Beyond ‘Best Practice’ – A Data-Driven Framework for Smarter Campaigns

For anyone managing a large e-commerce account, Performance Max presents a paradox. It promises unparalleled reach and efficiency, yet it can often feel like you’ve handed over the keys to a black box. You see the budget being spent, but on what?

Why do the same handful of products soak up all the attention while thousands of others gather digital dust? This isn’t a bug in the system; it’s a feature. And it was the exact challenge facing one of our clients, a major retailer with an inventory of over 335,000 products. Their move to PMax had led to a frustrating decline in performance.

Advertisers should monitor these changes closely as they may impact campaign performance, bidding strategies, and return on ad spend.

Despite their best efforts to segment campaigns by category, price, and ROAS, they were grappling with muddled attribution and felt they were losing their strategic grip. Our mission was to move beyond the standard playbook, decode the machine’s behaviour, and align its power with what truly drove the client’s bottom line.

Automated systems are designed to follow patterns, and at scale, this can lead to outcomes that require intervention: A workshop with the client’s marketing, commercial, and analytics teams confirmed the core issue: the campaign’s definition of “success” was not aligned with the business’s. The solution wasn’t to restrict the algorithm, but to provide it with a richer, more accurate set of data signals. We developed a proprietary classification engine, to translate the client’s deep business knowledge into a clear signal that PMax could act on. This intelligence layer was built on four pillars: This classification engine produced a five-tier segmentation system that sorted every product by its strategic role: Critically, this was a dynamic system.