Once your Labelizer buckets are available as a custom label in Google Ads, you can split your products by performance and spend your budget where it works hardest. This is where Labelizer turns into results.
Before you start
Make sure the labels are live in Google Ads first. Check that the custom label slot you chose is populated in Merchant Center and has appeared in Google Ads (this can take up to 24 hours). See Using your Labelizer labels in Google Ads.
Two common approaches
Approach 1: Segment inside one campaign. Keep a single Shopping or Performance Max campaign and use listing groups (or product filters in Performance Max asset groups) to split products by the custom label value. This is the simplest way to start and keeps your data together.
Approach 2: Separate campaigns per bucket. Create a dedicated campaign for each bucket, or for the buckets you care about most, and filter each one to a single custom label value. This gives you the most control over budget and targets per bucket, at the cost of more campaigns to manage.
If you are new to this, start with Approach 1 and move to separate campaigns only when you want tighter control.
How to filter by the label
In your campaign, create a listing group or product filter and select the custom label slot Labelizer writes to. Filter on the bucket value you want, for example over-index for top performers or under-index for the products draining budget. Repeat per group or per campaign depending on the approach you chose.
How much budget and target per bucket
These are starting points. Adjust to your margins and goals.
Over-index: your best performers. Give these the largest share of budget and your most ambitious targets.
Index: solid, around target. Fund them steadily.
Near-index: close to target with potential. Give them room to gather data before you judge them.
Under-index: spending more than they return. Reduce budget here, or set a stricter target, so they stop eating spend from better products.
No index: barely shown. Put them in a small, controlled campaign to give them a chance to gather data and prove themselves, without risking much budget.
Tips
Avoid running old, unsegmented campaigns over the same products at the same time. Overlapping campaigns compete with each other, split your data, and make optimization harder.
Give the structure time. Products move between buckets as their performance changes, and the algorithm needs stable data to learn.