Are we too focused on trivia?

As Annual Fund professionals we like to think that the precise details of our approach to prospective donors makes a difference in our rate of success. Some of our practices make so much sense, are so in tune with our instincts, that it seems absurd to bother testing them. Sometimes, though, a look at the data reveals that our carefully-crafted techniques aimed at engaging, convincing and converting make little or no difference.

At least, they make little difference when compared to what really matters: The emotions, opinions and feelings that would-be donors have when they think of our institution, organization or cause.

We should not be surprised that these feelings and emotions are not significantly influenced by whether we pay postage on the return envelope, or have Dr. So-and-So sign the letter, or many other, similar nuances that are the subject of the bulk of discussions on listservs that deal with Annual Fund and fundraising in general.

Yes, there are right and wrong ways to communicate with donors and would-be donors, but on the whole we have a hard time distinguishing between meaningful practices and mere refinements. We tinker with our letterhead, our brand, our scripts. We keep changing the colour of our sails in hopes the ship will go faster.

What is the non-trivial work we need to do? We need to get a whole lot better at identifying who likes us, and pay attention only to them. If they like us a lot, we need to ask them, thank them, upgrade them, stay with them on the journey — as all our fundraising experience and human instincts guide us to do. If they like us a little, perhaps we can do something to engage them. If they are indifferent, we must simply walk away.

That does not mean we should pay attention only to donors: There are all kinds of people who haven’t given, but will someday. They reveal their affinity in ways that most fundraisers don’t take into account. And among donors, these clues regarding affinity help define the donor who is ready to give much more, or remain loyal for a lifetime, or even leave a bequest.

I’m as guilty as anyone else. There are things I do in my Phonathon program only because they make strong intuitive sense and have no basis in the evidence of results. In my next post, I will give an example of a Phonathon “best practice” which seems beyond reproach but which (according to my data) has absolutely no effect on participation or pledge amount. I was surprised by what I learned from my study, and I think you’ll be too.

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Turning people into numbers?

(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.

Data mining is not a disruptive technology

Are you an experienced front-line fundraiser? Do you feel vaguely threatened by this whole data mining thing? Do you think all your ideas about how fundraising works are in danger of being supplanted by some computer-generated mumbo-jumbo?

Well, you needn’t.

Data mining is exciting, and powerful, and new to many of us. But in my view it is not a game-changer. Its arrival at your organization will not displace or invalidate anything you’ve come to know about how best to ask for a gift. It complements your strategy; it does not replace it.

In short, fundraising is still all about relationships. It will always be about relationships.

I’ll go even further, and say that front-line fundraisers are the real miners in the organization. Data miners are more like exploration geologists, pointing out the areas that might yield the most gold. “Dig here,” says the data miner to the fundraiser, “this ore body is likely to contain what you’re looking for.” And the fundraiser does the hard work, digging through the rock to find the nuggets within, using the same qualification tools he or she has always used: experience, personal contact, intuition.

The fundraiser can find gold without the data miner. But it’s not easy. He or she will dig anywhere, sinking dozens of useless shafts before striking a workable vein. Or maybe the fundraiser is into dowsing or uses amulets or crystals or something similarly occult to decide where to dig. That’s worked before. Not well, mind you. As they say, even a stopped clock is right twice a day.

The data miner, on the other hand, is useless without the fundraiser. Insight without action gets nowhere.

Marry the two, though, and you’ve got a powerful force. The data miner brings core samples and geological maps and insights into what lies hidden beneath the sod. Dig here, don’t dig there. No one’s skills become obsolete – the fundraiser still needs all his or her ‘traditional’ knowledge while digging. Only now, the chances that the digging will earn a return on investment are vastly improved. See? One set of skills complements the other.

What characterizes a fruitful relationship between these two camps is a sense of boundaries, a willingness to cooperate, and a certain amount of faith in methods employed by the other. The data miner may not really grasp the art of asking for a gift, the human element. And doesn’t need to. The fundraiser may not really grasp the science of segmenting the prospect pool for propensity to give. And doesn’t need to.

Both sides can learn more about what the other does, but it’s more important that each should treat the others’ domain of knowledge with respect.