If you have encountered any of these scenarios, read on. If you haven’t, call me! I have many questions for you.
What Is the Original Design of Capacity Ratings?
Let’s start with the original use of capacity ratings as they were introduced in the late 1990s.
To put prospects into the right pool and the right solicitation segments.
That was it. When you get screening results back, it’s tempting to start qualifying the results in descending order of capacity rating, but really, the rating levels are designed for portfolio assignment.
“But wait, Marianne,” you may be asking. “Where does the ask amount come in?” That’s our great divide, right? Normally, the research officer would say, “That’s the art part of the work. The gift officer decides upon the ask amount based on the prospect’s liquidity, affinity, and time of life (or of wealth).” The gift officer might say, “That’s the piece we’re missing and we’re being underserved by our research team.”
Let’s explore further, then.
How are capacity ratings built?
Here’s the next contributor to the divide. Let’s watch the cascade of steps from looking at your prospect as a specific individual to assuming your prospect is just like every other high net worth individual:
How Can We Best Use Capacity Ratings?
Because we’re still talking about process, the next topic I have to mention is the ask amount. Think about it: Often the capacity rating is the floor level of the prospect’s ability to give. However, have you heard of a prospect giving more than a gift officer asked for? Yes, it happens with direct mail, and even phonathons, but I have not heard of a prospect being asked for $100,000 and giving $1 million instead. So, the ask amount is, in effect, the ceiling.
That floor and ceiling range can be our widest divide. Mind, we know that a lot of factors can lower a prospect’s actual gift from her highest possible gift, including:
Because the research officer may never meet the prospect, the gift officer does have a responsibility to get the feel for the prospect’s affinity and timing. But some of the other factors can be addressed through research and modeling (note that the WealthX product provides liquidity ratings as well, something that was new to the market when they started).
However, notes on a prospect’s liquidity cannot be plugged into a formula as easily as the prospect’s stock holdings. And plugging values into a formula is the best way to ensure consistent ratings across the pool. Some narrative is necessary to help the gift officer frame one real estate mogul vs. one venture capitalist who just cashed out, and how do we include that in a rating?
But, Marianne, Are You Trashing Capacity Ratings?
I am absolutely NOT trashing capacity ratings. Before we used them, we had no way of sorting our major gifts pools. It was hit or miss. Now that we have capacity ratings, though, we have the opportunity to use them in the best possible way. And one of the best possible ways includes modeling our capacity ratings.
Let’s look at that idea: capacity rating modeling. What if these ratings would inform the gift size, but after each asset/income variable was passed through a donor model instead of a wealth mix formula? What if the capacity ratings are right about the prospect’s wealth, but the prospect’s affinity actually names the gift amount? We’ll explore these.
Donor modeling is taking a variety of internal and appended data and finding the most likely gift amount for each prospect. The tools we use for modeling still work on averages, but the data can also include qualitative data, like the prospect’s hometown or whether s/he is married. Donor modeling has been used for the past 20 years to identify prospects for the research team to qualify, and some shops are now short-cutting that process and putting modeled prospects straight into gift officer pools.
I don’t like that second approach, though. A gift officer is a development shop’s most expensive human resource. I prefer to follow the healthcare model: Have the doctors do the exams and diagnoses, the nurses do the data gathering and post care, and the blood technicians collect the blood. Each of these three jobs carries decreasing levels of pay and responsibility, and that is an efficient system (if they talk to each other). In a fundraising shop, when the gift officer has to do field research on a prospect that a research officer has not yet examined, then the gift officer becomes a very expensive research officer – with added travel expense.
So, now we’re in a space where I am saying that capacity ratings by themselves do not do the best job of setting the ask amount, given our rating methods. I’m also saying that modeling alone doesn’t do the trick, either.
A Modeled Approach to Capacity Ratings
My recommendation is to add a modeling approach to capacity ratings. In other words, instead of letting Capgemini tell us what percentage of a rating is made up from the prospect’s total real estate holdings, let the modeling against the highest gifts from major donors set the ask instead.
The wonder of modeling (and machine learning) is that anything can be included – from income estimates to specific phrases in contact reports – and so affinity, liquidity, interest, and timing can all be accommodated. That means that a capacity rating can be based on something besides dollar totals. And that’s why we recommend a custom rating for each organization.
A custom rating would be based on each prospect’s likely highest gift amount. The rating for a given prospect, then, becomes the highest gift level given by prospects who look like him/her. Both the frustrating and the nice part of using actual gift amounts is that each organization weights different wealth indicators according to those prospects that give to the organization. In other words, a cause-related organization that primarily attracts gifts from hedge funds can model those characteristics (and prospect ratings) differently from a healthcare organization that primarily attracts gifts from small business owners.
Remember, though, that the highest gift a prospect gives is likely to be in response to an ask, not to the prospect’s internal capacity assessment. So, using highest gift amount from the CRM may have an inherent discount rate at the onset. That can create a myopic view of prospect capacity, including limiting the models to the characteristics of only those who have given.
How do we work around that? Good research would include known gifts to other philanthropies, but certainly not all. Using a standard capacity rating as a comparison – the kind that estimates wealth and then rates the likely gift from it – could also raise expectations if both the research and major gifts team tend to be too conservative.
As we continually reconsider how we assign and how we use capacity ratings, we have an opportunity to blend in analytics to refine our ask amounts. We also have an opportunity to bring back the method of discussing wealth in paragraphs rather than simple formulas in order to help frontline staff have better initial and solicitation conversations with prospects.
Finally, these thoughts bring up (again) the need for gift officers and research officers to talk to each other in person, rather than to pass data across a transom. Partnering in the work makes a lot of good things happen more often and more quickly. And every organization benefits from that.