The Performance Max Control Challenge: Why Audience Signals Matter More Than Ever

Performance Max campaigns have fundamentally changed how advertisers interact with Google Ads. Instead of the granular control you once had with traditional Search and Shopping campaigns, you now work with a streamlined system that relies heavily on Google's AI to make optimization decisions. This shift from maximum control to just a few levers—setting a target ROAS and budget at the campaign level—has created a significant challenge for advertisers who are accustomed to precision management.

One of the most frustrating limitations is the restricted ability to add negative keywords directly to Performance Max campaigns. While Google finally rolled out campaign-level negative keywords in January 2025 after it being Performance Max's number one complaint since launch, this feature still doesn't provide the same level of control you have in Search campaigns. You can't add negative keywords at the asset group level, and the campaign-level implementation is limited compared to traditional negative keyword list management.

This is where audience signals become your most powerful compensation strategy. By strategically leveraging audience signals, you can guide Google's AI toward the right traffic while minimizing waste—effectively creating a positive targeting approach that compensates for limited negative keyword control. In this comprehensive guide, you'll learn exactly how to use audience signals to maintain campaign efficiency and protect your budget when direct negative keyword management isn't an option.

Understanding Audience Signals: What They Are and How They Actually Work

According to Google's official documentation, audience signals allow you to add audience suggestions that help Google AI optimize for your selected goals. Although adding audience signals is optional, they help you guide machine learning models on the ideal way to optimize your campaign.

Here's the critical distinction that many advertisers miss: audience signals are not used for direct targeting. Instead, they function as training data for Google's AI. Performance Max may show ads to relevant audiences outside of your signals if they have a strong likelihood of converting to help you meet your performance goals. Think of audience signals as hints or preferences rather than hard boundaries.

How Audience Signals Influence the Algorithm

When you add audience signals to your Performance Max campaign, you're essentially telling Google's machine learning algorithm, "Here's what my ideal customer looks like." The algorithm then uses this information during its initial learning phase to identify patterns and characteristics that correlate with conversions. Over time, as the campaign gathers more conversion data, the algorithm becomes more confident in expanding beyond your initial signals to find similar high-value users.

This learning process typically takes one to two weeks or more, depending on your budget and conversion volume. During this period, audience signals have their strongest influence on campaign performance. As the campaign matures and accumulates conversion data, the AI becomes increasingly confident in making its own decisions, potentially moving beyond your original audience parameters if it identifies profitable opportunities.

Types of Audience Signals You Can Leverage

Performance Max supports multiple types of audience signals, each with different strengths and use cases. First-party data represents the gold standard: your own customer lists, website visitors, and past converters. These signals are based on real user behavior within your business ecosystem, making them the most reliable predictors of future conversions.

Google audience segments include several categories: detailed demographics (long-term life facts like education or homeownership status), life events (important milestones like moving or getting married), affinity audiences (based on passions, habits, and interests), and in-market audiences (users actively researching purchases in your category). These segments cast a wider net and are particularly useful when you lack robust first-party data.

Custom segments allow you to reach ideal customers based on the keywords they're searching for, apps they're using, and URLs they're visiting. This signal type is particularly powerful for compensating for limited negative keyword control because you can proactively define the search intent and browsing behavior that aligns with your ideal customer profile.

The Compensation Strategy: Using Audience Signals to Replace Negative Keyword Control

When you can't exclude irrelevant traffic through comprehensive negative keyword lists at the asset group level, you need to flip your strategy from defensive to offensive. Instead of blocking bad traffic, you'll focus on aggressively signaling what good traffic looks like. This positive targeting approach requires a more sophisticated understanding of your ideal customer and a strategic layering of multiple signal types.

Step One: Define Your Ideal Customer Profile with Precision

Before you can effectively use audience signals as a compensation strategy, you need a crystal-clear picture of your ideal customer. This goes beyond basic demographics to include behavioral patterns, intent signals, and conversion likelihood indicators. Start by analyzing your existing conversion data to identify common characteristics among your highest-value customers.

Review your Google Ads conversion data from the past 90 days, focusing on users who not only converted but also demonstrated high customer lifetime value or strong engagement metrics. Look for patterns in demographics, interests, device usage, geographic location, and time of conversion. Export this data and create a detailed profile that includes both broad attributes (age range, income level, interests) and specific behavioral indicators (specific search queries that led to conversion, particular website sections visited before converting).

Your customer match lists from your CRM represent the most valuable data for this exercise. Upload lists of your best customers—those with repeat purchases, high order values, or strong engagement—to create a "golden audience" signal. Google's algorithm will use these real conversion profiles to find lookalike audiences with similar characteristics and behaviors.

