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.
