Creating a Better Fundraising Model for Non-Profits

May 16, 2014
Behavioral Science, Social Entrepreneurship

We've all gotten the call: a non-profit is asking for recurring donors to build a steady source of funding to maintain operations.

Professor Dan Ariely and post-doctoral fellow Lalin Anik believe they've found a way to help such groups achieve their goals.

In recent research, Ariely and Anik discovered that more people are willing to become regular donors during pledge drives when there is a promise to match donations if three quarters of donors pledge to give on a recurring basis.

Anik elaborates on the findings in a Fuqua Q & A.

Q) It might surprise some people that 75 percent is the magic number. After all, wouldn't a lower number seem more attainable to donors? What did your research show about why 75 percent yields the best returns?

Across three studies, Dan Ariely, Michael Norton of the Harvard Business School and I found that 75 percent is the sweet spot that sends a strong signal that many people are expected to become recurring donors and that the goal is attainable.

Imagine the following scenario: As you are making an online donation to your favorite charity, you are asked to sign up to become a monthly recurring donor. If you are like most donors (and we know that 60 percent of donors give once but never come back) you will probably not make such a commitment. Now imagine that on the check-out page, you see another message informing you that an anonymous donor will match all donations made that day if, and only if, X percent of donors agree to become recurring donors. What percent would motivate you?

If the match were set to kick in at too low a percentage, you might think that very few people are expected to become recurring donors. In contrast, if the number were set too high, you may think that many people are expected to become monthly donors, but you also might find it unlikely that enough people will agree. So, we find that 75 percent is motivating because it is the magic number that simultaneously signals that enough other people will do it and that the target is attainable.

Q) For donors, the 100 percent participation rate was too high a threshold. What is it about that all-or-nothing benchmark that influences their choices?

Though 100 percent might signal that many other donors are expected to give on a recurring basis, it makes donors stop and think, "Will everyone become a recurring donor?" The answer is a quick "no" for most. I think this comes from our everyday experiences. In a wide range of situations, we know that there is at least one person who will not follow through. The 100 percent requirement, therefore, becomes demotivating for donors. 
Q) Did your research discover anything about the effectiveness of a similar strategy for getting more one-time donations? 

Indeed, it did! We started our examination when a large online non-profit approached us with the simple but major challenge of finding a new model to turn one-time donors into recurring donors. We replicated the results across two large-scale field experiments and then further studied whether 75 percent is also magical for getting non-donors to donate. We found that the same strategy also works for motivating people to become one-time donors.

Q) Your research focused on one particular non-profit. How can other groups use this information when it doesn't apply specifically to donations?

We propose that our model might be used more broadly beyond the context of non-profits. In fact, if anything, the way we tested our model may provide a relatively conservative test: donors did not have any information about the behavior of other donors and donors were fully anonymous to each other.

Imagine instead an office manager who wants each employee in her office to get a flu shot. Applying our model, this office manager could inform employees that each person who receives a flu shot will get a $5 gift card to Starbucks but that the incentive will double to a $10 gift card if 50 percent of employees receive flu shots and quadruple to a $20 gift card if 100 percent comply.

In such settings, not only could the office manager provide progress reports, but the compliance of employees could be made public so that those employees who had already received flu shots could attempt to convince those who had not. This additional public pressure might enhance the effectiveness of our model.

Q) What do you hope non-profits and other organizations take away from your research?

Our model proved to be more effective at turning one-time donors into recurring donors than the standard matching model where all donations are matched. Though standard match is the most widely used motivator by non-profits, our results suggest that it might not be the best one.

In light of our findings, we would recommend that non-profits and other organizations encourage behavior by signaling that other people are also interested while framing these behaviors with realistic and attainable goals. One problem facing researchers and policymakers who seek to change people's behavior is that data often does not point in the right direction - for instance, when policymakers wish to encourage people to vote but must admit that only 25 percent of people voted in some previous election. Our model instead sends a signal of how many people are expected to engage in some behavior and is not bound by previous actual behavior. As a result, it may be particularly useful to invoke in situations where the frequency of the desired behavior is currently low.

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