Chateau Winery (A): Unsupervised Learning
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.
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