# Segment your Google Ads campaigns with Labelizer buckets

> Use your custom labels to structure campaigns and direct budget to the products that earn it.

Once your [Labelizer](/docs/labelizer/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](/docs/labelizer/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.

## Use the same buckets in ChatGPT Ads

The buckets you just used to structure Google Ads campaigns can drive ChatGPT Ads campaigns too,
without rebuilding the segmentation by hand. Flowboost reads the Labelizer output directly and
builds the campaigns for you. See
[Build product campaigns from Labelizer](/docs/chatgpt-ads/build-product-campaigns-from-labelizer).