Building Major Gifts Caseloads – Why Traditional Methods Fail to Accurately Qualify Donors

It’s a problem as old as fundraising. How do you find, attract, and qualify people who can give major gifts to your nonprofit organization? Many methods have been attempted over the years. And as technology and data analytics have progressed, so have the various methods for building caseloads.

But what continues to distress gift officers and others who work at large nonprofits is – even with the newest technologies like AI – their caseloads continue to be bloated with too many people who aren’t qualified to give major gifts. Gift officers lose so much time on prospects who don’t want to give, don’t want to talk, and don’t have the capacity to give transformational gifts even if they wanted to.

Some organizations still rely on Dunbar’s number and ensure there are always 150 people in each caseload, even though there are better ways of determining caseload size and who should be added to them.

The traditional methods aren’t working.

And yes – AI is already a ‘traditional’ method. Why? Because even though it’s dressed up in shiny new clothes and has a lot of PR people working on its behalf, underneath all that, it relies on many of the same assumptions and failed beliefs that the older methods like RFM and wealth screeners use.

We’re going to unpack why none of the modern methods for building caseloads consistently works at identifying and qualifying major donors, and weeding out the rest.

Donor discovery and qualification is hard work. There is a way to do it better than all these insufficient strategies. But before we can explain why it’s better, you need to know why the traditional methods don’t work.

And before we get too deep into any of these methods, let us first make clear – we are not saying there is no value at all to using these methods for building caseloads of qualified major gifts donors. We’re simply saying they are inadequate for doing the job well. They fall short. And the reasons they fall short are inherent to each method and cannot be fixed. You need a new method.

The 2 Common Flaws in All Caseload-Building Strategies

One thing to say up front is that all these methods have two major flaws in common.

Flaw 1 – They Rely Only on Quantitative Data

Quantitative refers to hard, numerical data. Dates. Times. Amounts. Quantities.

Every method you’re about to see – RFM, wealth screening, predictive analytics, propensity, and AI – relies almost exclusively on quantitative data.

But it is qualitative data that tells you the most valuable information about a donor. Qualitative data refers to things like motives, interests, past experiences, emotions, and other non-numerical factors. You’ll see how the absence of these inhibits the traditional donor qualification methods from working as well as they promise to.

Flaw 2 – They Ignore Non-Donating Supporters

All donors are supporters. But not all supporters are donors – yet. You have passionate supporters who haven’t yet donated. They may be volunteering. They may advocate for you in various ways. They may engage or follow your work on social media. They might sign petitions and respond to letter-writing campaigns you send to your email list. And yes, these people are on your email list.

But every traditional method for building major gifts caseloads ignores supporters who haven’t donated. Why? Because there is no quantitative data that indicates they want to give, since they haven’t done so yet.

But there are a lot of wealthy people who volunteer. One study found that 30.4% of high net worth individuals volunteer, and nearly half of those do so with just one organization. And volunteers often become donors.

If you aren’t engaging and cultivating these supporters, you may be missing some of your biggest potential major donors. And if you rely on the strategies listed below, these people will likely not find their way to your gift officers’ caseloads.

Let’s go through these caseload-building strategies one at a time.

RFM – Relies on Past Behavior

What is RFM? RFM stands for recency, frequency, and monetary value. It scores potential donors from 1 to 5 on those three qualifications. How recently did they give their last gift – of any size? How frequently do they give? And how large are their gifts?

Donors who give frequently, in large amounts, and who gave recently will score high, and are added to the caseloads of your gifts officers.

But are these donors really qualified? Consider everything this model leaves out.

Maybe the donor gets an annual bonus that’s kind of large and chooses to give it to your organization, but outside of that, they aren’t actually very wealthy. Maybe the donor is giving to other organizations too and is already being generous beyond their capacity. Maybe they are saving up money to pay for their kids’ college, or an upcoming house purchase, or a big medical procedure.

There are countless scenarios we can imagine that would make it immediately clear that even someone who scores a 5 on all three RFM characteristics is a terrible candidate for major giving.

RFM simply looks at past behavior, and makes assumptions about what that means. It interprets what someone has done as an indication of what they will do. But it doesn’t consider the many other possibilities. It leaves that work to the gift officer.

So, gift officers using RFM have to sort through lists of major gifts prospects and weed out the ones who never should have been there in the first place. This takes time. It’s emotionally and mentally draining. And as you’ll see in a bit, it’s almost entirely avoidable.

