Beyond Wealth Screening: How Behavioral Science Is Changing Prospect Research

Understanding the Difference Between Capacity and Readiness

Imagine two prospects with similar estimated giving capacities and comparable lifetime giving histories.

On a traditional wealth-based prospect list, they might receive nearly identical rankings.

But a closer look reveals something different.

One prospect recently attended an event, expressed interest in a particular program, participated in a meaningful conversation with a development officer, and agreed to a follow-up meeting.

The other hasn't had a documented meaningful interaction in more than a year, has no actionable next step, and has not responded to recent outreach.

Both prospects may have the same financial capacity.

But their immediate fundraising strategies shouldn't look the same.

The first prospect may be ready for a deeper conversation. The second may need reengagement or further qualification before a solicitation discussion makes sense.

The difference isn't wealth. It's demonstrated behavior and the context surrounding it.

That's where behavioral science becomes valuable.

Behavioral Science Has Been Telling Us This for Years

The relationship between human behavior and charitable giving isn't a new idea.

In their influential review of more than 500 academic studies, researchers René Bekkers and Pamala Wiepking identified eight mechanisms influencing charitable giving. Among them were awareness of need, solicitation, personal values, psychological benefits, and the perceived effectiveness of a gift.

Their research reinforces something experienced fundraisers already understand: philanthropic decisions involve far more than someone's financial resources.

Read the research: Eight Mechanisms That Drive Charitable Giving

More recently, a 2025 study published in Nonprofit Management and Leadership examined how peer-to-peer donors become direct organizational donors.

Researchers found that post-donation communication was associated with substantially greater odds of continued organizational giving. A machine-learning model also demonstrated that donor behavior could be predicted with meaningful accuracy in that particular setting.

These findings don't establish a universal model for major-gift readiness. They do, however, illustrate why relationship activity and behavioral information deserve consideration alongside traditional financial indicators.

Read the 2025 machine-learning study

Another useful perspective comes from the Fundraising Effectiveness Project.

Its October 2026 report showed that fundraising dollars had increased 6.6% through the second quarter of 2026, even as the number of donors declined 1.6%.

The report also found striking differences in donor retention based on giving frequency. Among donors who gave seven or more times in 2025, 90% had been retained through the second quarter of 2026. Among one-time donors, that figure was 14.4%.

That doesn't mean frequent annual donors are automatically ready for major gifts. It means patterns of behavior contain useful information that aggregate giving totals alone can obscure.

Read the October 2026 Fundraising Effectiveness Project findings

The research is there. The next challenge is applying what we know to the decisions fundraisers make every day.

We Already Have More Behavioral Data Than We Think

One of the things I've become increasingly interested in is how much useful information already exists inside our CRMs.

Think about what's available in a typical fundraising database.

We have giving histories showing when someone made a gift, how frequently they give, and which programs they support.

We have contact reports documenting conversations, interests, questions, concerns, and potential opportunities.

We have next steps showing whether there is an actual plan to move a relationship forward.

We have proposals showing whether a conversation has progressed toward a specific opportunity.

And we may have event attendance, volunteer involvement, board relationships, and other forms of engagement.

Individually, these records tell us pieces of a story.

Together, they may help us better understand where a donor is in their relationship with the organization.

The challenge is that much of this information exists across different fields, reports, and systems.

It can take considerable time to review hundreds of records and identify meaningful patterns.

This is where Excel, AI, and professional curiosity can change how we work.

Not because AI somehow knows what a donor is thinking. It doesn't.

But because it can help us organize information, identify inconsistencies, surface patterns, and identify records that deserve closer human review.

Instead of simply producing another ranked list, we can begin asking better questions.

Adding Behavioral Readiness to CAP Models

Capacity, Affinity, and Propensity are familiar concepts in prospect development.

Capacity helps estimate financial ability.

Affinity helps evaluate a person's demonstrated connection to the organization or its mission.

Propensity helps assess the likelihood of philanthropic participation using giving history and other relevant indicators.

I believe there's an opportunity to strengthen this approach by explicitly evaluating behavioral readiness.

Readiness is different from general propensity. It's focused on evidence that may help us determine the next appropriate action in a donor relationship.

For example, has the donor recently participated in a meaningful two-way conversation? Have they expressed interest in a specific program? Has a next meeting been accepted? Is there a proposal being discussed? Has the donor initiated contact?

These details can help us distinguish a prospect with considerable long-term potential from one whose relationship may require action this week.

Why I Recommend Starting With 15%

My suggestion is to give behavioral readiness approximately 15% of an overall prospect prioritization score, while continuing to rely on capacity, affinity, and propensity for the remaining components.

Why 15%?

