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• Case | HBS Case Collection | August 2017 (Revised August 2018)

  Busbud: Building a Data Company

  Srikant M. Datar, Alistair Croll and Caitlin N. Bowler

  The case features the work of LP Maurice (HBS '08) as he decides to take on the fragmented bus travel industry and launch an online business that aggregates and shares bus schedules for routes around the world. His first challenge: finding that the data he needs is scattered, siloed, and sometimes offline. The case follows him and his team as they take on challenges as the start-up evolves and develops. Topics include acquiring data, web-based prototyping to understand customer preferences, and customer acquisition and retention metrics (SEO & SEM), and strategic choices around transforming from a B-to-C organization to a B-to-B enterprise. This case was written for the second-year MBA course "Managing with Data Science." The course provides MBA students with no programming experience an introduction to the field of data science and its applications in business. Students learn to (1) carefully articulate the business ask, (2) reason carefully from the “ask,” through metrics and models, and outputs; and (3) evaluate outputs from models to (4) develop a plan for action.

  Keywords: data science; Data and Data Sets; Business Startups; Knowledge Acquisition; Customers; Measurement and Metrics; Transportation Industry;

  Citation:

  Datar, Srikant M., Alistair Croll, and Caitlin N. Bowler. "Busbud: Building a Data Company." Harvard Business School Case 118-011, August 2017. (Revised August 2018.)  View Details

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• Case | HBS Case Collection | August 2018

  Predicting Purchasing Behavior at PriceMart (A)

  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 a new baby, parents’ shopping habits became quickly ingrained. She hypothesized that if she could get households expecting a new baby to make PriceMart a part of their routines before becoming parents, she might keep them as customers for the next several years, winning significant additional revenue.
  
  Technical topics covered:
  o Collecting data and preparing a dataset; constructing training, validation, and holdout sets; cross-validation
  o Linear regression as a modelling technique; statistical tests
  o Logistic regression as a modelling technique for estimating predictions between 0 and 1; maximum likelihood estimation; log likelihood
  o Comparing model outputs

  Keywords: data science; Data and Data Sets; Analysis; Consumer Behavior; Forecasting and Prediction;

  Citation:

  Datar, Srikant M., and Caitlin N. Bowler. "Predicting Purchasing Behavior at PriceMart (A)." Harvard Business School Case 119-025, August 2018.  View Details

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• Supplement | HBS Case Collection | August 2018

  Predicting Purchasing Behavior at PriceMart (B)

  Srikant M. Datar and Caitlin N. Bowler

  This case builds directly on the case Predicting Purchasing Behavior at PriceMart (A), in which VP of Marketing, Jill Wehunt, and analyst Mark Morse build a logistic regression model to predict whether a customer household is expecting a baby. 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
  

  Keywords: data science; Data and Data Sets; 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.  View Details

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