Generating Insights from Clustering Large Donors

Track: Data Analytics Symposium

Session Number: 2056
Date: Wed, Jul 31st, 2019
Time: 3:30 PM - 4:30 PM
Room: Grand Saguaro West

Description:

Stakeholders frequently ask questions like “what other donors look like this group?” or “what is the pathway to elevated giving?” Clustering techniques within the class of unsupervised learning methods let analysts generate insights into these questions for their most generous donors, even without a dependent variable they are trying to predict.

In this talk, the presenter will discuss a variety of clustering techniques, how and when the techniques can be applied, and how the results from clustering algorithms can be used. Segmenting an organization’s most generous donors into distinct groups can allow fundraisers, researchers, and analysts approach these important constituents in more targeted and personalized ways.

Additionally, the presenter will comment on some of the larger trends in fundraising. These trends include the increasing concentration of giving, the continued deviation from the 80/20 Pareto principle, and the potential disruption in philanthropy caused by the ultra-wealthy.
Session Type: Breakout Session (60min)

Primary Competency: DA:Competency 4: Statistical Techniques and Competencies
Secondary Competency: DA:Competency 5: Visualization/Reporting Techniques and Competencies
Tertiary Competency: DA:Competency 3: Data Manipulation Skills
Intended Audience Level: Level I
Recommended Prerequisites: Some knowledge of R is helpful, as well as basic understanding of data manipulation techniques like centering and scaling variables, and basic statistical knowledge (e.g. calculating means and standard deviations).
Learning Objective #1: Attendees will learn the principles for implementing and interpreting some popular unsupervised learning techniques.
Learning Objective #2: Attendees will learn about the increasing influence of large donors on philanthropy, enforcing the importance of exploring data for this group.
Shop Size: All Shop Sizes
Session Type: Breakout Session (60min)

Primary Competency: DA:Competency 4: Statistical Techniques and Competencies
Secondary Competency: DA:Competency 5: Visualization/Reporting Techniques and Competencies
Tertiary Competency: DA:Competency 3: Data Manipulation Skills
Intended Audience Level: Level I
Recommended Prerequisites: Some knowledge of R is helpful, as well as basic understanding of data manipulation techniques like centering and scaling variables, and basic statistical knowledge (e.g. calculating means and standard deviations).
Learning Objective #1: Attendees will learn the principles for implementing and interpreting some popular unsupervised learning techniques.
Learning Objective #2: Attendees will learn about the increasing influence of large donors on philanthropy, enforcing the importance of exploring data for this group.
Shop Size: All Shop Sizes