The Data Sourcing Strategy for Now

Reach With Precision:

The Data Sourcing Strategy for Now

Why legacy sourcing models may be blocking the new audiences you want to reach

President + CRO, Path2Response

Advantage now comes from harnessing those differences in data and modeling to drive incremental results and uncover new audiences.

Reduce list cost. Narrow the number of sources. Prioritize overlap as validation. Manage the denominator to improve ROI. It worked. I stand by that model for that moment in time. But today, data sources and modeling methods are no longer homogeneous. Signals differ. Recency differs. Refresh cadence differs. Modeling agility differs. Processing sophistication differs. When differentiation was about scale, the strategy held. Now that performance demands scale plus recency, signal richness, and speed, that same strategy constrains incremental performance.

In short, leading marketers understand that greater value now comes from growing the numerator — new customers and incremental revenue — not from obsessing over reducing the denominator.

Ask agencies and data leaders what they’re looking for now and the answer is consistent:

New audiences that perform at or above goal.

Not recycled names. New customers they aren’t already promoting. Yet many sourcing frameworks still operate as if data were homogeneous. Circulation plans favor tenure and overlap. The same names resurface across providers – and performance converges. If inputs are similar, outcomes converge.

Meaningful differences now exist between data providers — and those differences drive performance. Some providers update data daily. Others monthly. Some can model and back-test same day. Others operate in rigid batch cycles. Some integrate high-recency intent signals with verified transaction data. Others rely on slower refresh environments.

Recency is no longer a minor variable. It is a competitive advantage.

When modeling adapts as signals change — not weeks later — audience quality improves. When data refreshes daily, high-intent prospects can be reached earlier in their decision cycle.

When refresh cycles lag, opportunity does too. This is no longer a market of interchangeable inputs. It is a market where value lives in the differences.

The opportunity is not in narrowing sources. It is in recognizing where differentiated signals create differentiated performance — at scale.

Traditional sourcing models disproportionately optimize the denominator in the ROI equation — cost. List cost. CPM. Reducing the number of sources to control for expense. Lower cost per name feels like efficiency. But data is not a commodity input. It is as critical as the offer and creative. Real value is created in the numerator — sales or donations in non-profit organizations.

The better question isn’t, “How inexpensive were the names?”

It’s, “Did they generate new customers or new donors?”

When differentiated data identifies higher-intent prospects earlier in their decision cycle, the lift shows up in response, average order value or gift amount, and downstream touches. Especially across second and third contacts, incremental contribution becomes measurable. The right metric isn’t the lowest cost per name. It’s incremental ROAS driven by new customer growth and dollars generated. And incremental ROAS is unlocked when differentiated signals are allowed into the plan — not filtered out in pursuit of cost control or overlap.

This shift isn’t theoretical.

Across direct mail acquisition campaigns in verticals including fine jewelry, specialty food, and humanitarian fundraising, differentiated audience sourcing has indexed 10% to nearly 50% higher in response than legacy mixes. In one humanitarian campaign, response increased 16%, average gift rose 379%, and total donations delivered a 3x return on the campaign. In direct mail retargeting across retail categories such as home furnishings, apparel, footwear, home décor, and subscription food, precision-driven audience selection has delivered 2x–7x lifts in response rates and meaningful gains in revenue per piece. In multiple campaigns, incremental ROAS reached strong double-digit multiples.

The pattern is consistent:

When differentiated signals are introduced, performance lifts. When sourcing frameworks prioritize discovery over duplication, results scale.

The original framework was built in the best interest of marketers at the time. Evolving it today comes from the same place. Marketers want new audiences that perform at or above goal. Reach with precision delivers that outcome. If your data sourcing strategy hasn’t evolved, it may be limiting your growth. Success now comes from embracing where data is different — and designing strategy around those differences. Now is a good time to connect with your agency partners and explore how richer, more differentiated data sources can be incorporated into your acquisition strategy.

Chris McDonald is President + CRO of Path2Response. His experience spans multiple data product and service organizations across diverse verticals and offer types. He believes strong partnerships fuel innovation — and that listening closely to marketers is the fastest path from insight to impact. He welcomes thoughtful dialogue.

