Google Ads Audience Signals in 2026: From Targeting Users to Training the Algorithm

TLDR

Audience signals in Google Ads are no longer targeting constraints, they’re training data for Google’s AI. In 2026, the algorithm uses your first‑party data, CRM-driven profit signals, creative assets, and search themes to predict and find your highest‑value customers across Performance Max, Demand Gen, and Broad Match.

If you’re still approaching Google Ads like it’s 2020, obsessing over keyword lists and match types—you’re fighting yesterday’s war. In 2026, the game has fundamentally changed. Your job is no longer to target users. Your job is to train the algorithm.

Answer First: What Are Google Ads Audience Signals in 2026?

Audience signals are no longer about “who you target.” They’re about how you train Google’s AI to find your best customers. This shift, accelerated through Performance Max and Demand Gen campaigns, means the signals you provide are hints, not handcuffs.

In plain language, audience signals are the data cues you give Google about your ideal customers: first party data from your CRM, behaviors tracked on your website, detailed demographics, creative assets, and search themes. These signals guide machine learning models toward high-intent users, but they’re not rigid targeting constraints. Google’s algorithms analyze these inputs and then expand—often well beyond the audiences you specify—to find users likely to convert.

For small and mid-sized businesses, this changes everything. Audience signals are now as critical as (or more critical than) your keyword list, especially in Performance Max campaigns, Demand Gen, and AI-native Broad Match. The hierarchy has flipped: first-party data and profit signals sit at the top. Keywords are now just one signal the system considers among many.

From my perspective—17 years managing multi-million dollar enterprise accounts, serving as President of the Paid Search Association—this article focuses on practical ways to use audience signals without big-agency retainers. The strategies I’m sharing are the same ones enterprise accounts use, made accessible for business owners who want control over their own ad spend.

The 2026 Google Ads Signals Hierarchy: What Actually Matters Now

Between 2023 and 2026, Google shifted from keyword-centric control to AI-led, signal-driven optimization. This wasn’t subtle—it was a complete paradigm flip that caught many advertisers off guard.

Here’s the hierarchy you need to understand:

Tier	Signal Type	What It Does
Tier 1	First-party data & CRM-integrated conversions	Tells Google who actually generates profit
Tier 2	Creative & assets (asset groups, images, video, copy)	Functions as implicit targeting through Vision AI
Tier 3	Predicted intent signals (broad match, search themes, user behavior)	Interprets context beyond literal search terms
Tier 4	Context & demographics (location, device, household income, age)	Refines delivery without controlling it
Tier 5	Keywords & search terms	One input among many—no longer the primary control layer

Google’s automation now optimizes across all of these signals simultaneously in every auction. But here’s where you get leverage: by feeding the top tiers—high-quality customer data and high-value signals in Google Ads—you train the algorithm to prioritize what actually matters to your business.

A quick example: I recently audited a B2B SaaS account that had doubled their leads in 2025 using aggressive Performance Max expansion. The problem? Profit had dropped by 40%. The algorithm was chasing volume, not value. When we shifted their signals to prioritize closed-won customers and integrated offline conversion data showing actual contract values, their ROAS recovered within 60 days—and they were generating fewer leads that were worth significantly more.

The rest of this article shows you how to structure audience signals in Performance Max and data within this hierarchy across Performance Max, Demand Gen, and search campaigns.

The Foundation: First-Party Data & CRM-Driven Profit Signals

In 2026, your customer list is more important than your keyword list. Full stop.

This isn’t marketing fluff. Your CRM data tells Google who actually generates profit, not just who fills out a form. Without this foundation, you’re training the algorithm on noise.

First-Party Data Sources to Prioritize

Customer Match lists from your CRM:

• Closed-won accounts

• High-LTV customers (repeat purchasers, long-tenure clients)

• Customers with above-average order values

Remarketing audiences from your site and app:

• GA4 audiences segmented by value (not just recency)

• High-engagement visitors (multiple sessions, key pages viewed)

• Cart abandoners with high AOV

Offline conversion data:

• Opportunity won events from your CRM

• Actual revenue amounts (not just “conversion happened”)

• Churn flags to help the algorithm avoid similar profiles

Setting Up Profit Signals: A Practical Walkthrough

Enable Enhanced Conversions:

1. Navigate to Goals > Conversions > Settings in Google Ads

2. Enable enhanced conversions for your primary conversion actions

3. Choose either automatic setup (Google Tag) or manual implementation

4. Verify data is flowing correctly within 48-72 hours

Set Up Offline Conversion Tracking (OCT):

1. Create an offline conversion action in Google Ads

2. Choose your import method: scheduled uploads, API integration, or CRM connector (Salesforce, HubSpot)

3. Map your CRM stages to conversion events (e.g., “SQL Created,” “Opportunity Won,” “Revenue Closed”)

4. Include conversion values that reflect actual profit or revenue

Map Conversion Values to Real Business Outcomes:

• Set ROAS targets based on margin, not gross revenue

• Create value rules that weight higher-LTV customer profiles

• Use data segments to apply different values to different customer types

These signals feed directly into bidding strategies like Maximize Conversion Value and Target ROAS, elevating “profit” as the optimization target instead of just “lead submitted.”

