E-commerce SEO Audit

Product-catalogue SEO is its own discipline. A generic technical audit treats every page like a blog post — crawlability, sitemap, Core Web Vitals. That misses what actually causes e-commerce sites to underperform: incomplete product schema, broken filter-navigation canonicalisation, variant pages cannibalising each other, out-of-stock pages lying to Google about availability, thin PLPs, and review schema that never triggers rich results.

What the e-commerce SEO audit covers

We inspect product-catalogue artefacts specifically — PDPs (Product Detail Pages), PLPs (Product Listing Pages), filter navigation, and supporting infrastructure — across eight weakness areas known to suppress both traditional rankings and AI-platform citations:

1. Product schema completeness — every PDP's Product, Offer, and AggregateRating markup validated for required properties, correct availability values, and pricing accuracy
2. Filter navigation — whether ?color=red&size=M URL parameters are canonicalised correctly, blocked from indexation, or allowed to flood the index with near-duplicates
3. Variant pages — whether colour or size variants sit on one canonical URL with structured data, or on separate URLs cannibalising each other
4. Category (PLP) pages — taxonomy depth, thin-content detection, internal linking to PDPs, category-level schema
5. Out-of-stock handling — what your site returns when a product is sold out (HTTP status, Offer.availability, sitemap inclusion, notification CTAs)
6. Sitemap coverage — PDPs, PLPs, and faceted URLs all correctly included or excluded
7. Review schema — whether customer-review markup generates rich results, passes validation, and connects to trust signals
8. PDP performance — image alt text, srcset, LCP, CLS on product pages

Why product schema completeness determines AI citation eligibility

AI platforms — ChatGPT, Perplexity, Google AI Overviews — cite products by name, price, and availability when the schema supports it. Incomplete or invalid Product markup means your products remain invisible to AI answer surfaces even when your pages rank in classic SERPs. We audit every field that matters — name, brand, description, sku, image, offers.price, offers.priceCurrency, offers.availability, aggregateRating — and flag gaps by severity.

Sample sizes scale with catalogue size

The audit examines a representative sample of PDPs and PLPs rather than every SKU. Sample sizes: 1,000 SKUs or fewer → 10 PDPs + 3 PLPs; 1,000–10,000 SKUs → 18 PDPs + 5 PLPs; 10,000+ SKUs → 25+ PDPs + 8 PLPs. We select across high-velocity SKUs, low-velocity SKUs, variant-heavy products, and out-of-stock items to expose issues specific to each pattern.

Delivery timeline: 5–14 working days depending on catalogue size. The output is a diagnostic report plus a prioritised implementation roadmap. Implementation is a separate engagement — the audit itself is pure diagnosis and recommendation.