How first-party data drives better outcomes in AI-powered advertising

As AI-driven bidding and automation transform paid media, first-party data has become the most powerful lever advertisers control.

In this conversation with Search Engine Land, Julie Warneke, founder and CEO of Found Search Marketing, explained why first-party data now underpins profitable advertising — no matter how Google’s position on third-party cookies evolves.

What first-party data really is — and isn’t

First-party data is customer information that an advertiser owns directly, usually housed in a CRM. It includes:

• Lead details.
• Purchase history.
• Revenue.
• Customer value collected through websites, forms, or physical locations.

It doesn’t include platform-owned or browser-based data that advertisers can’t fully control.

Why first-party data matters more than ever

Digital advertising has moved from paying for impressions, to clicks, to actions — and now to outcomes. The real goal is no longer conversions alone, but profitable conversions, according to Warneke.

As AI systems process far more signals than humans can handle, advertisers who supply high-quality customer data gain a clear advantage.

CPCs may rise — but profitability can too

Rising cost-per-clicks are a fact of paid media. First-party data doesn’t always reduce CPCs, but it improves what matters more: conversion quality, revenue, and return on ad spend.

By optimizing for downstream business outcomes instead of surface-level metrics, advertisers can justify higher costs with stronger results.

How first-party data improves ROAS

When advertisers feed Google data tied to revenue and customer value, AI bidding systems can prioritize users who resemble high-value customers — often using signals far beyond demographics or geography.

The result is traffic that converts better, even if advertisers never see or control the underlying signals.

Performance Max leads the way

Among campaign types, Performance Max (PMax) currently benefits the most from first-party data activation.

PMax performs best when advertisers move away from manual optimizations and instead focus on supplying accurate, consistent data, then let the system learn, Warneke noted.

SMBs aren’t locked out — but they need the right setup

Small and mid-sized businesses aren’t disadvantaged by limited first-party data volume. Warneke shared examples of success with customer lists as small as 100 records.

The real hurdle for SMBs is infrastructure — specifically proper tracking, consent management, and reliable data pipelines.

The biggest mistakes advertisers are making

Two issues stand out:

• Weak data capture: Many brands still depend on browser-side tracking, which increasingly fails — especially on iOS.
• Broken feedback loops: Others upload CRM data sporadically instead of building continuous data flows that let AI systems learn and improve over time.

What marketers should do next

Warneke’s advice: Step back and audit how data is captured, stored, and sent back to platforms, then improve it incrementally.

There’s no need to overhaul everything at once or risk the entire budget. Even testing with 5–7% of spend can create a learning roadmap that delivers long-term gains.

Bottom line

AI optimizes toward the signals it receives — good or bad. Advertisers who own and refine their first-party data can shape outcomes in their favor, while those who don’t risk being optimized into inefficiency.

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