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How commerce data actually works — identity, matching, history, provenance and the protocols that make a catalogue legible to machines.
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What is commerce intelligence?
Commerce intelligence is the layer above price monitoring: an index of what exists, who sells it, at what, and what that means.
Why percentile beats percent-off
A percentile against a product's own history is a defensible measure of discount depth; percent-off a reference price is not.
Product matching and why it fails quietly
How cross-seller product matching works, the three ways it fails, and why published match confidence matters.
GTIN, UPC, EAN and ASIN
How product identifiers relate, why check digits matter, and what to do when there is no identifier at all.
Data provenance in commerce data
Why every price should carry its origin and a confidence weight, and what goes wrong when it does not.
Agentic commerce, explained
How AI agents are changing online purchasing, and which protocols matter for making a catalogue legible to them.
Using MCP for commerce data
How to design Model Context Protocol tools for commerce so agents answer questions instead of stitching fragments.
Designing webhooks people can rely on
Idempotency keys, signature versioning, retry ladders and replay — the four things that separate usable webhooks from a support burden.
Store observations, not prices
Why an append-only observation model beats mutable current-price rows, and what that unlocks.
How often should you re-check a price?
Refresh cadence should be driven by observed volatility and by what a source costs to read — not by a fixed nightly schedule.
Unit price normalisation
Comparing prices across pack sizes requires normalising to a common unit — and treating pack size as part of identity.
Why shopping agents need memory
Durable, namespaced memory lets an agent carry preferences and constraints between sessions instead of re-interviewing the user.
How to get price history data
What price history data is, why it cannot be backfilled, and how to get it through an API instead of building collection yourself.
Building price collection versus buying it
An honest comparison of running your own price collection against using an API, including the costs that only appear in month three.
How to build a price comparison engine
The architecture behind a working price comparison site: canonical products, landed cost, per-seller history and published match confidence.
How price drop alerts actually work
The mechanics behind reliable price drop alerts: standing conditions, change detection, signed delivery and why context makes them worth opening.
Competitor price monitoring, done properly
How to monitor competitor pricing without drowning in noise: pick the competitive set, react to events, and require confidence before acting.
Dynamic pricing versus surveillance pricing
The difference between competitor-driven dynamic pricing and personalised pricing from consumer data — and why the distinction now matters legally.
MAP pricing explained
What minimum advertised price policies are, how violations are detected, and why evidence matters more than detection.
The buy box, and why it moves
How marketplace buy-box ownership works, why it flips, and what a seller change signals before the price move that follows.
Why landed cost is the only comparable price
Shipping, handling and thresholds mean sticker price comparisons are frequently wrong. Landed cost is the number that decides purchases.
Product data feeds, explained
How merchant product feeds work, what fields matter, and why feeds are becoming the primary way catalogues reach machines.
How to choose a product data API
The questions that actually separate commerce data vendors: coverage definition, freshness, match quality, provenance and export.
How AI agents actually buy things
The mechanics of agent-driven purchasing: how agents find products, evaluate price, and what makes a catalogue legible to them.