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2021
Working Paper

Time and the Value of Data

By: Ehsan Valavi, Joel Hestness, Newsha Ardalani and Marco Iansiti

Managers often believe that collecting more data will continually improve the accuracy of their machine learning models. However, we argue in this paper that when data lose relevance over time, it may be optimal to collect a limited amount of recent data instead of... View Details

Keywords: Economics Of AI; Machine Learning; Non-stationarity; Perishability; Value Depreciation; Analytics and Data Science; Value

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

2019
Article

Does Big Data Enhance Firm Innovation Competency? The Mediating Role of Data-driven Insights

By: Maryam Ghasemaghaei and Goran Calic

Grounded in gestalt insight learning theory and organizational learning theory, we collected data from 280 middle and top-level managers to investigate the impact of each big data characteristic (i.e., data volume, data velocity, data variety, and data veracity) on... View Details

Keywords: Analytics and Data Science; Innovation and Invention; Learning

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June 2019
Supplement

Improving Worker Safety in the Era of Machine Learning: Introduction to Predictive Analytics

By: Michael W. Toffel and Dan Levy

June 2019
Teaching Note

Improving Worker Safety in the Era of Machine Learning: Introduction to Predictive Analytics

By: Michael W. Toffel and Dan Levy

Teaching Note for HBS No. 618-019. View Details

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. View Details

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... View Details

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

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

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... View Details

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

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... View Details

Keywords: Data Science; Analytics and Data Science; Analysis; Consumer Behavior; Forecasting and Prediction

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... View Details

Keywords: Data Science; Analytics and Data Science; Analysis; Customers; Household; Forecasting and Prediction

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... View Details

Keywords: Data Science; Clustering; Analytics and Data Science; Customers; Marketing; Analysis

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... View Details

Keywords: Data Science; Data Analytics; Investing; Loans; Investment; Financing and Loans; Analytics and Data Science; Analysis; Forecasting and Prediction; Business Model

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.
... View Details

Keywords: Data Science; Data Analytics; Decision Trees; Investment; Financing and Loans; Analytics and Data Science; Analysis; Forecasting and Prediction

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... View Details

Keywords: Data Analytics; Data Science; Investment; Financing and Loans; Analytics and Data Science; Analysis; Forecasting and Prediction

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... View Details

Keywords: Geographic Location; Local Range; Transition; Analytics and Data Science; Measurement and Metrics; Forecasting and Prediction

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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February 2018 (Revised December 2020)
Supplement

People Analytics at Teach For America (Data Set)

By: Jeffrey T. Polzer

This data set is a supplement to the People Analytics at Teach For America (A) case. View Details

Keywords: Analytics; Human Resource Management; Data; Workforce; Hiring; Talent Management; Forecasting; Predictive Analytics; Organizational Behavior; Recruiting

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