Step Two: Layer Multiple Signal Types Strategically

The compensation power of audience signals comes from strategic layering. Rather than relying on a single signal type, you'll combine multiple complementary signals to create a comprehensive picture of your ideal customer while simultaneously reducing the algorithm's tendency to explore irrelevant traffic.

Structure your signal layering in a hierarchy of specificity. At the foundation, add your most specific, highest-quality signals: customer match lists of past converters and high-value customers. This gives the algorithm concrete examples of proven success. Next, layer in website visitors and engaged users—people who've demonstrated interest but haven't yet converted. This second layer expands reach while maintaining relevance.

The third layer should include Google's in-market audiences that align with your product or service category. These users are actively researching purchases and represent high-intent traffic. Finally, add complementary affinity audiences that match the interests and lifestyle characteristics of your ideal customers. This outer layer helps the algorithm understand the broader context of your target market.

Custom segments deserve special attention in your compensation strategy. By creating custom segments based on specific search keywords related to your products or services, you're essentially providing a positive alternative to negative keywords. For example, if you sell premium enterprise software, you can create custom segments targeting users searching for "enterprise solution," "business platform," and "professional tools" while the algorithm naturally deprioritizes users searching for "free," "student discount," or "personal use" terms that would typically be negative keywords.

Step Three: Segment Asset Groups by Intent and Customer Type

One of the most effective compensation techniques is creating separate asset groups within your Performance Max campaign, each with distinct audience signals tailored to specific customer segments or intent levels. This approach gives you more granular control over how Google's AI explores different audience territories.

Create an asset group specifically for high-intent audiences with signals focused exclusively on users who've already demonstrated strong purchase intent: cart abandoners, product page viewers, past converters, and in-market audiences in your specific category. Pair these signals with conversion-focused creative assets that speak directly to ready-to-buy customers. This asset group essentially replaces the need for negative keywords by concentrating budget on users least likely to generate irrelevant clicks.

Build a separate prospecting asset group with broader signals designed to find new customers: lookalike audiences based on your customer match lists, affinity audiences matching your ideal customer's interests, and custom segments targeting relevant research-phase keywords. Use more educational, awareness-focused creative assets in this group. By segmenting this way, you can monitor performance independently and adjust budgets based on which customer segments deliver the best results.

A third asset group dedicated to remarketing with signals exclusively focused on your own first-party data (website visitors, video viewers, app users) creates a protected zone of known-quality traffic. This is particularly valuable as a compensation strategy because remarketing traffic typically generates lower wasted spend than cold prospecting, reducing your overall need for aggressive negative keyword management.

Advanced Techniques for Maximizing Audience Signal Effectiveness

Avoid Signal Conflicts and Maintain Message Coherence

One critical mistake that undermines the compensation strategy is adding conflicting or unrelated signals to the same asset group. When you mix completely different audience types—for example, combining first-time homebuyers with retirement planning audiences in the same asset group—you confuse the algorithm and reduce its ability to optimize effectively. This confusion leads to exactly the kind of unfocused traffic exploration that you're trying to prevent.

Equally important is ensuring your creative assets and landing pages match your audience signals. If your signals target high-income professionals but your ad creative emphasizes budget pricing and discounts, you create a mismatch that reduces relevance scores and wastes budget. The algorithm performs best when there's clear alignment between who you're targeting (signals), what you're saying (creative), and where you're sending them (landing pages).

Use Search Themes to Complement Audience Signals

Starting in 2025, Google allows you to add up to 50 search themes to each Performance Max asset group. Search themes represent another powerful compensation tool that works synergistically with audience signals. While audience signals tell Google who to target, search themes tell Google what search contexts are most relevant to your business.

Use search themes to define the positive keyword territory where you want your ads to appear, effectively creating a positive alternative to negative keyword exclusions. For example, instead of adding "cheap" as a negative keyword, you can add search themes like "premium," "professional," "enterprise," and "high-quality" to signal the type of search context you want. The algorithm will naturally prioritize these contexts when combined with your audience signals.

Coordinate your search themes with your audience signals for maximum impact. If you're using customer segments targeting enterprise decision-makers, your search themes should reflect enterprise-level language and concepts. This coordination reinforces the algorithm's learning and helps it understand the complete picture of your ideal customer journey.

