When we think of the science of analytics, we consider it as a part of mathematics. And math is a set of instructions, even more than science. For instance, it doesn’t matter how you set up your equation, 3 + 3 + 5 = 11. Set differently, the equation would still be: 5 + 3 + 3 = 11. And: 3 + 5 + 3 = 11.
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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. When starting a data mining project, we are often challenged by our management to “go do some data mining.” We can get stuck from there, unless the tool that we’ll use has already been chosen, like a screening service or modeling vendor. Even then, making sure that we understand the question involved helps frame how we set up the study and then what we do with the results.
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 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 marianne@staupell.com. 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. |
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