Reach out directly at inquiries@path2response.com to share your ideas and insights.

Why Discovery Is the New North Star for Donor Acquisition

SVP, Nonprofit Solutions

For years, we relied on a predictable formula for nonprofit direct mail acquisition: find strong lists, control costs, and use overlap between files as a form of validation. It was a strategy built for a more homogeneous market, and for a long time, it served us well.

But systems that work well often create an unintended consequence—eventually, everyone uses the same system.

If every organization is sourcing donors from the same limited pools, are we truly discovering new supporters—or simply circulating the same ones more efficiently?

Every acquisition strategy reflects a belief about where future donors come from. Some prioritize efficiency. Others prioritize discovery. Today, those models are hitting a ceiling—and the difference matters more than ever.

In today’s environment, data is not a commodity—it is a competitive lever. And the most meaningful difference is now timing. Some signals update daily, while others lag by weeks. That gap determines how closely our campaigns align with real donor intent.

Overlap may feel like validation—but it also concentrates our attention on donors we’ve already found. True growth happens when we shift focus from cost efficiency to discovery: identifying new donors earlier in their giving journey, before the rest of the sector finds them.

We’ve seen firsthand the impact of moving away from “recycled names”. In one humanitarian campaign, shifting to higher-recency, precision-driven signals delivered:

The goal of acquisition is not just to reach donors—it is to find those who have not yet given, but are most likely to. These audiences almost always exist just beyond our traditional, “safe” targeting approaches. Organizations that embrace differentiated data and prioritize discovery will be best positioned to find their next generation of lifetime supporters. And sometimes, that growth begins the moment we stop looking at the same old lists—and start looking somewhere new.

Bruce Hammer Senior Vice President, Nonprofit Solutions, is a Direct Marketing Professional with over 3 decades of experience helping Non-Profits Organizations acquire new, high-value donors.

Your Next Best Customer May Be Invisible

Convert anonymous browsers into buyers and optimize your ad spend

VP, Client Development

The majority of your website visitors leave without identifying themselves. No purchase. No form fill. No cookie. Just gone — taking with them every dollar you spent on SEO, paid search, social, and your catalog to get them there in the first place.

The demand was real. The intent was there. The opportunity is still recoverable.

Path2Ignite was built to do exactly that: convert anonymous website visitors into your next best customer.

Most of your traffic is anonymous, traditional retargeting chases cookies and pixels indiscriminately.

Traditional retargeting doesn’t distinguish between your current customers, many of whom may have already made a purchase, high-intent prospects likely to convert, and casual buyers. Unless you have insight into who is on your site and those who will convert with fast follow-up, you’re squandering dollars and opportunity.

Path2Ignite is that infrastructure.

Identify. Path2Ignite deploys a website pixel that not only matches, but then qualifies anonymous visitors against a cooperative database of hundreds of millions of consumer records — turning unidentifiable traffic into a reachable, named audience ready for activation.

Score. Reach without precision is wasted spend. Path2Ignite’s Path2Acquisition model scores every identified visitor using real-world transaction history and offline behavioral data. Casual browsers are filtered out. What remains is a refined,high-intent audience that has demonstrated actual purchase behavior — not just a click.

Activate — fast. Once a visitor is scored, it reaches the printer within 24 to 48 hours. The mailer lands in-home within three to five days of the original site visit — while purchase intent is still at its peak.

Digital channels are saturated. Display ads get ignored. Promotional emails go unread. A Path2Ignite postcard has no algorithm blocking it, no spam filter, and no competing notifications.

It arrives at a moment of calm, in a physical space where purchase decisions actually get made. For a consumer who visited your site just days ago, that postcard isn’t an interruption — it’s a timely, tangible reminder from a brand they already considered.

Path2Ignite bridges digital precision with physical delivery. You’re not choosing between online and offline. You’re using one to make the other dramatically more effective.

Path2Ignite runs on billions of purchase records updated multiple times per week — because data recency directly impacts in-market relevance and return on ad spend.

