By Hugh Bennett
Associate Director, Prospect ID
Massachusetts Institute of Technology
As many of us consider upcoming conferences, a note on how Research is not dead. The Research field may seem to be morphing into two other valuable, but different fields - Prospect Management and Data Analytics. No one seems to be giving any Research track presentations anymore. Doesn't seem exciting enough. Some also question profiles as a service.
Well, there are trends in Research that show Research is not dead. Looking for relationships, linkage, and inclination come to mind as big drivers for many research projects today.
Another example - my small Research group (within a broader Research department) turns the definition of research on its head. We don’t look at an assigned record; confirm if the person is rich; seek inclination; and then write about them.
Instead, we troll external wealthy sites, places, people, industries, events, etc - with no promise of our alumni being present – and then see if any of those people are matches to people that we wish we had known about. It’s a different kind of block and tackling. All manual; one at a time; often timely.
An opportunity – a large percentage of publicly traded corporations do not identify schools of attendance for their executives on their websites or in SEC filings.
Another – most company M&A sales can be analyzed and ballpark estimated for proceeds to top executives can be applied, even with privately owned companies.
And – screening the top executives affiliated with a HNW industry (ex: every hedge fund with over $1B in assets under management) can churn up MG alumni unknown to be working in the industry or at that company.
No vendor service or screening does this. This is project based one-at-a-time trolling with name matching being done manually. Is that "John Cheng" who was just made a managing director at Goldman Sachs in Hong Kong, the same person as the "Xiaofan J. Cheng" in our database who has no career or address info?
This is productive for HNW individual identification and can be a valuable source for data in a database. It has to come from somewhere first, right?
We find information that is new to records and add it to records so they can become identifiable as major gift prospects. In our 250,000 plus records, many have next to no information or dated information. It is a big hole in every database. These holes cannot be matched in screening and are difficult to model or data mine from. So, we find new data by trolling likely wealthy marketplaces for matches that are currently unknown. We think this is really what should be defined as Prospect Identification. These are totally new prospects, not people who have been unsolicited, unassigned, or forgotten about.
We have added new major gift rate-able data to over 7,500 of our database records. And also added near MG rate-able data to thousands of more records that are now more up-to-date than before (so possible future upgrades). We have more than doubled our known, confirmed pool of wealthy rated prospects. We have also added significant industry, interest, wealth, and other coding to these records for future segmentation and contact ease of use (ie: data mining). And we have received major gifts from new prospects.
These new prospects did not come from screening, data mining, predictive modeling, or analytics. But now these records can be accurately used by data mining for the first time.
But wasn't data analytics employed by us for attempts at new MG prospect ID? They were. Experts here in analytics served up several generated pools of likely suspects. The people were "likely" due to inclination factors like event attendance, but also giving, wealthy towns of residence, average comp from zip codes, etc. But major gift capacity was not found by us in detailed researching of these pools. Researching these pools kept us from our otherwise productive efforts. Part of the problem is that we had already done a good job identifying the "low hanging fruit" of prospects that had suggestive data in their records. The new pools mostly served up connected, participatory, "super giver" type annual fund level capacity alumni who were accumulated lifetime donors or frequent small donors. What we needed was a way to find wealthy donor prospects where we didn't have data.
Analytics has been poor at telling us about the data that is not in a record. If you look in your database, I suspect that you will find lots of holes and dated info. For instance, 75% of our business info was over five years old. This is the kind of thing we can flesh out from external wealthy source data.
Our efforts seem different - few other higher ed orgs do this. But, we have made a case for another kind of research, and our results back it up. We are also lucky to be at a major institution where highly successful people have existed in many fields (sometimes unrecognized).
Analytics and Prospect Management can help you process what is in your database to make sure they are assigned, solicited, disqualified, etc. Or, Analytics may be useful to annual fund departments. But it is not how one actually finds new major gift prospects in a fairly static pool like ours at a higher ed org.
It is important to revitalize your pool with new prospects. Otherwise, you may be just circling the drain asking the same people for money over and over. Even if they are well analyzed and managed. Research can help.
© 2018 New England Development Research Association