The main objective was to increase investment in Performance Max campaigns to drive business growth. However, this increase posed a critical challenge: avoiding the loss of efficiency associated with expanding advertising coverage.

As the budget increased, certain risks began to emerge:

• Decreased targeting precision

• Reaching audiences with lower purchase intent

• Difficulty maintaining consistent performance

• Increasing reliance on the algorithm without sufficiently qualified inputs

In an environment where automation plays a central role, the challenge was not only to scale, but to do so while maintaining traffic quality, ad relevance and campaign profitability.

To address this challenge, a strategy was designed focused on improving the quality of the signals feeding the Performance Max algorithm, with a particular emphasis on targeting and campaign structure.

The solution was structured around three main pillars:

Signal enrichment through first-party data and advanced audiences
Demographic data, interests and first-party audience lists were integrated, allowing the system to more accurately identify profiles with a high probability of conversion. This approach helped reduce reliance on generic signals and improve targeting quality.
 

The implementation of the new strategy generated a direct and significant impact on key performance indicators, demonstrating improvements in both traffic quality and campaign profitability:

• +100% in conversion rate, reflecting a greater ability to attract users with high purchase intent

• +96% in ROAS, consolidating a substantial improvement in return on ad spend

• –47% in CPA, optimizing acquisition costs and increasing operational efficiency

The key outcome was the ability to increase investment while maintaining control over performance, which is especially relevant in automated environments such as Performance Max.