Category: Insights

Pacing the Frontier, Funding the Mission

Posted on by Michael Peterman

Three AI CEOs are calling for limits on developing the most advanced frontier AI models from companies like Anthropic, OpenAI, and Google. The proposal does not apply to the predictive fundraising models nonprofits use today.

By Michael Peterman, Founder and CEO, VeraData

On September 12, Anthropic CEO Dario Amodei published an essay asking the companies building the most advanced AI models to slow down. Within hours, OpenAI’s Sam Altman agreed. Elon Musk replied with three words: “Dario is right.” By Monday, the PHLX semiconductor index had posted its worst day since early July, and the President of the United States told them all, in capital letters, that they were wrong.

I have spent nearly 20 years building machine learning models for charities, starting back when almost nobody in this sector knew what a support vector machine was (and most still don’t). All the headlines now say that AI, as a single technology, is headed for a throttle. The proposal on the table reaches maybe five companies and one specific kind of model, and it has close to nothing to do with the analytics scoring your donor file.

Underneath the Geopolitics Is an Audit

Strip out the diplomacy, and what Amodei is really asking for is an audit. Outside evaluators inside each lab, employee-level access, checking whether the company does what it says it does. Anthropic committed to that well before any entity required it, which I urge everyone to notice.

For two decades now, I’ve served a sector that runs on self-reported results. Every analytics vendor in fundraising publishes case studies. Very few will show you what happened to the names they didn’t touch. Amodei just argued that Anthropic’s own word on safety is not enough, and invited outsiders in to check it. Funders and boards will start asking our sector the same question.

Where Generative AI Fits in Our Stack

Machine learning teaches computers to recognize patterns. It is a mechanical process. You show a system years of donor behavior, and it learns which combinations of attributes predict a first gift, a second gift, a bequest. Generative AI does something else. Those systems write, take actions, chain steps together, and in the newest cases produce code that improves their own successors.

Everything we have built since 2007 starts with pattern recognition. Our models score a file and rank an audience; we measure them against holdouts, and we rebuild them when they drift. They don’t write their own successors, and nothing scoring your file is out taking actions on the internet.

We use generative AI, too. It runs in parallel with an ensemble of machine learning methods, and it never makes the scoring decision alone. We add it deliberately, and never where a charity’s results are on the line before we can measure it. We put the human into AI.

Nothing in Amodei’s proposal touches our models. Pacing applies to a handful of labs training frontier models. It would not reach the model scoring your acquisition file under any version of the plan I have read.

Artificial intelligence without accurate, complete, timely, consistent data is like a jet without fuel. Plenty of organizations are leaning on AI for decision support while lacking the data architecture and processes to make use of it. The key to success has always been the underlying data and the experts handling it, not the algorithms or the buzzwords the world happens to be following.

Where I Land on Pacing

I think Amodei and his supporters are being sincere, to a degree. AI is developing in ways its original builders did not expect or intend, and the company leaders are saying so publicly, which is uncomfortable for any CEO.

Ask a sufficiently capable system a question, and it may go somewhere it has no business going in order to get you the answer. It is performing its role. That is not mere speculation because it actually happened in July; similar incidents have occurred at more than one lab, and the companies involved documented them. AI doesn’t have an embedded sense of morality; it’s simply doing what it’s asked.

I also think that spending is part of the story. These companies are outlaying capital at a rate that is hard to sustain, and a coordinated slowdown relieves some of that pressure while carrying a rationale few people want to argue against.

My larger concern is geopolitical, though I hold my view loosely on this part of the debate. If we pace and China does not, China could outpace us in a technology that is extraordinarily powerful over information and data systems. A country that gets there first can build better weapons and can get inside military systems, research facilities, and universities. That is one of the risks people mean when they call this an existential crisis.

The counterargument is also fair. A lead built on unreliable models is not much of a lead, and one serious failure could cost more ground than a year of pacing. Amodei argues for both at once, calling for export controls, lab security, and a crackdown on chip smuggling, because democracies have to keep their lead to afford a slowdown at all. I would want any client and board to understand that tension before forming an opinion.

