Multi-Channel Merchant Feed Pipeline

You build a feed-as-product-data discipline. In 2026, feed management is less about producing "a feed" and more about running a data product: continuously modifying attributes, enriching missing fields, validating quality, and tying changes to performance.

============================================================ === PRE-FLIGHT ===

• Product source of truth: Shopify, BigCommerce, WooCommerce, Magento, custom DB, ERP (NetSuite, SAP).
• Target channels: which of GMC / Meta / TikTok Shop / Pinterest / Bing / Snap?
• Catalog size: < 500 SKUs → single feed file is fine. 5k+ SKUs → use Content API / Commerce API for incremental updates.
• Product types: physical goods, digital, services, software, age-restricted (alcohol/tobacco/cannabis — extra scrutiny). Subscription products require additional Google attributes (subscription_cost).
• Brand assets: high-res images, GTINs, MPNs. Without GTIN, disapprovals climb 30-50%.
• Localization: feed per country / language? Currency? Local tax/shipping?

Recovery:

• Missing GTINs: flag SKUs with identifier_exists=false (Google allows this for niche brands) but expect lower performance.
• Image quality issues: route through a transform CDN (Cloudinary, imgix, Vercel Image Optimization) to auto-meet 500×500 minimum + AVIF/WebP output.

============================================================ === PHASE 1: CANONICAL PRODUCT SCHEMA ===

Build the single source of truth. Every channel feed projects from this:

{
  "id": "SKU-12345",
  "title": "...", // primary attribute for Google match
  "description": "...",
  "link": "https://...",
  "mobile_link": "https://...", // optional but recommended
  "image_link": "https://.../primary.jpg", // ≥ 500×500 (2026 requirement)
  "additional_image_link": ["...", "..."], // up to 10
  "availability": "in_stock|out_of_stock|preorder|backorder",
  "availability_date": "2026-08-15", // if preorder
  "price": "29.99 USD",
  "sale_price": "24.99 USD",
  "sale_price_effective_date": "2026-05-23T00:00-07:00/2026-06-30T23:59-07:00",
  "brand": "...",
  "gtin": "00012345678905",
  "mpn": "...",
  "identifier_exists": true,
  "condition": "new|refurbished|used",
  "google_product_category": "Apparel & Accessories > Clothing > Shirts & Tops",
  "product_type": "Mens > Shirts > T-Shirts",
  "color": "navy",
  "size": "L",
  "material": "100% cotton",
  "gender": "male|female|unisex",
  "age_group": "adult|kids|toddler|infant|newborn",
  "item_group_id": "TSHIRT-001", // for variant grouping
  "shipping": [{ "country": "US", "service": "Standard", "price": "4.99 USD" }],
  "tax": [{ "country": "US", "region": "CA", "rate": 9.0, "tax_ship": true }],
  "custom_label_0": "high_margin", // for ad bidding strategy
  "custom_label_1": "summer_collection",
  "custom_label_2": "best_seller",
  "custom_label_3": "new_arrival",
  "custom_label_4": "low_inventory",
  "video": "https://.../product.mp4" // critical for TikTok Shop
}

Persist as products.json with versioning (so feed history is auditable).

VALIDATION: 100% of products have id, title, description, link, image_link, availability, price, brand. ≥ 90% have GTIN.

============================================================ === PHASE 2: TITLE OPTIMIZATION ===

Title is the highest-leverage attribute for Google matching. Use this structure:

[Brand] [Product Type] [Key Attribute] [Variant] [Size/Quantity]

Examples:

• ❌ "Awesome T-Shirt"
• ✅ "Nike Dri-FIT Men's Running T-Shirt — Navy, Large"
• ✅ "Apple AirPods Pro (2nd Gen) — USB-C, Active Noise Cancellation"

Title cap: 150 chars (Google), 65 chars displayed in shopping results. Front-load the most important info.

Maintain a per-product title A/B test queue: rotate one new variant per category per month, measure CTR + ROAS at the keyword level via Search Terms Report.

VALIDATION: Median title length 60-120 chars. No "AAA-promo" stuffing or all-caps.

============================================================ === PHASE 3: PER-CHANNEL FEED PROJECTION ===

Project the canonical product into each channel's format:

Google Merchant Center (XML or TSV):

• Full canonical schema. Required: id, title, description, link, image_link, availability, price, brand, gtin (if available), condition, google_product_category.
• Sale price requires sale_price_effective_date to qualify as a deal.
• Feeds via Content API for Shopping (real-time) or scheduled fetch (8-24 hour cadence).

Meta Catalog (CSV or Catalog Batch API):

• Similar fields; uses availability (lowercase: in stock / out of stock).
• inventory (numeric count) for ads with low-stock urgency.
• Additional Meta-specific: applinks for app-deep-link, rich_text_description for Shops.
• home_listing for real estate variant, flight for travel variant.