Implement a Protected Keyword Strategy Alongside Audience Signals

While this article focuses on audience signals as a compensation strategy, the most sophisticated approach combines signals with strategic use of the limited negative keyword functionality that is available. Add campaign-level negative keywords to block the most egregious irrelevant terms—words like "free," "cheap," "used," "DIY," or specific product names you don't sell—while using audience signals to proactively steer toward quality traffic.

This dual approach creates both offensive and defensive protection. Your negative keywords act as guardrails preventing the worst traffic, while your audience signals actively guide the algorithm toward the best traffic. For agencies managing multiple accounts, this strategy becomes especially valuable because you can develop standardized negative keyword lists for campaign-level application while customizing audience signals for each client's unique customer profile.

Tools like Negator.io's AI-powered negative keyword management become essential in this context, helping you identify which irrelevant search terms are still slipping through despite your audience signal strategy. By analyzing search term reports with context from your business profile and keywords, you can identify emerging waste patterns and add them to your campaign-level negative lists, creating a continuously improving compensation system.

Monitoring and Optimization: Making Your Compensation Strategy Work Long-Term

Track Audience Performance Through Insights Reporting

Google has significantly improved Performance Max reporting in 2025, providing deeper audience and search category reporting inside the Insights tab. Use this to understand whether your campaigns lean more toward branded or generic searches, and which audience segments are driving the majority of your conversions. This visibility helps you evaluate whether your audience signal compensation strategy is actually working or if irrelevant traffic is still consuming budget.

Review your audience insights at least weekly during the first month of any new Performance Max campaign, then bi-weekly once performance stabilizes. Look specifically for audience segments that show high impression volume but low conversion rates—these represent potential waste areas where your signals may not be strong enough. Conversely, identify high-performing segments that you may want to emphasize more heavily in your signal configuration.

Pay particular attention to search category insights. If you notice your campaign is triggering on search categories that seem tangentially related or completely irrelevant to your business, this indicates your audience signals need refinement. For example, if you sell B2B software but see significant impressions in consumer-focused categories, you need to strengthen your B2B-specific signals and potentially add consumer-related terms to your campaign-level negative keywords.

Refresh and Optimize Your Signals Regularly

Audience signals are not a set-it-and-forget-it solution, especially when you're using them as a compensation strategy for limited negative keyword control. Your customer base evolves, market conditions change, and Google's algorithm continuously learns. Regular signal refresh ensures your compensation strategy remains effective over time.

Update your customer match lists at least monthly, ideally every two weeks if you have sufficient conversion volume. Google requires lists to be refreshed every 540 days to maintain optimal performance, but more frequent updates ensure your signals reflect your most recent customer acquisition patterns. Focus particularly on uploading lists of customers acquired in the last 30-60 days, as these represent current market conditions and buyer behavior.

As you gather performance data, prune underperforming signals and double down on successful ones. If certain affinity audiences or custom segments consistently show poor performance metrics, remove them from your signal configuration. This pruning prevents the algorithm from wasting exploration budget on low-probability audience territories. Conversely, when you identify high-performing signals, consider creating dedicated asset groups specifically for those audiences to maximize their impact.

Adjust Budget Allocation Based on Signal Performance

One advantage of segmenting asset groups by audience intent is the ability to adjust budget allocation based on which signals deliver the best results. While Performance Max handles budget distribution automatically within a campaign, you maintain control at the campaign level, allowing you to create multiple Performance Max campaigns with different signal strategies and budget priorities.

Consider creating separate Performance Max campaigns for distinctly different signal strategies: one focused exclusively on first-party data and high-intent signals with the majority of your budget, another for prospecting with broader signals and a smaller test budget, and potentially a third for specific geographic or demographic segments if your business has distinct regional variations. This structure gives you more control over how much budget can be allocated to different compensation strategies.

Review campaign-level performance monthly and reallocate budgets based on efficiency metrics. If your high-intent signal campaign consistently delivers 5:1 ROAS while your prospecting signal campaign struggles at 2:1, shift budget accordingly. This dynamic allocation ensures you're maximizing the effectiveness of your audience signal compensation strategy rather than treating all signal approaches equally.

Common Mistakes and Pitfalls to Avoid

Mistake One: Using Audience Signals with Insufficient Size

One frequent mistake is adding audience signals that don't meet Google's minimum size requirements or are too small to provide meaningful training data. Audience lists must have more than 1,000 active and eligible users to be effective. When you use undersized audiences, the algorithm essentially ignores them, rendering your compensation strategy ineffective.

If your first-party data doesn't meet minimum thresholds, expand your signals by increasing the lookback window (from 30 days to 90 or 180 days of website visitors, for example) or by combining multiple related smaller audiences into broader segments. For customer match lists, ensure you're uploading comprehensive data including email, phone number, and address information to maximize match rates.