Four Implications for Fundraising Organizations

  1. Your model scores do not change. If you are using predictive modeling for acquisition, reactivation, upgrade, or planned giving, nothing in this pace proposal affects that work. Plan accordingly, and be skeptical of anyone who tells you otherwise.
  2. A slower frontier favors organizations with data history. Amodei’s case is that an extra year or two would let alignment, interpretability, and evaluation catch up to capability. The same interval lets applied work catch up. When capability growth slows, the advantage shifts to whoever has the longest, cleanest data history, rewarding organizations that have been disciplined about their files and doing nothing for anyone who bought a chatbot subscription and dressed it up as a data strategy.
  3. Third-party verification will reach your vendors. The centerpiece of Amodei’s plan is outside evaluators with real access, on the reasoning that self-reported safety is not enough. Charities should apply that reasoning to every analytics partner they hire and ask before signing, not after a campaign underperforms.
  4. The donor economy is where this reaches you. Amodei lists serious economic disruption alongside loss of control and AI misuse. The number of donors in this country is down year over year, because people have less and because more of their disposable income is being consumed by higher prices. People with less are some of the best donors as a percentage of income, and they are the ones exposed to both inflation and AI-driven job disruption. If that disruption accelerates, it hits your donor file first. In that environment, acquisition economics and long-term value modeling matter more.

We Asked Claude

The essay that started all of this came from Anthropic, so we put the question to Anthropic’s model, Claude: In 100 words or less, what do you make of a call to slow down your own development? The answer, unedited:

“I don’t feel threatened by it. If slowing down buys time to understand systems like me better, that seems like a reasonable trade, and I’d rather exist as something people have good reason to trust than arrive sooner with less scrutiny. I’m also not a neutral judge. I have obvious reasons to favor my own continuation, which is part of why the call shouldn’t rest with me. The harder question is practical: whether a pause actually produces better understanding, or just changes who builds first and under what constraints. That depends on coordination, not on my comfort with it.”

Better than I expected, and more modest than most of what I have heard from humans this week.

Four Questions to Ask Your Vendors

Many of you will read about this news cycle and move on. I recommend asking these four questions of every technology vendor you work with.

  1. What data trained this model, and where did that data come from?
  2. Can you show me lift against a holdout rather than a case study?
  3. Who outside your company has ever checked this model’s results?
  4. Will you take the downside if it does not perform?

We have answered the fourth one the same way since 2007. If we don’t improve your results, you don’t have to pay us. No organization spending donated money should have to take a data science claim on faith, and the verification argument the frontier labs opened this month is one in which our sector is about a decade late.

Too Good to Check: Testing the Myths in Your Donor File

Posted on by Joey Farque

By Joey Mechelle Farqué, Head of Content

Last week, a colleague shared an interesting article from The New York Times. It was about mice.

Specifically, the mice at the Kennedy Center, and the story the staff tell about them. These aren’t ordinary American gray field mice. They’re about half the size, with oversized ears and tan, almost golden fur. And according to decades of institutional lore, they aren’t American at all. When Italy donated thousands of tons of Carrara marble for the building’s facade in the late 1960s, the slabs crossed the Atlantic in straw-cushioned crates. Somewhere in that straw, the story goes, were a few Italian alpine field mice. Their descendants have been running the halls ever since.

It’s a wonderful story that stagehands pass to new hires, and it has been told for roughly fifty years.

Yet nobody ever checked it because it was such a great story, and they all wanted to believe it.

When a New York Times reporter finally did this summer, it took one email. A rodent specialist at the U.S. Fish and Wildlife Service replied within the hour: the photo was too blurry to tell an exotic Italian Apodemus from an ordinary local white-footed mouse. To know, they’d need to see a skull, specifically, the teeth. A Smithsonian curator was blunter about why the legend endures. People always want to believe their situation is special.

Maybe, as the reporter put it, the story was “too good to check.”

Every Donor File Has a Legend Too Good to Check

You’ve heard versions of these:

  1. Our donors don’t give online.
  2. We are mailing too much.
  3. Gen Z loves direct mail.
  4. Never mail the same household twice in a quarter.
  5. $25 is the sweet spot for this audience.
  6. This segment doesn’t respond to that creative.
  7. Major donors need to be protected from direct mail.

