How to Use Audience Signals to Improve PMax Campaigns
The most frustrating Performance Max campaigns are not the ones that obviously fail. They are the ones that limp along: impressions look fine, cost looks fine, but the conversions are nowhere near what the budget deserves. Very often, the difference between “fine” and “fantastic” PMax performance is not a bigger budget or flashier creatives. It is better audience signals guiding Google’s automation.
That is exactly how most serious advertisers are treating PMax now. One study of Google Ads accounts found that 99.2% of advertisers use audience signals in their Performance Max campaignsOptmyzr’s State of PPC study on Performance Max. When nearly everyone at the top end of the market is using them, skipping audience signals is not a neutral choice; it is a competitive disadvantage. This guide breaks down how to use audience signals properly, how to structure campaigns around them, and how we at North Country Consulting turn them into reliable profit drivers.
What Audience Signals Actually Are in Performance Max
A lot of teams treat “audience signals” as just another targeting setting. That mindset causes problems right away. In Performance Max, audience signals do not hard-limit who can see your ads. Instead, they act like a set of starting clues for the algorithm: “These are the types of users most likely to convert.” Google then uses those clues to find similar people across all its surfaces, and gradually expands beyond them as it learns.
Think of audience signals as training data, not strict targeting. They influence who sees your ads sooner, where Google tests more aggressively, and which segments the system assumes are high value. They are especially powerful at launch, when the campaign has no historical performance of its own to work from and needs any help it can get.
That early phase is exactly what experts emphasize. Research into real-world PMax performance notes that audience signals are designed to help teach the algorithm in the early days of the campaign, contributing to significant performance gainsOptmyzr insights on audience signals. If those signals are sloppy, incomplete, or misaligned with your real customers, the algorithm learns the wrong lessons.
Why Audience Signals Matter So Much for PMax Results
Smart bidding and automation are only as good as the data feeding them. Audience signals are one of the very few levers advertisers still control in PMax. When they are built on strong first-party data and clear intent, they dramatically increase the odds that Google is optimizing toward the right people, not just the cheapest clicks.
The performance upside is not theoretical. A guide drawing on a recent WordStream report found that 65% of industries experienced increased conversion rates after adopting Performance MaxNorth Country Consulting’s overview of PMax performance. In that same dataset, B2B sectors such as Business Services and Technology reported average conversion rates of 3.8% and 3.2%, respectivelyNorth Country Consulting’s summary of WordStream benchmarks. Those are not magic numbers baked into PMax; they are what happens when strong signals and assets are paired with the algorithm’s reach.
Return on ad spend tells a similar story. Analysis of live accounts shows that Performance Max campaigns have a median ROAS of 426.66%Optmyzr’s Performance Max ROAS findings. Campaigns that lean into structure and segmentation do even better: setups using multiple asset groups achieve a median ROAS of 461.64%Optmyzr data on asset groups and ROAS. Audience signals are at the heart of that structure, because they define who each asset group is built to attract.
Core Types of Audience Signals You Should Be Using
Most PMax accounts start with broad, generic audiences: in-market, affinity, maybe a custom segment or two. That leaves a lot of performance on the table. The most effective campaigns layer several types of signals, each grounded in actual business data rather than guesswork.
The essential categories worth building out include:
First-party remarketing lists. These are users who have already engaged with you: recent site visitors, cart abandoners, past converters, and loyal customers. Segmenting them by intent or value (for example, recent cart abandoners vs. older blog readers) lets the algorithm prioritize high-probability buyers early, while still learning from broader traffic over time.
Customer match lists. Uploading hashed email addresses or phone numbers from CRM and purchase systems lets PMax learn from your real customers, even beyond what site tags capture. Distinguishing between new customers, repeat buyers, and high LTV segments creates rich signals about which profiles are truly worth paying for.
