Presented by James Cheng of Memorial Sloan Kettering Cancer Center
As information become more voluminous, velocious, veracious, varied, and valuable, the worlds of research and data science within fundraising have evolved as “wonder twins” to support the prospect development pipeline. While we as development professionals have come to rely on and collaborate with researchers, however, we have less of an understanding of the various forms data science have taken within fundraising. Thus, using Memorial Sloan Kettering Cancer Center’s Development Analytics group as a case study, this session will explore how data science is employed in the automated estimation of capacity for roughly 9 million constituents in the database for identification purposes. The session will also go over the calculation of giving likelihood via predictive modeling to aid in prospect identification and verification by researchers. Finally, the concept of unsupervised learning for portfolio optimization will be introduced.
Presented by Melissa Bank Stepno, Target Analytics at Blackbaud
Successful major gift fundraising is not always about finding the wealthiest person in your database. It is about focusing efforts on prospects who have the wealth and interest to make a major contribution to your organization. Are you targeting your best prospects or simply focusing on the most obvious candidates? Or, are you not confident in your ask amounts because you don’t know if you are asking too much or too little? If you don’t have a professional researcher on your side to help you, how can you quickly assess your organization’s best prospects to focus your major gift efforts appropriately? In this session, we will introduce the non-researcher to internet resources available to do some basic prospect research and we will also review easy strategies to help you synthesize data for effective fundraising.
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