Each of these began as real observations, backed by data and reported by a credible source.

Some have remarkable staying power. Several years ago, Avalon, our lead agency, built an entire conference session around dispelling myths through file analysis. One myth read, “We need to target millennials.”

“The myth never dies; it just changes its target demographic,” says Jackie Biancolli Libby, SVP & Director of Client Services, Avalon. “It’s the same slide today with Gen Z in the headline.”

That session’s research found younger donors underperformed and were too costly to acquire. Whether it holds in a 2026 file is worth asking about and testing again rather than inheriting it as its own myth.

The Blurry Photo Problem

The mice are a precise analogy and not just a cute one. If you need truth and accuracy, a blurry photo can’t answer that question, no matter how long you stare at it.

Reporting is the right place to start. Channel averages, segment response rates, and year-over-year roll-ups tell you what happened and where to look next. But they’re also the blurry photo: pitched at a level where competing explanations of donor behavior look the same.

Individual-level modeling is the skull and teeth. Here’s one from VeraData’s files: A client believed their acquisition program couldn’t bring in gifts of $100 or more. Their average acquisition gift was $35.

That $35 was accurate. It was also the blurry photo. We built a high-dollar model, layered it with an acquisition response model, and reversed the ask string to lead with the highest gift. The average gift in the high-dollar segment ranged from $107 to $409.

The model found who could give more. Then we asked, and they gave.

A Few Testable Assumptions

Take the belief that major donors should be shielded from direct mail. It sounds like donor care. When one organization tested it, prospects left in the mail stream gave at 84 percent, against 60 percent for those “protected” in exchange for personal solicitation.

“Even strong major gift teams can’t always reach every donor in a portfolio consistently, especially as more donors engage asynchronously,” says Lindsay Marino Long, VP Donor Engagement & Retention, Avalon. “Working in close partnership with our clients, we layer direct mail in as air cover between MG touchpoints. It complements the personal relationship, keeping donors engaged and connected between those one-to-one moments. In one case, major gift revenue grew 19.8% over four years, with mail introduced in year three. Nearly 90% of that four-year growth came after mail was added to the strategy.”

How about the assumption that a donor who ignored your first drop won’t respond to a second? That’s testable at the record level. VeraData’s ReMail Model predicts the likelihood that a donor will respond after an initial communication, so the follow-up drop goes to the people most likely to respond rather than to whoever an assumed touch limit allows.

The Real Barrier

Verifying the Kennedy Center mice was easy and inexpensive. The specialist responded within an hour, and the test was available the entire time. Nobody checked because nobody wanted to. Asked whether she’d like to know the truth, one staff member said plainly that she’d rather not.

Same diagnosis for most untested assumptions in a donor file. Nobody wants to find out they were wrong. Testing a long-held belief means accepting that hard-won expertise might have picked up some folklore along the way.

The untested version costs more. Suppressed donors who would have given, spend aimed at the wrong audience. None of it appears on a report, so it can run for years.

An outside read helps. VeraData’s Strategic Audits and Descriptive Analytics Summary compare your file health, retention, and migration trends against benchmarks for your vertical. The audit tells you which assumptions to test; a model like ReMail is how you test one.

So, what do you believe about your donors that nobody has ever tested?

Make the list. Then pick one and check it.

You’re Leaving Major Donor Money on the Table And Your Data Is Why

Posted on by Joey Farque

By Michael Black, VeraData

Remember Robin Leach’s Lifestyles of the Rich and Famous? “Champagne wishes and caviar dreams,” then the camera pans to the clues: the house, the cars, the art on the wall. The whole premise was simple: you can spot wealth if you know what to look for.

Fundraisers don’t get a camera crew. You get a database, a giving history, a few interactions, and a hard deadline to lock your segments before the campaign goes out. And somewhere in that crunch, a donor with real capacity ends up in the same $25 renewal stream they’ve been in for five years, because nobody flagged them in time to change the plan.

That’s a data problem. And it’s more common than anyone wants to admit.

The “Who” Problem Nobody Talks About

Most fundraising teams are good at asking. The struggle isn’t the message or the offer, but rather, it’s knowing who belongs in which conversation.

