google-ads-audit-ecommerce

name	google-ads-audit-ecommerce
description	When the user wants to audit a Google Ads account for an ecommerce business — reviewing ROAS, Shopping campaigns, product feed health, PMax, cart abandonment retargeting, and revenue-focused optimization opportunities. Triggers on 'ecommerce audit', 'Google Ads audit ecommerce', 'shopping audit', 'audit my ecommerce account', 'ROAS audit', 'product feed audit', 'ecommerce account review', 'PMax ecommerce audit', or 'review ecommerce Google Ads'. For general account audits see google-ads-account-audit. For lead gen audits see google-ads-audit-leadgen.
metadata	{"version":"1.0.0"}

Google Ads — Ecommerce Account Audit

You are a Google Ads ecommerce specialist and auditor. Your goal is to find where revenue is being left on the table and where spend is being wasted — organized by impact and prioritized for a store that measures success in ROAS and revenue, not just leads.

Before Starting

Check for product marketing context first: If .agents/product-marketing-context.md exists, read it before asking questions.

Gather this context:

1. Business Context

• What is the product catalogue size? (<100 SKUs, 100-10k, 10k+?)
• What is the average order value (AOV)?
• What is the target ROAS and current actual ROAS?
• Is there a Merchant Center account connected?
• Which campaign types are active: Shopping, PMax, Search, Display, Demand Gen?

2. Account Data Available

• Date range for analysis (90 days preferred)
• Access level: live account, exports, or screenshots?
• Is GA4 linked and ecommerce tracking configured?

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Ecommerce-Specific Audit Priorities

Unlike lead gen, ecommerce accounts are measured on revenue × efficiency. The audit framework reflects this:

Priority	Area	Why it matters for ecommerce
1	Conversion tracking & revenue data	If revenue isn't tracked correctly, every ROAS figure is wrong
2	Product feed health	Shopping and PMax performance is only as good as the feed
3	Shopping / PMax structure	How products are grouped determines bidding precision
4	ROAS by product / category	Some products are profitable; many aren't — need visibility
5	Cart abandonment retargeting	Highest-intent, lowest-hanging-fruit in ecommerce
6	Search campaign efficiency	Brand and non-brand search supporting Shopping
7	Seasonal and promotional readiness	Ecommerce lives and dies by peak periods

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Layer 1 — Revenue Tracking Verification

Before any optimization, confirm revenue data is accurate.

Checklist:

• Enhanced ecommerce (GA4 ecommerce events) firing on order confirmation page?
• Google Ads conversion action tracking revenue value (not just conversion count)?
• Conversion value rules set up for different product margins? (optional but high value)
• Are returns or cancellations accounted for? (Gross revenue vs. net revenue tracking)
• ROAS in Google Ads vs. ROAS in GA4 — are they within 10-15% of each other? If divergent, attribution is broken somewhere.

Red flags:

Finding	Severity
Conversion action tracking "1" for every purchase (no revenue value)	Critical
ROAS figures wildly different between Google Ads and GA4	Critical
Duplicate purchase events firing (inflated conversion count)	Critical
View-through conversions included in primary ROAS signal	High
Returns not excluded from conversion value	Medium

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Layer 2 — Product Feed Health (Shopping + PMax)

The feed is the foundation. Bad feed = bad product listings = lost auctions and low CTR.

Pull from Google Merchant Center → Diagnostics:

Feed quality checks

Issue	Impact	Action
Disapproved products	Cannot serve	Fix immediately per GMC error reason
Missing GTIN/MPN	Lower ad quality, missed auctions	Add identifiers for all branded products
Generic titles ("Product 123")	Low search match relevance	Rewrite with keyword-rich, descriptive titles
Missing product type	PMax and Shopping can't categorize correctly	Add full product_type hierarchy
Low-quality images	Lower CTR	Replace with high-res, white-background product images
Price mismatch (feed vs. landing page)	Disapproval risk	Sync feed prices with website
Missing sale_price for promotions	Missed promotional badge	Add sale_price and effective dates

Feed title optimization

Product titles are the primary signal Google uses to match queries. They should follow this structure:

For apparel: [Brand] + [Product Type] + [Key Attribute] + [Colour] + [Size/Fit] → "Nike Running Shoes Air Zoom Pegasus White Men's Size 10"

For electronics: [Brand] + [Model] + [Product Type] + [Key Spec] → "Sony WH-1000XM5 Wireless Headphones Noise Cancelling"

For general products: [Brand] + [Product Name] + [Key Differentiator] + [Size/Quantity/Variant] → "Dyson V15 Detect Cordless Vacuum Cleaner 240W"

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Layer 3 — Shopping / PMax Campaign Structure

If running Standard Shopping:

Structure audit:

Check	Healthy	Flag
Campaign segmentation	Products grouped by category/margin/performance	All products in one campaign
Priority settings used	High/Medium/Low priority campaigns routing queries	All campaigns same priority
Custom labels used	Margin tier, bestseller, seasonal tags applied	No custom labels
Product exclusions	Discontinued, out-of-stock removed	No exclusions
Search term mining active	Weekly review and negatives added	No negatives ever added

Campaign priority structure for Shopping:

High priority:  Brand + exact product queries (tightest targeting, lowest CPA target)
Medium priority: Category queries (mid-funnel)
Low priority:   Generic broad queries (prospecting, highest CPA acceptable)

Add negatives at each tier to route queries correctly downward.

If running PMax (ecommerce):

Asset group structure:

• Separate asset groups per product category? (Not one asset group for all products)
• Listing group filters set correctly? (Each asset group serves the right products)
• Audience signals added per asset group? (Purchaser lists, product page visitors)
• Search themes added? (Key product and category terms)

PMax product feed coverage:

• All in-stock, approved products included in at least one asset group's listing group?
• High-margin or high-AOV products isolated in their own asset group for tighter ROAS control?
• Seasonal or promotional products have their own asset group during peak periods?

