How to Structure PMAX Campaigns for Optimal Asset Group Performance

If you’ve ever managed a Google PMAX campaign, you’ll know there’s a world of difference between throwing all your products into a single asset group and building a structure that truly reflects intent, audience, and actual business goals. The secret sauce for hitting that sweet spot where automation works for you rather than against you? It’s all about clever campaign architecture. Rooted in real data, intentional segmentation, and a stubborn refusal to let good signals go to waste.

Campaign Structure Isn’t Just a Nerdy Detail

Let’s be real. PMAX campaigns boom because of automation. They promise reach, efficiency, and those buttery smooth incremental conversions. But automation is only as smart as the structure you feed it. If your asset groups are a jumbled mess, you send all the wrong signals, and Google’s machine learning can’t connect the dots on relevance, intent, or value.

In several hands-on attempts, I’ve seen underperforming PMAX campaigns come alive with one change: asset group reorganization based on product category and intent. It’s not rocket science, but it is easy to overlook when new features are rolling out and teams are short on time.

Asset Group Organization: More Than a Checkbox

Who hasn’t been tempted to jam all products into a single group? Maybe each asset gets a line-up of generic headlines, universal images, and a broad audience signal. Sure, it runs. But the results are usually underwhelming.

Here’s what actually works:

• Organize asset groups by distinct product categories. Each group gets creative, feed, and audience signals tailored to those products. Think shoes separate from jackets, premium services split from budget tier.
• Segment by intent whenever you can. Are certain users browsing your product vs others ready to buy? Build groups to mirror those differences. In practice, I’ve split asset groups for “browsers” (using broader audience signals like website traffic or YouTube viewers) and “high-intent” segments (like cart abandoners, or custom lists of return customers). The data has shown marked ROAS lifts and cleaner reporting.
• Leverage strong audience signals. Wherever you can, drop in first-party data. Remarketing lists, past purchasers, or custom segments defined by real engagement. These aren’t just buzzy features. They’re proven performance drivers, often referenced in trusted industry studies.

Budget and Bidding: The Overlap Trap

Ever felt that budgets disappear but results don’t add up? In the field, overlapping asset groups or campaigns bleed into each other, driving up costs while splitting valuable signals. I learned this the hard way juggling two PMAX campaigns for a fashion retailer. One targeting “new customers” and one “all users.” Both ended up targeting similar audiences because the signals weren’t exclusive. Performance dipped, and spend spiked.

What’s clear from recent research is that overlapping campaign structure can dilute optimization impact, making it harder for automation to decide what to prioritize. Stick to one clear intent, audience, or product focus per campaign where possible. If you’re testing multiple strategies, set clear budget caps and monitor for overlap in search term or audience reports.

Customer Acquisition Goals: Guiding Your Structure

Google keeps evolving their PMAX capabilities, with an increased focus on customer acquisition metrics. From first-hand experience, setting “New Customer Acquisition” goals within your campaign provides a direct signal to prioritization systems. If your business is growth-focused, you’ll want at least one PMAX campaign laser-focused on acquisition, supported by first-party audience lists that are always kept fresh.

I’ll never forget the difference this made for a direct-to-consumer startup: isolating high-value lookalike audiences, layering on new customer acquisition goals, and making sure the campaigns were structured so that “repeat” and “prospecting” asset groups never crossed wires. It saved dollars and made reporting much less of a headache.

Audience Signals and Data-Driven Decision Making

It’s easy to get distracted by shiny new automation features, but the foundation always circles back to data. Feed your campaigns with robust, up-to-date audience signals: CRM uploads, re-engagement audiences, site visitors segmented by funnel stage. Research regularly highlights that PMAX works best when fuelled by hard data, not just guesswork or broad interests.

Testing is key here. I’ve always started with the highest-confidence audiences, then gradually expanded to test new combinations, tracking uplift without risking the core performance.

Avoiding Common Structural Mistakes

Now, let’s get honest about what drags down PMAX performance. There are a few cardinal sins I see far too often:

• Lumping every product or service into one mega group: You lose all nuance. Google can’t tell what’s most relevant to whom.
• Ignoring product feed hygiene: Mismatched or outdated feeds send bad signals, plain and simple.
• Overlapping campaigns or asset groups with indistinct intent: You pay more for less, while blurring data for everyone on your team.
• Neglecting negative signals: If you know some searches or audiences never convert, exclude them. Don’t leave Google guessing.

Best Practices Recap

If you’re keen to boost ROAS without handcuffing automation, lay down a PMAX structure rooted in:

• Clear separation by category, audience, or intent level
• Exclusive audience signals where feasible
• Concrete testing and review cycles. Never just “set and forget”
• Tight control over budget and bid strategies to prevent costly overlap
• A firm commitment to first-party data and ongoing audience refreshes

The Payoff: ROAS, Clarity, and Automation You Can Trust

Crafting a streamlined, intentional PMAX structure isn’t a chore. It’s a lifeline for campaign clarity, automation efficiency, and reliable reporting. When you feed the system with clean, well-segmented signals, you invite Google’s machine learning to do its best work for you.

In my own campaigns, performance doubled simply by making the structure more deliberate. Wins weren’t instant, but they were consistent, and the peace of mind knowing you’ve minimized waste? Worth every extra minute spent in the setup.

If you’re ready for smarter automation and stronger results, make your next PMAX campaign a study in purposeful organization. Start small, review data often, and let your structure amplify what you know about your customers.

Take action now: Review your active PMAX campaigns and look for asset groups where intent, audience, or product overlap. Set aside an hour to reorganize. Your metrics will thank you.

Frequently Asked Questions

How many asset groups should a PMAX campaign have for best results?

There’s no universal number, but research and hands-on application point to “as many as needed to reflect distinct categories, intents, and audience segments. Without overlap.” Quality always wins out over quantity, and too many groups can dilute signals.

What types of audience signals drive the strongest performance in PMAX?

First-party signals, such as remarketing lists, past purchasers, and engaged site visitors, typically yield the best results. Data-driven custom segments are valuable, especially when they tie directly to buying signals or recent engagement.

Can I run multiple PMAX campaigns without cannibalizing performance?

Yes, but it requires careful planning. Ensure each campaign serves a clear, unique objective with non-overlapping audience signals, distinct product sets, and mindful budgeting. Always monitor for overlap using Google’s reporting tools to catch inefficiencies.

How often should I review and restructure my PMAX asset groups?

Set a monthly or bi-monthly review schedule, especially after major promotions or product launches. Make changes only when data supports a clear benefit; avoid constant tinkering without justification.

What’s the most common mistake with PMAX structure, and how can I spot it?

Overlapping intent or audience between asset groups tops the list. Regularly review search term reports, audience insights, and conversion tracking to catch and fix duplication. Clean structure means cleaner data and better results.