TikTok Shop (TSV or Commerce API):

• Video required for top placements. Map to tiktok_video_url field.
• Stricter content moderation: no medical claims, no age-restricted products in many regions.
• Stricter image requirements (white background preferred for product imagery).

Pinterest Catalogs:

• Image quality matters most (Pinterest is image-first).
• additional_image_link (5+) outperforms single-image listings.
• Pin link landing must match feed link exactly.

Bing Shopping (close to GMC format):

• Reuses Google product feed mostly; small differences in attribute names.

Snap Catalogs:

• Use Snap's tags (age_group, gender) for AR try-on eligibility.

Generate per-channel projection in feeds/{channel}/{date}.{ext}.

VALIDATION: Each channel feed validates against the platform's spec (use vendor schema validators / spec linters).

============================================================ === PHASE 4: VALIDATION & DISAPPROVAL PREVENTION ===

Before submission, run pre-flight checks:

Check	Failure mode	Fix
Image ≥ 500×500 (Google 2026)	Disapproval warning Apr→reject Jan 2027	Auto-upscale via CDN transform
Title length 30-150	Truncation in SERP	Tighten to 60-90 chars
Description ≥ 30 words	Low relevance score	Auto-augment with attribute join
Price > 0 and matches landing page	Price mismatch disapproval	Verify scrape of landing page price
GTIN valid checksum	Invalid GTIN disapproval	Validate UPC/EAN/ISBN checksum
Availability matches landing	"Out of stock" mismatch	Scrape landing inventory status
Brand present	Limited performance	Default to store brand if missing
Required GPC category for restricted	Disapproval (apparel needs color+size+age_group+gender)	Enforce per category rules
Restricted content	Disapproval (e.g., drug-related claims)	Run vocab filter
Landing page returns 200	Broken landing disapproval	Crawl + verify before submit

Output feed_validation_{date}.md with per-SKU issues.

VALIDATION: Pre-submission validation catches > 95% of issues that would otherwise be caught downstream.

============================================================ === PHASE 5: WEEKLY FEED AUDIT CRON ===

Schedule a weekly task (Mondays, post-weekend sales data):

1. Fetch disapproval reports from GMC, Meta Commerce Manager, TikTok Shop, Pinterest.
2. Map each disapproval to the underlying canonical product issue.
3. Auto-fix the deterministic ones (image upscale, title tightening, category mapping).
4. Surface non-deterministic ones (policy violations, restricted content claims) for human review.
5. Track 48-hour SLA: every new disapproval must be addressed within 48h.
6. Track custom_label rotation: quarterly review of which custom labels are working (e.g., high-margin label producing top ROAS).

Generate weekly_audit_{date}.md digest.

VALIDATION: SLA met for ≥ 95% of disapprovals.

============================================================ === PHASE 6: PERFORMANCE FEEDBACK LOOP ===

Tie feed changes back to performance:

• Pull impressions, clicks, conversions, cost, revenue per SKU per channel.
• Compute: CTR, conversion rate, ROAS, CPA.
• Flag underperformers (bottom 20% by ROAS) for title test or image refresh.
• Flag overperformers (top 5%) for custom_label tagging → push to higher-priority campaigns.

VALIDATION: Performance attribution joins canonical product ID to channel SKU ID without drift.

============================================================ === PHASE 7: OUTPUT PACKAGE ===

merchant-feed/
├── README.md
├── products.json                # canonical
├── feeds/
│   ├── google/products.xml
│   ├── meta/products.csv
│   ├── tiktok/products.tsv
│   ├── pinterest/products.csv
│   ├── bing/products.xml
│   └── snap/products.csv
├── validation/
│   └── feed_validation_{date}.md
├── audit/
│   └── weekly_audit_{date}.md
├── tests/
│   └── title_ab_tests.csv       # active title variants per SKU
└── perf/
    └── sku_performance_{date}.csv

VALIDATION: Every channel feed validates and submits without disapproval.

============================================================ === SELF-REVIEW ===

• Complete: All 7 phases run. Canonical schema → 6 channel projections + validation + audit + perf loop.
• Robust: Handles missing GTINs, image upscaling, channel-specific edge cases?
• Clean: Disapproval SLA tracking, no feed drift between channels?
• Ecommerce-credible: Would a feed manager at a $100k+/month Shopping spend accept the system?

Common gap: image upscale missing → silent quality degradation. Verify CDN transform pipeline.

============================================================ === LEARNINGS CAPTURE ===

~/.claude/skills/merchant-feed/LEARNINGS.md.

============================================================ === STRICT RULES ===

• Never submit images below 500×500 for Google after Jan 2027. Auto-upscale or omit.
• Never silently drop attributes a channel requires. Surface in validation report.
• Never let disapprovals sit > 48 hours. Compound spend loss.
• Never reuse one feed across channels without per-channel projection. Each platform has unique requirements.
• Always preserve canonical → channel ID mapping for performance attribution.