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All HBS Web
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- Faculty Publications (103)
- June 2019
- Supplement
Improving Worker Safety in the Era of Machine Learning: Practicum in Predictive Analytics
By: Michael W. Toffel and Dan Levy
- June 2019
- Supplement
Improving Worker Safety in the Era of Machine Learning: Practicum in Predictive Analytics
By: Michael W. Toffel and Dan Levy
- June 2019
- Teaching Note
Improving Worker Safety in the Era of Machine Learning: Practicum in Predictive Analytics
By: Michael W. Toffel and Dan Levy
Teaching Note for HBS No. 618-019.
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- 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...
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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.)
- February 2019
- Case
Miroglio Fashion (A)
By: Sunil Gupta and David Lane
Francesco Cavarero, chief information officer of Miroglio Fashion, Italy’s third-largest retailer of women’s apparel, was trying to bring analytical rigor to the company’s forecasting and inventory management decisions. But fashion is inherently hard to predict. Can...
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Keywords:
Inventory Management;
Demand Forecasting;
Artificial Intelligence;
Machine Learning;
Forecasting and Prediction;
Operations;
Management;
Decision Making;
AI and Machine Learning;
Apparel and Accessories Industry;
Fashion Industry
Gupta, Sunil, and David Lane. "Miroglio Fashion (A)." Harvard Business School Case 519-053, February 2019.
- 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...
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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.)
- November 2018
- Case
Komatsu Komtrax: Asset Tracking Meets Demand Forecasting
By: Willy Shih, Paul Hong and YoungWon Park
Komatsu's Komtrax system started as a way of remotely monitoring and tracking equipment for the purpose of improving operational efficiency. This case follows its evolution towards other uses including demand forecasting for its sales, marketing, and production...
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Keywords:
Big Data;
Manufacturing;
Manufacturing Industry;
Data Strategy;
Internet Of Things;
Construction;
Production;
Analytics and Data Science;
Strategy;
Performance Efficiency;
Forecasting and Prediction;
Industrial Products Industry;
Construction Industry;
Japan
Shih, Willy, Paul Hong, and YoungWon Park. "Komatsu Komtrax: Asset Tracking Meets Demand Forecasting." Harvard Business School Case 619-022, November 2018.
- August 2018 (Revised September 2018)
- Case
Predicting Purchasing Behavior at PriceMart (A)
By: Srikant M. Datar and Caitlin N. Bowler
This case follows VP of Marketing, Jill Wehunt, and analyst Mark Morse as they tackle a predictive analytics project to increase sales in the Mom & Baby unit of a nationally recognized retailer, PriceMart. Wehunt observed that in the midst of the chaos that surrounded...
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Keywords:
Data Science;
Analytics and Data Science;
Analysis;
Consumer Behavior;
Forecasting and Prediction
Datar, Srikant M., and Caitlin N. Bowler. "Predicting Purchasing Behavior at PriceMart (A)." Harvard Business School Case 119-025, August 2018. (Revised September 2018.)
- August 2018 (Revised September 2018)
- Supplement
Predicting Purchasing Behavior at PriceMart (B)
By: Srikant M. Datar and Caitlin N. Bowler
Supplements the (A) case. In this case, Wehunt and Morse are concerned about the logistic regression model overfitting to the training data, so they explore two methods for reducing the sensitivity of the model to the data by regularizing the coefficients of the...
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Keywords:
Data Science;
Analytics and Data Science;
Analysis;
Customers;
Household;
Forecasting and Prediction
Datar, Srikant M., and Caitlin N. Bowler. "Predicting Purchasing Behavior at PriceMart (B)." Harvard Business School Supplement 119-026, August 2018. (Revised September 2018.)
- August 2018 (Revised April 2019)
- Supplement
Chateau Winery (B): Supervised Learning
By: Srikant M. Datar and Caitlin N. Bowler
This case builds directly on “Chateau Winery (A).” In this case, Bill Booth, marketing manager of a regional wine distributor, shifts to supervised learning techniques to try to predict which deals he should offer to customers based on the purchasing behavior of those...
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Datar, Srikant M., and Caitlin N. Bowler. "Chateau Winery (B): Supervised Learning." Harvard Business School Supplement 119-024, August 2018. (Revised April 2019.)
- August 2018 (Revised September 2018)
- Case
LendingClub (A): Data Analytic Thinking (Abridged)
By: Srikant M. Datar and Caitlin N. Bowler
LendingClub was founded in 2006 as an alternative, peer-to-peer lending model to connect individual borrowers to individual investor-lenders through an online platform. Since 2014 the company has worked with institutional investors at scale. While the company assigns...
