CoolData blog

25 November 2010

Turning people into numbers?

Filed under: Front-line fundraisers, skeptics — Tags: , — kevinmacdonell @ 1:18 pm

(Image used via Creative Commons license. Click image for source.)

I tend to hear the same objections from presentation audiences, my own and others’. They’re not objections so much as questions, and very good questions, and always welcome. But no one yet has voiced a reservation that I know some must be thinking: This predictive modeling stuff, it’s all so … impersonal.

We already work in a profession that refers to human beings as “prospects” and “suspects”. Doesn’t sticking scores and labels on people perpetuate a certain clinical coolness underlying how fundraising is carried out today? Predictive modeling sounds like bar-coding, profiling, and commodifying people as if they were cattle destined for the table. Maybe we can be so busy studying our numbers and charts that we lose our connection with the donor, and with our mission.

Apologies in advance for setting up a straw man argument. But sometimes I imagine I see the thought forming behind someone’s furrowed brow, and wish it would be brought into the open so we can discuss it. So here we go.

(First of all, how many fundraising offices do you know that carry out their work with “clinical coolness”? We should be so lucky!)

More seriously: Data mining and predictive modeling will never interfere with the human-to-human relationship of asking for a gift, whether it’s a student Phonathon caller seeking an annual gift, or a Planned Giving Officer discussing someone’s ultimate wishes for the fruit of a lifetime of work. It’s a data-free zone.

What predictive modeling does is help bring fundraiser and would-be donor together, by increasing the odds (sometimes dramatically increasing the odds) that the meeting of minds will successfully converge on a gift.

Here’s how I frame it when I talk about predictive modeling to an audience that knows nothing about it. If all we know about a constituent is their giving history (or lack of it), we’re treating everyone the same. Is one non-donor just as likely as another to be convinced to make an annual gift? Is one $50-a-year donor just as likely as another to respond to an appeal to double their pledge this year, or be receptive to having a conversation with a Planned Giving Officer?

The answers are No, No, and NO!

What I say is, “Everyone is an individual.” If they played sports as a student, if they lived on campus, if they attended an event — we can know these things and act accordingly, based on what they tell us about their engagement with our institution. We just have to tune in and listen.

“Everyone is an individual.” Catchy, eh? Well, it’s trite, but it’s true — and it’s no less true for data miners than it is for anyone else.

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