This blog appeared in the APRA Upstate New York newsletter in the fall of 2009.
As fundraisers, we focus our resources on major gifts prospects. However, with Big Data bringing out a lot more information from social networking, we now see the value of using modeling and mining tools to help annual giving, membership, and events programs. Our recent conversations have centered on engagement – a rather nebulous term we use to try to understand what our donors feel about us before they give for the first time.
This blog post explores using a popular measure, RFM (explained below), as a modeling tool. There is some debate about its use in modeling major gifts, and so I share my thoughts here. If you have used RFM to measure your prospect giving behaviors, let us know at firstname.lastname@example.org.
In data mining, we frequently choose the dependent variable that is easiest to acquire from our primary databases. Most often, we start with the prospect’s lifetime giving (I admit that I teach that as well). However, we want to be careful when thinking about which giving variable is going to actually answer the question we have, and we have to make the best choice among all of our giving values, including single gifts and totals, to get the information that we need to make strategic decisions. Let’s take a look at some of these and how we might use them in visualizations, modeling, and mining.
This article was featured in the APRA Upstate New York newsletter. Used with permission.
In this blog entry, we cover specific fundraising issues and our analytics suggestions to solve them. We wrote this short starter list to help you explore how using statistics boosts your shop’s performance.