Mistake Two: Making Excessive Signal Changes During Learning Period

The learning period is critical for Performance Max campaigns, and excessive changes to audience signals during this phase can reset the algorithm's progress. Some advertisers, anxious about performance during the initial weeks, continuously tweak signals, add new audiences, or remove existing ones. This constant change prevents the algorithm from completing its learning cycle and identifying meaningful patterns.

Allow at least two weeks—ideally four weeks—of stable signal configuration before making significant changes. During this period, Google's AI needs to accumulate at least 30-50 conversions to establish reliable optimization patterns. If you must make changes during learning, do so incrementally: add one new signal type at a time rather than completely overhauling your signal strategy.

Mistake Three: Ignoring Conversion Tracking Quality

Your audience signal compensation strategy is only as effective as your conversion tracking. If your conversion tracking is inaccurate, incomplete, or tracking low-value actions, the algorithm will optimize toward the wrong outcomes regardless of how well-crafted your audience signals are. Google's AI uses conversion data as the ultimate truth signal, so if that truth is flawed, everything else fails.

Audit your conversion tracking before implementing sophisticated audience signal strategies. Ensure you're tracking actions that represent genuine business value, implement enhanced conversions for improved accuracy, and consider offline conversion tracking if your business involves phone calls or in-person interactions. For lead generation businesses, implementing lead quality scoring and importing that data back into Google Ads creates a feedback loop that helps the algorithm distinguish between high-quality and low-quality leads, making your audience signals more effective at finding the right users.

Integrating Audience Signals with Your Broader Performance Max Strategy

Coordinate with Traditional Search and Shopping Campaigns

Your audience signal compensation strategy shouldn't exist in isolation. The most effective approach coordinates Performance Max with traditional Search and Shopping campaigns to create a comprehensive account structure where each campaign type serves a specific purpose. Tailoring your negative keyword strategies by campaign type helps you understand how Performance Max fits into your broader optimization framework.

One critical coordination point is branded traffic management. Use Search campaigns to capture branded queries where you have maximum control over keywords, ad copy, and landing pages. Then exclude branded terms from your Performance Max campaigns using campaign-level negative keywords, forcing Performance Max to focus on non-branded, prospecting traffic where audience signals provide the most value. This division of labor ensures your audience signal strategy focuses on the most challenging traffic acquisition scenarios.

Use your Search and Shopping campaigns as testing and proving grounds for Performance Max expansion. When you identify high-performing keywords, audience segments, or product categories in your traditional campaigns, incorporate those insights into your Performance Max audience signals and search themes. This creates a continuous improvement cycle where your granular campaign learnings feed your automated campaign optimization.

Leverage Account-Level Negative Keywords Strategically

While your audience signals work at the campaign and asset group level, don't overlook the power of account-level negative keyword lists that apply across all campaigns. Create standardized lists of universally irrelevant terms—competitor brand names you don't sell, quality indicators you want to avoid ("free," "cheap," "pirated," "cracked"), and product/service categories completely outside your business scope.

Maintain several account-level lists for different categories: a competitors list, a quality-filter list, an unrelated-products list, and a job-seekers list (if applicable to your business). Apply these lists to all campaigns including Performance Max. This creates a baseline level of protection that works in concert with your audience signal strategy, ensuring your signals are guiding the algorithm within a pre-filtered landscape rather than starting from completely unfiltered Google search traffic.

Scale Your Strategy Across Multiple Accounts (For Agencies)

For agencies managing multiple client accounts, developing a standardized audience signal compensation framework creates consistency and efficiency. While each client requires customized signals based on their specific customer profile, the overall strategic approach can be templated and scaled.

Create a standardized framework that includes: a customer data collection template that identifies what first-party data each client should provide, a signal hierarchy structure that determines the layering order for different signal types, an asset group segmentation model that defines how to split campaigns by intent level, and a monitoring dashboard template that tracks the key metrics indicating whether the compensation strategy is working.

This framework becomes particularly powerful when combined with specialized tools. Managing negative keyword hygiene across multiple agency accounts is exponentially more complex than managing a single account, and audience signal strategies multiply that complexity. Tools that automate the identification of wasted spend patterns across your entire account portfolio allow you to continuously refine your signal strategies based on collective learnings rather than managing each account in isolation.

