In a world where wealth now includes mining rights, fracking revenue, and other intangible assets like patents, copyrights, and trademarks, it can be confusing to figure out whether some assets add value to a prospect’s capacity rating. Here, we'll explore the intrinsic value of patents and how to include them in a prospect’s profile.
Most data mining and donor modeling projects include one dependent variable compared to several independent variables. Let’s review these concepts briefly before we get into the meat of this article.
There are two reasons to use giving variables in a prospect model. First, unlike any other part of a fundraising shop, gift records are audited. That means that they are the cleanest and most consistent of all the data you would use. Second, past behavior often indicates future behavior – past giving can show the pattern of future giving. Using giving history judiciously can improve your major gifts model well.
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.