If you’re still leaning on probabilistic data and expiring cookies to buy media in 2025, you’re already late to the party. 

The new performance edge for programmatic traders is deterministic commerce data, signals tied to real credit-card transactions that plug straight into your DSP.

Media buyers who can activate that data at scale are already winning auctions and compressing customer-acquisition costs; the only question now is whether you join them or chase from behind.

DTC Data is the Truth

Direct-to-consumer brands already generate 14.9% of all US ecommerce sales and keep growing—even as overall ecommerce growth cools. They’re pouring those revenues back into programmatic with a killer advantage: first-party checkout data.

• Brands that activate deterministic purchase data see 8x higher ROI and 25% lower CPA  than peers relying on probabilistic segments.
• CTV campaigns that merge retail-purchase data with premium inventory deliver up to 67 % higher ROAS and 2× sales revenue YoY (AlixPartners case study, May 2025).  

Translation for traders: Every time you bid blind, a DTC challenger armed with verified purchase signals can win the same impression at a smarter eCPM—and siphon sales away from your portfolio brands.

Deterministic Purchase Data: A Sharper Signal Than Modeled Audiences

Most programmatic audiences are built on probabilistic inference: infer that somebody might be in-market for shoes because they read sneaker blogs or live near a sporting goods store. 

Deterministic datasets flip that logic. 

They start with a verified transaction—a SKU-level receipt, a basket value, a timestamp—and then anonymise the record into hashed IDs that can flow through DSPs safely.

Because every ID is anchored in a real purchase, deterministic segments behave differently:

True intent, zero guesswork: A consumer who spent $120 on premium pet food last week is a far stronger prospect for a higher-end CPG brand than someone who merely visited a pet-care article.

Lean, efficient reach: There’s no need to inflate the segment to find scale; smaller audiences convert at higher rates, trimming impression waste.

Easy portability: Hashes slot directly into UID2, Yahoo ConnectID, ID5 and virtually all major retail-media clean rooms.

Better AI fuel: Machine-learning bid models such as The Trade Desk’s Kokai improve faster when the training labels are guaranteed conversions; no noise means faster optimization loops. Two-thirds of TTD clients already run Kokai models as of Q1 2025.

What does this have to do with Proxima?

Glad you asked!

Over the last 3 years, we’ve built the most valuable dataset in DTC. As of this writing, we’ve amassed a continuously updated graph of 300 million receipt-verified transactions across 12 retail verticals—beauty, fashion, home, food, pet, health and more. 

Every row is hashed at the person and household level, refreshed daily, and never modeled or back-filled. 

For traders, that means an audience layer that behaves like a precision instrument instead of a guess.

Meet the High-Value Shoppers Inside Proxima’s Dataset

Understanding who sits inside those hashes explains why they beat generic third-party segments—and why they bring something genuinely new to programmatic buying.

Affluent, younger, and spend-ready

eMarketer’s latest generational retail survey shows that 60% of US Gen Z adults and 58% of Millennials now make “most of their purchases” online, compared with 47 % of Gen X and barely one-third of Boomers. In the same study, Gen Z and Millennials are 15 percentage points more likely than older cohorts to describe themselves as “brand-first shoppers.”

Why does this matter to programmatic traders? 

Because household income is still the strongest predictor of average order value. 

By mapping every Proxima ID to an estimated income decile you get prospect lists whose basket sizes routinely outstrip standard interest-based look-alikes. In a world where most modeled segments lump Gen Z into the same bucket as college students on debit cards, deterministic receipts let you cherry-pick actual discretionary spenders.

Digitally native problem-solvers

High-value DTC buyers finish checkout flows faster, navigate self-service returns, and rarely phone customer support. 

That frugal behaviour has a direct media implication: brands don’t have to “over-message” them with onboarding nudges, so impression budgets can tilt toward acquisition instead of retention.

For CTV buyers, that means a lower recommended frequency (five to seven exposures) without fear of drop-off, freeing budget to widen reach. For display retargeting, it means shorter look-back windows (often seven days instead of the default 30) because this audience converts quickly or moves on. 

Deterministic time stamps make that cadence adjustment trivial.

Social-commerce accelerators

TikTok reached 40.7 million US social buyers in 2024, gaining more shoppers than Facebook, Instagram, and Pinterest combined.

Yet TikTok’s own on-platform ad tools give buyers limited visibility beyond last-touch clicks. 

When a user views a creator haul and then completes checkout in a browser, the platform often loses the signal. 

Proxima’s commerce hashes solve that attribution gap: by matching post-purchase receipts to TikTok exposure IDs inside a clean room, you can run true incremental-lift studies that prove the social video spend is doing more than cannibalising lower-funnel retargeting. 

Traditional third-party segments can’t offer that closed-loop proof.

Subscription-prone repeaters

McKinsey’s 2025 personalisation outlook notes that 78% of consumers who feel “valued by a brand” are more likely to repurchase, and that propensity spikes in categories with refill or replenishment cycles.

