Predictive Models - Publications - Faculty & Research - Harvard Business School

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March 2020
Supplement

People Analytics at Teach For America (B)

By: Jeffrey T. Polzer and Julia Kelley

This is a supplement to the People Analytics at Teach For America (A) case. In this supplement, situated one year after the A case, Managing Director Michael Metzger must decide how to apply his team's predictive models generated from the previous year’s data. View Details

Keywords: Analytics; Human Resource Management; Data; Workforce; Hiring; Talent Management; Forecasting; Predictive Analytics; Organizational Behavior; Recruiting; Analytics and Data Science; Forecasting and Prediction; Recruitment; Selection and Staffing; Talent and Talent Management

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... View Details

Keywords: Visual Perception; Bayesian Modeling; Perception; Judgments

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Article

Counterfactual Explanations Can Be Manipulated

By: Dylan Slack, Sophie Hilgard, Himabindu Lakkaraju and Sameer Singh

Counterfactual explanations are useful for both generating recourse and auditing fairness between groups. We seek to understand whether adversaries can manipulate counterfactual explanations in an algorithmic recourse setting: if counterfactual explanations indicate... View Details

Keywords: Machine Learning Models; Counterfactual Explanations

January 1996
Article

Real Business Cycle Models and the Forecastable Movements in Output, Hours and Consumption

By: J. J. Rotemberg and Michael Woodford

Keywords: Forecasting and Prediction

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2018
Working Paper

Bayesian Ensembles of Binary-Event Forecasts: When Is It Appropriate to Extremize or Anti-Extremize?

By: Kenneth C. Lichtendahl Jr., Yael Grushka-Cockayne, Victor Richmond R. Jose and Robert L. Winkler

Many organizations face critical decisions that rely on forecasts of binary events. In these situations, organizations often gather forecasts from multiple experts or models and average those forecasts to produce a single aggregate forecast. Because the average... View Details

Keywords: Forecast Aggregation; Linear Opinion Pool; Generalized Additive Model; Generalized Linear Model; Stacking.; Forecasting and Prediction

March 2024 (Revised January 2025)
Case

Hippo: Weathering the Storm of the Home Insurance Crisis

By: Lauren Cohen, Grace Headinger and Sophia Pan

Rick McCathron, CEO of Hippo, considered how the firm’s underwriting model could account for the effects of climate change. Along with providing smart home packages, targeting risk-friendly customers, and using data-driven pricing, the Insurtech used technologically... View Details

2025
Working Paper

Dynamic Personalization with Multiple Customer Signals: Multi-Response State Representation in Reinforcement Learning

By: Liangzong Ma, Ta-Wei Huang, Eva Ascarza and Ayelet Israeli

Reinforcement learning (RL) offers potential for optimizing sequences of customer interactions by modeling the relationships between customer states, company actions, and long-term value. However, its practical implementation often faces significant challenges.... View Details

Keywords: Dynamic Policy; Deep Reinforcement Learning; Representation Learning; Dynamic Difficulty Adjustment; Latent Variable Models; Customer Relationship Management; Customer Value and Value Chain; Foreign Direct Investment; Analytics and Data Science

July 2024
Article

Chatbots and Mental Health: Insights into the Safety of Generative AI

By: Julian De Freitas, Ahmet Kaan Uğuralp, Zeliha Uğuralp and Stefano Puntoni

Chatbots are now able to engage in sophisticated conversations with consumers. Due to the ‘black box’ nature of the algorithms, it is impossible to predict in advance how these conversations will unfold. Behavioral research provides little insight into potential safety... View Details

Keywords: Autonomy; Chatbots; New Technology; Brand Crises; Mental Health; Large Language Model; AI and Machine Learning; Behavior; Well-being; Technological Innovation; Ethics

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December 2014
Case

Groupon: A New CEO Takes Charge

By: Lynda M. Applegate and Arnold B. Peinado

On August 7, 2013, Eric Lefkofsky, the chairman and largest shareholder of Groupon was named CEO, replacing founder Andrew Mason, who had run the company since its inception in 2009. When Groupon had its initial public offering (IPO) in November 2011, the company's... View Details

Keywords: Entrepreneurial Management; Disruptive Technologies; Growth Strategy; Customer Relations; Service Management; International Business; Business Models; Strategy Execution; Entrepreneurship; Growth and Development Strategy; Business Model; United States

2021
Working Paper

Time Dependence and Preference: Implications for Compensation Structure and Shift Scheduling

By: Doug J. Chung, Byungyeon Kim and Byoung G. Park

This study jointly examines agents’ time dependence—period effects within instantaneous utility—and time preference—behavior on discounting future utility. The study considers the start- and end-of-period effects for time dependence and exponential and hyperbolic... View Details

Keywords: Time Preferences; Present Bias; Hyperbolic Discounting; Compensation; Dynamic Structural Models; Identification; Time Management; Motivation and Incentives; Behavior; Performance; Compensation and Benefits

October 2017 (Revised November 2017)
Case

NYC311

By: Constantine E. Kontokosta, Mitchell Weiss, Christine Snively and Sarah Gulick

Joe Morrisroe, executive director for NYC311, had some gut instincts but no definitive answer to the question he was just asked by one of the mayor’s deputies: “Are some communities being underserved by 311? How do we know we are hearing from the right people?” Founded... View Details