Performance is validated using campaign hold groups to isolate true incremental ROAS, ensuring every lift is attributable to Path2Ignite — not organic trends:

Performance Benchmarks (RR Index) The RR Index measures response rates against a control group; for example, an index of 355 indicates a response 3.55x higher than the baseline.

Across categories, the pattern is consistent: brands using Path2Ignite convert more high-intent visitors than their digital campaigns alone.

The demand is already there. The visitors are arriving. Path2Ignite gives you the speed, precision, and physical presence to act on that intent before it fades — turning anonymous traffic into named prospects, and named prospects into customers.

Ready to convert your anonymous visitors into your next best customers? Let’s talk about Path2Ignite.

I have been working in artificial intelligence since

before most people in this industry started using the term.

At NASA’s neurocomputing institute in the 1990s, I built neural

networks. Real ones — not the marketing version of AI, the

engineering version, where you are accountable to whether the

system actually works.

So when I hear that a competitor has moved to a single

generative AI model for data science, I am not impressed. I am

concerned — for their clients.

Let me be precise, because the term AI is being used to describe

two very different things right now. There is true AI — statistical

models, machine learning, expert systems. And there is

generative AI — large language models that synthesize content

based on patterns in training data.

Generative AI is genuinely powerful for what it does. It is not

what statistical modeling requires.

When I am building a model to identify who is likely to respond

to a specific offer, the model needs to find real signals in real

data. Generative AI will generate something that looks right.

But it does not know that the offer is a seasonal promotion, that

there is a 20% coupon on the cover, or what the macroeconomic

environment looks like this mail season.

Humans know those things. Data scientists with domain

expertise know those things. My job — and what we built this

platform to do — is to give those humans better tools, not

replace them with a system that generates plausible-sounding

outputs.

The companies going all-in on generative AI for modeling are

going to see their results fall. Some already are.

Path2Response was built without the co-op playbook. I came

from processing massive, complex data sets for airlines. I had

never built a data cooperative, and I did not want to replicate how

they had always worked.

We embrace data in its natural, unstructured form. No fixedwidth

fields, no translation loss, no signal trimmed at the edges

to fit a predefined structure. Data arrives intact, the models

see it intact, and intact data processed at high recency is where

performance comes from.

We run on elastic cloud infrastructure — hundreds of servers,

thousands of nodes when a client needs it, scaled back down

when they do not. Same-day and next-day model turnaround

is standard. That speed enables iteration: run a model, review it,

adjust it, run it again. The best results come from that loop, not

from a single output handed back after a long wait.

And we let the model work. When clients and brokers overspecify — loading up pre-selects and forcing the model toward

a predetermined answer — performance degrades. When we take those constraints back, apply the right variables, and let

the model find its own signals, it consistently outperforms what human pre-selection would have produced. The model finds nuances that experienced people miss.

That requires human expertise at the front end to provide

context — and then the discipline to step back.

Those results do not come from a generative AI model. They

come from a platform built with the right philosophy, by people

who understand what data actually is.

Michael Pelikan is Co-Founder and Chief

Technology Officer of Path2Response.

Path2Response’s formal AI usage policy

prohibits generative AI in production modeling

or decisioning. Client data is never used to

train models and is never shared with external

AI systems.

Reach out directly at inquiries@path2response.com to share your ideas and insights.

Solving the Publishing Paradox

Why Source Quality is the Only Metric That Matters

Mike Roots

Sr. Client Partner, Path2Response

Publishing today runs on the unforgiving math of

subscription economics: Subscriber Acquisition Cost (SAC) measured against Lifetime Value (LTV). Industry-wide, we are

seeing a tightening vise—acquisition rates have declined for three consecutive years while the cost to find those new readers has risen sharply.

The pressure of this “squeeze” ultimately exposes a fundamental

quality issue. Across the subscription economy, first-term

churn typically hovers around 25%. By contrast, publishing has one of the lowest overall voluntary churn rates globally, at

just 2.1%. This striking gap is largely determined by the types

of prospects acquired in the first place. The most effective

strategy, therefore, is to focus on acquiring prospects with a

higher likelihood of retaining beyond their initial term!

