How to Optimise Bidding in Performance Max Campaigns for Better ROI

Cracking the code on Performance Max (PMAX) campaigns isn’t just about letting Google’s automation take the wheel and hoping for the best. I’ve spent years hands-on in Google Ads accounts. Testing, tweaking, celebrating wins, and sometimes squinting at results that didn’t make a lick of sense. Optimising bidding in PMAX is one part science, one part art. And your choices can mean the difference between ROI that floats and one that soars.

Understanding Smart Bidding in PMAX: The Core

Let’s cut to the chase. PMAX campaigns operate on Smart Bidding, an automated system that uses real-time data to decide which ad, audience, and inventory channel gets your bid. Sounds magical, but it’s more formula than fairy dust.

Here’s the catch: the system only works as well as the inputs and boundaries you set. My experience has shown over and over that advertisers who “set and forget” often spend more than they should. And see less in return.

When you launch a PMAX campaign, you’re given a choice of bid strategies:

• Maximise conversions
• Maximise conversion value
• Target CPA (Cost Per Acquisition)
• Target ROAS (Return on Ad Spend)

I’ve found that Target ROAS is typically best suited for eCommerce brands where every sale has a crystal-clear value. For lead gen, Target CPA or Maximise conversions can be clutch, but be prepared to keep a close eye on early signals. Especially if your budget or conversion volume is low. Don’t be afraid to test, but always use real performance data to guide tweaks.

Segmenting Groups for Sharper Bidding

Ever heard the saying, “Don’t put all your eggs in one basket?” It couldn’t ring truer in PMAX. Google lumps your inventory together. Search, Display, YouTube, Discover, Maps, Gmail. Which is why campaign structure matters more than most folks realise.

Segmenting asset groups or product groups gives you tighter control. In practical terms, this means:

• Splitting out high-value SKUs from low performers. I’ve worked with retailers who doubled their ROAS by separating top-selling products into dedicated asset groups.
• Grouping together products or services with similar profit margins and customer lifecycles.
• Tailoring creative, headlines, and landing pages to match the intent and behaviours of specific segments.

Why bother? Because granular segmentation feeds cleaner data to Google’s Smart Bidding. The more context you provide, the more effectively it can learn. And the more accurate your bidding will be.

Audience Signals: Still Vital for Guiding AI

Too many advertisers treat audience signals as a box-ticking exercise. That’s a surefire way to miss out. While Google’s automation is sophisticated, it still leans heavily on the hints you provide.

A few points from the trenches:

• Upload your customer lists, remarketing audiences, and high-intent custom segments whenever possible.
• Add audience signals based on nuanced data. Repeat buyers, abandoned carts, known categories of interest.
• Refresh signals regularly. I’ve seen campaigns stagnate because audience groups were left untouched for months.

When you give Google’s AI a better starting point, it finds your ideal customer faster. That means less wasted spend and more meaningful results.

Fine-Tuning with Conversion Value Rules and Data Exclusions

Here’s something that takes PMAX results to the next level: using conversion value rules and data exclusions.

Conversion value rules let you assign higher values to certain actions or user types. For example, if you know a purchase from a loyal customer is worth 30% more than a first-time buyer, you can nudge the system to bid more aggressively for those loyalists. This approach takes some time to perfect. Analyzing customer lifetime value, purchase frequency, and tie-ins with offline data. But it pays off handsomely.

On the flip side, data exclusions are your shield against outliers. Say your conversion tracking was broken for a day and inflated results. By excluding that dodgy data, you keep the system learning accurately. When I started using this in mature accounts, wasted spend dropped and the algorithm’s learnings got a much-needed reality check.

Let’s be real, automation doesn’t mean giving up control. It means knowing when to intervene. And how.

Monitoring, Evaluating, and Course-Correcting

If you’re not actively monitoring your PMAX performance, you’re running blind. Google Ads’ Insights tab offers a goldmine of data, if you know where to look:

• Asset group breakdowns reveal which creative combinations are driving the most value.
• Search term insights highlight trends in shopper intent that you can act on fast.
• Top signals show which audiences and demographics your ads are resonating with.

I like to set up custom reports to track changes in cost per conversion, ROAS trends, and asset-level performance weekly. And don’t just stop at the Google dashboard. Use Google Analytics or another analytics platform to double-check that actions align with business outcomes.

If you’re brave enough, test incrementality by running controlled experiments. It’s the only way to truly know what impact PMAX is having above and beyond your other efforts. Just be sure to set a realistic time frame and budget. Wild swings in performance are common early on, but things should settle as the campaign matures.

A Few Real Lessons from the Field

Early in my PMAX journey, I assumed Google’s default settings would outperform manual optimisation. Turns out, context and intent matter more than any algorithm can guess. Mixing in audience insights from CRM data, excluding conversion spikes during promotions, and segmenting high-margin products have consistently delivered better results in both eCommerce and lead gen. Yes, the system can learn, but only if you give it rich, relevant, and timely information.

Wrapping Up With Actionable Takeaways

If you’re serious about squeezing maximum ROI out of PMAX:

• Choose your bidding strategy based on your business model, not just what Google suggests.
• Segment asset groups or products for clearer signals and smarter bidding.
• Treat audience signals like strategic assets, not afterthoughts.
• Leverage value rules to reward high-value behaviours. And don’t hesitate to exclude bad data.
• Rely on real data, monitor performance closely, and always be ready to pivot if things start heading south.

Winning with PMAX is an ongoing dance between automation and human insight. Let Google do the heavy lifting, but never underestimate the value of a sharp, strategic mind behind the scenes.

Ready to level up your PMAX results? Start with these strategies, keep learning, and don’t be afraid to test bold ideas. The rewards are worth the hustle.

Frequently Asked Questions

What is the best bidding strategy for new Performance Max campaigns?

The best bidding strategy depends on your goals. For eCommerce focused on sales value, Target ROAS is often the top pick. If you’re running lead generation, Target CPA or Maximise conversions tend to deliver more reliable results. Start with one that matches your objectives and refine as real data comes in.

How often should I update my audience signals?

Updating audience signals every month keeps your campaigns fresh. If you have seasonal swings or new product launches, increase the frequency. Regular updates help Google’s AI align with your most relevant and valuable user segments.

Should I segment asset groups or let Google handle everything?

Segmenting asset groups almost always leads to clearer data and smarter bidding. While Google’s automation is impressive, it can’t read your business priorities unless you communicate them. Manual segmentation gives you more control and often better results.

How do I use conversion value rules in PMAX?

Access conversion value rules in your campaign settings. Assign higher values to high-value customers, repeat buyers, or priority categories. This pushes the system to bid more for actions that mean more to your business, improving overall ROAS over time.

How do I know if PMAX is really adding value compared to my other campaigns?

Run controlled experiments or uplift studies. Compare performance during periods when PMAX is active versus when it’s paused, and analyze against your baseline campaigns. This gives you clear insight into the incremental value PMAX brings to your overall advertising efforts.