What I Find in Audits

In my Google Ads audits, I consistently find:

• “Leads” optimized as primary conversions with zero quality feedback to Google

• Enhanced conversions not enabled (leaving significant signal data on the table)

• No CRM integration, so Google can’t distinguish junk leads from high-value deals

• Stale customer lists from 2021-2023 still being used as signals

My recommendation: fix your conversion tracking and first-party data before over-focusing on tweaking individual audience segments or relevant keywords. The foundation determines everything above it.

The Bridge: Audience Signals & Asset-Based Modeling (Creative as Targeting)

In 2026, especially in Performance Max and Demand Gen, your creative assets function as powerful audience signals. Google’s Vision AI and language models infer who your ad is “for” based on what they see and read.

Each asset group’s combination of elements—headlines, descriptions, images, logos, video creative, landing pages, and attached audience signals—helps Google model which user intents and profiles are the best match.

Structuring Asset Groups Around Audience Concepts

Segment by customer type:

• “CFO buyers” with financial imagery and ROI-focused copy

• “Marketing leaders” with growth metrics and team collaboration visuals

• “Existing customers” with upsell messaging and loyalty themes

Segment by value proposition:

• “Cut wasted ad spend” for cost-conscious prospects

• “Scale your best campaigns” for growth-focused buyers

• “Get transparency and control” for agency-fatigued business owners

Treating Creative as Targeting

Use imagery that matches context:

• Industry-specific visuals (manufacturing floor for industrial B2B, modern office for SaaS)

• Role-appropriate imagery (executive settings vs. hands-on implementation)

• Buying stage context (research mode vs. decision mode)

Write copy that names specific problems:

• “Tired of opaque agency retainers in Google Ads?”

• “Not sure if your ad spend is actually profitable?”

• “Running Performance Max but can’t see what’s working?”

Align landing pages with the same signals:

• Headlines that mirror ad copy themes

• Case studies featuring similar company profiles

• Testimonials from recognizable role types

In my audits, I often see one generic asset group trying to serve everyone. This dilutes your signals. Instead, build a lean but segmented structure—typically 3-6 focused asset groups per Performance Max campaign.

In Demand Gen specifically, Google now converts lookalike segments into AI optimization signals (as of March 2026), relying heavily on creatives to discover new customers with similar profiles.

Understanding Google Ads Audience Signals: From Segments to Search Themes

The “audience signals” interface in Google Ads covers multiple components: your data lists, custom segments, demographics, and in Performance Max, search themes that hint at what people are searching for.

Main Audience Signal Types

Your Data:

• Customer Match lists

• Remarketing audience lists (website visitors, app users)

• GA4 audiences exported to Google Ads

Custom Segments:

• Search-based (people who searched for specific terms)

• URL-based (people who browse certain websites)

• App-based (people who use specific apps)

Interests & Detailed Demographics:

• In market audience segments (actively researching products/services)

• Affinity audience segments (long-term interests)

• Life events (recently moved, started a business, new parents)

• Demographics: age, gender, parental status, household income

Search Themes (Performance Max):

• Keyword-like hints to train the algorithm

• Not strict targeting—they guide initial learning

Signals vs. Targeting: The Critical Distinction

Here’s what many advertisers miss: audience signals guide who to test first. They’re suggestions, not constraints. Google can and will go beyond those groups to find cheaper or better-converting traffic when conversion data points in that direction.

You retain limited control through:

• Brand exclusions at campaign level

• Negative keyword themes

• Account-level negative keywords

Practical Example 1: B2B Lead Generation For a B2B consulting Performance Max campaign, I’d layer:

• High-value customer list (closed-won accounts from the past 2 years)

• Custom segment: people searching for “Google Ads audit” and “PPC consultant”

• In-market: “Business & Productivity Software,” “Marketing & Advertising Services”

Practical Example 2: E-commerce For an e-commerce brand:

• Product-interest GA4 audiences (viewed specific category 3+ times)

• Cart abandoners with AOV above $150

• Custom segment: competitor product page URLs

These signals become most powerful when combined with automated bidding strategies. Manual or enhanced CPC bidding can’t synthesize multiple data points per auction the way machine learning can.

