Mobile Proxy Retail Data
Retail data is the part of commerce that never left the physical world: what is actually on the shelf in a particular store, at a particular price, today. Almost all of it is gated behind a postcode and a local visitor.
- Stock is per store, not per website — availability endpoints answer for a branch and need a location to answer at all.
- A postcode is part of the query — without one, most grocers and chains show you nothing useful.
- Promotions are regional — the same chain runs different offers in different parts of one country.
- Daily is the right rhythm — shelves change daily, and store endpoints are not built for constant polling.
Query availability the way a nearby shopper does.
Reach stock and offers that only exist in the retailer app.
Retail Data Beyond the Website
Retail data is the part of commerce that never moved online. It describes physical stores: what is on the shelf in a particular branch, at what price, today. The unit of analysis is the store rather than the catalogue, and almost everything interesting is gated behind a location.
That produces a specific access shape. Availability endpoints, click-and-collect slots and local pricing are all functions of a branch, so the retailer asks where you are before it will tell you anything. Supplying a postcode from a foreign address frequently returns a partial answer or a rejection, which is why a local exit and a local postcode go together — either alone tends to produce data that looks fine and is not.
| What varies by store | Why |
|---|---|
| Stock and availability | Physical inventory sits in a building, not on a website |
| Price | Local competition, store format and regional cost all move it |
| Promotions | Chains run regional campaigns that never appear nationally |
| Range | Store size determines what is carried at all |
| Collection slots | Capacity is per branch and changes through the day |
Store-Level Stock and Availability
The most valuable field in retail data is one no retailer publishes: how long a product was actually unavailable at a given branch. Availability snapshots are commonplace and fairly uninformative. Out-of-stock duration is what tells you about supply reliability, distribution failures and demand spikes — and like time on market in property, it exists only if you were collecting continuously.
store_id … postcode_area … product_id … in_stock true | false | limited observed_at 2026-08-24T09:14:02Z market GB price … # per store, not per chain promotion … # regional campaigns show up here
Cadence should be daily. Availability and promotions turn over on that rhythm, and faster collection adds noise rather than signal — a product going out of stock at eleven and back in at three is not something anybody can act on. Store-level endpoints are also comparatively fragile and were never designed for constant polling, which is a second reason patience pays here.
Local Pricing and Promotions
In many markets, prices genuinely differ between stores in the same chain, and it is one of the more interesting things retail collection reveals. Convenience and forecourt formats frequently price well above the same chain’s supermarkets, and that is deliberate strategy rather than error. None of it is visible from the national website.
Regional promotions behave the same way. A chain may run a campaign in one part of a country to answer a local competitor, and the national site will show no sign of it. Collecting per region rather than per chain is what surfaces this, and it is usually the finding that justifies the programme to whoever is paying for it.
Scaling Retail Collection
The temptation is to cover every store. It is almost always wrong. A well-chosen sample — a handful of branches per region, spanning the formats the chain operates — captures the variation that matters at a fraction of the volume, and a smaller footprint is also considerably less likely to attract attention from endpoints that were never built for this.
- Sample stores, do not enumerate them — Variation comes from region and format, not from store count.
- Hold the store as part of the key — Chain-level aggregation destroys exactly the differences you collected for.
- Pair a local exit with a local postcode — Either on its own produces answers that look complete and are not.
- Watch the app surface too — Some chains expose stock and offers in their app that the website never shows.
- Alert on collection health — A store endpoint that starts returning empty looks identical to a store with nothing in stock.
For the online half of the same question see ecommerce data, and for continuous price watching, price monitoring.
Query Stores the Way Local Shoppers Do
Live PXM2 locations — pick the countries whose chains you track and collect store-level data from inside each:
France
India
Singapore
Frequently Asked Questions
How does retail data differ from ecommerce data?
Ecommerce data describes a website that ships nationally. Retail data describes physical stores, and the unit of analysis is the branch rather than the catalogue. That changes everything: stock is per store, prices can vary between stores in one chain, promotions run regionally, and the useful question is what is on the shelf near a particular customer rather than what the retailer sells in principle.
Why is a postcode involved?
Because store-level endpoints cannot answer without one. Availability, click-and-collect slots and local pricing are all functions of a specific branch, so the retailer asks where you are before it will tell you anything. Supplying a postcode from a foreign address frequently produces a partial or rejected answer, which is why pairing a local exit with a local postcode is the combination that actually works.
Do prices really differ between stores in the same chain?
In many markets yes, and it is one of the more interesting things retail data reveals. Chains adjust for local competition, store format and regional cost, and the differences are invisible from the national website. Convenience and forecourt formats in particular frequently price well above the same chain’s supermarkets, and that is a deliberate strategy rather than an error.
What cadence does shelf data need?
Daily is right for availability and promotions, both of which turn over on that rhythm. Faster adds noise rather than signal — a product going out of stock at eleven in the morning and back in at three tells you nothing you can act on. Store-level endpoints are also comparatively fragile and were never built for constant polling, which is another reason patience pays here.
What is the most useful thing to record?
Out-of-stock events with their duration, per store. Availability snapshots are commonplace; how long a product was actually unavailable at a given branch is not, and it is the number that tells you about supply reliability, distribution problems and demand spikes. Like time on market in property, it only exists if you were collecting continuously.
Related Mobile Proxy Guides
Retail is the physical half of commerce; the online half and the pricing half sit next door.
Business use cases
Core mobile proxy guides
Query Stores From Inside the Region
Dedicated 4G/5G modems with unlimited bandwidth and unlimited rotations — carrier IPs that make a local postcode return a real answer.
Get a Mobile Proxy