shopping-campaign-audit

name	shopping-campaign-audit
description	Google Ads Shopping / retail audit — product feed coverage, listing-group granularity, zero-impression products, bidding, top wasted spend
version	1.0.0
author	Cogny AI
requires	cogny-mcp
platforms	["google-ads"]
user-invocable	true
argument-hint	[campaign name or id]
allowed-tools	["mcp__cogny__google_ads__tool_list_accessible_accounts","mcp__cogny__google_ads__tool_execute_gaql","mcp__cogny__google_ads__tool_get_gaql_doc","mcp__cogny__google_ads__tool_get_reporting_view_doc","mcp__cogny__create_finding","WebFetch","WebSearch"]

Shopping Campaign Audit

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.