Where and How to Set Up Audience Signals in Performance Max & Demand Gen

While you can add audiences across many campaign types, Google treats “Audience Signals” as a formal configuration primarily in Performance Max and Demand Gen in 2026.

Performance Max Setup

Audience signals are attached at the asset group level. This is critical: avoid dumping every audience into one asset group. Instead, align one primary audience concept per asset group.

You can include:

• Your data lists

• Custom segments

• Demographics

• Search themes

All together as signals for a single asset group.

Demand Gen Setup

Lookalike segments are treated as optimization signals, not strict targeting constraints. This widens your reach while still using your seed audiences to guide the algorithm.

Layer your data, detailed demographics, and interests while letting Google use lookalikes as hints rather than fences.

Step-by-Step Process for Adding Audience Signals

1. Create or edit a Performance Max or Demand Gen campaign

2. Navigate to the asset group you want to configure

3. Open “Audience signals” settings

4. Add “Your data” lists (prioritize high-value customers)

5. Add 1-2 custom segments based on high-intent searches or competitor URLs

6. Optionally refine with demographics where it truly matters (e.g., B2B roles, high-income thresholds)

7. Save and allow 2-3 weeks for initial learning

Naming conventions matter. Use clear labels like “PMax_Audience_HVL_Customers_US_2026Q1” so signals are easy to audit and adjust later.

One important limitation: you can’t see performance per individual audience signals. However, you can test different audience bundles in different asset groups and compare outcomes over a 30-60 day window.

Types of Audience Signals You Should Actually Use (2026 Playbook)

Let’s move from “what’s available” to “what’s actually worth the effort” based on high-impact consulting experience.

Priority 1: Your Data

This is your highest-leverage signal category:

Data Type	Why It Matters
Closed-won customer lists	Shows Google exactly who converts to revenue
High-value lead segments (SQLs, high pipeline value)	Trains toward quality, not volume
Cart abandoners with high AOV	Captures demonstrated purchase intent
Repeat purchasers	Identifies retention-worthy profiles

Strategy Note: Segment by value, not just by recency. A customer who bought once three years ago is fundamentally different from a customer who bought five times last quarter.

Priority 2: Focused Custom Segments

Search-based custom segments:

• Mirror high-intent queries and buying signals

• Avoid generic informational terms

• Example: “Google Ads audit services” not “what is PPC”

URL-based segments:

• Competitor product pages

• Industry review sites (G2, Capterra for SaaS)

• Niche directories your ideal customers frequent

App-based segments:

• Use only when clearly relevant

• Specific SaaS tools or industry apps your target audience uses

• Example: Salesforce users for a B2B data tool

Priority 3: Interests & Demographics (Use Sparingly)

In-market segments:

• Use narrowly relevant categories

• For a PPC consultant: “Business & Productivity Software,” “Marketing & Advertising Services”

Affinity audiences:

• Caution against stacking too many broad affinity segments

• They can dilute intent signals when overused

Demographics to consider:

• Exclude clearly irrelevant age brackets

• Use household income tiers for luxury goods or premium services

• Focus on specific life events only when strongly tied to your product

Best Practice Summary

1. Start narrow with high-value first-party lists + 1-2 strong custom segments

2. Let the algorithm prove it can find more conversions before expanding

3. Add broader interests or demographics only after stable performance and sufficient volume

Predicted Intent Over Search Terms: Moving to AI-Native Broad Match

By 2026, “match types” are less about control and more about feeding intent to an AI system that interprets user context, history, and user behavior beyond the literal search term.

The Role of AI-Native Broad Match

Broad Match now uses audience signals, device, location, and historical behavior to predict user intent. When combined with smart bidding (Max Conversions, Target CPA, Target ROAS), Broad Match can outperform Exact Match alone—if your conversion signals and negatives are set correctly.

