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Cross-Entity Attribution

How AxonRow connects entities across data sources to answer questions no single dimension can — the complete profit chain.

The Problem

You have a creator who made a video. You promoted that video with GMV Max. It sold 3 products. One of those products had a 40% return rate. The question: was this entire chain profitable?

No single data source can answer this. The Ads API knows the spend. The Shop Analytics API knows the video views. The Settlement API knows the actual fees. The Creator API knows the commission. Only by connecting all four do you get the real answer.

This is what makes AxonRow different from a dashboard that shows each entity in isolation. We don't just show you "Creator X had $50K GMV." We show you "Creator X drove Product Y through Video Z with Ad Campaign W, and after all fees the chain netted $2,100."

The Four Entities

Every transaction on TikTok Shop touches up to 4 entities:

  • Product: The SKU that was sold — its COGS, platform fees, shipping cost
  • Creator: The affiliate who drove the sale — their commission rate, content quality
  • Ad: The campaign that amplified reach — its spend, targeting, bid type
  • Content: The specific video or live session — its production cost, organic reach, conversion rate

Not every order involves all 4. Organic orders from your own shop page have no creator or ad. Direct search purchases have no content attribution. But for affiliate-driven, ad-boosted content — which is where most TikTok Shop GMV comes from — all 4 are in play.

How the Data Connects

The Join Keys

TikTok's APIs don't provide a single "transaction chain" endpoint. AxonRow builds the connections from shared identifiers:

  • Product ↔ Settlement: product_id in settlement_transaction_skus — exact fee attribution per SKU per order
  • Creator ↔ Product: creator_open_id + product_id in creator performance data — who sold what
  • Ad ↔ Product: item_group_id in GMV Max product reports — which SKUs got ad spend
  • Ad ↔ Content: item_id (video/creative ID) in GMV Max creative reports — which content was promoted
  • Content ↔ Creator: video metadata contains creator_id — who made the content
  • Content ↔ Product: video analytics contain product-level GMV attribution

Time Alignment

A critical detail: settlement data is timestamped by order_create_time (when the customer placed the order), not by settled_at (when TikTok paid you, 7-30 days later). AxonRow aligns all entity data on the order date so that:

  • Ad spend on June 1 is compared to profit from orders placed June 1
  • Creator performance on June 1 reflects orders they drove that day
  • Not orders that happened to settle on June 1 (which were actually placed weeks earlier)

Cross-Entity Questions

Once entities are connected, you can ask questions that span dimensions:

Creator × Product

  • "Which creators sell my high-margin SKUs vs my low-margin ones?"
  • "Creator A drives volume but only on products with 5% margin. Creator B drives less volume but targets 30%+ margin SKUs."
  • "If I shift Creator A to my top-margin products, what's the projected impact?"

Creator × Ad

  • "Am I spending ad money to boost creators who are already profitable organically?"
  • "Which creator content has the highest paid ROAS vs organic ROAS gap?" (Content that only works with ads = expensive dependency)
  • "If I cut ad spend on Creator X's content, do they still generate positive contribution?"

Product × Ad × Settlement

  • "True ROAS by SKU — not platform ROAS, but profit after all 47 fee categories divided by ad spend"
  • "Which products are ad-spend traps? High GMV ROAS but negative profit ROAS because of thin margins + high return rates"

Creator × Content × Product (The Full Chain)

  • "Creator A's Video #123 promoted Product B via Campaign C. The video got 500K views, generated $12K GMV across 3 SKUs. After commission ($1,800), ad spend ($950), platform fees ($2,100), COGS ($3,200), and returns ($1,400) — net profit was $2,550. Chain ROI: 2.55x on investment."

Practical Application

Budget Allocation

Cross-entity data lets you optimize spend across all levers simultaneously:

  • Increase commission rates for creators who sell high-margin products (they'll prioritize you)
  • Cut ad spend on content that only drives low-margin SKU sales
  • Route inventory to creators whose content drives profitable conversions, not just volume

Creator Selection

Traditional creator selection looks at followers, engagement rate, and content quality. Cross-entity attribution adds the financial dimension: does this creator's audience actually buy profitable products and keep them?

A creator with 10K followers but a 2% return rate on high-margin SKUs might be worth more than a creator with 1M followers driving volume on razor-thin products with 25% returns.

What AxonRow Shows You

  • Full attribution chains: Creator → Content → Product → Profit (with ad spend factored)
  • Cross-entity matrices: Creator × Product profit grid, Content × SKU performance
  • Chain ROI: total investment across all entities vs net profit for the complete flow
  • Anomaly detection: chains that look good on one dimension but lose money end-to-end

The insight isn't in any single entity. It's in the connection between them.