What to check before you touch anything
- Campaign structure. Are different product categories currently split into separate campaigns/groups, or is everything running through one shared structure (especially relevant for Shopping/Performance Max)? Without structural separation, it's technically impossible to assign different targets to different categories in the first place.
- Traffic type. How much do the categories differ in demand type — impulse, low-cost purchases versus considered, expensive ones, one-time buys versus high-repeat-frequency products? Different traffic types naturally settle at different ROAS levels, and that's a reason to differentiate the target, not to force it to match.
- Demand capacity. How large is each category by demand volume? A low-volume category may simply not have enough data for a separate target to ever learn properly.
- Competition. Does the competitive environment differ between categories (Auction Insights for each one separately)? A high-competition category and a low-competition one will naturally need different target ROAS, even if product margins are identical.
- How much does margin differ between categories? If the gap is large, one blanket tROAS across all categories will systematically undervalue the high-margin ones and overvalue the low-margin ones.
- What value is being passed as conversion value for each category — revenue or profit? If the methodology is mixed, comparing ROAS across categories and deciding on a shared or separate target will be unreliable.
Possible approaches
- If categories differ meaningfully in margin, traffic type, and competition, it makes sense to set different tROAS targets for each, structurally splitting them into separate campaigns, or — for Shopping/Performance Max — into separate product groups/asset groups with their own target.
- If categories are similar in economics but differ in volume (one large, one small), you can keep a shared target while separately tracking whether the low-volume category is dragging the aggregate up or down and distorting the account-wide picture.
- Google broadly supports segmenting Shopping/Performance Max campaigns specifically by product groups with different economics — this is a standard use case for product groups and asset groups, not a workaround.
- If a low-volume category can't learn on its own separate target, you can leave it in the shared structure with a unified target for now, tracking its contribution separately, and spin it out into its own group only once enough data has accumulated.
- If the comparison shows differences between categories are mainly explained by competitive environment rather than margin, it's worth adjusting the target to each category's auction first, not just its economics — since competition determines how easy or hard it is to physically hit a given ROAS.