Old approach (pre-2024):

• Tightly control Phrase and Exact Match

• Build massive keyword lists

• Manual bid adjustments by query

New approach (2026):

• Train Broad Match with strong audience signals

• Feed robust conversion data (profit, not just leads)

• Use negatives strategically rather than exhaustively

• Let AI find new profitable pockets you’d never discover manually

Performance Max Search Themes

Search themes function similarly to keyword hints:

• They signal topics and products to prioritize

• They help the system understand relevant queries

• They’re not hard targeting—they’re training inputs

Control Levers That Still Exist

You’re not powerless. These controls remain:

Brand Exclusions:

• Prevent cannibalizing your branded search campaigns

• Protect brand integrity in Performance Max

Negative Keyword Themes:

• Block obviously irrelevant queries at scale

• Account-level negatives (via support or MCC tools) for universal exclusions

Example negatives to consider:

• “free” or “cheap” for premium services

• Competitor brand names (unless you’re explicitly conquesting)

• Job-seeker queries (“careers,” “jobs,” “salary”)

For many small businesses, the winning 2026 combination is:

• AI-native Broad Match on search campaigns

• Performance Max with strong first-party signals

• A limited, carefully curated negative and brand exclusion structure

• Weekly monitoring of search term reports

Interface Shift: Audience Signals in AI Overviews & Conversational Search

Google’s AI Overviews and conversational search—rolled out wider through 2024-2025—changed how users search. Instead of typing “google ads consultant,” they now ask, “How do I know if my Google Ads agency is wasting my budget?”

In this world:

• Queries are longer and more contextual

• Google relies more on user-level signals and past behavior

• Content relevance matters more than exact keyword matching

How Audience Signals Intersect with AI Mode

First-party lists tell Google which types of users should see your brand inside AI Overviews or conversational ad slots. The algorithm connects the dots between your best customers and similar users asking complex questions.

Creative assets that answer real questions act as strong relevance signals:

• Q&A style copy

• Explainer videos

• “How it works” visuals

Optimizing for Conversational Search

Ad copy and landing pages:

• Mirror natural language queries and objections

• Address “how,” “why,” and “when” questions directly

• Use the actual language your customers use in sales conversations

Landing page structure:

• Add FAQ sections that answer specific questions

• Include structured data markup

• Create clear, scannable content that AI systems can confidently reference

The better your first-party data and conversion feedback, the more likely Google’s AI surfaces your ads to similar high-intent users in these AI-driven interfaces.

In my audits, I often find a mismatch between what users actually ask (in calls, emails, sales conversations) and what ads and landing pages address. Align your creatives with real conversational language—not just the keywords you think people search for.

Practical Setup: Structuring Audience Signals in Performance Max

This section is a practical mini-playbook for structuring audience signals—not a generic UI walkthrough.

Recommended Asset Group Structure

Build one asset group per major audience concept:

• “Existing clients” — Upsell and retention messaging

• “In-market prospects” — Active research, comparison content

• “Competitor switchers” — Pain point focused, switching benefits

• “High-value lookalikes” — Based on your best customer profiles

For most small to mid-sized accounts, maintain 3-6 well-defined asset groups rather than one mega-group trying to do everything.

What to Include in Audience Signals

For each asset group, add:

Component	Recommendation
Your data segments	Match the asset group focus (e.g., “Closed Won 2024-2026” for lookalikes)
Custom segments	1-2 derived from high-intent searches or competitor URLs
In-market/affinity	Only the most relevant segments—don’t stack broadly

What to Avoid

• Mixing radically different segments (cold prospects and past buyers) in one asset group

• Adding every possible interest and demographic

• Generic creative that tries to speak to everyone

Testing Timeline

• Initial learning: Expect 2-3 weeks of volatility

• Evaluation window: 4-6 weeks before making structural changes

• Exception: Adjust sooner only if performance is clearly unacceptable (e.g., zero conversions, 10x CPA)

Quick Audit Checklist

From my consulting work, here’s what I check first:

• [ ] Do all asset groups have clearly named, focused audience signals?

• [ ] Is at least one asset group driven primarily by high-value first-party data?

• [ ] Are search themes aligned with profitable queries from past search campaigns?

• [ ] Are there brand exclusions preventing Performance Max from cannibalizing branded search?

Using Google Analytics 4 and CRM to Refine Audience Signals

GA4 and your CRM are essential partners in shaping and updating audience signals—they’re not just reporting tools.

Leveraging GA4

Build behavioral audiences:

• Time on site (top 25% of engaged users)

• Specific pages viewed (pricing page, case studies)

• Funnel steps completed (started checkout, viewed demo)

Identify high-conversion patterns:

• Which landing pages drive the most valuable conversions?

• Which product categories attract your best customers?

• Use these URLs in custom segments

Export audiences to Google Ads:

• Connect GA4 to Google Ads

• Use GA4 audiences as “Your data” for remarketing and seed lists

Using CRM Data

Identify high-value customer attributes:

• Industry vertical

• Company size

• Geographic region

• Job title or role

Apply these insights to signals:

• Inform Customer Match list segments

• Shape creative positioning (“Built for 10-50 person marketing teams”)

• Guide demographic targeting choices

Recurring Refresh Process

Set a monthly or quarterly cadence:

1. Refresh Customer Match lists with new high-value customers

2. Remove churned or low-value segments from “best customer” lists

3. Update GA4 audiences based on new behavior patterns or product launches

4. Review which audiences are actually driving closed revenue (not just conversions)

Start with one or two high-quality, manually curated lists rather than dozens of lightly qualified audience segments. Quality beats quantity at the signal layer.

