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.

Rethinking calling priority in your phonathon campaign

Year after year, I see the results of the propensity model for phonathon and I become increasingly convinced that calling priority must be based solidly on the predictive score. Call your high scorers first, regardless of their giving history.

Does it not make sense to use giving history to determine strategy, and stop using it to assign call priority? What is so magical about LYBUNTs? Why are we determined to contact every one of last year’s donors, including the ones least likely to renew, when we should be moving on to broadening the base by targeting the high-scoring never-donors who are most ripe for conversion?

(When I say “we”, I’m not referring to my own institution. I’m talking about all phonathon people out there.)

The high scorers in my model have the highest rates of participation, give the largest gifts, are the most likely to convert or renew or return (if lapsed), and are the most likely to increase their pledges year over year. Those high scorers come from every donor category, from loyal donors to first-time donors, to people who’ve never given us a dime before.

The evidence tells me this: We make a huge mistake whenever we call low scorers ahead of high scorers, simply for the sake of squeezing dollars out of a calling group we have composed based on our assumptions.

Not entirely false assumption, of course. We are mostly right to assume that last year’s donors are more likely to give this year than any other group. But calling ALL last year’s donors first assumes that they are uniformly likely to renew, which they most certainly are not.

A constituent’s giving history determines your goals and strategy. For non-donors, the goal is conversion. For first-time donors, the goal is renewal. For loyal donors, the goal is upgrading. There is no reason why these goals can’t be persued simultaneously, or nearly so. In my opinion, it is never wrong to make it a priority to work a segment of never-donors, IF they have a high propensity score.

These days, when it takes multiple calls to get even a loyal donor on the line, call priority is very important. If your model is trained on the most phone-receptive people in your database, then use it.