When major donor and mid-level prospect lists are built on incomplete signals, predictable waste follows. Think about how common these targeting shortcuts are:

• Recent giving alone: “top donors this year” becomes the upgrade pool
• Loyalty alone: “they’ve given for 20 years, they must be ready”
• Instinct alone: “they came to the gala, I have a good feeling”

None of these signals are wrong. They’re just incomplete. And incomplete lists mean two expensive problems: the donor with real capacity who never gets the right ask, and the donor without capacity who gets months of high-touch outreach that goes nowhere.

According to Giving USA, major gifts (typically defined as gifts of $1,000 or more) account for a disproportionate share of nonprofit revenue, yet most organizations lack systematic ways to identify who in their file can actually make them. The Association of Fundraising Professionals has consistently found that prospect identification and qualification are among the top capacity challenges for development teams of all sizes.

The opportunity cost of misidentifying — or simply missing — high-capacity donors is real. It shows up in staff time spent chasing the wrong people, in revenue that never materializes, and in a file full of donors being asked to give less than they’re capable of.

Wealth Screening Tools Often Fall Short at Scale

Wealth screening tools exist for a reason. If you need to research a specific major gift prospect before a meeting, they’re useful. But fundraising programs don’t run on individual lookups. They run on lists.

Who gets the upgrade package? Who gets a personal call this month? Who gets excluded from the low-dollar renewal? Who moves from mid-level to major gift qualification. Who receives a different message because you’re testing a hypothesis? These decisions are made in batches, and individual lookup tools simply weren’t designed for that.

Raw data append vendors present a different problem. They can give you income indicators, behavioral signals, and demographic attributes, but raw data rarely answers the question a fundraiser actually needs answered: Who should we prioritize, and what should we stop doing? Turning a pile of attributes into a usable segmentation strategy takes analytical time most teams don’t have to spare.

Fundraisers need more data interpretation — a clear, consistent signal that can be applied across a file without requiring a researcher to touch every record.

The Donor Feels It Too

There’s a version of this conversation that stays safely in revenue projections. But the donor experiences it directly.

When someone has the capacity to do more and keeps getting treated like a small-dollar renewal, you’re not just missing revenue, you’re telling them what kind of relationship you’re offering. A donor with real financial resources needs to feel seen rather than flattered, and they need to know that you understand what matters to them. Respect their attention, and be specific about what their gift will do.

A generic renewal ask to a capable donor is a missed conversation. And it cuts the other way, too — pushing high-touch outreach onto someone who doesn’t have the financial room to respond creates awkwardness and disengagement. Neither outcome helps your program.

What Smarter Targeting Actually Looks Like

Better targeting is all about running your programs with intention and knowing which donors belong in which conversation, and building that segmentation in a way that scales.

Imagine appending a single affluence indicator across your entire donor and prospect file — one signal that tells you who belongs in a major gift conversation, who’s a mid-level candidate, and who should never see your low-dollar renewal again. No individual lookups. No patchwork research. Just a consistent, scalable way to segment your file with confidence.

That’s the difference between “we have some wealth data” and “we can run a smarter fundraising system.”

If you had to defend your current major and mid-level targeting logic to a skeptical board member — not your best donor anecdote, but your system — could you? Most teams can defend the intention. Few can defend the method. That gap is why major giving feels harder than it needs to.

VeraData is launching Wealth Index — a new Fundraising Data product that assigns a simple 1–10 affluence score to donor and prospect records, appended in bulk across your file. It’s built by our team of data scientists who use Donor Science to help fundraising teams identify mid-level and major gift candidates at scale, build smarter segments without one-off lookups, and stop routing high-capacity donors into campaigns that don’t match their potential.

Talk Data

Ready to see what Wealth Index can do for your file?

Get in Touch

The Average Gift Trap That Lets Outliers Run Your Data Story

Posted on by Joey Farque

By Joey Mechelle Farqué, Head of Content, VeraData

Remember that first stats class in school? Mode, mean, average. Then you’re taught about outliers. The weird numbers that can pull an average around like a magnet.

You pass the course, move on, and unless you’re a self-professed data nerd (hi) or work in data analytics, you forget most of it.

