A common mistake that I encounter with clients is the assumption that data analytics is an absolute process. It is not. It is a highly effective process in helping identify, segment and prioritize, allowing non-profits to focus their energy strategically. However, as I wrote about in a previous post On the Importance of Qualifying Prospects data analytics projects are about Identification. Qualification (or Disqualification) of results is essential too.
In On the Importance of Qualifying Prospects, I focused on questions a gift officer should ask assessing both his/her current portfolio as well as newly identified prospects. I did not focus on the role that Researchers can play in this next and crucial Qualification step. Nor did I talk about still needing to occasionally prospect outside of your data analytics project results to truly find all of your organization’s potential. With that in mind, I want to share two Research-related examples:
First, to the point about needing to qualify your predictive modeling and electronic screening results:
While working with a client’s data recently I was reviewing a list of their best prospects for major giving potential. My list was initially based on strong predictive modeling scores. I then used past giving to my client’s organization and assets found through an electronic screening to further narrow my list. One prospect caught my eye – a seven figure donor to my client’s organization who had relatively unremarkable identified assets from the electronic screening.
Who is this person? A quick Google search revealed that this average middle-class person was covered in local newspaper after winning one of those mega-million type lotteries that many states participate in. Now, I realize that this was a ‘known’ prospect since the person had already given my client a large portion of their winnings. But it reinforced the point that modeling scores can sometimes reveal good potential even when electronic screenings results reveal very little.
Conversely, there was also a prospect whom I encountered at one point who had terrific modeling results and strong assets from an electronic screening, but the prospect had recently passed away. Clearly a case where disqualification was needed!
Second, to the point of sometimes needing to do good old fashioned research:
A posting to an APRA (Association of Professional Researchers for Advancement) LinkedIn Group that I belong to brought me to a blog post by Dan Blakemore. Dan shared an example of reviewing a mass media listing of “Hidden Billionaires” and his work researching each person on the list to see if they had any connection to the organization he works for. The short of the story is that one of the billionaires is married to an alum of his organization and he states that there is “very little possibility that [he] would have stumbled upon this information at a later date, because [the organization] did not even know that this alumnus was married.”
Now, I don’t know how this alumnus would have fared through a predictive modeling or electronic screening. I don’t personal work with Dan’s organization so I’m not even sure if they have ever embarked on such a project. But, what I can say is that it was a person, not an analytics project, who qualified this alum’s capacity. Hopefully Dan finds that the alum has a strong affinity to his organization too!
I can share many other stories in this same vein, but I’d love to hear from you! Do you have any good tales of the trade related to prospect Qualification? Please share them on this site or email me at firstname.lastname@example.org.
*Melissa Bank Stepno is a consultant for Target Analytics. You may reach her at Melissa.email@example.com
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