This blog appeared in the APRA Upstate New York newsletter in the fall of 2009.
As fundraisers, we focus our resources on major gifts prospects. However, with Big Data bringing out a lot more information from social networking, we now see the value of using modeling and mining tools to help annual giving, membership, and events programs. Our recent conversations have centered on engagement – a rather nebulous term we use to try to understand what our donors feel about us before they give for the first time.
Under the engagement umbrella is participating in events and communities, and so we explore this topic through this blog post. Even if our constituents participate only by reading our newsletter, we still have need to find and interpret the trail they leave behind when doing so.
Some participation measurements are easy if one has the data: number of events attended (in my own studies, number of events refused was an indicator of major giving, since one indicated a close relationship to my organization by regretting an invitation), most recent event attended, number of contacts received. These measures go along with number of historical addresses tracked and depth of family information tracked: They show that the constituent is sharing personal information if the organization diligently inputs the data.
Some analysis needs to be thought out. Examples include distance between the first newsletter sent and the first gift; response to direct mail; response to a podcast (how does an organization measure who listened to it?); mention of the organization as one’s top philanthropy. Some of these can be known by surveying, but surveying itself raises data entry problems and subsequently requires careful data manipulation.
Comparing the date of the first gift the first event attended, or to the first volunteer spoken with (if tracked), or to the first volunteership (and what if the voluneership came first?) are also great ways to understand constituent movement along the engagement (and then giving) ladder.
Using event dates in analysis can help one determine if a prospect likes to go only to, say, opening night, or only to events designed for high-end donors. Also, understanding the interplay between event attendances especially and giving allows for getting at the picture of those who like to go to events and don’t give and those who are further engaged after events and begin their giving history.
Annual giving can rely on RFM analysis (see our blog post on this) to assist with measuring a prospect’s increasing giving, including moving a donor up the giving ladder based on triggers, instead of designing the annual giving program segments based on program needs, for instance.
Also, a careful study of how different populations respond to different fundraising techniques (do engineers and Midwesterners prefer to be solicited by email?) is useful, and their response to the follow up technique is even more useful. For instance, if a prospect is acquired as a donor by direct mail after buying a duck at a duck race, then is she best solicited next by a phonathon, by a personalized letter, or by another event invitation? What are the stages and their progressions? And how does an organization cipher the different audiences? Like Harry Potter’s constantly moving stairways, an annual giving program would get completely lost to try to segment its population down to pools of, say, 5% of the population assigned to each pool. That’s 20 segments. It would be difficult to write that much copy.
The question of measuring participation falls flat on its face if the data is not there, even if it’s there in a way that it can be connected to the central database. For instance, I was able to talk to a museum which measured members’ activities while they swiped their membership cards. I could find out when they arrived, what day they liked to come, whether they bought in the gift shop, and their preferred café lunch hour. The catch is that the museum’s best prospects were giving at the patron level, and therefore walked through the concierge desk to be admitted: No swiping. We could not get data on the very people we cared about the most.
Remember, however, that data gives itself away in many ways. My favorite data example is the Smithsonian. They were trying to measure the most favorite exhibits. It took a while, but they finally figured out that counting the tile replacements in front of exhibits worked. I call that getting there by proxy.
What data do you have that can be a shadow of an indicator? Tweet us @Staupell or leave a comment below.