Publications
Publications
- August 2018 (Revised September 2018)
- HBS Case Collection
Predicting Purchasing Behavior at PriceMart (B)
By: Srikant M. Datar and Caitlin N. Bowler
Abstract
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 logistic regression.
Wehunt and Morse then compare the models and select the model most effective at correctly classifying households as expecting. Students explore the relationship between the model’s confusion matrix, which organizes the model’s correct and incorrect classifications, the cutoff point on the curve that matches true positives and true negatives, and the payoff matrix Wehunt and Morse construct. Students can then follow the link directly from their model to their marketing strategy.
Technical topics covered:
o Ridge logistic regression (or L2 regularization) as a modelling technique
o Lasso logistic regression (or L1 regularization) as a modelling technique
o Comparing models, thinking about coefficients, and selecting model for deployment
o Evaluating model output; ROC curve, cutoff point, confusion matrix; payoff matrix as a framework for utilizing the model to carry out marketing strategy
Wehunt and Morse then compare the models and select the model most effective at correctly classifying households as expecting. Students explore the relationship between the model’s confusion matrix, which organizes the model’s correct and incorrect classifications, the cutoff point on the curve that matches true positives and true negatives, and the payoff matrix Wehunt and Morse construct. Students can then follow the link directly from their model to their marketing strategy.
Technical topics covered:
o Ridge logistic regression (or L2 regularization) as a modelling technique
o Lasso logistic regression (or L1 regularization) as a modelling technique
o Comparing models, thinking about coefficients, and selecting model for deployment
o Evaluating model output; ROC curve, cutoff point, confusion matrix; payoff matrix as a framework for utilizing the model to carry out marketing strategy
Keywords
Data Science; Analytics and Data Science; Analysis; Customers; Household; Forecasting and Prediction
Citation
Datar, Srikant M., and Caitlin N. Bowler. "Predicting Purchasing Behavior at PriceMart (B)." Harvard Business School Supplement 119-026, August 2018. (Revised September 2018.)