The Reality Check: When Audience Signals Aren't Enough

Acknowledge Performance Max's Inherent Limitations

While audience signals provide a powerful compensation strategy for limited negative keyword control, it's important to be realistic about Performance Max's inherent limitations. Scaling Performance Max campaigns without blowing your budget requires acknowledging that even the best audience signal strategy won't provide the same level of control you had with traditional Search campaigns.

Google's AI has significant autonomy in Performance Max campaigns, and audience signals are advisory rather than restrictive. The algorithm will explore beyond your signals if it identifies conversion opportunities, which means some level of irrelevant traffic is inevitable. The goal isn't to eliminate all waste—that's impossible in Performance Max—but rather to minimize it to acceptable levels while maximizing the campaign's unique advantages in cross-channel reach and automated optimization.

Recognize When Traditional Campaigns Remain Superior

For certain business models and campaign objectives, traditional Search campaigns with comprehensive negative keyword control remain the superior choice despite Performance Max's automation benefits. If your business has very specific, narrow targeting requirements, deals with highly regulated industries where ad placement control is critical, or operates in competitive spaces where branded traffic protection is paramount, the limitations of Performance Max may outweigh its benefits.

The most sophisticated approach for many advertisers is a hybrid strategy that uses traditional campaigns for core, high-control situations and Performance Max for expansion and prospecting where audience signals can guide exploration into new territory. This hybrid approach recognizes that different campaign types serve different strategic purposes, and audience signals work best when they're expanding reach rather than trying to replicate the precision control of traditional campaigns.

Accept That Continuous Monitoring Is Non-Negotiable

Using audience signals as a compensation strategy for limited negative keyword control requires more active monitoring than traditional campaigns with robust negative keyword lists. You're essentially relying on positive guidance rather than explicit exclusions, which means you need to continuously verify that the algorithm is interpreting your signals correctly and not drifting into irrelevant territory.

Plan to invest significant time in the first 4-6 weeks of any new Performance Max campaign with this compensation strategy, reviewing search term insights, audience performance data, and conversion quality metrics at least twice weekly. As campaigns mature and demonstrate consistent performance, you can reduce monitoring frequency, but Performance Max campaigns with audience signal strategies never reach the truly set-it-and-forget-it status that some advertisers hope for.

This monitoring requirement highlights a critical truth about modern PPC management: Google Ads automation still needs human context. The algorithms are powerful, but they lack business context, industry knowledge, and the ability to distinguish between technically valid conversions and genuinely valuable business outcomes. Your audience signal strategy provides some of this context, but ongoing human oversight ensures the automation stays aligned with your actual business goals.

Conclusion: Your Action Plan for Implementing Audience Signal Compensation

The limited ability to add negative keywords directly to Performance Max campaigns represents a genuine control challenge, but audience signals offer a viable compensation strategy when implemented thoughtfully. By shifting from defensive blocking to offensive targeting, you can guide Google's AI toward quality traffic while minimizing wasted spend on irrelevant clicks.

Start by defining your ideal customer profile with precision, analyzing your existing conversion data to identify the characteristics and behaviors that predict success. This foundation makes every subsequent decision about audience signals more effective.

Layer multiple signal types strategically, beginning with your highest-quality first-party data and expanding through increasingly broad audience segments. Create custom segments that define positive search intent, effectively replacing the need for extensive negative keyword lists.

Segment your asset groups by intent level, creating separate groups for high-intent converters, mid-funnel prospects, and cold traffic acquisition. This segmentation provides more granular control and allows you to monitor which signal strategies deliver the best results.

Coordinate audience signals with search themes, campaign-level negative keywords, and account-level negative lists to create a comprehensive compensation system that combines positive targeting with strategic exclusions.

Monitor performance continuously, particularly during the critical first 4-6 weeks, and be prepared to refine your signals based on actual performance data rather than assumptions about what should work.

Refresh your customer data regularly, update underperforming signals, and double down on audience segments that demonstrate strong conversion performance.

Finally, recognize that even the best audience signal strategy benefits from specialized tools that help identify where waste is still occurring. When you're relying on positive targeting rather than explicit exclusions, having visibility into what search terms are actually triggering your ads becomes even more critical. The combination of strategic audience signals and intelligent negative keyword management creates the most comprehensive approach to maintaining efficiency in Performance Max campaigns.

The reality of Performance Max is that you'll never have the same granular control you once enjoyed in traditional Search campaigns. But with a sophisticated audience signal compensation strategy, you can maintain campaign efficiency while leveraging Performance Max's unique advantages in cross-channel reach and automated optimization. The key is approaching audience signals not as a simple targeting tool, but as a comprehensive strategic framework for guiding algorithmic decision-making in an environment with limited direct control.