CoolData blog

19 April 2015

Planned Giving prospect identification, driven by data

Filed under: Planned Giving, Prospect identification — Tags: , , , — kevinmacdonell @ 6:28 pm

I’m looking forward to giving two presentations in my home city in connection with this week’s national conference of the Canadian Association of Gift Planners (CAGP). In theory I’ll be talking about data-driven prospect identification for Planned Giving … “in theory” because my primary aim isn’t to provide a how-to for analyzing data.

 

Rather, I will urge fundraisers to seek “data partners” in their organizations — finding that person who is closest to the data — and posing some good questions. There’s a lot of value hidden in your data, and you can’t realize this value alone: You’ve got to work closely with your colleagues in Advancement Services or with any researcher, analyst, or IT person who can get you what you need. And you have to be able to tell that person what you’re looking for.

 

For a shop that’s done little or no analysis of their data, I would start with these two basic questions:

 

  1. What is the average age of new expectancies, at the time they became known to your organization?
  2. What is the size of your general prospect pool?
The answer to the first question might suggest that more active prospect identification is required, of the type more often associated with major-gift fundraising. If the average age is 75 or older, I have to think that earlier identification of bequest intentions would benefit donor and cause alike, by allowing for a longer period for the conversation to mature and for the relationship to develop.

 

The answer to the second question gives an indication of the potential that exists in the database — but also the challenge of zeroing in on the few people (the top 100, say) in that universe of prospects who are most likely to accept a personal visit. Again, I’m talking about high-touch fundraising — more like Major Gifts, less like Annual Fund.

 

As Planned Giving professionals get comfortable asking questions of the data, the quality of the questions should improve. Ideally, the analyses will move from one-off projects to an ongoing process of gathering insights and then applying them. Along those lines, I will be giving attendees for both presentations a taste of how some simple targeting via data mining might work. As Peter Wylie and I wrote in our book, “Score!”, data mining for Planned Giving is primarily about improving the odds of success.

 

I said that I’m giving two presentations. Actually, it’s the same presentation, for two audiences. The first talk will be for a higher ed audience in advance of the conference, and the second will be for a more general nonprofit audience attending the conference proper. I expect the questions and conversations to differ significantly, and I also expect some of my assertions about Planned Giving fundraising to be challenged. Should be interesting!

 

Since you’ve read this far, you might be interested in downloading the handout I’ve prepared for these talks: Data-driven prospect ID for Planned Giving. There’s nothing like being there in person for the conversation we’re going to have, but this discussion paper does cover most of what I’ll be talking about.

 

If you’re visiting Halifax for the conference, welcome! I look forward to meeting with you.

 

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