CoolData blog

13 December 2011

Finding connections to your major gift prospects in your data

Guest post by Erich Preisendorfer, Associate Director, Business Intelligence, Advancement Services, University of New Hampshire

(Thanks to Erich for this guest post, which touches on something a lot of prospect researchers are interested in: mapping relationships to prospects in their database. Actually, this work is more exciting than that, because it actually helps people find connections they may not have known about, via database queries and a simple scoring system. Is your Advancement Services department working on something like this? Why not ask them? — Kevin.)

Data miners often have an objective of exploring sets of data to determine meaningful patterns which can then be modeled for predictive patterning, hopefully to help meet their organization’s end goal(s).  However, there may be a time when the end behavior is not inherent in your database. Such a situation recently came up for my Advancement organization.

Our prospecting team recently started a program wrapped around peer recommendations: A prospect recommends new suspects to us based on the prospect’s interactions with the suspects. The question then became, what can we provide to the prospect to help get them thinking about potential suspects?

We currently do not have any type of data which would allow us to say, “Yes, this is what a relationship looks like,” outside of family relationships. We had to find a different way to identify potential acquaintances. I looked back at my own relationships to determine how I know the people I know. My friends and acquaintances largely come from some basic areas: school, work, places I’ve gone, etc.

Transforming my experience with relationships into what we have for useable data, I saw three key areas where relationships may exist: work history, education history, and extracurricular activities including one-time events. Fortunately, I was able to pinpoint our constituents’ time in each of the above areas to help isolate meaningful, shared experiences amongst constituents. Our work and extracurricular history includes to/from dates, and we have loads of educational history data that includes specific dates. Using this data, I am able to come up with potential relationships from a single prospect.

Prospect Profile (generated by entering a single prospect’s ID):

  • John Adams
  • Widget Factory, Employee 01/05/1971 – 06/16/1996
  • Student Activities: Football, Student Senate 09/1965-05/1966
  • Bachelor of Arts, Botany 1966

Potential Relationships (each item below is a separate query, using the Prospect Profile results):

  • Those employed by the Widget Factory who started before John ended, and ended after John began.
  • Those students who participated in Football and had a class year within +/-3 years of John.
  • Those students in Student Senate at the same time as John, similar to the Widget Factory example.
  • Those students who were in the same class year as John.
  • Those students who share John’s major.

Currently,since I have no way of proving the value of one point of contact over the other, each row returned in the potential relationships earns the constituent one point. Since my database stores historical records, I may get more than one row per constituent in any one category if they met more than one of John’s associated records – say they participated in Student Senate and played Football. This is great, because I want to give those particular constituents two points since they have more than one touch point in common with John.

I end up with a ranked list of constituents who share potential relationship contacts with my main prospect. The relationship lists provide our prospect researchers a starting point in putting together a solid list of high capacity constituents a single person may have some sort of relationship with, thus a greater insight into potential giving.

As of now, the report is in its infancy but looks to have high potential. As we grow the concept, there are multiple data points where further exploration could result in a higher level of functioning. As prospects use the lists to identify people they know, we can then deconstruct those choices to determine what is more likely a relationship. Should shared employment be ranked higher than shared class year? Should Football rank higher than Student Senate? I would guess yes, but I currently do not have supporting data to make that decision.

Another interesting concept, raised at the recent DRIVE 2011 conference, would be: “How are these two prospects potentially related by a third constituent?”  The result could mean the difference between two separate, forced conversations and one single conversation with three prospects shared over nostalgic conversations, drinks and, hopefully, money in the door!

Erich Preisendorfer is Associate Director, Business Intelligence, working in Advancement Services at the University of New Hampshire.

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1 Comment »

  1. It would be great to use the information in your database to create a visual map. We recently launched a tool called Kumu that allows you to import relational data and create maps with customized displays based on the underlying data. Take a look at a sample here and let me know if you are interested about learning more: http://www.kumupowered.com/jeffmohr/the-power-of-kumu/web-2-0.

    Comment by Jeff Mohr — 7 June 2012 @ 10:09 pm


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