Week Twelve: Acceptable Risk
Happy holidays, readers! Wherever you are as you read this, I hope have been able to relax for awhile and enjoy the company of family and friends.
In my last entry, I wrote about home and included a poll for the first time in my blog. Thanks to everyone who voted in my unscientific poll – looks like the majority of those who answered felt that their current city of residence wins the title of home. I’ll work on changing my mindset. Maybe by this time next year…
I begun this week in my former city, Chicago, for the CASE V Conference. It was nice to be home! The conference was great. I especially appreciated that you could attend the entire conference paper-free if you wanted. Conference organizers put the entire schedule, attendees and all into an app called Guidebook. I had never used it before but it was pretty cool- you could pick sessions to add to your calendar and even contact other attendees. I hope more conferences go this route in the future.
Earlier this week, I got an email from LinkedIn:
While good intentioned, LinkedIn is showing me jobs across the spectrum of organization type, job responsibilities, and region of the country that are connected to one or two common threads from my LinkedIn profile. Were LinkedIn a person, I’d think s/he hasn’t really done his or her homework before shooting this email off to me.
This got me thinking about big data. Adapted from the for profit world, our industry is embracing the big data trend, and prospect development shops of all sizes and types are integrating analytics to improve prospect identification and portfolio rebalancing efforts. Prospect researchers (who live in the qualitative world) are often tasked with verification efforts following predictive modeling or wealth screening projects. This becomes daunting, and researchers are faced with a dilemma: when to “call it.” Every researcher has the nightmare of delivering a meticulously prepared profile on a prospect, only to have someone in senior leadership inform them that they missed a critical detail in the report.
As researchers, we understand the need to get it right when completing a full profile, but how much uncertainty is acceptable when completing a “first pass” research assessment? Are we comfortable handing over partially verified information? If so, how do we communicate to our partners in fundraising the extent to which this information has been confirmed? And what do we do to safeguard against inaccurate or misleading information in an efficient manner?
Don’t lose hope- there’s an answer to all of these questions and more: Prospect Identification! Separating out a portion of your prospect research department (or your role if you’re a one-person team) into prospect identification work will allow you to establish, refine, and communicate guidelines around what goes into initial verification work. Make sure you communicate where this function lives in the development cycle (pre-qualification), and be clear about the type of information that you will verify or include (examples: one asset only, demographic data, basic biographic data). Finally, work with senior leadership and frontline fundraisers to make sure the delivery method is something they can use (via a data visualization tool rather than a spreadsheet).
I realize this isn’t a new idea, so I welcome feedback from any of you regarding your prospect identification efforts. What has worked? What hasn’t?