Using Data Science to Improve Fundraiser Metrics and Optimize Portfolios

Track: Data Science

Session Number: 2082
Date: Thu, Aug 1st, 2019
Time: 2:15 PM - 3:45 PM
Room: Grand Saguaro North

Description:

Every fundraising office strives to be as efficient as possible, particularly when it comes to portfolio management. To achieve this, you must put the correct systems in place, but defining what might be correct for your organization can be a challenge. In this session we will present a case study of how the University of Iowa Center for Advancement applied a mathematical model to understand the past and current behaviors of our fundraisers, establish detailed fundraiser metrics, and optimize fundraiser portfolios. We will explore in detail the analytics that brought us to our current metrics and systems to improve performance across the organization.
Session Type: Breakout Session (90min)

Primary Competency: DA:Competency 1: The Data Science Umbrella
Secondary Competency: DA:Competency 3: Data Manipulation Skills
Tertiary Competency: RM:Competency 9: Fundraiser Performance
Intended Audience Level: Level II
Recommended Prerequisites: Understanding of major gift fundraising; basic inclination to analytics; basic stats background; working knowledge of analytics tools like Excel, R, SQL, Sas, etc.
Learning Objective #1: Attendees will explore how a mathematical model can be applied to analyze fundraiser performance and develop metrics based on industry standard but influenced by the institution's historical data.
Learning Objective #2: Attendees will learn how to finalize their findings and present their data to fundraising leadership and staff.
Shop Size: Mid-Size/Large
Session Type: Breakout Session (90min)

Primary Competency: DA:Competency 1: The Data Science Umbrella
Secondary Competency: DA:Competency 3: Data Manipulation Skills
Tertiary Competency: RM:Competency 9: Fundraiser Performance
Intended Audience Level: Level II
Recommended Prerequisites: Understanding of major gift fundraising; basic inclination to analytics; basic stats background; working knowledge of analytics tools like Excel, R, SQL, Sas, etc.
Learning Objective #1: Attendees will explore how a mathematical model can be applied to analyze fundraiser performance and develop metrics based on industry standard but influenced by the institution's historical data.
Learning Objective #2: Attendees will learn how to finalize their findings and present their data to fundraising leadership and staff.
Shop Size: Mid-Size/Large