Analytics Expert Offers Advice on How to Use Data to Raise Money

Diana Deaver

Fundraisers have long debated whether their discipline is an art or a science. But Steve MacLaughlin, director of analytics at fundraising-software company Blackbaud, argues that’s the wrong question. It’s obvious, he says, that raising money takes both.

“There’s an art to having a conversation with an individual about making a $10 million gift to your organization,” he says. “You cannot replace that with robots or science.”

The real dilemma charities face is how to analyze their data to identify the right people to talk to and understand their giving habits. Mr. MacLaughlin got a taste of that challenge early in his career when he worked at Indiana University. In his new book, Data Driven Nonprofits, he profiles groups working on an array of causes that have figured out how to apply the power of data to fundraising.

Says Mr. MacLaughlin: “Hopefully organizations can look at those nonprofits and say, ‘Wow, look what they’re doing. It’s possible.’ "

Mr. MacLaughlin talked about data-driven fundraising with The Chronicle. The interview has been edited for length and clarity.

Why is data something nonprofits should care about?

We’re living in an age where the use of data to help drive decisions and improve results is happening on an increasing scale. If we look at the corporate sector, it’s not just Google and Facebook who are doing this. It’s John Deere. It’s the Fitbit device. It’s Disney. It’s how Walmart knows when a storm is coming that they need to stock up on strawberry Pop-Tarts because that’s what people are going to buy.

There’s the equivalent type of value and insights that nonprofits can get: Which donors are my best donors? Which donors are most likely to stop giving to my organization? Which donors should I choose to invest more in? Which donors or people who have been activists or volunteers in the past are likely to be my best donors in the long-term? Who are people who have traditionally given through direct mail who are most likely to become planned-gift donors in the future? All these things are possible.

What’s the current state of data use in fundraising?

A lot of organizations spend a lot of time using data to drive using the rearview mirror. They look at reports. They’re constantly tweaking and adjusting those reports, but reports only show you what’s happened before. They don’t show you what’s happening right now, and they don’t really show you what’s likely to happen in the future.

Can you talk about a nonprofit that increased its fundraising by focusing on data?

One of the more interesting stories in the book is the Denver Rescue Mission. A guy by the name of Griff Freyschlag worked in banking for over 20 years before he started running development and fundraising at the Denver Rescue Mission.

This is an organization that’s been around for over 100 years, but up until the mid-1980s, they’d been a staff of four people, and they only had about $200,000 in revenue. Fast forward to today, and it’s a $32 million-a-year organization.

A lot of that transformation has to do with building a culture around using data. As Griff tells the story, he’d always been a numbers guy. And so when he came in, he looked at things like, Where are we growing? Where are we not growing? Where are the best opportunities to do things?

They’ve been able to grow their major-giving program from nothing, literally nothing, to a significant part of their revenue. They chose to invest in it, realizing that it wouldn’t happen overnight. You couldn’t just go hire someone who had major-gift experience, bring them in, and then suddenly they’d be raising a lot of money. They knew they had to invest in that over a number of years to build up a program, to get donors interested.

What effect does organizational culture have on the way nonprofits use data?

When I started working on the book, I thought I would spend a whole lot of time talking about data and technology skills. You need to have those things, but it’s not enough.

There’s the Peter Drucker quote: Culture eats strategy for breakfast. It’s absolutely true when it comes to trying to become more data-driven. You can have the data. You can have the technology. You can have people with the right skill set. It’s not enough. You have to have a culture that values data and is willing to do something with it.

What are some examples of cultures that promote data use?

Public broadcasting has a culture of sharing. They’ve been doing benchmarking with each other for 30 years. The public radio station in D.C. doesn’t feel like it competes with the public radio station in San Francisco, so they’re willing to share data.

That’s very different than the culture of testing at the World Wildlife Fund. They’re not a sharing culture, but they are a testing culture, in part because of their direct-marketing roots. They test a lot of things in direct mail, and what happened was other people in the organization would see that and go, “Wow, you tested that? Could you come test something that we’re trying to do?”

A culture of testing helps to settle long-held beliefs about something that may or may not be true.

There’s an example in the book from the Royal National Institute of Blind People in the U.K. For many years, they had run a raffle program that involved millions of donors, and there was an internal debate over whether or not they should ask those donors to also donate to the organization. There was a standoff. There was one group that said we should, and there was one group that said we shouldn’t. But no one actually went and did the analysis to find out, Should we be asking these people?

So they went to the Fundraising Insight Unit at RNIB — it’s like CSI, but for nonprofit data. They did some analysis on the data, and they found out that, yes, [they] should be asking these people to make an additional, recurring gift. Asking people who have already participated in the raffle to also participate in their recurring-gift program is worth about £1 million [$1.3 million] a year to the organization. The only reason that they didn’t know that before is they didn’t want to test it.

Is data-driven fundraising just for large charities?

If you’re a large health-care nonprofit and you raise $100 million a year, if you miss out on a $1 million or $2 million gift, you’ll be OK. You’ve got a pipeline. You’ve got other donors. You’ve got other things in the works. If you miss something, you’ll find a way to fill that hole and meet your goal.

If you’re a smaller nonprofit — let’s say you’re a $1 million-a-year organization — if you miss out on an opportunity for a $50,000 gift or a $250,000 gift, that actually has a bigger impact. If you’re a smaller organization, the importance and potential upside of data is huge.