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  • August 2018 (Revised April 2019)
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Chateau Winery (A): Unsupervised Learning

By: Srikant M. Datar and Caitlin N. Bowler
  • Format:Print
  • | Language:English
  • | Pages:17
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Abstract

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; Analytics and Data Science; 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.)
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About The Author

Srikant M. Datar

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Related Work

    • August 2018 (Revised April 2019)
    • Faculty Research

    Chateau Winery (B): Supervised Learning

    By: Srikant M. Datar and Caitlin N. Bowler
    • August 2018 (Revised April 2019)
    • Faculty Research

    Chateau Winery (A): Unsupervised Learning

    By: Srikant M. Datar and Caitlin N. Bowler
Related Work
  • Chateau Winery (B): Supervised Learning By: Srikant M. Datar and Caitlin N. Bowler
  • Chateau Winery (A): Unsupervised Learning By: Srikant M. Datar and Caitlin N. Bowler
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