From surprise success to analytic success

publication date: Dec 30, 2018
 | 
author/source: Matthew Dubins

The news about the surprise multi-million dollar bequest by frugal banker turned social worker, Alan Naiman, is quite lovely, but this doesn't look like something that happened as a result of a well-entrenched planned giving strategy on the part of the charities that were given the generous gifts.

If you don't have a very well-established program in place, you will not have a viable Planned Giving model to develop analytics. My recommendation is to send surveys to your donors to assess Planned Giving readiness. Have events. Engage your donors. Find those whose life missions align with your organization. As well, find those who are single and don’t have kids. These people will be the foundation of your program.

Planned Giving solicitation and fulfillment is a lengthy process that should start long before the last year of your donor's life. It needs a pretty incredible confluence of strategy, people skills, cultivation, conversations, events, and a damned good relationship with your donors.

Charities: what is your plan for next year? Who here would like to know which donors are at risk of lapsing? Also, which of your donors will reactivate? No need for a crystal ball, when you have analytics on your side. Research on US charitable giving reveals less low and mid level donation money, and more in the major donor level. It would be good to have metrics on Canadian giving, but it's not hard to imagine the same thing happening here!

That said, I can't emphasize enough that the strategy has to come first. Planned gift predictive models are not magic. They are statistical algorithms that learn from previous examples of the behaviour in question.

I'm very much of the philosophy that in the right circumstances, predictive modelling can be an excellent supplement to a planned giving program, directing the activities of the gift officers to people they might not have previously considered. This modeling will find your best bets along the way! Once you have enough success with your PG program, then the modelling will be a great idea. In sum: strategy first, analytics after.

Matthew Dubins is Chief Donor Scientist at Donor Science Consulting: a truly Canadian consulting agency using predictive analytics, data visualization, dashboarding, and address correction to help you do better fundraising with the help of your data! You can reach him at matt.dubins@donorscience.ca and read more at www.donorscience.ca.



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