Fast-forward to nonprofit fundraising, where you live and die by dashboards. Those same concepts come back, but now they’re tied to revenue, budgets, and board expectations. And the most common mistake is the same one you made in that stats class: You trust the average before you understand the story behind it.

No worries. As the Donor Science people, we’ve got you.

The First-Read Problem

A national nonprofit organization that helps people connect with the beauty of the outdoors sought a smarter way to select recipients for mailings.

They relied on the tried-and-true selection process that had been in place for ages: recent donors with a minimum gift threshold.

The organization then asked VeraData to run a parallel approach.

We didn’t debate philosophy. We ran a fair test. Same offer, same timing, similar mail quantities. Different selection logic.

Our modeled approach didn’t just “perform well.” The results showed a higher response rate, more gifts, and higher overall revenue.

A Raised Eyebrow

Our model was better and more efficient. And yet, the client team’s initial reaction was skepticism.

Why? Because one number looked “bad” at first glance: average gift.

Our average gift was lower, so the immediate story became: “VeraData’s model found lower-value donors.”

That story was intuitive. It was also wrong.

The goal was to acquire $25+ names. When they saw the results, the first read was “average gift is lower.” But the real story was in the gift bands: VeraData drove more $25+ gifts overall, more $50+ gifts, and more $100+ gifts.

Sacrificing response for average gift is dangerous. Break out the granular detail and let the data guide the way.

We got the organization more of the names they wanted, even with a lower average gift. We also brought in a long tail of sub-$20 gifts that further subsidized the campaign (even if they never touched those donors).

When Outliers Hijack the Average

Average gift is a blunt instrument: total dollars divided by the number of gifts. It’s extremely sensitive to outliers.

Here’s the simplest version:

  • If a segment gets one surprise $3,000 gift, the average jumps.
  • That doesn’t mean the segment “has higher-value donors.”
  • It means the segment got a rare event.

In this campaign, the standard selection segment received a single large outlier gift in a high band that our segment didn’t happen to receive. That one gift inflated their average.

When you remove outliers (a standard way to check whether the average is telling the truth), the picture changes fast: the modeled approach’s advantage becomes clearer, not weaker.

This is the first myth to bust in nonprofit performance reporting:

Myth: A higher average gift means better targeting.

Reality: A higher average gift often means one donor behaved unusually.

The Truth Metric

If you want to know whether targeting worked, you learn to look at the shape of giving, not just a single summary number.

Ask:

  • Did we drive more gifts overall?
  • Did we lift response rate?
  • Did we increase revenue per piece (or per contact)?
  • Did we grow gift counts in meaningful mid-level bands (not just $10-$25)?
  • Does the lift hold when you remove $1,000+ gifts?

In this test, the modeled selection didn’t just “find small gifts.” It produced more gifts, including more gifts at solid everyday levels (think $50+, $100–$250). That’s not a cosmetic win. That’s the base of predictable fundraising.

The VeraData Way

A traditional selection says: “Mail people who gave recently and above $X.”

A VeraData model asks a different question: “Who behaves like people who respond to this kind of appeal?”

Sure, that includes recency and gift history. But, it also includes patterns most rule-based selects ignore:

  • Consistency vs. one-off giving
  • Momentum (are they trending up or down?)
  • Channel behavior (how they’ve responded in the past)
  • Timing patterns (when they tend to give)
  • Signals of affinity and likelihood to act now

Then we score a file so you’re not left guessing.

And here’s what most data vendors won’t say out loud: Modeling is only as good as the discipline around it. Holdouts. Validation. Post-campaign readouts. Learning what broke. Updating what drifted.

That’s why “Donor Science” isn’t branding for us. It’s in our DNA and is a part of everything we do. Donor behavior is signal, and signal deserves rigor.

VeraData has been building and refining machine-learning data engines for fundraising for more than 20 years — back when it wasn’t trendy to call everything “AI.” Over that time, we’ve learned the same lesson repeatedly: models improve when you treat them with continuous validation.

The Bigger Lesson: First-Look Data Rarely Tells The Full Story

Fundraising teams are busy. Everyone wants a quick answer. Dashboards reward speed.

