...
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- September 2018
- Article
Discretionary Task Ordering: Queue Management in Radiological Services
By: Maria Ibanez, Jonathan R. Clark, Robert S. Huckman and Bradley R. Staats
Work-scheduling research typically prescribes task sequences implemented by managers. Yet employees often have discretion to deviate from their prescribed sequence. Using data from 2.4 million radiological diagnoses, we find that doctors prioritize similar tasks...
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Keywords:
Discretion;
Scheduling;
Queue;
Healthcare;
Learning;
Experience;
Decentralization;
Operations;
Service Operations;
Service Delivery;
Performance;
Performance Effectiveness;
Performance Efficiency;
Performance Improvement;
Performance Productivity;
Decisions;
Time Management;
Cost vs Benefits;
Health Industry
Ibanez, Maria, Jonathan R. Clark, Robert S. Huckman, and Bradley R. Staats. "Discretionary Task Ordering: Queue Management in Radiological Services." Management Science 64, no. 9 (September 2018): 4389–4407. (Working paper available here. Winner of the 2017 Best Paper Competition of the POMS College of Healthcare Operations Management. Featured in Forbes, Quartz, and Inc.)
- June 2018
- Article
Personal and Social Usage: The Origins of Active Customers and Ways to Keep Them Engaged
By: Clarence Lee, Elie Ofek and Thomas Steenburgh
We study how digital service firms can develop an active customer base, focusing on two questions. First, how does the way that customers use the service postadoption to meet their own needs (personal usage) and to interact with one another (social usage) vary across...
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Keywords:
Customer Engagement;
Adoption Routes;
Word-of-Mouth;
Digital Marketing;
Bayesian Estimation;
Customers;
Communication;
Consumer Behavior;
Marketing;
Internet and the Web;
Analytics and Data Science
Lee, Clarence, Elie Ofek, and Thomas Steenburgh. "Personal and Social Usage: The Origins of Active Customers and Ways to Keep Them Engaged." Management Science 64, no. 6 (June 2018): 2473–2495. (Lead Article.)