The Perils of Wealth Screening Only

Every vendor will tell you the one bit of data they have is the most important for segmentation. For years, we used RFM segmentation because it was what we had (if you still are using RFM, check out our Audience-First Marketing Playbook). F2F canvassing organizations talk about age as the predictive variable of whether a donor will retain because they have it. Email marketing systems make it easy to stop sending emails to those who haven’t opened in a while. Generally, these single-variable vendors are like the blind people describing an elephant—expert at telling you what their part feels like; inexpert in giving a full view of the situation.

One of the biggest purveyors of a single-variable view is wealth screeners. For a low price, these vendors will tell you the income and assets every individual on your file has (heck, we can do this as part of SimioEnhance data appends).

But to do this as the entirety of your mid- or major-donor conversion data strategy is to confuse a necessary condition (e.g., you need at least $1,000 to make a gift of $1,000) and a sufficient condition (e.g., just because you have $1,000 doesn’t mean you are ready and willing to give $1,000).

There are wealthy people who do not give.

There are wealthy people who only give smaller gifts.

There are wealthy people who give bigger gifts but won’t to you.

In short, in this case, wealthy people are just like people who are not wealthy but with more money.

This is why major donor meetings that start with “who do we know at the Gates Foundation?” make both direct marketers and major gift experts want to commit one of the -cides; whether homicide or suicide depends on temperament. Capacity doesn’t equal desire.

More importantly, a single variable focus ignores data that can help you answer some of these questions. For example, looking at co-op data can potentially tell you if that $20 donor with the capacity to give more has ever given more than $20 to any of the organizations they support. If not, getting them to make the jump to a four-figure gift is likely skipping several very important steps.

Similarly, a review of the ten most predictive factors for planned giving found seven highly predictive behavioral, demographic, and transactional variables to go with three wealth indicators.  The same is true in most major donor models—there’s a variety of transactional, wealth, demographic, and organization-specific variables that create the best model with no one variable or set of variables with the same power. Organization-specific and behavioral information is particularly important because even if you know if someone has the capacity and desire to make a large gift, you need to know that you are in the consideration set to receive that gift. It is not good enough to be loved; you must be preferred.

So is it good to know wealth data on your donors? Absolutely. But it functions best as part a robust data stew rather than a standalone predictor.