Fundraising at St. Camillus Hospital

Srikant M. Datar; Caitlin N. Bowler

Case | HBS Case Collection | October 2018

Fundraising at St. Camillus Hospital

by Srikant M. Datar and Caitlin N. Bowler

Abstract

St. Camillus is a fictional non-profit hospital in rural Maine facing a serious budget deficit. As Director of Marketing, Victoria Stern is building a team to modernize the hospital fundraising efforts. An interview with a promising candidate, who is also a digital native, forces her to confront shifting attitudes toward issues of data and privacy across generations against the backdrop of continued cybercrime and data breaches within the healthcare industry. Students grapple with the tension between data as "useful" or private within an organization. In this context they explore the ethical issues they will face around data security and data governance in their roles as managers.

Keywords: data analysis; data privacy; data governance; non-profit; health care; fundraising; data security; Data and Data Sets; Safety; Governance; Ethics; Health Care and Treatment;

Language: English

Format: Print

10 pages

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Citation:

Datar, Srikant M., and Caitlin N. Bowler. "Fundraising at St. Camillus Hospital." Harvard Business School Case 119-027, October 2018.

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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Fundraising at St. Camillus Hospital

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