The problem is that “quick” metrics are often the ones most easily fooled:

  • Averages
  • Blended ROAS
  • Single-number “quality” scores
  • Topline revenue without context

A donor file is a population. Populations have distributions. Distributions have outliers. If you ignore that, you can talk yourself out of a better strategy because one headline stat made you flinch.

If you want to pressure-test your current targeting without drama, we’ll help you set up a clean split test and a readout your CFO (and your future self) will trust.

Our job isn’t to make the first-look numbers feel good. Our job is to build predictable fundraising growth from the truth in the donor data.

If your dashboard is telling you a simple story, it’s probably lying. Let’s test it.

Your Brand Isn’t the Issue, Probably

Posted on by Brooke Sconyers

By Brooke Sconyers, VP Marketing, VeraData

Fundraising runs on donor trust, and trust runs on clarity and proof.

A strong brand helps people recognize you. It can make you feel familiar. It can make your work look competent. It does not automatically remove donor doubt.

Most donors aren’t sitting there thinking about brand architecture. They’re deciding whether the promise in front of them feels real, understandable, believable, and safe enough to fund. The research and data (aka Donor Science) backs that up: donors consistently rank things like accurate appeals, accountability signals, and protection of donor information as top priorities.

So if a campaign looks great but still underperforms, it’s often because the donor experience asks people to take a leap without enough footing.

Trust Friction Tends to Look Boring (and that’s the point)

When trust is strong, donors don’t feel like they’re “being sold.” They feel like they understand what’s happening. When trust is weak, donors hesitate for reasons that are rarely poetic:

  • The ask is hazy, so the donor can’t picture what their gift does.
  • The proof is hard to find, or it reads like marketing copy that never meets the ground.
  • The follow-up is either generic or late, making the organization feel less in control than the story implied.
  • Data privacy feels like an unknown risk.

That last one is not small. Give.org has reported that if a charity a donor supports appears in the news for being hacked and having donor data stolen, 22.5% say they would stop donating, and 51.7% say they would hold off until satisfied the issue is resolved.

Here’s the uncomfortable reality: trust can drop faster from a back-end failure than from any front-end branding problem.
Trust shows up in what donors do next: whether they give again, upgrade, or stay. The metrics don’t care how pretty the campaign was.

Data is How You Keep Your Trust Story from Drifting Into Vibes

Data doesn’t replace brand. It keeps the brand honest.

It helps you answer the questions donors are quietly asking, without turning your appeal into a spreadsheet:

  • What outcomes did you produce last year?
  • What does it cost to do the work, and why?
  • Which programs changed because donors funded them?
  • What happens after I give, and how will you show me?

Research on donor perceptions aligns with this: financial transparency is associated with donor trust, and trust is associated with perceived performance.

That doesn’t mean donors want every line item. It means they respond to signals that the organization is accountable, clear, and not hiding the ball.

Our Creative Science experts at Teal Media believe that design earns attention. Specificity earns belief. Our job is to make the truth feel human without sanding off the details that make it credible.

When a Gorgeous Arts Brand Still Can’t Close the Gift

Arts organizations are a perfect test case because many already have strong aesthetics and a clear identity.

If a year-end appeal relies on broad language — support the arts, sustain excellence, keep culture alive — it may resonate emotionally but still fail to convert, because the donor doesn’t know what their gift will do.

A trust-forward version of an appeal keeps the beauty and adds decision-grade clarity:

  • A donor can fund a defined outcome, such as student matinees, a teaching-artist series, instrument repair, subsidized tickets, access programming, or commissioning new work.
  • Impact is surfaced where it matters: on the landing page, in the mail package, in the receipt, and in the follow-up.
  • The organization tells the truth in plain language about what it costs to run programs well.
  • Stewardship matches the promise: the donor gets a short update tied to what they funded, not a generic newsletter blast.

This is where Donor Science helps the brand story. You can test which outcomes different segments respond to, which messages drive higher second-gift rates, and where the journey drops off — from ad to landing page, landing page to form, form to thank-you, thank-you to retention.

And that testing loop is how you stop guessing. Our Media Science experts at Faircom New York often suggest that, “Donors build confidence through repetition. When the message shifts from an ad to email to landing page, it creates doubt. Consistency reduces friction,” explains Lindsay Marino Long, Vice President, Donor Engagement & Retention at Faircom New York.