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Keywords:
Data Science;
Data Analytics;
Investing;
Loans;
Investment;
Financing and Loans;
Analytics and Data Science;
Analysis;
Forecasting and Prediction;
Business Model
Datar, Srikant M., and Caitlin N. Bowler. "LendingClub (A): Data Analytic Thinking (Abridged)." Harvard Business School Case 119-020, August 2018. (Revised September 2018.)
- August 2018 (Revised September 2018)
- Supplement
LendingClub (B): Decision Trees & Random Forests
By: Srikant M. Datar and Caitlin N. Bowler
This case builds directly on the LendingClub (A) case. In this case students follow Emily Figel as she builds two tree-based models using historical LendingClub data to predict, with some probability, whether borrower will repay or default on his loan.
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Keywords:
Data Science;
Data Analytics;
Decision Trees;
Investment;
Financing and Loans;
Analytics and Data Science;
Analysis;
Forecasting and Prediction
Datar, Srikant M., and Caitlin N. Bowler. "LendingClub (B): Decision Trees & Random Forests." Harvard Business School Supplement 119-021, August 2018. (Revised September 2018.)
- August 2018 (Revised September 2018)
- Supplement
LendingClub (C): Gradient Boosting & Payoff Matrix
By: Srikant M. Datar and Caitlin N. Bowler
This case builds directly on the LendingClub (A) and (B) cases. In this case students follow Emily Figel as she builds an even more sophisticated model using the gradient boosted tree method to predict, with some probability, whether a borrower would repay or default...
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Keywords:
Data Analytics;
Data Science;
Investment;
Financing and Loans;
Analytics and Data Science;
Analysis;
Forecasting and Prediction
Datar, Srikant M., and Caitlin N. Bowler. "LendingClub (C): Gradient Boosting & Payoff Matrix." Harvard Business School Supplement 119-022, August 2018. (Revised September 2018.)
- 2018
- Working Paper
Measuring Gentrification: Using Yelp Data to Quantify Neighborhood Change
By: Edward L. Glaeser, Hyunjin Kim and Michael Luca
We demonstrate that data from digital platforms such as Yelp have the potential to improve our understanding of gentrification, both by providing data in close to real time (i.e., nowcasting and forecasting) and by providing additional context about how the local...
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Keywords:
Geographic Location;
Local Range;
Transition;
Analytics and Data Science;
Measurement and Metrics;
Forecasting and Prediction
Glaeser, Edward L., Hyunjin Kim, and Michael Luca. "Measuring Gentrification: Using Yelp Data to Quantify Neighborhood Change." NBER Working Paper Series, No. 24952, August 2018.
- 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...
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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
Glaeser, Edward L., Hyunjin Kim, and Michael Luca. "Nowcasting Gentrification: Using Yelp Data to Quantify Neighborhood Change." AEA Papers and Proceedings 108 (May 2018): 77–82.
- February 2018 (Revised December 2020)
- Supplement
People Analytics at Teach For America (Data Set)
This data set is a supplement to the People Analytics at Teach For America (A) case.
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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...
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Polzer, Jeffrey T., and Julia Kelley. "People Analytics at Teach For America (A)." Harvard Business School Case 418-013, February 2018. (Revised December 2020.)
- February 2018
- 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, Managing Director Michael Metzger must decide how to extend his team’s predictive analytics work using Natural Language Processing (NLP) techniques.
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- Article
How Did the Great Recession Affect Charitable Giving?
By: Arthur C. Brooks
A great deal of research has studied the effects of income and tax changes on charitable giving. However, little work has focused on how these relationships were affected by the Great Recession. This article estimates the tax and income effects using the 2009 Panel...
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Keywords:
Charitable Giving;
Great Recession;
Philanthropy;
Philanthropy and Charitable Giving;
Financial Crisis;
Taxation;
Policy
Brooks, Arthur C. "How Did the Great Recession Affect Charitable Giving?" Public Finance Review 46, no. 5 (September 2018): 715–742.
- January 2018 (Revised January 2020)
- Case
People Analytics at McKinsey
By: Jeffrey T. Polzer and Olivia Hull
A private equity–backed fast food chain has hired McKinsey’s new People Analytics group to help it improve performance. As the final client workshop approaches, Associate Partner Alex DiLeonardo ponders the best way to present the team’s findings, especially those that...
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Keywords:
Talent and Talent Management;
Customer Relationship Management;
Forecasting and Prediction;
Cost Management;
Human Resources;
Employees;
Recruitment;
Retention;
Selection and Staffing;
Measurement and Metrics;
Performance;
Performance Capacity;
Performance Efficiency;
Performance Evaluation;
Performance Improvement;
Consulting Industry;
Service Industry
Polzer, Jeffrey T., and Olivia Hull. "People Analytics at McKinsey." Harvard Business School Case 418-023, January 2018. (Revised January 2020.)