Shopping Campaign Audit

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

Works with

Call list_integrations once. Without a store, the audit runs on Google Ads alone.

Step 0: Cogny check

If no mcp__cogny__* tools exist (no list_integrations, no cogny_status), print this and stop:

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Shopping Campaign Audit needs your marketing data through Cogny's MCP gateway.

1. Sign up at https://cogny.com and connect Google Ads (plus Microsoft Ads, Shopify or
   WooCommerce if you use them).
2. Add Cogny to .mcp.json in your project:
   {"mcpServers": {"cogny": {"type": "http", "url": "https://app.cogny.com/mcp"}}}
3. Restart Claude Code; it opens a browser for the OAuth login on first use.

Nothing to run without Cogny.

If Cogny is connected but mcp__cogny__mcp__Google_Ads__tool_execute_gaql is missing, say "connect Google Ads in Cogny" (or "reconnect Google Ads in Cogny" if it's listed but unhealthy or a read returns 401/permission denied) 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

Google Ads money fields are micros (divide by 1,000,000). An empty GAQL result (no rows, or the tool returns no output) means zero: no matching entities or no activity in the window. Report it as 0, not as an error.

1. Find the retail campaigns

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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

Confirm which campaigns have a Merchant Center feed attached:

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SELECT campaign.id, campaign.name, campaign.advertising_channel_type,
  campaign.shopping_setting.merchant_id, campaign.shopping_setting.feed_label
FROM campaign
WHERE campaign.advertising_channel_type IN ('SHOPPING', 'PERFORMANCE_MAX')
  AND campaign.status != 'REMOVED'

Treat a PMax campaign as retail only if merchant_id is set (non-zero). Report the Merchant Center ID(s), and flag Shopping campaigns pointing at different merchant IDs or feed labels than expected.

If there are no SHOPPING campaigns and no PMax campaign with a merchant_id, report "no retail campaigns" (and Microsoft Ads Shopping totals, if any) and stop. Skip steps 2 onward.

Microsoft Ads (if connected): tool_get_campaigns (account_id, campaign_types: ["Shopping", "PerformanceMax"]) and tool_get_report (account_id from tool_list_accounts, report_type: "campaign", campaign_ids). Report spend, conversions and revenue in its own block; product-level depth is Google-only here.

2. Product-level performance

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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

Enhanced when connected: real sales. Shopify tool_list_orders (query_filter: "created_at:>=YYYY-MM-DD financial_status:paid") or WooCommerce get_top_sellers_report (date_min, date_max). Bestsellers in the store that get little Shopping spend are scale candidates; products Ads says convert well but rarely sell in the store point to attribution or tracking issues. Compare totals as a ratio ("Ads conversion value = 0.6x store paid revenue"), never as a corrected ROAS.

3. Feed coverage — products that never run

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

shopping_performance_view only returns products that had impressions, so this query cannot find zero-impression products on its own: it gives the list of products that ran. Finding the ones that never ran needs the full catalog to compare against, from the store (Shopify / WooCommerce, below) or from Merchant Center. Without a store connected, report "products with impressions: N", say the zero-impression count needs the catalog, and tell the user to check Merchant Center → Products → "Not eligible / Disapproved".

Products in the catalog but absent here are usually disapproved, out of stock, missing required attributes, or priced uncompetitively.

Enhanced when connected: check against the store catalog.

• Shopify: mcp__cogny__mcp__Shopify__tool_list_products (status: "ACTIVE", first: 250, paginate with after). Shopify feed IDs usually look like shopify_<country>_<productId>_<variantId>; match on the variant ID or SKU.
• WooCommerce: mcp__cogny__mcp__WooCommerce__list_products (status: "publish", per_page, page); match on sku or product ID.

Catalog products absent from the query above are the zero-impression products. Split them into in stock and active in the store (a real problem, the expensive one) and out of stock or draft (expected). For the first group, Ads data can't tell "disapproved or not eligible" from "never reached the feed"; Merchant Center shows which. Also flag spending products that are out of stock or archived in the store: that's spend on clicks that can't convert.

4. Listing-group granularity

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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

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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)
- Of the zero-impression products: N active + in stock in [Shopify/WooCommerce] (fix these)
- 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

jsonCopy code
{
  "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"
}

Cloud uses create_ticket with the same content, fields mapped per section 2 of the routing guide.

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

Recording findings

Use create_ticket when the workspace has it (Cloud) and create_finding otherwise (Solo). The fields differ; see section 2 of ../marketing-engineer/references/mcp-routing.md.

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. Store write tools (tool_update_product, update_product) are never called by this audit; if the user asks for a fix, the skill asks first.
5. Store and Ads numbers stay side by side. Name the source on every figure.