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.