See more problems with RFM

Wealth Screening – Relies on Outdated Data

Wealth screening is the ‘candy’ version of major donor qualification. It’s just so delightful to get a list full of names and contact information of wealthy people and imagine all the money on that list.

Wealth screening, as mentioned earlier, can be used for good purposes if you understand what it can do well. But if you rely on it to build caseloads of qualified major donors, you might be consistently disappointed and frustrated with the results.

Wealth screeners attempt to solve some of the problems in RFM, particularly the measurement of wealth capacity. As the examples listed in the RFM section revealed, just because someone gives a large gift a few times doesn’t mean they have capacity for a major gift. Maybe a distant relative died and left them $20,000. And they decide to give that to your organization. But they don’t have real, lasting wealth capacity.

Wealth screeners attempt to determine who has actual wealth capacity to give a major gift. So that’s a good goal. You should know if a donor prospect has capacity to give before connecting them with a gift officer.

The biggest problem is, wealth screeners rely on outdated information. They get their data from places like SEC filings, property records, and tax assessments. They may look at employment history, alumni records, and business ownership.

All of this information has to be updated frequently to remain trustworthy. But many wealthy people go out of their way to prevent governments and other entities from knowing their true wealth capacity. Furthermore, life situations change, and these data sources are often the last to know about it. And there are other major problems with using wealth screening data to populate major gifts caseloads.

The lure of wealth screening data is that it lets you avoid having to actually talk to people. You get the list. You presume it’s mostly filled with rich people. So then you create a direct mail, phone, email, or other sort of campaign to reach out to them.

But major gift fundraising and qualification cannot be done without personal interaction. It is a relationship-driven fundraising context.

And – when that interaction happens matters. As you’ll see in a bit, there are ways to ‘interact’ on a personal level using software automation that protects the valuable time of your gift officers until a donor prospect is fully qualified.

Predictive Analytics – Relies on Insufficient Data

Both RFM and wealth screening have obvious flaws. They rely on past behavior and old data. We hope that data is still accurate and that it truly implies about the donor what we think it implies. Often, it doesn’t.

Predictive analytics recognizes this problem, and attempts to use data to make predictions about future donor behavior.

When predictive analytics works, it can anticipate future events such as when an airplane engine part might fail. But this sort of prediction relies on having large amounts of data that gets continually updated. Furthermore, data on engine parts and anything else that is repeated and used in mass quantities allows computer models to accurately identify when the statistics are changing, and to then predict what may happen next.

But with major donors, almost none of that is true.

The sample sizes are far too small to engage in this sort of data-driven statistical modeling. There just aren’t enough wealthy people who also support your cause to come even close to making accurate predictions, no matter how much data you may have on them (and as you’ve seen, you don’t usually have enough).

So while it has good intentions, predictive analytics is rife with many of the same problems as the other methods for building caseloads of qualified major donors.

Propensity – Relies on Uncertain Assumptions

Propensity has some similarities to predictive analytics. But here, you’re focusing on two particular types of data to try to determine the likelihood of making a major gift:

  • Past gifts to your organization
  • Gifts and support for other nonprofits similar to yours

The problem is, propensity does the same thing as wealth screening. It gives the gift officer a false sense of security that they have a list of qualified donors, when in fact they do not. It forestalls the inevitable need to actually get out there and talk to people.

Major gifts fundraising is about relationships. And relationships can never be boiled down to quantitative data. Propensity models will look for data such as number of gifts, frequency of gifts, size of gifts, lifetime giving totals, and monthly giving habits.

Does this sound familiar?

It is.

Past data is no guarantee of future behavior. And past data can be outdated, inaccurate, incomplete, and misinterpreted. The only way to know for sure – which even propensity modeling services will tell you – is to go talk to donor prospects. You can’t escape that step.

Why? Because that’s the only way to collect the qualitative data that tells you what you really need to know.

And as for gifts supporting similar organizations, there are even more problems with this. See 6 reasons why a donor would reject giving to an organization with a similar mission.

AI and Machine Learning – Relies on Quantitative Data

Okay. You probably see the flaws in the donor qualification methods we’ve looked at so far. But what about AI? What about machine learning? AI, especially since ChatGPT hit the scene, is “new.” It’s different, right?

Well, yes and no.

What AI does differently is that it takes all the data you have on donors and prospects – from within your database and from external sources – and uses it to answer new questions.

