When AI groups campaigns incorrectly: check names and UTMs

Correct numbers, wrong groups

You ask for ROAS by product group over the last 14 days, but the result combines prospecting and retargeting campaigns. Even if the arithmetic is correct, that grouping makes a budget decision harder.

Names such as new_test_2 or conversion_0501 may not identify the product or audience. Check campaign settings and the team’s mapping table. Leave a campaign unclassified when the evidence is still insufficient.

Find the same campaign across reports

Consistent campaign names and UTMs make it easier to find the same campaign across ad platforms, GA4, and shared reports. Start by deciding which fields your team uses for classification.

GA4, too, breaks out source, medium, and campaign from your UTM values. Google recommends using utm_source, utm_medium, and utm_campaign consistently, and notes that values with different capitalization are treated as different. SpringSale and spring_sale may look like the same campaign to a person, but they can show up as separate rows in your reports.

The minimum AI needs to read a campaign

You don’t need to cram every detail into the campaign name. When names get too long, no one follows the convention. Instead, define the minimum set of fields AI needs to run analysis.

For example, meta_purchase_prospecting_skincare_broad_202605 records platform, objective, funnel, product group, audience strategy, and period in order. If your standard is skincare, map older variants such as skin_care or skin to that value.

When names aren’t enough, lean on UTMs and a mapping table

Use a mapping table for information that does not belong in the name. If a report joins data by campaign name, renaming may affect its connection to historical data. Check the reporting setup first. You can apply new rules to new campaigns and map existing ones by ID.

UTMs are how GA4 splits your traffic; the mapping table is how your team interprets campaigns. The tracking-parameter tooling differs by channel—Google Ads URL options, for one—but in the end GA4 and Neuro need to read consistent source, medium, campaign, and product-group values.

Record the campaign ID, product group, and funnel in the mapping table, and save interpretation rules in workspace or Gem instructions. Mark campaigns covering several product groups as mixed instead of forcing them into one category.

In Neuro, start with a naming audit

Before you ask AI to run performance analysis, have it audit the account’s classification rules. The goal isn’t to interpret performance—it’s to confirm AI can read the account reliably in the first place.

Review unclassified campaigns with the largest spend share first. Update the mapping rules, rerun the same request, and compare which gaps were resolved and which remain.