Because I want behavioral information to have enough influence to change priorities when there is meaningful evidence, without allowing incomplete contact reports or a single interaction to overwhelm the established measures.

I don't consider 15% a scientifically validated universal weighting. It's a working recommendation: a reasonable place to begin testing and refining your own model.

Every institution will have different available data, reporting practices, and fundraising processes.

Some development officers consistently document contact reports. Others may be less consistent.

That shouldn't automatically determine whether a donor is considered engaged.

An absence of documentation is not proof of an absence of interest.

A well-designed model should recognize incomplete information, incorporate multiple relevant indicators, and avoid counting the same behavior several times across different scoring categories.

This is where the prospect researcher brings something AI cannot provide independently: an informed understanding of the institution's data, its fundraising practices, and what the available evidence actually means.

You Don't Need an Expensive New Platform to Begin

Another important point is that this doesn't necessarily require purchasing an entirely new technology platform.

You may already have what you need.

Start with a CRM export containing prospect IDs, giving history, estimated capacity, engagement information, relevant contact reports, next steps, and proposal activity.

Use Excel to organize and connect the fields.

Then, using an approved AI environment and appropriate data protections, begin evaluating patterns.

For example, you might ask AI to identify prospects with strong capacity and affinity who have not received a meaningful follow-up in the past 90 days.

Or identify prospects whose recent documented activity suggests an opportunity for a deeper conversation.

You might also examine proposals without actionable next steps or identify qualification prospects whose engagement has increased.

Importantly, AI-assisted analysis of contact reports should distinguish actual documented donor statements from interpretations generated by the model.

Every recommendation should be traceable to the underlying information.

And the first analysis should always be treated as a test, not a finished predictive model.

Review a sample of records. Check dates and calculations. Talk with development officers about whether the suggested priorities make sense. Track whether the recommendations lead to more meaningful conversations, completed next steps, appropriately timed proposals, and improved pipeline movement.

Over time, those results can help determine whether your behavioral weighting is appropriate.

The goal isn't to produce a more complicated spreadsheet. It's to produce better decisions.

The Role of the Prospect Researcher Is Evolving

I don't believe AI eliminates the need for skilled prospect researchers.

I believe it increases the importance of their judgment.

There was a time when gathering information from different sources, building detailed profiles, and organizing large amounts of data accounted for much of the visible work of prospect research.

Those responsibilities aren't disappearing. Accurate information still matters enormously.

But the opportunity to add value is expanding.

We're increasingly being asked not just to identify potential donors, but to help fundraisers understand their portfolios, prioritize relationships, evaluate opportunities, and decide what actions will make the greatest difference.

That requires more than the ability to operate a screening platform or write a good research profile.

It requires curiosity, analytical thinking, an understanding of human behavior, and a willingness to examine how data can support better fundraising decisions.

AI makes some of that analytical work faster and more accessible. But it also introduces responsibilities.

We need to protect confidential donor information, recognize bias, validate outputs, and avoid presenting AI-generated assumptions as established facts.

The Association of Fundraising Professionals' recent guidance on ethical AI use reinforces the importance of human review, donor privacy, and genuine relationship building.

Read AFP's guidance on responsible AI in fundraising

Ultimately, no model can establish with certainty whether an individual is ready to make a gift.

But a thoughtful model can help us recognize evidence, identify unanswered questions, and recommend a more appropriate next conversation.

Moving From Research to Strategy

For me, this is the most important part of the conversation.

Whether your title is Prospect Researcher, Prospect Development Analyst, Prospect Manager, or something entirely different, our work should help people make better decisions.

That means moving beyond a list of names ranked by estimated capacity.

It means developing a more complete picture of the relationship by considering both financial potential and documented behavior.

And it means giving development officers something they can use immediately.

Who needs attention?

Why does that person need attention now?

What is the next appropriate action?

Those questions should be at the center of how we evaluate our work.

After all, the purpose of prospect research isn't simply to identify wealthy individuals.

It's to help organizations build stronger relationships with people who care about their missions.

Capacity tells us what may be possible. Behavioral evidence helps us decide what to explore next. Professional judgment connects the two.

Join the Conversation: November 13, 2026

On Friday, November 13, I'll be presenting a webinar for the Ohio Prospect Research Network:

Leveling Up: Turning Prospect Profiles Into Fundraising Strategy

During the session, I'll demonstrate how AI-assisted analysis can help connect giving history, contact reports, next steps, proposals, and recorded capacity to evaluate donor engagement and readiness.

We'll explore how prospect researchers can turn existing CRM information into practical recommendations while keeping human judgment and verification central to the process.

The examples will use fictional data, and the approach is applicable across different CRM platforms.

Ryan Clement, MS | Director of Prospect Research, Cleveland State University