E-commerce SEO Analysis Comprehensive product page optimization, marketplace intelligence, and competitive pricing analysis. Works standalone (on-page + schema) and with DataForSEO Merchant API for live Google Shopping and Amazon data. Commands Command Purpose DataForSEO? /seo ecommerce Full e-commerce SEO analysis of a product page or store Optional /seo ecommerce products Google Shopping competitive analysis Required /seo ecommerce gaps Keyword gap: organic vs Shopping visibility Required /seo ecommerce schema Product schema validation and enhancement No 1. Product Page Analysis (No DataForSEO Needed) Fetch and parse any product page for on-page SEO quality. Workflow 1. python scripts/fetch_page.py → raw HTML 2. python scripts/parse_html.py --url → SEO elements 3. Analyze product-specific signals (below) Product SEO Checklist Title Tag Contains primary product keyword Includes brand name Under 60 characters (no truncation in SERPs) Format: [Product Name] - [Key Feature] | [Brand] Meta Description Contains product keyword + benefit Includes price or "from $XX" (triggers rich snippet interest) Call-to-action present (Shop now, Buy, Free shipping) Under 155 characters Heading Structure Single H1 matching primary product name H2s for: Features, Specifications, Reviews, Related Products No duplicate H1 tags across product variants Product Images Alt text includes product name + distinguishing feature File names are descriptive (not IMG_001.jpg ) WebP format served (with JPEG fallback) At least 3 images per product (hero, detail, lifestyle) Image dimensions >= 800px for Google Shopping eligibility Lazy loading on below-fold images only Internal Linking Breadcrumb navigation: Home > Category > Subcategory > Product Related products section (cross-sell / upsell) Link back to category page with keyword-rich anchor Reviews section links to full review page (if separate) Content Quality Unique product description (not manufacturer copy-paste) Word count >= 200 for product description body Specs table present (not just prose) User reviews on-page (UGC signals) Scoring Category Weight Criteria Schema completeness 25% Required + recommended Product fields Title & meta 15% Keyword placement, length, format Image optimization 20% Alt text, format, sizing, count Content quality 20% Unique description, specs, reviews Internal linking 10% Breadcrumbs, related products, categories Technical 10% Page speed, mobile rendering, canonical 2. Google Shopping Intelligence (DataForSEO Merchant API) Live competitive analysis from Google Shopping results. Cost Guardrail (MANDATORY) Before EVERY Merchant API call: python scripts/dataforseo_costs.py check merchant_google_products_search "status": "approved" -- proceed "status": "needs_approval" -- show cost, ask user "status": "blocked" -- stop, inform user After each call: python scripts/dataforseo_costs.py log merchant_google_products_search < cost

Workflow

Product search: who sells what at what price

python scripts/dataforseo_merchant.py search "" --marketplace google

Seller analysis: merchant ratings and dominance

python scripts/dataforseo_merchant.py sellers ""

Normalize results for analysis

python scripts/dataforseo_normalize.py results.json --module merchant Analysis Outputs Pricing Intelligence Price distribution: min, max, median, P25, P75 Price outliers (> 2 standard deviations from median) Price-to-rating correlation Currency normalization to USD (or user-specified) Seller Landscape Top 10 sellers by listing count Merchant rating distribution Free shipping prevalence New vs established sellers Product Listing Quality Title keyword patterns in top listings Average rating and review count benchmarks Image count per listing Availability status distribution Load references/marketplace-endpoints.md for full API parameter details. 3. Amazon Marketplace (DataForSEO) Cross-marketplace intelligence comparing Google Shopping and Amazon. Cost Guardrail (MANDATORY) python scripts/dataforseo_costs.py check merchant_amazon_products_search Amazon endpoints are in the warn_endpoints set -- always requires user approval. Workflow

Amazon product search

python scripts/dataforseo_merchant.py search "" --marketplace amazon

Cross-marketplace comparison

python scripts/dataforseo_merchant.py compare "" Cross-Marketplace Report Metric Google Shopping Amazon Avg price $ $ Median rating X.X X.X Avg review count N N Top seller share % % Free shipping % % % 4. Marketplace Keyword Gaps Identify mismatches between organic and Shopping visibility. Workflow Fetch organic rankings via seo-dataforseo: dataforseo_labs_google_ranked_keywords for domain Fetch Google Shopping presence via Merchant API: merchant_google_products_search for top organic keywords Cross-reference results Gap Types Gap Type Meaning Action Organic Only Ranks organically but no Shopping ads Create Google Merchant Center feed, bid on these keywords Shopping Only Shopping visibility but weak/no organic Create content (buying guides, comparison pages) for these keywords Both Present Visible in both channels Optimize: ensure price consistency, enhance schema Neither No visibility in either Low priority unless high volume Output Format

Keyword Gap Analysis: example.com

Opportunities: Organic → Shopping (12 keywords)