For example, if you asked an AI fundraising system to identify donors who have capacity to give between $5000 and $25,000 and who prefer to be contacted by phone, it could produce that list faster and more accurately than these other methods.

AI is able to solve problems and answer questions. Some AI can even propose new questions you wouldn’t have thought to ask.

AI can also analyze more data, and more types of data. For example, AI can look at various characteristics of college alumni to identify those who have a high likelihood of giving and high wealth capacity.

AI can also identify more specific segments of potential donors for particular purposes, such as giving matching gifts, sponsoring an event, or likelihood of making a planned gift.

But all of this – all of it – again gets you back to the same place you started. The same limitations. The data is quantitative. What you put into software is what you get out. The AI does not know your donors on a personal level. It doesn’t know why they care about your mission, or who influenced them to get involved years ago.

It can probably outperform propensity and predictive analytics, but it can’t truly qualify your potential major donors. Your gift officers still have to take the list and start reaching out to the people on it.

There is no way to avoid this. And having bad leads and unqualified prospects wastes time and resources, and leads to annoyed and upset donors. See more problems with AI and machine learning.

Qualitative Data – the Path to Real Donor Qualification and Better Caseloads

Yes. There is a way to qualify major donors before your gift officers talk to them. It IS possible. You CAN do what these other methods of building major gifts caseloads are trying to do.

To succeed at this, you first need to know what you’re actually looking for.

What defines a ‘qualified major donor’ who is ready for outreach from a gift officer?

That is THE question of major gifts fundraising. Who is ready to hear from you, and how do you know?
Once you know a person is ready to hear from you, the barriers to successfully reaching out to them and getting a major gift are dramatically lower. You’ll experience far less resistance.

5 Traits of a Qualified Major Donor

Once a prospect exhibits all five of the following traits, you can confidently add them to a gift officer’s caseload.

  1. Timing – the timing is right in their life for a major gift
  2. Reason – they have a powerful emotional reason to give (usually because of their life story, their values, and their desire for belonging due to the people they care about or who cared for them)
  3. Engagement – they are engaged with your organization and mission
  4. Capacity – they have capacity to give big as reported to you by THEM
  5. Permission – they have signaled interest in hearing from you

Think about those for a moment. You will see why you need all five of these before a major gift is even possible. Take out any of them, and the chances of a transformational gift plummet.

No capacity? No gift.

Don’t want to hear from you? Hard to get a gift out of them.

Bad timing? They’re not ready.

No engagement? Then why would they give to you?

No emotional reason to support your cause? Then there’s nothing to motivate a large gift.

You need all five of these. And all the traditional methods of building major gifts caseloads can, at best, help you know items 3 and 4. The data can show you wealth capacity and past engagement. Might not be accurate. Might not be relevant in their lives anymore. But the data is there.

But the other items on that list – particularly items 1 and 2 – come from qualitative data that you can only learn by asking them.

Item 5 is a simple yes or no question – are you open to being contacted by someone from our organization? But hardly any nonprofits bother to ask that question. How much less resistance will your gift officers experience if the person they’re reaching out to gave permission to be contacted?

How to Collect Qualitative Data

The best way to collect the data you need to truly qualify major donor prospects is with surveys. You can do surveys over the phone or by mail. But at scale, and with lowest costs, the best way is by emailing surveys to people who have opted in to hear from you through this channel.

Surveys are powerful. They value the person’s opinion enough to ask for it. They value their time by letting them respond when it’s convenient. And they respect their privacy by allowing them to share as much or as little as they want. Surveys provide a way to help your mission.

See 6 reasons why surveys work so well at qualifying major donor prospects so you can build caseloads.

With surveys, you can collect information on all five donor qualification traits. And once a prospect has been identified as having all five – at that point you can add them to the gift officer’s caseload and prepare them for outreach.

How do you send out email surveys that achieve this? How do you know what to ask, or when to ask it? And how do you know when a prospect can check off all five qualities and be added to a major gifts caseload?

That’s what MarketSmart’s software was built to do.

Our system does what no other caseload-building method can do – it collects the qualitative data that is essential to accurate donor identification, qualification, and pre-cultivation. Our system tells you when a prospect is ‘outreach-ready.’

With our system, your gift officers never lose time and resources reaching out to people who have no interest, insufficient wealth capacity, or other reasons for not wanting to give transformational gifts.

Learn more about donor qualification


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