Teaching Interest

Overview

Paul is primarily interested in teaching data science to management students through the case method. This includes technical topics (programming and statistics) as well as higher-level management issues (digital transformation, data governance, etc.) As a research... View Details

Keywords: A/B Testing; AI; AI Algorithms; AI Creativity; Algorithm; Algorithm Bias; Algorithmic Bias; Algorithmic Fairness; Algorithms; Analytics; Application Program Interface; Artificial Intelligence; Causality; Causal Inference; Computing; Computers; Data Analysis; Data Analytics; Data Architecture; Data As A Service; Data Centers; Data Governance; Data Labeling; Data Management; Data Manipulation; Data Mining; Data Ownership; Data Privacy; Data Protection; Data Science; Data Science And Analytics Management; Data Scientists; Data Security; Data Sharing; Data Strategy; Data Visualization; Database; Data-driven Decision-making; Data-driven Management; Data-driven Operations; Datathon; Economics Of AI; Economics Of Innovation; Economics Of Information System; Economics Of Science; Forecast; Forecast Accuracy; Forecasting; Forecasting And Prediction; Information Technology; Machine Learning; Machine Learning Models; Prediction; Prediction Error; Predictive Analytics; Predictive Models; Analysis; AI and Machine Learning; Analytics and Data Science; Applications and Software; Digital Transformation; Information Management; Digital Strategy; Technology Adoption

October 2013
Article

Ad Revenue and Content Commercialization: Evidence from Blogs

By: Monic Sun and Feng Zhu

Many scholars argue that when incentivized by ad revenue, content providers are more likely to tailor their content to attract "eyeballs," and as a result, popular content may be excessively supplied. We empirically test this prediction by taking advantage of the... View Details

Keywords: Ad-sponsored Business Models; Media Content; Blog; Revenue Sharing; User-generated Content; Platform-based Markets; Blogs; Business Model; Digital Platforms; Commercialization; Digital Marketing

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October 2023
Article

Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA

By: Matthew S. Johnson, David I. Levine and Michael W. Toffel

We study how a regulator can best target inspections. Our case study is a U.S. Occupational Safety and Health Administration (OSHA) program that randomly allocated some inspections. On average, each inspection averted 2.4 serious injuries (9%) over the next five years.... View Details

Keywords: Safety Regulations; Regulations; Regulatory Enforcement; Machine Learning Models; Safety; Operations; Service Operations; Production; Forecasting and Prediction; Decisions; United States

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February 2018 (Revised December 2020)
Case

People Analytics at Teach For America (A)

By: Jeffrey T. Polzer and Julia Kelley

As of mid-2016, national nonprofit Teach For America (TFA) had struggled with three consecutive years of declining application totals, and senior management was re-examining the organization's strategy, including recruitment and selection. A few months earlier, former... View Details

Keywords: Recruitment; Selection and Staffing; Analysis; Forecasting and Prediction

April 12, 2023
Article

Using AI to Adjust Your Marketing and Sales in a Volatile World

By: Das Narayandas and Arijit Sengupta

Why are some firms better and faster than others at adapting their use of customer data to respond to changing or uncertain marketing conditions? A common thread across faster-acting firms is the use of AI models to predict outcomes at various stages of the customer... View Details

Keywords: Forecasting and Prediction; AI and Machine Learning; Consumer Behavior; Technology Adoption; Competitive Advantage

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20 Oct 2011
Research & Ideas

Getting the Marketing Mix Right

their FSL model, however, the results provided much greater detail about the potential effects of different marketing investments. For example, the model predicted that sales gains from DTCA and M&E... View Details

Keywords: by Dina Gerdeman

May 2018
Article

Nowcasting Gentrification: Using Yelp Data to Quantify Neighborhood Change

By: Edward L. Glaeser, Hyunjin Kim and Michael Luca

Data from digital platforms have the potential to improve our understanding of gentrification and enable new measures of how neighborhoods change in close to real time. Combining data on businesses from Yelp with data on gentrification from the Census, Federal Housing... View Details

Keywords: Forecasting Models; Simulation Methods; Regional Economic Activity: Growth, Development, Environmental Issues, And Changes; Geographic Location; Local Range; Transition; Analytics and Data Science; Measurement and Metrics; Economic Growth; Forecasting and Prediction

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April 12, 2022
Article

Evaluation of Individual and Ensemble Probabilistic Forecasts of COVID-19 Mortality in the United States

By: Estee Y. Cramer, Evan L. Ray, Velma K. Lopez, Johannes Bracher, Andrea Brennen, Alvaro J. Castro Rivadeneira, Michael Lingzhi Li and et al.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models... View Details

Keywords: COVID-19; Forecasting and Prediction; Health Pandemics; Mathematical Methods; Partners and Partnerships

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June 2016
Teaching Note

HubSpot: Lower Churn through Greater CHI

By: Jill Avery, Asis Martinez Jerez and Thomas Steenburgh

HubSpot, a web marketing startup selling inbound marketing software to small- and medium-sized businesses, is under pressure from its venture capital partners to rapidly acquire new customers and to maintain a low level of customer churn. The B2B SaaS company is in the... View Details

Keywords: CRM; Customer Acquisition; Customer Retention; Churn Management; SaaS Business Models; Customer Lifetime Value; Venture Capital; Startup; Software; Monitoring And Control; Marketing; Customer Relationship Management; Marketing Strategy; Accounting; Technology Industry; United States

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