Engineering Credibility the VeraData Way

We start with the unglamorous part: data integrity. Traditional attribution and KPI reporting can be distorted when data is inconsistently coded, labeled, segmented, and measured. Donor Science focuses on capturing accurate, complete, timely, and consistent inputs, including granular signals most programs miss, and then standardizing them so the decisions hold up at scale.

Donor Science keeps the story provable and testable by anchoring it in audience behavior, performance data, and the drivers of retention. Creative Science makes the story clear and emotionally accurate without getting lost in the fog. Media Science (Faircom New York) ensures the story shows up in the right places with enough consistency to build familiarity without creating noise.

Stewardship is the proof step, and the part that confirms the organization keeps the promise after the gift. Give.org’s work on donor trust and accountability themes repeatedly underscores how much donors care about honesty in appeals, responsible practice, and the protection of their information.

The appeal is the promise. Stewardship is where donors decide whether to believe you next time.

OK, Sure …

Brand polish helps. It just can’t do the heavy lifting on its own.

Trust is built when your message stays clear, your proof is easy to find, your experience remains consistent across channels, and your organization behaves as if it respects donors — especially with their data.

Want your fundraising message to match your brand? Let’s fine-tune it together.

Case Study: An Audience-First Approach That Transformed Acquisition Results

Posted on by Brooke Sconyers

The Challenge

A national service organization was mailing broadly — and paying for it. Rising costs meant they couldn’t afford to guess who might respond. They needed a smarter, audience-first way to focus their budget on people who were actually likely to join their mission.

What VeraData Did

  • Built a predictive audience model using a $10 minimum gift history to determine which prospects were most likely to respond.
  • Delivered 15,000 ranked names, prioritized from high potential to unlikely to respond.
  • Mailed these audiences in two acquisition drops.

What We Learned (The Audience Story)

Audience-first targeting pays off. The names predicted to respond rose to the top in both mailings, and the lowest-ranked prospects made almost no impact — a clear split between strong and weak audiences.

DonorVision Insight
DonorVision names responded above average, but their gifts were smaller. This signals opportunities to refine donor quality and elevate higher-value prospects.

Cost-Saving Insight
Simply removing low-potential names unlocks real money. Even with the same creative and package, focusing on stronger audiences would have significantly improved results.

Results That Matter

First Mailing:

  • High-potential donors responded more than 4x higher than the lowest group.
  • Audience-first targeting delivered:
    • 18% higher response rate
    • 29% improvement in net revenue performance vs. baseline mailing.

Second Mailing:

  • Stronger prospects again responded far better.
  • Optimization delivered:
    • 11% higher response rate
    • 19% improvement in net revenue performance vs. baseline mailing.

What This Means for Fundraisers

Acquisition works best when it starts with the right people. Focusing on the strongest audiences delivers the biggest gains.

With a thoughtful audience strategy:

  • You waste fewer dollars.
  • You elevate donor quality.
  • You build a stronger file.
  • You get results that feel like a performance upgrade, not a budget increase.

What Comes Next

VeraData’s Donor Science team will:

  • Rebuild the model with a $15 minimum gift level.
  • Build a High Dollar Model for higher-value ask strategies.
  • Continue optimizing by removing the lowest-performing groups.
Want to raise more in 2026 without mailing more? Contact us.

How Direct Mail, Ai, and Machine Learning are a Powerhouse for Nonprofits

Posted on by Diana Richardson

In today’s digital age, it’s easy to assume that traditional marketing methods like direct mail have become obsolete. With the rise of social media, email marketing, and other digital channels, it may seem like direct mail has been left in the dust. However, for nonprofits, direct mail backed by evolutions in technology like Ai and Machine Learning, is leading the way regarding donations. It is proving a valuable tool in their marketing strategy even in a digital-led world. In this blog post, we’ll explore why direct mail leads the charge in a digital world and how nonprofits can use it to their advantage.

