Margin-based segmentation in Performance Max: how i-run maximized its profitability ?

Context

We supported i-run in optimizing their Google Shopping strategy, facing a challenge common to e-commerce players: the difficulty of steering profitability within Performance Max campaigns, where every product is treated uniformly by the algorithm, regardless of its real contribution to margin. The goal was clear: stop allocating advertising budgets indiscriminately across a product catalog with heterogeneous margins, and regain control of performance by directing investment toward the most profitable references.

Solution

We designed and deployed a margin-based segmentation strategy within Performance Max, leveraging the FeedMax solution to restructure product feeds and take back control of the algorithm's serving logic.

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Product segmentation by margin level

• Isolating low-margin products in a dedicated campaign, in order to control and limit their serving without excluding them entirely from the setup.
• Creating a specific campaign for high-margin products, maximizing their visibility and impression share on the most profitable references for i-run.
• Adapting campaign objectives by margin level, enabling finer performance steering and better alignment between business intent and algorithmic logic.

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Data-driven steering via FeedMax

• Implementing the FeedMax feed segmentation solution to structure product data according to profitability criteria, not just volume or popularity.
• Setting up a granular reading of performance by margin type, making budget trade-offs and client-side decision-making easier.
• Monitoring over a 3-month period to measure the real and incremental impact of the new campaign architecture.