Path2Acquisition identifies direct mail

prospects with first-segment response

indexes of 121 to 177, maintaining above-index

performance well into the second segment.

Without the granular data to distinguish a long-term loyalist from a “one-and-done” seeker, most programs spend against the average. And in this market, the average is getting more and more expensive every year.

To break this cycle, we have to move beyond blanket prospecting.

Path2Acquisition, our specialized direct mail prospecting models

find prospects whose purchasing history and behavioral signals

indicate they won’t just subscribe—they’ll stay.

By screening out those with high churn propensity before the first

mailer ever hits a mailbox, we build “payback” into the acquisition

model as a variable. This approach not only helps mitigate SAC,

but also drives our consistently over-index “Payup” rates. The

results span the entire spectrum of the industry:

- Scientific & Academic: High-intent professional audiences.

- Celebrity & Lifestyle: Mass-market appeal with selective retention.

- Faith-Based Content: Values-driven, long-term loyalists.

Across these diverse titles, the Path2Acquisition models

consistently identify direct mail prospects with first-segment

response indexes of 121 to 177. Perhaps more importantly,

the model depth is strong enough to maintain above-index

performance well into the second segment. Our focus is quality at

the source, ensuring it holds through the first renewal and beyond.

In an era of rising costs, the goal isn’t just to find any subscriber—it’s to find the right subscriber. Publishers can finally stop spending against the average and start investing in growth that sticks.

Mike Roots is Sr. Client Partner, Path2Response, with 20+ years using data to power new customer acquisition campaigns for leading publishers and non-profits.

Reach out directly at inquiries@path2response.com to share your ideas and insights.

We’ve all seen the marketing briefs. They usually

look something like this: “Our target audience for home

or auto protection plans is women, aged 35–55, with an HHI of

$100k+, living in suburban zip codes.”

For decades, this demographic-first approach was the gold

standard. But in today’s hyper-connected world, relying on

broad demographics is like trying to find a needle in a haystack

by buying more hay. More importantly, there is a new “buzz” in

the air: Generative AI. Attach “AI” to your company name, and

suddenly you’re an innovator.

At our core, we believe the hard truth is that correlation does not

equal causation. Just because a consumer fits a profile doesn’t

mean they intend to buy a protection plan today. To move the

needle for warranty brands, you have to be willing to do data

different.

While the industry rushes toward the latest generative AI trends,

let’s call it what it is: a race to cut costs. Many data partners

are trying to replace seasoned human decision-making with

automated algorithms designed to boost their own bottom line,

not yours.

When a competitor uses generative AI to “automate” audience

selection, they are creating an economic trap for your brand.

These systems often act as duplication engines, focusing on

“lookalikes” that have already been tapped out. You aren’t

reaching new prospects; you’re just paying a premium to find the

same customers you already have.

We use AI in the service of data scientists, not to replace data

scientists. Frankly, this strategic approach is why we are

delivering strong results.

Instead, the focus must be on Discovery over Duplication. True

scale comes from identifying massive pockets of new prospects

that traditional models—and cost-cutting AI—are completely

blind to.

For warranty companies, acquisition relies on a critical two-step

performance engine: sending targeted direct mail to get the right

prospect to pick up the phone, and ensuring those inbound calls

land with a highly qualified lead ready to buy.

When your data strategy is optimized for this two-step process,

it changes everything:

Step 1: Opening the Decision Window

(The Right Mailbox at the Right Time)

A consumer doesn’t buy a warranty because of their age, zip

code, or income bracket; they buy it because they have just

entered a highly specific Decision Window. This window opens

the moment a life change occurs—whether they just bought

a pre-owned vehicle, purchased a new home, or experienced

a major appliance failure—creating an immediate sense of

vulnerability.

We don’t guess who is in that window based on static profiles.

We track the behavioral and transactional footprint in real time.