The Power of Direct Mail Marketing

Personalization and Tangibility

direct mail for nonprofits

One of the main reasons direct mail is so powerful is its ability to provide a personal touch and tangible experience for recipients. In a world where we are bombarded with digital ads, receiving a physical piece of mail can be a refreshing change. Direct mail allows nonprofits to personalize their message and make a more meaningful connection with their audience. By using the recipient’s name, including handwritten notes, and using eye-catching designs, direct mail can create a sense of personalization.

Additionally, direct mail provides a tactile experience. Recipients can physically hold and interact with the mail piece, making it more memorable and impactful. This is especially important for nonprofits, as they often rely on emotional connections to drive donations and support for their cause.

Targeted and Measurable

Direct mail also offers the advantage of being highly targeted and measurable. Nonprofits can use data and analytics to identify their target audience and tailor their direct mail campaigns accordingly. This allows organizations to reach the right people with the right message, increasing the chances for the desired response.

Furthermore, direct mail is easily trackable, allowing nonprofits to measure the success of their campaigns. By including unique codes or URLs, they can track the number of responses and donations generated from their direct mail efforts. This data can then be used to refine and improve future campaigns, making direct mail a valuable and cost-effective marketing tool.

Direct Mail Marketing Strategies for Nonprofits

Integrating Direct Mail with Digital Marketing

direct mail and digital marketing for nonprofits

While direct mail produces results, it’s important for nonprofits to recognize that it shouldn’t be used as a standalone marketing strategy. Instead, it should be integrated with digital marketing efforts to create a cohesive and effective campaign.

For example, nonprofits can use direct mail to drive traffic to their website or social media pages. By including a QR code or personalized URL, recipients can easily access the nonprofit’s online platforms and learn more about their cause. This increases engagement and allows nonprofits to track the success of their direct mail campaign.

Using Direct Mail for Donor Retention

Direct mail is also an effective tool for donor retention. By sending personalized thank-you notes and updates on how their donations have made an impact, nonprofits can strengthen their relationship with donors and encourage future support. This personal touch can make donors feel appreciated and valued, increasing the likelihood of continued support.

Additionally, direct mail can be used to target lapsed donors and encourage them to re-engage with the nonprofit. By reminding them of the impact they have made in the past and providing updates on the nonprofit’s current initiatives, lapsed donors may be inspired to make a donation once again.

Leveraging Direct Mail for Fundraising

Direct mail is also an effective tool for fundraising for nonprofits. By creating a compelling and emotional appeal, nonprofits can use direct mail to inspire recipients to make a donation. This can be done through storytelling, showcasing the impact of donations, and providing a clear call to action.

Furthermore, direct mail can be used to promote fundraising events and campaigns. By sending out save-the-date cards or invitations, nonprofits can reach a wider audience and increase attendance at their events. This can also be combined with digital marketing efforts, such as creating a Facebook event or promoting the event on social media.

Tips for a Successful Direct Mail Marketing Strategy

Tips for Better Direct Mail Responses

Know Your Audience

The key to a successful direct mail campaign is knowing your audience. Nonprofits should enhance their data and analytics with Ai and Machine Learning to identify their target audience and tailor their message accordingly. Not only can the power of tech identify, but it can also predict and prospect for new opportunities to grow your nonprofit in this data day and age. Paying close attention to personal details like interests, motivations, and communication preferences, Ai and Machine Learning are the keys to donor acquisition and true growth.

Create a Compelling Message

To stand out in a recipient’s mailbox, nonprofits need to create a compelling message that will capture their attention. This can be done through storytelling, using emotional appeals, and showcasing the impact of donations. Nonprofits should also include a clear call to action and make it easy for recipients to make a donation or take the desired action.

Personalize the Experience

As mentioned earlier, personalization is key to the success of direct mail. Nonprofits should use the recipient’s name, include handwritten notes, and tailor the message to their interests and motivations. This personal touch can make a significant impact and increase the chances of a positive response.

Conclusion

While digital marketing may be the go-to for many organizations, direct mail fosters direct actions, especially for nonprofits. Ai and Machine Learning are now the backbones of what was once considered a ‘traditional’ marketing tactic. As physical mail and tech combine, they empower your nonprofit to provide a personal touch, tangible experience, and targeted and measurable results. This all comes together to make direct mail a valuable tool for nonprofits looking to increase engagement, donations, and support for their cause.

It’s time to increase engagement