E-commerce SEO Analysis

Shared Data Cache

Step 0 -- Check shared data cache:

Before gathering, check .seo-cache/ for reusable context from related SEO skills. Reference: ../seo/references/shared-data-cache.md for schemas and dependency map.

Check these cache files when present:

• .seo-cache/site-meta.json for domain, business type, industry, and crawl context

• .seo-cache/audit-scores.json for prior full-audit priorities

• .seo-cache/pages/{url-slug}/page-analysis.json for page-level context when a URL is provided

• If found: parse and use clearly valid fields (note "Using cached [X] from [date]")

• If missing, corrupt, or irrelevant: continue with fresh evidence

• If the user says "refresh" or "re-run": ignore cache reads and overwrite on write

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 <url>	Full e-commerce SEO analysis of a product page or store	Optional
/seo ecommerce products <keyword>	Google Shopping competitive analysis	Required
/seo ecommerce gaps <domain>	Keyword gap: organic vs Shopping visibility	Required
/seo ecommerce schema <url>	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 <url>         → raw HTML
2. python scripts/parse_html.py --url <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 "<keyword>" --marketplace google

# Seller analysis: merchant ratings and dominance
python scripts/dataforseo_merchant.py sellers "<keyword>"

# 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 "<keyword>" --marketplace amazon

# Cross-marketplace comparison
python scripts/dataforseo_merchant.py compare "<keyword>"

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

1. Fetch organic rankings via seo-dataforseo: dataforseo_labs_google_ranked_keywords for domain
2. Fetch Google Shopping presence via Merchant API: merchant_google_products_search for top organic keywords
3. 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)
| Keyword | Organic Pos | Volume | CPC | Recommended Action |
|---------|------------|--------|-----|-------------------|

### Opportunities: Shopping → Organic (8 keywords)
| Keyword | Shopping Rank | Volume | CPC | Content Type Needed |
|---------|-------------|--------|-----|-------------------|

---

5. Product Schema Enhancement

Validate and generate Product schema following Google's current requirements.

Required Properties (Google Merchant)

{
  "@context": "https://schema.org",
  "@type": "Product",
  "name": "",
  "image": [""],
  "description": "",
  "brand": { "@type": "Brand", "name": "" },
  "offers": {
    "@type": "Offer",
    "url": "",
    "priceCurrency": "USD",
    "price": "0.00",
    "availability": "https://schema.org/InStock",
    "seller": { "@type": "Organization", "name": "" }
  }
}

Recommended Properties (Enhance Rich Results)

• sku -- product identifier
• gtin13 / gtin14 / mpn -- global trade identifiers
• aggregateRating -- star rating + review count
• review -- individual reviews (minimum 1)
• color, material, size -- variant attributes
• shippingDetails -- ShippingDetails with rate and delivery time
• hasMerchantReturnPolicy -- MerchantReturnPolicy with type and days

Validation Rules

1. price must be a number string, not "$29.99" (no currency symbol)
2. availability must use full Schema.org URL enum
3. image should be array with >= 1 high-res image URL
4. priceCurrency must be ISO 4217 (USD, EUR, GBP)
5. brand.name must not be empty or "N/A"
6. Dates in priceValidUntil must be ISO 8601
7. If aggregateRating present: ratingValue and reviewCount required

Schema Scoring

Completeness	Score
All required fields	50/100
+ aggregateRating	65/100
+ sku/gtin/mpn	75/100
+ shippingDetails	85/100
+ merchantReturnPolicy	90/100
+ reviews (3+)	100/100

---

Cross-Skill Integration

Skill	Integration Point
seo-schema	Delegates Product schema generation; reuses validation logic
seo-images	Product image audit (alt text, format, dimensions)
seo-content	Product description E-E-A-T and uniqueness analysis
seo-dataforseo	Organic keyword rankings for gap analysis
seo-technical	Core Web Vitals for product pages (LCP on hero image)
seo-google	Google Merchant Center feed validation via GSC

---

Error Handling

Error	Cause	Response
No Product schema found	Page lacks JSON-LD	Analyze page content, generate recommended schema
DataForSEO credentials missing	Env vars not set	Run analysis without marketplace data, note limitation
Cost check blocked	Daily budget exceeded	Inform user, offer free-only analysis
Empty Shopping results	No products for keyword	Suggest broader keyword, check location settings
Amazon API timeout	Network/rate limit	Retry with backoff, fall back to Google-only
Invalid URL	Malformed input	Validate via google_auth.validate_url(), show error
Non-product page	URL is category/homepage	Detect page type, suggest /seo ecommerce schema instead

---

Output Template

## E-commerce SEO Report: [URL or Keyword]

### Overall Score: XX/100

### Product Page SEO
- Schema Completeness: XX/100
- Title & Meta: XX/100
- Image Optimization: XX/100
- Content Quality: XX/100
- Internal Linking: XX/100

### Marketplace Intelligence (if DataForSEO available)
- Google Shopping Listings: N products found
- Price Range: $XX - $XX (median: $XX)
- Top Seller: [name] (XX% market share)
- Amazon Comparison: [available/not checked]

### Top Recommendations
1. [Critical] ...
2. [High] ...
3. [Medium] ...

Generate a PDF report? Use `/seo google report`

Write to shared data cache

After completing all work, write a concise JSON summary to .seo-cache/ when the workflow produced durable findings. Use the schemas and naming rules in ../seo/references/shared-data-cache.md; include at least cache_type, analyzed_at, source URL/domain, key findings, issues, recommendations, and tool limitations. Add .seo-cache/ to .gitignore if it is missing.