By fusing verified offline purchase history with billions of daily

digital intent signals, we identify the exact moment a consumer

enters their Decision Window. This high-recency modeling

ensures your direct mail piece lands in their mailbox at the

precise time they are actively weighing risk, prompting them to

pick up the phone.

Step 2: Capitalizing in the Sales Center

(Converting Within the Window)

The ultimate measure of a direct mail campaign isn’t the number

of mailpieces sent, or even the initial call volume—it’s how many

inbound calls convert into bound policies at the sales center.

When you mail to a random database list, your call center agents

spend their days trying to push cold prospects into a Decision

Window that doesn’t exist for them, resulting in low conversion

rates and wasted agent time. But when an inbound call is

driven by an audience optimized for the Decision Window, the

script flips. These leads are already highly qualified; they have

a verified need and true purchase intent before they even dial.

Because you reached them while their Decision Window was

wide open, they are exponentially more likely to convert, driving

up your call center efficiency and your bottom line.

Is the shift from demographics to intent worth the effort? The proof

is in the performance when you choose to get different results:

Intent is the new demographic, and behavior is the new metric

for success. If your data partner’s “innovation” is making

their operation cheaper but your customer acquisition more

expensive, it’s time to ask: who is that innovation really for?

They’re cutting costs. We’re doing data different to boost your

inbound calls, your call center conversions, and your bottom line.

Ready to see what an intent-driven warranty audience looks

like for your brand? Contact us today for a personalized

performance benchmark report. Let’s do data different and

find your next high-converting customer together.

For acquisition marketers, the playbook is changing rapidly.

Budgets are under constant pressure, and performance

is paramount. Relying on prospect audiences based largely or

entirely on inferred interest and behavior leaves marketers

falling short of their acquisition goals. Legacy data sourcing

models and generic targeting segments are no longer enough.

To survive and scale in today’s landscape, performance-oriented

DTC marketers and acquisition professionals need to move away

from guesswork and lean into deterministic, transaction-based

intent targeting. That’s exactly where Custom Digital Audiences

(link to page) come in. Here is how you can leverage highperforming

modeled audiences to find your next best customers

with absolute precision.

When marketers seek to scale their acquisition efforts, broad

demographic targeting is often described as a blunt instrument.

While painting with a very broad brush isn’t ideal, demographics

aren’t actually the primary offender causing wasted ad

spend today, and here’s the part that gets overlooked: nearly

every targeting model, deterministic ones included, treats

demographics as a secondary signal at best. That’s not where the

real divide is.

The real divide is between fact and inference. Facebook lookalike

modeling largely builds outward from content engagement, what

someone likes, comments on, or scrolls past. Google’s targeting

relies heavily on site visits and browsing context. Both are reading

interest: what someone seems to care about, based on where

their attention goes. That’s a genuinely useful signal. It’s also, by

design, an inference. A page visit or a content “like” can indicate

a dozen different things, and assuming a future purchase from

digital context alone is where ad spend quietly disappears.

This is where true deterministic data changes the game. Our

models don’t start from attention or affinity; they start from the

transaction itself. Instead of inferring what someone might buy

from what they browse or who they follow, we build from records

of what someone has already bought, tied to core demographics

and submitted monthly by a growing network of contributing

retail and nonprofit partners, and layered with daily retail

site-visitor signals and nonprofit gift-donation data to capture

real-time intent.

That data is tied to 273 million individuals and mapped directly

to their households, so targeting stays precise even at scale. And

it goes deep: exact purchase dates and dollar amounts, tracked

across 40 retail categories and 19 nonprofit causes, the difference

between knowing someone follows fitness influencers and

knowing they bought running shoes three weeks ago. Because

the underlying data relies on recent transactions and daily visitor

signals rather than a static file, the resulting audiences reflect

current, active buying behavior rather than a stale snapshot.

For DTC marketers, this is the shift worth understanding:

moving away from “audiences that look like they might care”

and moving toward “audiences built from evidence that people

are already behaving like your customers.”

Historically, testing a new audience source meant absorbing

upfront costs before knowing whether the data would actually

perform. Marketers faced development fees, onboarding charges,

and heavy minimum commitments, a structure that put the risk

almost entirely on the brand.