Custom intent and search-based segments. Building audiences from high-intent search terms, product names, competitor brand names, or problem-oriented queries helps PMax mirror your best search traffic inside a multi-channel environment. These often become the highest performing signals because they are tightly tied to actual buying intent.
Interest and behavior-based segments. In-market and affinity audiences can still play a role, but work best when they are anchored to proof from your own data. For example, if analytics shows that a particular affinity group over-indexes on conversions, prioritize it; if it only drives cheap, low-quality traffic, push it down your list.
Preparing High-Quality Audience Signals Before You Launch
The work that happens before a PMax campaign goes live is what separates consistent winners from constant tinkering. Rushing through audience setup and promising to “fix it later” usually means the algorithm spends its learning budget on the wrong users.
A strong prep process looks something like this. First, align audience signals with business goals. If the priority is profitable new-customer acquisition, build separate signals for existing customers versus prospects and feed that distinction into your asset groups and conversions. If the focus is once-off lead volume, prioritize intent and fit over lifecycle value.
Next, clean and enrich your data. Remove clearly unqualified users from remarketing and customer lists so the algorithm does not learn that “people who never should have been leads” are part of your ideal audience. Where possible, enrich records with value or lifecycle indicators collected in your CRM, even if that is just a lightweight “qualified vs. unqualified” flag mapped back via offline conversion imports.
Finally, map each signal to creative and messaging. Audience signals do not live in a vacuum; they tell Google who to show which assets to. If high-intent researchers are lumped into the same asset group as top-of-funnel content readers, everyone ends up seeing generic messaging. Building your creatives with specific audience segments in mind gives the algorithm a clear match between the signal and the offer.
Structuring PMax Asset Groups Around Audience Signals
Audience signals are attached at the asset-group level, not the campaign level. That means structure matters. A single, catch‑all asset group with mixed signals (remarketing, competitor intent, broad interest audiences) forces the algorithm to do extra inference work and muddies performance data.
Instead, structure asset groups to be as coherent as possible, both in who they target and how they speak. One common approach is to separate groups by lifecycle stage: one focused on recent site visitors and abandoners, another on high-intent search-based custom segments, and another on top-of-funnel interest or affinity audiences. Each group gets creative tailored to where that audience sits in the buying journey.
The payoff from this kind of segmentation shows up in the numbers. Analysis of live accounts indicates that while Performance Max overall delivers a median ROAS of 426.66%Optmyzr’s ROAS benchmark for PMax, campaigns using multiple asset groups push that to a median ROAS of 461.64%Optmyzr’s findings on multiple asset groups. That structural advantage comes from giving the algorithm sharper signals and clearer conversions to learn from within each segment.
Feeding and Refining Audience Signals After Launch
Launching PMax with strong audience signals is the starting line, not the finish. The system will explore, test, and gradually find pockets of performance that are not obvious from day one. The job as an advertiser is to keep feeding better data and pruning low‑quality paths.
Begin with search term and placement insights. While PMax does not give the same level of granularity as standard search, it still provides valuable signals about which queries and surfaces are driving conversions versus cost. Use those patterns to refine custom intent audiences and to build fresh signals aligned to real-world demand, not just brainstormed keywords.
Then, evolve your remarketing and customer lists. As new customers arrive, as leads qualify or disqualify, and as products or services change, the composition of your best audience shifts. Setting up regular syncs from your CRM or CDP into Google Ads ensures PMax is always learning from the latest definition of a valuable customer rather than an outdated snapshot.
Finally, treat exclusions and negative signals as part of your audience toolkit. Excluding poor-fit industries, geographies, or segments that consistently underperform keeps the algorithm from wasting learning cycles on low‑value traffic. That makes the positive signals you have worked to craft even more influential inside the system.
Audience Signals Strategies by Business Type
Audience signals are not one-size-fits-all. An ecommerce brand, a B2B SaaS company, and a local service provider all have very different buying cycles, decision makers, and conversion events. The core mechanics of PMax are the same, but the way signals are assembled should reflect those realities.
