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  • 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
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  • September–October 2020
  • Article

Managing Churn to Maximize Profits

By: Aurelie Lemmens and Sunil Gupta
Customer defection threatens many industries, prompting companies to deploy targeted, proactive customer retention programs and offers. A conventional approach has been to target customers either based on their predicted churn probability or their responsiveness to a... View Details
Keywords: Churn Management; Defection Prediction; Loss Function; Stochastic Gradient Boosting; Customer Relationship Management; Consumer Behavior; Profit
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  • 2019
  • Working Paper

Managing Churn to Maximize Profits

By: Aurelie Lemmens and Sunil Gupta
Customer defection threatens many industries, prompting companies to deploy targeted, proactive customer retention programs and offers. A conventional approach has been to target customers either based on their predicted churn probability, or their responsiveness to a... View Details
Keywords: Churn Management; Defection Prediction; Loss Function; Stochastic Gradient Boosting; Customer Relationship Management; Consumer Behavior; Profit
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  • March–April 2023
  • Article

Pricing for Heterogeneous Products: Analytics for Ticket Reselling

By: Michael Alley, Max Biggs, Rim Hariss, Charles Herrmann, Michael Lingzhi Li and Georgia Perakis
Problem definition: We present a data-driven study of the secondary ticket market. In particular, we are primarily concerned with accurately estimating price sensitivity for listed tickets. In this setting, there are many issues including endogeneity, heterogeneity in... View Details
Keywords: Price; Demand and Consumers; AI and Machine Learning; Investment Return; Entertainment and Recreation Industry; Sports Industry
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  • Web

Data Science for Managers 2 - Course Catalog

module will examine several concepts key to machine learning: e.g., autocorrelation, time series, Lasso and Ridge regressions, confusion and payoff matrices, ROC curves, neural networks, gradient boosting... View Details
  • 04 Dec 2018
  • First Look

New Research and Ideas, December 4, 2018

Gradient Boosting & Payoff Matrix 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 View Details
Keywords: Dina Gerdeman
  • 17 Sep 2013
  • First Look

First Look: September 17

depends on the number of customers the firm decides to target for its retention campaign. We propose a predictive model that accounts for all these elements. Our optimization algorithm uses stochastic View Details
Keywords: Sean Silverthorne
  • 17 Apr 2007
  • First Look

First Look: April 17, 2007

literature. Download the paper: http://www.hbs.edu/research/pdf/07-058.pdf Media Markets and Localism: Does Local News en Español Boost Hispanic Voter Turnout? Authors:Felix Oberholzer-Gee and Joel Waldfogel Abstract Since the dawn of... View Details
Keywords: Martha Lagace
  • 30 Jan 2018
  • First Look

January 30, 2018

Sandino Abstract—Many service organizations rely on information sharing systems to boost employee creativity to meet customer needs. We conducted a field experiment in a retail chain, based on a registered report accepted by Journal of... View Details
Keywords: Sean Silverthorne
  • Web

Print View - Course Catalog

confusion and payoff matrices, ROC curves, neural networks, gradient boosting and algorithmic bias, as well as NLP (Natural Language Processing), including sentiment analysis, naïve Bayes classification, and... View Details