In my audits, I often uncover stale lists from 2021-2023 still being used as signals. Use explicit naming: “HVC_Customers_Updated_2026-01” so you know exactly what you’re working with.

Audience Signal Best Practices & Common Pitfalls

Most performance problems I see in 2024-2026 accounts trace back to weak signals, not “bad AI.” The algorithm is doing exactly what you trained it to do—the question is whether you trained it correctly.

Best Practices

• Start with your highest-quality first-party data, even if it’s small. Precision beats volume at the signal layer.

• Combine customer data with a few high-intent custom segments rather than many broad interests.

• Keep asset groups and audience signal bundles tightly themed. One concept per group.

• Regularly review search terms and placement insights to adjust negatives and search themes.

• Document every major signal change (date, what changed, why) to interpret performance shifts accurately.

Common Pitfalls

Pitfall	Why It Hurts
Strict Targeting Assumption	You assume you know who’s seeing ads—you don’t (Google uses signals as hints, not hard borders).
Overloaded Asset Groups	Muddles the signal and confuses the algorithm by mixing unrelated audiences.
Form-Fill Optimization	Trains toward volume, not profit; ignores CRM-verified revenue.
Ignoring Exclusions	Wastes ad spend on low-intent queries and negative themes.
Stale Customer Lists	Signals are based on outdated buyer profiles that no longer reflect your ideal customer.

Simple Monitoring Routines

Weekly:

• Check search terms and asset group performance

• Adjust negatives and budgets as needed

• Review any significant CPA or ROAS shifts

Monthly:

• Cross-reference which audiences align with actual closed revenue in your CRM

• Update negative keyword themes based on emerging irrelevant traffic

Quarterly:

• Refresh first-party lists with new customer data

• Revisit your Signals Hierarchy based on new business goals

• Audit asset group structure for relevance

The aim isn’t perfect control. It’s better training data so Google’s algorithm finds more of your ideal customers.

How Sarah Stemen Can Help You Fix Your Audience Signals

I’m a 17-year Google Ads veteran and President of the Paid Search Association. My specialty is translating enterprise-level signal strategies into actionable playbooks for small and mid-sized businesses—without locking you into long-term retainers.

What My Google Ads Audits Evaluate

• The quality and structure of your audience signals in Performance Max, Demand Gen, and search campaigns

• Whether your conversion tracking feeds profit-level signals (enhanced conversions, OCT integration)

• How your asset groups and creatives are—or aren’t—functioning as effective signals

• Where your ad spend is leaking to low-value traffic

Services Relevant to Audience Signals and broader Google Ads strategy & consulting

One-Hour Clarity Calls: Focused diagnosis of why your Performance Max or Broad Match campaigns aren’t performing as expected. We identify the signal gaps and conversion tracking issues in a single session.

90-Day Method: Rebuild your campaigns around the 2026 Signals Hierarchy. I train you or your team to manage it independently—no ongoing retainer required.

“Second Opinion” Account Reviews: For agency-managed accounts, I uncover ad spend waste and false positives masked by vanity metrics. Get transparency on what’s actually driving revenue.

Your Next Step

If you suspect your audience signals and conversion data are holding back your Google Ads performance in 2026, let’s talk. Schedule an audit or clarity call to get a clear picture of what’s working, what’s wasting money, and exactly how to fix it.

The bottom line: In 2026, your success in Google Ads depends less on clever keyword lists and more on the quality of signals you feed the algorithm. First-party data, profit-level conversion events, well-structured audience signals, and creative that speaks directly to your best customers—these are the levers that matter now.

The businesses winning this year are those who treat audience signals as training data, not targeting constraints. The hierarchy has flipped. It’s time your strategy reflected that.

FAQs About Audience Signals

What are Google Ads audience signals in 2026? Audience signals are data cues—first‑party lists, CRM values, creative assets, and search themes—that train Google’s AI to find high‑intent, high‑value customers.

Are audience signals the same as targeting? No. Signals guide initial learning, but Google expands beyond them to find profitable users.

Which audience signals matter most now? First‑party data, CRM revenue imports, enhanced conversions, and tightly themed custom segments.

How do audience signals affect Performance Max? They shape how asset groups learn, who Google tests first, and how quickly the algorithm finds profitable patterns.