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• Teaching Note | HBS Case Collection | May 2019

  Data Science at Target

  Srikant M. Datar and Caitlin N. Bowler

  Teaching Note for HBS No. 118-016.

  Citation:

  Datar, Srikant M., and Caitlin N. Bowler. "Data Science at Target." Harvard Business School Teaching Note 119-108, May 2019.  View Details

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• Case | HBS Case Collection | August 2018 (Revised April 2019)

  Chateau Winery (A): Unsupervised Learning

  Srikant M. Datar and Caitlin N. Bowler

  This case follows Bill Booth, marketing manager of a regional wine distributor, as he applies unsupervised learning on data about his customers’ purchases to better understand their preferences. Specifically, he uses the K-means clustering technique to identify groups of customers who have purchased any number of 32 specific “deals” Booth offered over the year, differentiated by the wine varietal as well as its country of origin and a minimum number of bottles to purchase. Insights from this analysis may help him understand themes across the deals that can inform construction of new deals in the future.
  
  Topics include unsupervised learning; similarity and proximity; K-means clustering, with measures of Euclidean distance and cosine similarity; Gaussian mixture models; and interpreting clusters.

  Keywords: clustering; data science; Data and Data Sets; Customers; Marketing; Analysis;

  Citation:

  Datar, Srikant M., and Caitlin N. Bowler. "Chateau Winery (A): Unsupervised Learning." Harvard Business School Case 119-023, August 2018. (Revised April 2019.)  View Details

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• Supplement | HBS Case Collection | August 2018 (Revised April 2019)

  Chateau Winery (B): Supervised Learning

  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 customers closest to them.
  
  Topics include supervised learning; collaborative filtering; K-nearest neighbor as a modeling technique, collaborative filtering with cosine similarity of customers, collaborative filtering with cosine similarity of products, and comparison of different prediction models.

  Keywords: data science; clustering; Data and Data Sets; Customers; Marketing; Analysis;

  Citation:

  Datar, Srikant M., and Caitlin N. Bowler. "Chateau Winery (B): Supervised Learning." Harvard Business School Supplement 119-024, August 2018. (Revised April 2019.)  View Details

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