Even though, I’ve been in prospect development for more than 25 years, I continue to learn new things every day. It amazes me. Just when I think I have things figured out, this field can humble me. Often, when I think I’ve found the right way to do something, something else comes along to challenge that best practice and show me another, better, more strategic approach.
To be honest with you, sometimes that just ticks me off.
I don’t mean that in a bad way (if that’s even possible). That bit of an angry feeling comes more in the form of “Oh crap… I wish I had known that sooner.” It also often leads to “Great, now I have to learn that thing.”
That bit of anger usually fades away and is replaced by a genuine curiosity about how to actually get better, be more strategic, and focused. That doesn’t necessarily happen overnight. You see, it’s not my nature to be an early adopter, but when something new comes along, I definitely pay attention.
Unfortunately, while I’m paying attention – that new thing usually flies past me and I have to play catch-up.
That’s how I feel right now about Artificial Intelligence (AI). I don’t completely understand it, but I see it coming like a title-wave. Some people like the folks at City of Hope (Nathan Chappell and Nathan Fay) are riding that wave, hanging ten on their surfboards and waving at the people on the beach, who are wishing they were on that wave with the two Nathan’s.
Nathan Fay and I sat down to lunch several weeks back and I could see how committed he was to AI and I walked away having no doubt that he was going to help City of Hope raise more money. I was envious. Not jealous, because I love City of Hope – just envious that he had the support and resources to move forward. Good for him. Good for City of Hope.
AI is overwhelming to me. It’s huge. It’s mega-big. It’s a game-changer.
I’m all about finding the story behind a donor’s gift and their connection to my organization and its mission. It takes a lot of work to find that story and then turn around and tell it to the right people.
And when I say a lot of work, I mean it. When I was at the Pancreatic Cancer Action Network (PanCAN), I came to realize I could spend every moment of every working day – finding and telling those stories. Every moment. Every day.
It’s a daunting task when you’re looking at a database of more than a million records. Heck, it’s even a daunting task when you’re looking at a few hundred thousand records. If AI can look at all that data and then go beyond to learn the story, I want in on that (By the way, it can).
Are you kidding me?
I don’t know about you, but I often feel a sense of urgency in the work I do. I especially felt that working at PanCAN and City of Hope because quite frankly, people were dying every day. Meeting volunteers, who lost someone to cancer will do things to you. Being someone who has lost family to cancer does things to you. In my case, it motivates me to do better. Be faster. More strategic.
I have a strong work ethic, but if I can work smarter – I want that. I know if I can combine the two, I’ll be a force to be reckoned with. I’m just one person, but I can make a difference. We all can.
Why am I writing all of this?
I recently listened to David Lawson’s podcast where he explains "How Big Data can translate into Big Good."
As always – David makes me think. He has this way of presenting new ideas that really piques my interest. Mind you, my interest was already piqued by conversations with Nathan Fay, but David’s podcast hit me in the face like a cold wave from the Pacific Ocean.
David addressed the bias we have in the work we do and the bias that often exists in our data. He told the story of a visit with an Ivy League school a number of years ago where he was trying to convince someone that younger donors could have a major impact. The response was “Our major donor’s average age is 72 years old.” David replied with “As long as that is who you’re going to focus on, that is going to be true.”
Bam. A self-fulfilling prophecy.
In my last blog post, I wrote about the predictive modeling results we just implemented. I mentioned that I saw this trend where our highest scores were often represented by constituents who were alums, parents and either faculty or staff. Those that had all three attributes “looked” like our best prospects.
I acknowledged that this made sense since they were the most engaged. It was our own self-fulfilling prophecy. I knew it was biased. I also realized that if we continued to focus on these constituents, our model wouldn’t change.
David’s podcast reinforced the idea that we often do things that become self-fulfilling prophecies.
It’s one of the reasons I set aside those prospects/donors who had all three attributes and began my implementation of the results by looking at other alums who had high scores – knowing we didn’t have the best engagement with our existing alums.
I wanted to change the focus of our major gift team (who has a history of focusing on new parents) and help them focus on a group of people that would have a more long term impact on our fundraising efforts.
As I listened to David’s podcast, my head began to spin.
I began to think about the fact that even though there is value in predictive modeling, there is even greater value in utilizing big data and more specifically, AI to do even more.
I know there is bias in modeling. That's not necessarily a bad thing, if you know that in advance. It has taken me some time to realize that. You see, it’s often a bit of a process for me to figure out what that bias actually is. AI helps us avoid some of that bias, if not all of it. At least, I think it does.
At PanCAN – we took the modeling results and really analyzed it to the point where we started to develop our own model of who our best prospects were. When I say “we” – I really mean our data analyst at the time – Victoria Merlo and I worked together. She did the heavy analysis and really led the effort.
We found very specific characteristics/data points in our donor records that really helped us focus on a specific group of people to target for major gift cultivation. The modeling scores were a part of the profile, but now we were armed with additional data to help us be more strategic.
Like I said earlier, I’m always learning. Every time I take on a project like a wealth screening or predictive modeling, I become more aware and more informed. My goal is to find what is truly predictive.
That’s the thing. That’s the secret sauce.
How can we know who is really most likely to give and how do we engage them? The answers appear to be in AI through machine learning.
AI can learn things faster than I can. It operates without bias and when operating in that realm, the data is never going to lie. This is real science. This is game changing.
I want in.
Right now, I have no choice but to do the work that needs to be done manually at times. It’s just my reality. I will continue to move forward; armed with the self-awareness, that there is bias in what I do and that it’s not going to be perfect. It can still be effective, but it’s far from perfect. My new objective isn't to just follow a predictive model - my goal is to change the model in a way that will make us more effective. It's an idea that Lawrence Henze of Target Analytics actually put in my head when we took delivery of our predictive modeling results.
Models change. We can impact that change. Lawrence has helped me realize that.
Models change. We can impact that change. Lawrence has helped me realize that.
I will continue to move us forward and continue to advocate for the use of AI. I know I may only be able to take baby steps; when in reality, I want to sprint forward and dive into the wave where I can join the Nathan’s of the world.
I’m trying to do that, by introducing companies like Gravyty to our organization. Unfortunately, we’re not using their product just yet. Fortunately, by introducing them to our team, we have opened the door for a conversation about AI. It’s a start and I am hopeful.
I know there may be resistance, but I can be like a dog on a bone when I think something is important. In the meantime, we are all doing the best we can with the resources we have.
It’s all about the big picture. Non-profits want to change the world and make it a better place. Those of us in prospect development can help drive that effort. If I’m going to have a self-fulfilling prophecy; let it be this.