Shopping Campaign Audit

SkillAI & models

Google Ads Shopping / retail audit — product feed coverage, listing-group granularity, zero-impression products, bidding, top wasted spend

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Shopping Campaign Audit skill

What this skill tells your AI

The instructions your AI receives, as published by cognyai/claude-code-marketing-skills in skills/shopping-campaign-audit/SKILL.md and read by ahel’s review.

A deep audit of your Shopping and retail campaigns — feed coverage, listing-group structure, products that never get impressions, and where the spend is leaking. Covers standard Shopping campaigns and the Shopping side of Performance Max.

Requires: Cogny Agent subscription ($9/mo) — Sign up

Prerequisites Check

If mcp__cogny__google_ads__tool_execute_gaql is not available, print the Cogny sign-up instructions (see /google-ads-audit) and stop.

This skill audits the Google Ads side of retail. It cannot see Merchant Center disapprovals directly — where a feed problem is suspected it says so and tells you to check Merchant Center.

Usage

/shopping-campaign-audit — audit every Shopping + retail PMax campaign /shopping-campaign-audit Shopping - All Products — audit one campaign

Steps

1. Find the retail campaigns

SELECT campaign.id, campaign.name, campaign.status,
  campaign.advertising_channel_type, campaign.bidding_strategy_type,
  campaign_budget.amount_micros, metrics.cost_micros, metrics.conversions,
  metrics.conversions_value
FROM campaign
WHERE campaign.advertising_channel_type IN ('SHOPPING', 'PERFORMANCE_MAX')
  AND segments.date DURING LAST_30_DAYS
ORDER BY metrics.cost_micros DESC

For PMax campaigns, confirm a Merchant Center feed is attached before treating them as retail.

2. Product-level performance

SELECT segments.product_item_id, segments.product_title,
  segments.product_type_l1, segments.product_brand,
  metrics.cost_micros, metrics.conversions, metrics.conversions_value,
  metrics.clicks, metrics.impressions
FROM shopping_performance_view
WHERE segments.date DURING LAST_30_DAYS
ORDER BY metrics.cost_micros DESC
LIMIT 200

Compute:

  • Wasted spend — sum the cost of products with spend and zero conversions
  • The 80/20 — what share of products drive 80% of revenue
  • ROAS spread — best vs worst product type

Flag:

  • High-spend, zero-conversion products (bid them down or exclude)
  • Product types where ROAS is below account break-even

3. Feed coverage — products that never run

SELECT segments.product_item_id, segments.product_title,
  metrics.impressions, metrics.clicks
FROM shopping_performance_view
WHERE segments.date DURING LAST_30_DAYS

Products with zero impressions are usually disapproved, out of stock, missing required attributes, or priced uncompetitively. Flag the count and tell the user to cross-check Merchant Center → Products → "Not eligible / Disapproved".

4. Listing-group granularity

SELECT ad_group.name, ad_group_criterion.listing_group.type,
  ad_group_criterion.listing_group.case_value.product_type.value,
  ad_group_criterion.cpc_bid_micros, campaign.name
FROM product_group_view
WHERE campaign.advertising_channel_type = 'SHOPPING'

Flag:

  • Everything in one "All products" node — no way to bid up winners or down losers
  • A unit (UNIT type) catching far too many SKUs to bid meaningfully
  • Bestsellers and clearance stock sharing the same bid

5. Bidding & priority

Flag:

  • Manual CPC on a large catalog (unmanageable — Smart Bidding or PMax fits better)
  • Target ROAS set far from achievable ROAS (starves or overspends the campaign)
  • Standard Shopping and retail PMax both enabled for the same products (PMax wins the auction — the Shopping campaign is just paying for reporting)

6. Campaign structure

Flag:

  • No separation of brand vs non-brand, or bestsellers vs long-tail
  • A single catch-all campaign — fine to start, but it caps optimization

7. Score and report

Shopping Campaign Audit — [Account Name]
Shopping Score: X/100  ·  30-day spend: [X]  ·  ROAS: [X.Xx]

Catalog health:
- Products with impressions: X of Y (Z% coverage)
- Wasted spend (0-conversion products): $X,XXX
- Top product type by ROAS: ... | Worst: ...

🔴 Critical   — zero-coverage catalog share, big zero-conversion spend
🟡 Important  — flat listing groups, bidding mismatch, PMax/Shopping overlap
🟢 Optimization — split out bestsellers, brand vs non-brand

Top 3 Actions:
1. [Highest $ impact]
2. ...
3. ...

8. Record findings

{
  "title": "31% of catalog gets zero impressions — likely feed disapprovals",
  "body": "412 of 1,330 active products had zero impressions in 30 days. This is almost always Merchant Center disapprovals or missing attributes (GTIN, image, price). Check Merchant Center → Products for the disapproval reasons; fixing them unlocks inventory you're already paying to advertise around.",
  "action_type": "feed_optimization",
  "expected_outcome": "More eligible products in the auction, higher revenue at the same budget",
  "estimated_impact_usd": 2200,
  "priority": "high"
}

Action types: feed_optimization, listing_group, bidding_strategy, campaign_optimization, negative_keyword.

Critical rules

  1. Feed coverage first. Products that never run are revenue you've already paid to build campaigns around.
  2. Be honest about Merchant Center's blind spot — name it, don't fake it.
  3. Quote real numbers: coverage %, wasted spend, ROAS by product type.
  4. Read-only. Recommend bid and exclusion changes; never make them.

Signals

GitHub stars
102
Forks
12
Last commit
Jun 2026
Advanced
Catalog kind
skill
Gateway key
shopping-campaign-audit
Source
github.com/cognyai/claude-code-marketing-skills