Fundraisers Reap Millions by Using Data ‘Gold Mine’

“Moneyball” has hit the fundraising department.

Like the data-driven, low-cost approach to building a competitive baseball team depicted in the popular book and movie, nonprofits are using powerful analytical techniques to make smarter fundraising decisions and boost donations.

Data analytics can help build sophisticated statistical pictures of donors and focus fundraising efforts on those most likely to give.

“Your database is a gold mine,” says Emily Courville, director of data analysis at the Humane Society of the United States. “There is so much you can learn about what your donors are doing if you just start to look.”

It seems to be working:

• A postcard mailing promoting charitable annuities that Johns Hopkins University sent to supporters chosen based on its analysis of past planned-gift donors brought in commitments totaling $2.4-million.

• World Vision analyzes information about its donors to decide who should receive which direct-mail appeals. The result: fewer solicitations. “If you know that by mailing 50 percent of the mail, you can get 90 percent of the income, why would you want to mail 100 percent just to get [the remaining] 10 percent?” says Lisa Pang, a senior director at the international aid group.

• CARE’s deep dive into its own data found a large number of donors on the border between direct mail and major gifts, so the organization started a new effort to court donors who make medium-size contributions.

• Boston College is using data analytics to help new big-gifts fundraisers get off to a quick start and to make decisions about how to deploy staff members with varying levels of skill and experience.

Nonprofits have long used data to drive their fundraising efforts. But the complexity of charities’ analyses has shot up as statistical tools have become easier to use, the cost of computing power has dropped, and more consultants and other vendors provide analytics services designed specifically for fundraising.

A small but growing number of organizations, including the Environmental Defense Fund, the Humane Society, and World Vision, have full-time analytics employees—and, in some cases, teams—in their fundraising departments.

Such positions are especially prevalent at large hospitals and universities. And those positions have gotten increasingly difficult to fill because charities must compete with for-profit companies to hire in-demand data analysts.

‘A Voice at the Table’

But even the strongest proponents of fundraising analytics say the focus on data can go too far.

Organizations using predictive models to home in on the people most likely to make large gifts can make the mistake of dropping long-established practices like asking board members for leads on potential donors, says Josh Birkholz, author of Fundraising Analytics: Using Data to Guide Strategy, he says, should supplement, not replace, existing development efforts.

“Data shouldn’t be your decision maker,” says Mr. Birkholz. “But it should be a voice at the table.”

It’s also critical for nonprofits to understand what their numbers are telling them, says Tim Sawer, vice president for marketing at World Vision. Poor use of data, he warns, can lead to costly mistakes.

As an example, he points to World Vision’s early days using direct-response television commercials to attract new donors. The organization bought mostly late-night airtime at a fraction of the rates for morning or late afternoon. The late-night ads had a good response rate, so initially the cost-benefit ratio looked favorable.

But when World Vision later compared the total amount donors gave over time, it became clear that the donors who responded to late-night ads were more likely to cancel their child sponsorships and ultimately gave less. The charity quickly moved its ads to the more expensive daytime hours.

“We realized from an analytics perspective that cost-per-acquisition in late night was a very poor indicator,” says Mr. Sawer.

Mr. Sawer, who is committed to data-driven fundraising, keeps a large whiteboard in his office to highlight the results of the charity’s child-sponsorship program and expose employees throughout the organization to the power of data.

Says Mr. Sawer: “If you’re coming to talk to me about something, you have to walk by the analytics before you come in.”

Screening Donors

One of the biggest challenges development offices face is figuring out how to make most efficient use of fundraisers who specialize in attracting big gifts.

Making a large donation is unusual behavior even among the wealthy, says Diane Korb, senior metrics analyst at the University of Texas MD Anderson Cancer Center.

Nonprofits, she says, can use sophisticated statistical software to see who in the donor database shares distinguishing traits with the people who have given big gifts in the past.

With wealth screening, MD Anderson can narrow the number of prospects from the 2 million constituents in its donor database to the 30,000 people who have the wealth to make a big gift, but that’s still many more than the 3,000 to 5,000 people that its fundraisers have time to seek out and ask for money, explains Ms. Korb.