The industry is moving toward a completely different model:

pay-for-performance data licensing, where the cost of building

and deploying a custom audience is folded into normal media

spend rather than charged separately.

At Path2Response, this plays out through a workflow built around

that exact principle. There is no model development fee and no

onboarding cost. Our team handles the full process, building

the model and delivering it directly to a client’s specific seat ID

in their preferred DSP, Social, or CTV platform, starting from

either a seed audience we already have available or one the client

provides. Payment happens only through the client’s normal

media buy, based on actual usage, and if the audience doesn’t

meet ROAS, reach, and scale targets, we don’t get paid either. That

structure aligns our incentives with the client’s from day one.

When you leverage a custom Path2Response modeled audience,

there is no payment to Path2Response. The audience targeting

fee is a component of your media buy. The exact amount

depends on your media platform. For social platforms and The

Trade Desk, your fee is a percentage of your media buy; for DSPs,

it’s CPM-based and included in your media cost.

If acquisition campaigns are hitting a wall using models that rely

heavily or exclusively on inferred behaviors, it’s time to shift to

precision modeling built on real, deterministic transaction data.

Kathy Huettl has worked in the direct

marketing and data-driven industries for over

20 years, spending the last 17 years in the

cooperative database management space.

Her background spans digital and database,

and encompasses all levels of multi- and

omni-channel marketing.

Curt Blattner is the Vice President of Digital

Strategy at Path2Response, bringing over 15

years of experience in digital advertising and ad

tech. An industry expert in audience targeting,

identity resolution, and digital campaign

measurement, he has worked extensively with

top brands, agencies, and platforms. His unique

background bridges both offline direct mail and

online marketing strategies.

Nonprofit development and acquisition marketers face a persistent, frustrating challenge: their

donor databases are quietly shrinking.

Every year, a meaningful share of hard-won donors slip into "inactive" status. They supported

your cause once, maybe twice, then lapsed. Traditional win-back strategies, blasting the entire

lapsed file with standard direct mail or generic email streams, often yield diminishing returns and

a high cost per dollar raised (CPDR).

But those inactive donors aren’t necessarily unreachable. They might just be active somewhere

else.

By leveraging high-recency data co-operatives (co-ops), nonprofit marketers can identify when

their lapsed donors are actively giving to other organizations and time re-engagement

campaigns accordingly. Here's how to use high-recency co-ops to bring inactive donor files

back to life, and how Path2Response's co-op puts this into practice.

The standard approach to lapsed donor reactivation relies almost entirely on internal data.

Marketers segment by RFM (Recency, Frequency, Monetary value) and try to guess when a 12-

to 24-month lapsed donor might be ready to give again.

The problem: internal data only tells you when someone stopped giving to you. It doesn't tell

you whether they're currently writing checks to a disaster relief fund, supporting a local animal

shelter, or contributing to an advocacy campaign.

If a donor is actively giving elsewhere, their philanthropic budget is open. If they haven't given to

anyone in two years, your acquisition dollars are likely better spent elsewhere. High-recency co-

ops close this information gap by showing you giving behavior your internal file can't see.

A data co-operative pools transaction data across participating organizations. In a high-recency

co-op, the focus is on speed: rather than looking at multi-year giving histories, the system tracks

real, recent transactional behavior, refreshed daily or weekly, not monthly or quarterly.

Path2Response's co-op, for example, draws on first-party transaction data from a robust,

growing network of retail and nonprofit organizations, updated five times a week, among the

freshest cadences in the industry, with audience segments built around 30-, 60-, and 90-day

giving windows. That means a development marketer can score a lapsed file against the co-op

and see, concretely, who has given to any organization in the last month, two months, or

quarter, while that donor is still in-market and in their decision window, rather than estimating it

from an aging internal record.

Co-op data doesn't just help with net-new donor acquisition. It's equally useful for optimizing the

reactivation of lapsed files.