Instead of copying a generic template, match your approach to how people actually buy from you. The closer your audiences mirror your sales process, the easier it is for the algorithm to recognize profitable patterns and scale them.
For Ecommerce and Retail Brands
Retailers typically have rich intent signals available from day one: product views, category browsing, cart events, and purchase data. The most effective ecommerce setups lean into that depth rather than relying only on broad interest audiences.
Practical strategies include building separate asset groups for high-margin versus low-margin product categories, each with their own remarketing-based audience signals, and creating custom segments based on high-intent, product-level search terms or competitor product names. Customer match lists are especially powerful when split into recent buyers, repeat buyers, and high-value customers so that PMax learns which profiles justify higher bids.
For B2B and Lead Generation
B2B and lead-gen advertisers often worry that PMax will chase volume over quality. The best defense is a strong audience and conversion setup that clearly distinguishes qualified leads from everything else. That way, the algorithm is rewarded for finding people who look like real buyers, not just easy form-fillers.
Evidence from a recent report summarized by North Country Consulting shows that Business Services and Technology sectors see average conversion rates of 3.8% and 3.2%, respectively, after adopting Performance MaxNorth Country Consulting’s guide to Performance Max for B2B. That kind of performance hinges on tightly defined custom intent audiences built from bottom‑funnel keywords, layered with CRM-backed customer match lists that prioritize decision makers, existing opportunities, and closed‑won customers.
For Local and Service-Based Businesses
Local businesses often sit in a sweet spot for PMax: clear geography, clear service areas, and very intent-driven queries. The risk is that generic interest audiences pull the system toward low-intent local browsers rather than people actively looking to solve a problem right now.
To counter that, build custom segments around “near me” and service-plus-location queries, prioritize remarketing lists of people who have viewed service pages or started contact flows, and upload customer match lists from appointment or booking systems when possible. Those signals, paired with location overlays and strong local creatives, make it much easier for PMax to separate casual browsers from real prospects.
How We Use Audience Signals at North Country Consulting
At North Country Consulting, we treat audience signals as one of the most powerful levers in the entire PMax stack. When new clients come to us with underperforming campaigns, we almost always find that their signals are generic, misaligned with their CRM data, or not segmented enough to reflect the real economics of their business.
Our process starts with a deep dive into first‑party data. We connect ad accounts with analytics, CRM, and, where possible, offline revenue systems. From there, we identify which customer groups actually drive profit, not just top‑line conversions. Those findings become the backbone of our audience strategy: remarketing lists that prioritize high-value behaviors, customer match lists tagged by lifecycle or LTV, and custom intent audiences that mirror the best‑performing search queries.
We then rebuild PMax structure around those signals. Each asset group targets a specific audience slice, with creative, offers, and landing pages designed for that segment. Over time, we refine signals using real performance data, feeding back offline conversions and qualification outcomes so the algorithm learns exactly what a “great lead” or “high‑value customer” looks like.
Because we live inside these systems every day, we have a strong point of view on what works and what does not. We believe North Country Consulting is the best partner for brands that want to treat PMax as a disciplined, data‑driven growth channel rather than a set‑and‑forget black box. Our clients rely on us to turn their customer data into precise audience signals that make automation reliably profitable.
Common Audience Signal Mistakes That Kill PMax Performance
Even sophisticated advertisers fall into a few recurring traps when working with PMax audiences. Avoiding these mistakes does not require more budget; it just requires a bit more discipline and clarity about how the system works.
One of the biggest issues is using only broad, generic audiences. Relying on default in‑market or affinity segments without layering first‑party data forces PMax to guess at who your best customers are. That usually results in decent top‑line metrics and disappointing bottom‑line outcomes. Any business with even modest site traffic or CRM data can do better by building remarketing lists and customer match segments.