Modeling, she says, not only helps the organization make smarter assignment choices, it can also flag donors that fundraisers might not have considered otherwise.

“Maybe we need to reach out to that person even though they’ve only made a few $25 gifts,” says Ms. Korb. “There’s a story there. So let’s call and let’s tell them how important the gift is to the overall mission of the institution.”

One of Ms. Korb’s biggest frustrations about analytics is that the most important information donors share with the organization is embedded in reports that fundraisers file after visiting with a potentially generous supporter, large “text blobs” that are difficult, if not impossible, to analyze.

Ms. Korb explains that if she is trying to compile a list of every donor who has been diagnosed with breast cancer or had a relative with the disease, she can search the reports for “breast cancer,” but she still must read every record to determine the context.

The problem, she says, isn’t insurmountable. With access to programmers with text-analytics skills, the fundraising office could make better use of the valuable information that resides in the visit reports, says Ms. Korb.

Making the Leap

A handful of nonprofits are making the leap to the next phase of analytics: studying the fundraising process itself.

Santa Clara University, which is just getting started with analytics, plans to study what separates its best big-gift fundraisers from lower performers.

Finding out will help the university, which has been seeking large donations for a relatively short time, improve its fundraising efforts, says Caroline Chang, assistant vice president for operations and campaigns at the university.

“Is it that they have a more mature portfolio, or is it that they ask more often?” Ms. Chang says. “Is it that they ask for bigger gifts? It’s probably a combination of all those things, but we don’t really know for a fact until we actually dig into the data.”

The University of Michigan’s data monitoring revealed that if a gift solicitation stays open for more than a year, it’s unlikely the donor will make the contribution. But until that point, there’s a good chance of getting the donation.

Being able to identify clear data points is helpful for managers, says Karen Isble, an executive in the university’s development office.

“That helps our gift officers understand that they need follow-up with donors where they have made an ask and understand that there are consequences to letting them languish on the vine,” she says.

Setting Targets

Data-driven forecasting also is taking some of the guesswork—and bravado—out of capital-campaign goals. Analytics helps nonprofits set realistic targets and determine how many additional fundraisers and how much increased productivity they need to meet an ambitious goal, says Sarah Williams, lead analyst and consultant at Marts & Lundy, a fundraising consulting company.

One of the big benefits, she says, is the impact on staff morale. “It used to be that the fundraising staff would hear the numbers that either the board or the president decided on and think, ‘Oh my God, we’re never going to be able to do this,’” she says. “And now they can see the data and think, ‘OK, this is a very attainable and achievable goal for us.’”

Charities can also show a modified version of their calculations to reassure donors they ask to make early large gifts, says Ms. Williams.

Despite the promise of analytics, the logistics of getting started can be daunting.

After the Humane Society decided to increase its focus on fundraising analytics, the organization looked for a year for a consulting company but failed to find a good fit, says Geoff Handy, a senior vice president at the Humane Society.

“We ultimately determined that staffing the analytics function in-house, where everyone on our marketing team could access it, where we’d actually learn to live and breathe data, would best serve us in the future,” he says.

The organization budgeted to hire a four-person analytics team. It took nine months to find the director, Emily Courville, who started in October. She hired her first analyst in March and hopes to fill the other two positions by the end of the year.

Competing against companies to hire experienced people and offer them attractive salaries is tough. The Humane Society is considering remote positions so the new hires won’t have to move to its headquarters in the high-cost Washington metropolitan area.

But more and more organizations are finding it worth the effort and expense to bolster their data operations.

Beth McDermott, associate vice president for development at Boston College, says data analytics has helped reveal new opportunities on its donor list.

“We may think that the person who we see regularly at events and who has been bobbing along as an annual leadership donor is our next best prospect for a six- or seven-figure campaign gift,” Ms. McDermott says. “But when we look at the data about the aggregate pool, we may find that someone like that is fairly far down on the list.”

How One Nonprofit Uses Data to Figure Out Who to Solicit

The University of Texas MD Anderson Cancer Center has 2 million people in its database. Each figure represents 5,000 people.

Wealth screening winnowed that list to the 30,000 people with the means to make a large gift.

Additional data analysis helps home in on the top 3,000 to 5,000 prospects to be assigned big-gift fundraisers.