seo-ecommerce

# 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:
```bash
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:
```bash
python scripts/dataforseo_costs.py log merchant_google_products_search <cost>
```

### Workflow

```bash
# 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)

```bash
python scripts/dataforseo_costs.py check merchant_amazon_products_search
```

Amazon endpoints are in the `warn_endpoints` set -- always requires user approval.

### Workflow

```bash
# 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 Gap