Shifting from static, internally modeled lists to dynamic, co-op-driven targeting has produced

measurable gains across sectors Path2Response has worked with:

Unlike net-new acquisition, where you're introducing your mission to a cold audience, this is

about reactivation: your lapsed file is full of people who already know your brand, believe in your

mission, and have a proven history of giving. They don't need to be introduced to who you are;

they need to be reached at the right moment.

By integrating high-recency co-op data into your development strategy, you stop guessing when

a donor is ready to return and start meeting them where their giving intent already is. That's the

difference between a leaking database and a predictable, repeatable growth engine.

Ready to see which of your lapsed donors are active right now? Contact Path2Response for a

free consultation to learn how our high-recency co-op data can help you reactivate and grow

your donor file.

Lindsay Asselmeyer is a Client Partner at Path2Response working with nonprofit, publishing, and commercial clients. She has been in the direct marketing industry for ten years.

Whether you joined us in the room at The Bridge

Conference or are reviewing these trends from afar,

one truth is clear: the donor relationship is fundamentally

changing. Below is a summary of the macro-trends

currently reshaping our sector and how non-profit

leaders must adapt.

Organizations are shifting from maximizing donor

acquisition to maximizing donor lifetime value. Donor

files continue to shrink, while retention, second-gift

conversion, sustainer programs, and lifetime value

receive greater investment. Rebuilding the pipeline of

everyday donors is essential to sustaining future midlevel,

major, and planned giving programs.

The organizations that thrive won’t simply keep more

donors—they’ll continuously create the next generation

of donors.

The conversation has shifted from having more data

to having better data. Organizations are investing

more heavily in first-party data, predictive modeling,

smarter segmentation, and audience intelligence.

Testing is becoming more strategic, with fewer but more

meaningful experiments focused on understanding

donor behavior.

Organizations that know their donors best will thrive.

Artificial Intelligence is changing how donors discover,

evaluate, and engage with nonprofits. AI search and

Answer Engine Optimization (AEO) are changing

how organizations are found online, while paid media

increasingly relies on machine learning and creative

optimization. Digital success increasingly depends on

integrating search, paid media, social media, email, and

content into one coordinated strategy.

AI is no longer simply a productivity tool—it is reshaping

the donor journey.

Privacy is evolving from a compliance concern into

a strategic fundraising consideration. State privacy

laws continue to expand and evolve, making data

collection, sharing, and activation increasingly complex. Organizations must balance personalization with donor

trust and regulatory compliance.

Privacy considerations are increasingly influencing

fundraising strategy—not just legal review.

Despite having more data than ever, understanding

what truly drives giving is increasingly difficult. Multichannel

donor journeys, DAF giving, privacy regulations,

and AI-driven discovery are complicating traditional

measurement and KPIs. Organizations are becoming

more comfortable using directional insights rather than

expecting perfect attribution.

Better decisions increasingly depend on understanding

influence—not simply measuring transactions.

Giving vehicles and donor preferences continue to

change, particularly with the growth of Donor-Advised

Funds (DAFs). Electronic giving continues to grow while

paper checks continue their long-term decline.

Organizations that understand and embrace emerging

giving behaviors will be better positioned to capture

future philanthropy.

Technology continues to evolve—but trust remains the

foundation of fundraising success. Donors increasingly

expect transparency, measurable impact, authentic

storytelling, and consistent experiences across channels. Trust influences both donor acquisition and long-term

retention.

Technology may help organizations reach more donors,

but trust is ultimately what inspires people to give.

Taken together, these trends point to something bigger

than a collection of new tactics or technologies.

The donor relationship is shifting. Donors are giving

differently, discovering organizations differently, and

moving across more channels. They expect more from

the organizations they support and are becoming harder

to measure through any single transaction or channel.

At the same time, the value of a lasting donor relationship

has never been greater. The future of fundraising depends

less on how well we optimize each transaction, and

more on how well we understand, earn, and sustain the

relationship behind it.

Every nonprofit leader in the health and advocacy space

knows the pressure of mission expansion. Whether

you’re delivering frontline medical care to imperiled global

populations, funding vital clinical research, or championing

human rights and healthcare access, your impact is directly

tethered to one critical engine: sustainable donor acquisition.

