The Center for Creative Leadership

Srikant M. Datar; David A. Garvin; Carin-Isabel  Knoop

Case | HBS Case Collection | February 2008 (Revised May 2009)

The Center for Creative Leadership

by Srikant M. Datar, David A. Garvin and Carin-Isabel Knoop

Abstract

The Center for Creative Leadership (CCL) was founded in 1970 on the notion that leadership was not innate but could be learned. CCL evolved into one of the world's top leadership development organizations, involved in both research and program design and delivery. This case explores CCL's approach to leadership and management education for executives and presents some of the challenges the CCL faces as many different types of leadership development providers continue to emerge.

Keywords: Competency and Skills; Learning; Training; Leadership Development; Personal Characteristics;

Format: Print

27 pages

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Citation:

Datar, Srikant M., David A. Garvin, and Carin-Isabel Knoop. "The Center for Creative Leadership." Harvard Business School Case 308-013, February 2008. (Revised May 2009.)

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.

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.

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The Center for Creative Leadership

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