Our biggest challenge was achieving a Return on Ad Spend (ROAS) of over 5x to ensure the campaign stayed profitable. Despite trying different campaign types like Search and Shopping, we struggle hit the target. The premium pricing of the products made it even harder to find and target the right audience effectively
Our main hurdle was ensuring a Return on Ad Spend
(ROAS) greater than 5x to keep our campaign in the
profitable zone. Despite testing different type
campaigns like Search and Shopping, achieving the
desired outcomes proved challenging. Additionally,
the products are premium priced, making it even
more difficult to identify and target the appropriate
audience effectively
Primary Objective: Our main goal was to boost conversions and the return on ad spend (ROAS) from our campaign, while also keeping costs under control.
Secondary Objective: Reduce the Cost per Acquisition (CPA).
We turned to Google’s Performance Max (pMax) campaigns, which use Machine Learning to run ads
across multiple Google platforms. This automated system helped us reach our goals by optimizing ad
delivery and increasing sales. To ensure the campaign could scale, we created a mix of creative assets,
including text, images, and videos, to appeal to a wider audience and maximize impact.
Results
This case study shows how combining Machine Learning, creative customization, and a cross-channel approach can significantly boost advertising performance and profitabilty.