Yet across conversations with development directors and

chief marketing officers in the sector, we see the same

dilemma play out: organizations know how to convert highaffinity

donors in small batches, but the moment they try to

scale their prospecting universe, efficiency plummets and

cost to acquire skyrockets.

Relying on conventional list rentals and basic donor

exchanges often leads to diminishing returns. When you

only acquire a few donors at a time, you are treading water

rather than building momentum.

To break through this plateau, health and advocacy

organizations are shifting their focus to daily donor signals

and multichannel transaction data. By leveraging these

real-time, comprehensive signals, organizations can identify

lookalike donors with the propensity and capacity to give,

moving well beyond traditional list-based approaches.

Below is a look at how three distinct organizations solved the

acquisition puzzle by combining broad audience modeling

with disciplined depth testing.

Known across the globe for delivering medical support to

imperiled populations, this client needed a shot in the arm

for its fundraising strategy. Specifically, leadership was seeking substantial new donor growth to help fund ongoing

expansion of the organization’s mission — but finding only a

few qualified donors at a time wasn’t sustainable at the scale

they needed.

The strategy: Path2Response identified a sizable target

universe for modeling and proposed a depth-testing

approach that would give the client clarity on rollout

potential in a near-term timeframe, drawing on donation

and transaction histories spanning hundreds of millions of

US consumers.

The results: The model and testing approach proved

successful enough that the client rolled out at substantial

scale — and kept delivering. Response Rate Index

performance held strong across three separate campaigns:

(An index of 10 0 represents the campaign average, so a score

of 293 means that campaign generated nearly triple the typical

response rate.) Consistency across three separate campaigns,

rather than a single lucky test, is what gave this client

confidence to keep scaling.

With a mission spanning research, education, and proactive

intervention programs, this health advocacy organization

needed a reliable, repeatable source of new donors — one

that could deliver solid performance and the scale to keep

feeding ongoing prospecting efforts, not just a one-off list.

The strategy: Path2Response tailored predictive models to

evaluate not just philanthropic participation but also broader

consumer spending behavior and giving cadence. The team

built a custom-modeled prospecting audience aligned with

the client’s goals, then layered in a strategic testing plan to

establish both baseline performance and the potential for

additional depth.

The strategy: Path2Response tailored predictive models to

evaluate not just philanthropic participation but also broader

consumer spending behavior and giving cadence. The team

built a custom-modeled prospecting audience aligned with

the client’s goals, then layered in a strategic testing plan to

establish both baseline performance and the potential for

additional depth.

The results: By testing top-tier prospect segments alongside

broader depth-test segments, the organization proved that

performance held strong even as audience volume grew:

(An IPPM index of 195 means that segment generated nearly

double the typical income per piece mailed, evidencing the model

was surfacing real high-capacity donors, not just high-volume

names.) The outcome: scaling prospect volume didn’t have to

dilute gift size or response quality.

Modern advocacy organizations operate across complex,

multichannel landscapes. For this international organization

advancing women’s rights and essential clinical health

services, direct mail and digital campaigns needed to work

in tandem to fuel aggressive growth targets; the client

needed maximum prospecting scale without sacrificing

campaign profitability or return on ad spend.

The strategy: By leveraging deep cooperative transaction

signals across the Path2Response database, the organization

deployed targeted audience expansion testing. The model

surfaced responsive prospective donors who were completely missed by standard affinity lists; then a straightforward testand-

rollout plan enabled the client to see early results and

scale up quickly.

The results: The campaign delivered positive ROI across

both the client’s core audience and its newly modeled

expansion segments, paving the way for a significant rollout

and sustained donor file growth as the organization works

toward its subscriber goals.

Across these three health and advocacy use cases, several

universal principles stand out for development teams

planning upcoming acquisition efforts:

If your organization is ready to expand its donor base with

predictability and scale, the Path2Response team is here to

help design a customized modeling and testing roadmap for

your next appeal.