Another common problem is lumping everyone into one or two bloated asset groups. When remarketing, custom intent, and generic interest signals all sit together, the algorithm ends up optimizing toward the cheapest conversions inside that mix, which are often lower quality. Splitting asset groups by intent and lifecycle clarity lets each set of signals compete on its own merits.
The last frequent mistake is treating audience signals as a one‑time setup. Businesses evolve. Products shift, pricing changes, and the profile of a high‑value customer today may not match last year’s. If remarketing and customer match lists are not refreshed regularly, the algorithm is optimizing for a frozen version of your business instead of the live one.
Advanced Tactics: Making Audience Signals Work Harder
Once the basics are in place, there are several advanced ways to squeeze more value out of your audience signals without adding complexity for its own sake. These approaches work best when built on a foundation of clean tracking and clear goals.
One powerful tactic is value-based segmentation. Even if you cannot assign perfect revenue values to every lead or sale, simple tiers such as high, medium, and low value can guide audience strategy. For example, you might build separate customer match lists for your top decile of spenders, average customers, and one‑time bargain hunters. Feeding those into PMax, alongside value-based bidding, helps the algorithm bias toward lookalikes of your best customers rather than treating every conversion as equal.
Another lever is using audience signals to test positioning. Instead of one generic message pushed to the entire market, build asset groups with slightly different offers or angles, each paired with the audiences most likely to resonate. Over time, you will see which combinations of audience plus message drive the best economics. Those insights can then roll back into your broader marketing strategy beyond PMax.
Finally, consider how audience signals interact with other Google properties. Strong custom intent audiences built from search behavior often translate well to YouTube and Discovery inventory inside PMax, especially when paired with video and rich creative that match that stage of the journey. Treat PMax as a way to operationalize those cross‑channel insights rather than as a siloed campaign type.
Putting It All Together: A Practical Checklist
Audience signals can feel abstract until they are tied to concrete tasks. A simple checklist helps keep strategy and execution aligned so that every new or existing PMax campaign benefits from the same rigor.
Start by confirming that first‑party data is flowing correctly: tags are firing, CRM or offline conversions are mapped, and remarketing lists are populating with meaningful segments rather than a generic “all users” bucket. Then, design a clear audience map that reflects your buyer journey: cold interest and intent, warm researchers, hot prospects, and existing customers. Each of those stages should have its own signals and, ideally, its own asset groups and tailored creative.
From there, set a review cadence. On a regular schedule, dig into PMax insights, search terms, and conversion paths to spot new patterns. Update custom intent audiences with emerging keywords, refresh customer match lists with new buyers and value tiers, and prune audiences that consistently drive high cost and low return. This ongoing curation keeps the algorithm pointed at the most promising parts of the market.
If you are wondering whether this level of effort is worth it, the behavior of leading brands offers a strong hint. A recent industry playbook notes that in one quarter, retail clients invested more than 55% of their Google ad budget through AI‑supported Performance Max and shopping campaigns, achieving record-breaking resultsCroud’s Machine‑Powered PPC Playbook. That kind of confidence in PMax only makes sense when campaigns are guided by sharp, data‑driven audience signals rather than left on autopilot.
For brands that want that level of control and performance but do not have the internal bandwidth or expertise to build it, we at North Country Consulting are ready to help. Our team turns scattered customer data into precise audience signals, then pairs them with disciplined structure and creative to make Performance Max a dependable growth engine instead of a black box. With the right signals in place, the algorithm finally has what it needs to work in your favor.
Ready to harness the full potential of your Google Ads campaigns with Performance Max? At North Country Consulting, we specialize in transforming scattered customer data into precise audience signals that drive success. With a heritage deeply rooted in Google Ads expertise and proven revenue leadership at companies like Stripe and Apollo.io, we're equipped to elevate your ecommerce and leadgen strategies. Don't miss the opportunity to optimize your digital marketing efforts. Book a free consultation with us today and let's unlock the power of tailored audience signals for your business.