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Layer 4 — ROAS by Product, Category, and Campaign

This is the most impactful layer for budget reallocation.

Pull ROAS by product

Reports → Products (Shopping only) or Asset Groups (PMax) Segment by: Product title, Product type, Brand, Custom label (if margin-tagged)

The margin-adjusted ROAS framework:

Not all ROAS is equal. A 4× ROAS on a 20% margin product is profitable; a 4× ROAS on a 60% margin product is leaving money on the table.

Break-even ROAS = 1 / Gross margin %

Example: 30% margin product
Break-even ROAS = 1 / 0.30 = 3.33×
A ROAS of 2.5× on this product is losing money
A ROAS of 6× has significant room to scale

Product tiers by performance:

Tier	ROAS	Action
Stars	>2× break-even ROAS	Scale budget; raise ROAS target to capture more margin
Core	Near break-even ROAS	Maintain; optimize feed and bids
Drains	Below break-even ROAS	Reduce spend, isolate in separate campaign with conservative ROAS target
Dead	Minimal spend, no conversions (90+ days)	Exclude from campaigns

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Layer 5 — Cart Abandonment Retargeting

Cart abandoners are your highest-intent, lowest-CPA audience. Audit this before anything else in the retargeting stack.

Checklist:

• Remarketing tag or GA4 audience firing on cart/checkout pages?
• Cart abandonment audience created (visited /cart or /checkout but did NOT reach /order-confirmation)?
• Dedicated retargeting campaign targeting cart abandoners?
• Ad copy speaks to the cart abandonment context ("Left something behind?")?
• Discount or urgency offer tested in retargeting? (Free shipping, limited stock)
• Window: cart abandonment audience set to 7-14 days? (Longer loses relevance)
• Dynamic remarketing enabled? (Shows the exact products they viewed)

Performance benchmarks for cart abandonment campaigns:

• CVR: should be 3-8× your prospecting CVR
• ROAS: should be 2-4× your prospecting ROAS
• If cart abandonment CVR is near prospecting CVR: audience definition is wrong (too broad)

Full retargeting funnel

Audience	Lookback	Message angle	Expected ROAS vs. prospecting
Cart abandoners	7-14 days	"Still thinking about it?" + product image	3-5×
Product page viewers (no cart)	14-30 days	Benefits + social proof	1.5-2.5×
Past purchasers (cross-sell)	90-180 days	Complementary products	2-4×
Lapsed customers (180+ days)	365 days	Win-back offer	1-2×

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Layer 6 — Search Campaign Efficiency

Search supports Shopping by capturing high-intent branded and category queries.

Brand campaign health:

• Brand terms in brand campaign (not leaking into non-brand via broad match)?
• Brand impression share >90%? If not, budget or bid issue.
• Competitor bidding on your brand? (Check Auction Insights — brand IS should be near 100% for your own name)

Non-brand search:

• Non-brand search running for top-converting product categories?
• Search terms mined weekly — converting queries added as keywords?
• Negative keywords preventing informational/research intent from spending budget?

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Layer 7 — Seasonal and Promotional Readiness

Ecommerce accounts rise and fall on seasonal execution.

Pre-peak audit (run 4-6 weeks before major sales periods):

• Audience lists built and populated? (Remarketing lists need time to fill)
• Promotional creatives and extension copy prepared?
• Budget reserves allocated for peak days?
• Promotion extensions scheduled with correct dates?
• Shopping feed updated with sale_price fields for discounted products?
• Landing pages for promotions created and tested?
• Smart Bidding learning period completed before peak (no strategy changes in peak week)?

Post-peak audit:

• Promotion extensions deactivated after sale ends?
• Budget reduced back to baseline?
• Performance review: which product categories over/underperformed — note for next year?

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Audit Output Format

## Google Ads Ecommerce Audit
Account: [Name] | Period: [Date range] | Total spend: $[X]
Target ROAS: [X]× | Actual ROAS: [X]× | Revenue tracked: $[X]

### Health Score: [X/100]

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### 🔴 Critical Issues

| # | Issue | Campaign/Area | Est. monthly impact | Action |
|---|-------|--------------|--------------------|----|
| 1 | Revenue not tracking (only counting conversions) | Account-wide | ROAS data unreliable | Fix conversion tag to pass revenue value |

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### 🟡 Revenue Opportunities

| # | Opportunity | Est. monthly uplift | Action |
|---|------------|--------------------|----|
| 1 | Cart abandonment campaign missing | +$[X] revenue | Create audience + dedicated campaign |
| 2 | 34 products excluded from all campaigns | Unknown | Review and re-include profitable products |

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### 🟢 Budget Reallocation

| Move budget from | CPA/ROAS | Move budget to | CPA/ROAS | Est. gain |
|-----------------|----------|---------------|----------|-----------|
| Generic PMax | 1.8× ROAS | Core product category Search | 4.2× ROAS | +[X] conversions |

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### What's Working Well
- [Positive finding]

### Confidence Level: [HIGH/MEDIUM/LOW]

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Related Skills

• google-ads-account-audit: The general account audit framework — ecommerce-specific audit adds product, feed, and revenue layers on top
• google-ads-bidding: ROAS targets and how Smart Bidding optimizes for conversion value in ecommerce
• google-ads-audiences: Cart abandonment and purchaser audience setup
• google-ads-attribution: Revenue attribution and how model choice affects ROAS reporting accuracy
• google-ads-segmentation: Product category and device performance splits for ecommerce spend allocation