Case Studies: Roadmap to Enhanced RFM/Amazon SageMaker

Recorded On: 10/06/2020


Back to Package

  • Register
    • Non-member - $85
    • Professional Member - Free!
    • Associate Member - $50

    Continue

A Roadmap to Enhanced RFM Scores: What You Can Do Beyond the Basics (With R and a Little SQL)

Andrew McMahon, Assistant Director, Prospect Analytics, US Holocaust Memorial Museum

Basic RFM analysis can go along way towards highlighting your best prospects for retention and upgrades. But, two enhancements can make your ratings more timely, granular, and useful to your fundraisers. This case study will introduce these two enhancements and serve as be a roadmap to help you implement them. First, we will cover how to create dynamic scores that update daily in your CRM–This may be easier than you think especially if you have the support of your database team or know a little SQL. Second, we will cover ways to use more involved techniques–like Markov Chains and clustering algorithms–to achieve more granular donor segments; we'll suggest specific R packages to help you implement these techniques. The goal of the case study is to provide an overview of two practical techniques to enhance your prospecting metrics.

Predictive Modeling in Amazon SageMaker

Maxwell Dakin, Development Analytics, John F. Kennedy Center for the Performing Arts

In an effort to cut costs on direct marketing, we used Amazon SageMaker – a cloud-based ML platform from Amazon Web Services – to develop a set of Machine Learning models for prospect identification. We are testing the results in direct mail and email campaigns over the next year, with the goal of reducing costs by bringing model development in-house and focusing our direct marketing efforts on the best leads. In this session, we will cover what we have learned from the project, why we chose this tool and how we plan to implement the results.

This session is part of the Data Science Now 2020 Bundle.

Key:

Complete
Failed
Available
Locked
Case Studies: Roadmap to Enhanced RFM/Amazon SageMaker
Open to view video.
Open to view video. A Roadmap to Enhanced RFM Scores: What You Can Do Beyond the Basics (With R and a Little SQL) Andrew McMahon, Assistant Director, Prospect Analytics, US Holocaust Memorial Museum Basic RFM analysis can go along way towards highlighting your best prospects for retention and upgrades. But, two enhancements can make your ratings more timely, granular, and useful to your fundraisers. This case study will introduce these two enhancements and serve as be a roadmap to help you implement them. First, we will cover how to create dynamic scores that update daily in your CRM–This may be easier than you think especially if you have the support of your database team or know a little SQL. Second, we will cover ways to use more involved techniques–like Markov Chains and clustering algorithms–to achieve more granular donor segments; we'll suggest specific R packages to help you implement these techniques. The goal of the case study is to provide an overview of two practical techniques to enhance your prospecting metrics. Predictive Modeling in Amazon SageMaker Maxwell Dakin, Development Analytics, John F. Kennedy Center for the Performing Arts In an effort to cut costs on direct marketing, we used Amazon SageMaker – a cloud-based ML platform from Amazon Web Services – to develop a set of Machine Learning models for prospect identification. We are testing the results in direct mail and email campaigns over the next year, with the goal of reducing costs by bringing model development in-house and focusing our direct marketing efforts on the best leads. In this session, we will cover what we have learned from the project, why we chose this tool and how we plan to implement the results.
You must register to access.

Andrew McMahon

Assistant Director, Operations and Prospect Development

United States Holocaust Memorial Museum

Andy McMahon is an Assistant Director, Operations and Prospect Development at the United States Holocaust Memorial Museum. Previously he worked on the Donor Operations team at Share Our Strength, a non-profit that strives to end childhood hunger in the U.S. He graduated from Carleton College in 2009 with a degree in philosophy. He is interested in every aspect of fundraising operations, including donor analytics. He's particularly passionate about using the R programming language, machine learning, SQL, and automation (whenever possible) to optimize the Museum's prospect management. When he's not fundraising or programming, he's dreaming about consuming as much ramen and kombucha as possible.

Maxwell Dakin

Assistant Director, Prospecting and Special Initiatives

UNICEF USA

As a member of the Prospect Intelligence team at UNICEF USA, Max manages data strategy and analytics projects to support prospecting, portfolio optimization, and pipeline development. Prior to UNICEF USA, Max was Assistant Manager, Development Analytics at the John F. Kennedy Center for the Performing Arts in Washington, D.C. Max currently serves as co-chair of Apra's Data Science Committee and is a member of NEDRA. He lives in Portland, Maine.