The cutting edge in fundraising is becoming even sharper. In an environment where raising money is an increasingly data-driven process, your organization cannot afford to be dull.
The most successful nonprofits now combine their privately-held donor information with public records and their donors’ social media activities, and throw it into a predictive model to tailor a specific message to be delivered to a specific donor at the most appropriate time.
Nonprofit managers looking for a specific, actionable example of this need look no further than an improv theater in Houston.
Officials at the Alley Theatre say that their method of identifying probable donors — a system which brings in an additional $100,000 in donations each month — owes its effectiveness to “dynamic scoring” which relies on updating the model with the latest information available. If someone’s likelihood to donate has changed, dynamic scoring will flag that change.
How It Works
The fundraising department should be getting constant updates from whoever is running the analytics side. In the case of the Alley Theatre, the fundraising department is updated weekly on what the analytics are saying about big donors. The analytics can determine which donors should be targeted for which events.
Details from American Theatre:
The Alley Theatre of Houston has embraced the change, beginning with a pilot project in 2013 that has blossomed into full-blown use of predictive modeling.
The idea is not just to look at a [donor’s] assets, she says, but how they interact with the theatre: what shows they see, whether they volunteer, and any patterns in their giving history.
In 2014, the Alley started doing hard-hat tours during building construction for major donors. The model list gave them 100 names to target. These people, later invited to a range of other events, gave combined gifts of $200,000 in 2013, and now give $325,000. The analytics are now being refined to focus on potential donors’ interests, whether the attraction is new plays or education or buildings. “One lady gave $5,000 every year, and we appreciated her consistency, but after we found she was interested in new works, we began inviting her to specific new-work events—and now she gives $25,000,” Franck says.
By 2014, according to the Chronicle of Philanthropy, the Alley Theater had blended two different predictive models into its database:
…one that predicts which supporters are most likely to make a gift of $10,000 or more, and one that identifies ticket buyers most likely to become donors. The major-gifts model analyzes hundreds of factors. Some are related to the donors’ giving, like their number of gifts, the amount of their first gift, and whether they gave more with their second gift.
If somebody on the Alley Theatre’s donor list posts on social media that he or she just enjoyed a play that information will be combined with Alley’s database. If the database reveals that person has not made a donation recently, then that probable donor will be immediately targeted with a tailored message calculated to tickle that donation urge.
What Tools They Use
The Theatre’s models were created by fundraising consultant Bentz Whaley Flessner.
Additionally, the Theater uses a software called Tessitura, which is a fundraising databases originally created by the Metropolitan Opera. The software is specifically tailored toward the arts and culture space; if your organization resides in a different sector, you will need to search for software tailored for you.
Would it Work For My Organization?
Analytics and predictive modeling in general works for all organizations, regardless of size. You don’t need to have a team of fundraisers or an in-house data scientist.
In fact, these techniques have a bigger impact on smaller organizations, because they produce data insights that would otherwise take 20 people.
The Alley Theater only has three fundraising staffers, and only one works on fundraising full-time.
This specific approach — dynamic scoring — works best for nonprofits that deliver an experience; for example, theaters or hospitals. There are other types of analytics and predictive models that better track donors for advocacy organizations.