Because Proxima tags every ID with historical purchase cadence, traders can split the audience into one-time, occasional, and subscription buyers—then tailor messaging appropriately. For example, a protein-powder brand might exclude confirmed subscribers from acquisition budgets (saving waste) while only showing upsell SKUs to its existing auto-ship cohort. 

Modes like this simply aren’t possible with probabilistic “fitness-enthusiast” segments that lack transaction depth.

Category-agnostic spenders

A November 2024 path-to-purchase study of households earning $150K+ found that the top reason those consumers try a new brand—across electronics, food, beauty, furniture, accessories, and shoes—is “superior product quality,” not discounts.

That cross-vertical curiosity means commerce data travels well: the same ID who buys a $90 serum often buys a $35 organic snack box and a $120 athleisure set. For media teams, this opens a compliant way to scale into neighbouring verticals without buying tenuously modeled “intender” segments. 

Activate one deterministic quality-seeker cohort, then test new creative tailored to each sub-category—the overlap delivers instant reach extension.

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Tapping this pocket of high-value shoppers is the difference between incremental ROAS and flat-lining performance—especially for traders who’ve already squeezed every drop from traditional look-alike pools.

Five Programmatic Tactics That Turn Deterministic Data Into Incremental ROAS

Before we jump to the table, note the common thread: each tactic layers deterministic purchase data on top of a standard DSP primitive (private deals, frequency caps, geo-testing, and negative targeting). 

That means zero special integrations, no custom workflows, and measurement that finance teams already recognize.

‍* Segments are live today in The Trade Desk under Proxima → Consumer/DTC tree.

Scaling and Optimizing Deterministic Campaigns

Creative sequencing matters

In tactic #3, insist on at least three creative variants—a teaser spot, a benefits-driven follow-up, and a closing offer. The deterministic ID lets you guarantee order, so lean into narrative arcs that modeled audiences can’t deliver.

Bid shading ≠ budget overrun

Because deterministic segments index so much higher on conversion, you can afford CPMs 10-20% above your usual clearing price and still hit a better nCPA. Set a hard CPA guardrail in the DSP and let the algo discover the sweet spot.

Scale through look-with (not lookalike)

Once a deterministic cohort proves out, widen reach by pairing it with contextual inventory—e.g., “High AOV Shopper” IDs inside luxury-fashion content bundles. This look-with approach keeps the anchor signal deterministic while adding incremental, citable volume.

Refresh cadences aggressively

Recency is a feature, not a filter. Replace 90-day cohorts every 30 days to keep intent hot; Proxima’s daily feed makes that easy, and most DSP seat IDs auto-update overnight.

Benchmark like-for-like

When you report lift from tactic #1, compare against your best modeled audience, not the universal average. Doing so guards against the “easy win” narrative and builds long-term budget trust.

Measuring Success: Incrementality, nCPA and Other Familiar KPIs

Finance wants proof, not promises. 

Deterministic data fits the same scorecard you already present at quarterly reviews:

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In January 2014, new IAB/MRC Retail-Media Measurement Guidelines standardized how logged purchase events feed lift studies, giving deterministic providers a clear third-party yardstick. 

How to Activate Proxima Audience Segments in The Trade Desk and Yahoo DSP

Proxima DTC audience segments are now available in Yahoo DSP and The Trade Desk. To activate on your programmatic campaigns:

1) Simply go to the 3rd party data marketplace within the DSP

2) Search for data provider “Proxima”

3) Select the segments that best fit your strategy 

Future-Proof Targeting With UID2, Clean Rooms and Gen-AI Bid Models

Every quarter brings tighter privacy APIs, yet deterministic purchase hashes remain durable:

• Multi-ID portability. One Proxima hash resolves to UID2, RampID, ConnectID, ID5 and major retailer IDs with minimal entropy.
  
• Clean-room ready. Brands can overlay first-party sales inside Snowflake, Amazon Marketing Cloud or Habu without exposing raw PII.
  
• AI-accelerated learning. The Trade Desk reports two-thirds of clients now run campaigns through its Kokai AI stack, which improves faster when deterministic labels replace modeled conversions .

Signal longevity plus AI-friendly training data equals campaigns that keep performing as cookies disappear.

From Audience Fatigue to Precision Growth

Programmatic buyers have reached a crossroads: rising inventory costs and shrinking signal quality make yesterday’s look-alikes an increasingly blunt tool. 

Deterministic purchase data—updated, privacy-safe and rooted in real spending—offers the precision necessary to restore efficiency and unlock net-new growth.

Curious how it performs in your own campaigns?

Activate a segment in The Trade Desk or Yahoo DSP.

Book a 30-minute insights session with Proxima’s commerce-data strategists here.

Fresh intent, proven spend—and the results to satisfy the sharpest finance team—are only a line item away.