Platform · Postmates

Postmates Data Scraping

It has not shut down — but it runs on Uber Eats infrastructure, so collecting both may be buying the same data twice.

Postmates data scraping collects menus, pricing and availability from a platform that is still operating as a consumer brand while running on Uber Eats infrastructure following the 2020 acquisition. That matters commercially before it matters technically: merchant and pricing overlap with Uber Eats is high, so we measure it before you commission both.

This page exists mainly to answer one question honestly: is it worth collecting alongside Uber Eats? The answer is measurable, and we measure it first.

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postmates.jsonl LIVE FEED

{"platform":"postmates","brand":"consumer_brand", "merchant_id":"pm-44120","city":"Example city", "item_price":13.49, "also_on_uber_eats":true, "price_differs_from_uber_eats":false, "note":"same merchant, same item, same price. shared infrastructure"} {"brand_exclusive_promo":true, "promo_text":"as displayed, this brand only", "note":"THIS is the part worth collecting — the difference, not the whole"} {"overlap_with_uber_eats":0.91, "recommendation":"collect divergence only, not a second full panel", "caution":"91% identical. buying both full panels benefits the vendor, not you"}

3 of 1,884,220 merchant-item rows · USoverlap measured BEFORE quoting · schema v1.0

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to Postmates or its owners. Postmates and related marks belong to their respective owners, used here only to name the publicly accessible source this service collects from.

Our Data Powers

B2C Marketplace

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NYKAA

D2C + Marketplace

Walmart

FMCG Marketplace

udaan

Food Delivery

Uber Eats

Quick Commerce

blinkit

Taxi Aggregator

Uber

E-Commerce

Tmall

Postmates at a glance

How we handle Postmates specifically

Platform-specific handling, not a generic retail template pointed at a different domain.

Status: Operating. Not shut down
Ownership: Acquired by Uber in 2020
Infrastructure: Orders fulfil through the Uber Eats system
Consumer brand: Separate app and marketplace
Consequence: High merchant and price overlap with Uber Eats
What we do: Measure the overlap before you commission both
Where it differs: Brand-level promotions and app presentation
Refresh: Daily. Matched to Uber Eats where both are collected

Platform specifics

A consumer brand on shared infrastructure

These are the reasons a Postmates dataset needs its own handling rather than a shared retail schema.

What the acquisition actually changed

Uber acquired this platform in 2020. Reporting since describes the outcome consistently: the consumer brand and app continue, while merchant and delivery networks merged and orders fulfil through the Uber Eats system.

So the honest framing is neither "it shut down" nor "it is an independent competitor":

It is a consumer-facing brand with its own app and marketplace.
It runs on shared merchant and delivery infrastructure.
Merchants signed to one are largely available on the other.
So listing and pricing overlap is high, though presentation and brand-level promotions can differ.

Which makes one number decide the engagement

overlap_with_uber_eats — the share of merchants and items that appear on both with the same price, measured on your own markets in the pilot.

If that share is very high, we will tell you not to commission both. Buying the same data under two brand names is the kind of thing a vendor benefits from and a client does not.

Where the brands genuinely differ, and how we capture it

Overlap being high is not the same as the platforms being identical. Where differences exist they are worth isolating.

Brand-level promotions can run on one and not the other.
Fee structures can differ at the brand level.
Merchant presentation — imagery, descriptions, category placement — is not always identical.
Geographic strength differs, reflecting the platform's historical footprint in the western US.

We record brand_exclusive_promo where a promotion appears on one brand only, and price_differs_from_uber_eats where the same merchant and item carry different prices — computed from paired records, never asserted.

Which is the useful engagement shape

Where overlap is high, the interesting collection is the difference rather than the whole. A targeted programme capturing brand-exclusive promotions and price divergence is far cheaper than a full second panel, and it is what the data actually supports.

Standard mechanics, and what we do not collect

Everything on the marketplace side works as our Uber Eats page sets out: merchant sets the price, modifier groups make the headline not the entry price, fees are separate fields, and availability is address-level.

Schedules must match

If the point is measuring divergence between two brands, they must be observed on the same schedule. A gap measured from records taken hours apart is partly a timing artefact, and on a delivery platform prices and availability move within the day.

panel_schedule_id is shared and observation_interval_minutes recorded.

What we do not produce

A claim that the platforms are identical, or that they are independent. We measure the overlap and report it.
Order volumes, merchant revenue or platform take rate. Not published.
Courier, customer or reviewer identity.

Scope

What we collect on Postmates, and what we do not

The right column matters more than the left. Anyone can list fields; the limits are what tell you whether the dataset will hold up.

✅ What we collect

overlap_with_uber_eats measured in the pilot, before commissioning
A recommendation against collecting both where overlap is very high
brand_exclusive_promo where a promotion appears on one brand only
price_differs_from_uber_eats computed from paired records
Shared schedule across both brands where divergence is the question
Modifier groups with minimum realisable price
Fees as separate fields
Address-level availability
Review counts and ratings, without reviewer identity

❌ What we do not, and why

Both brands sold as a full panel without measuring overlap
A price difference asserted rather than computed from paired records
The two brands observed on different schedules and compared
A claim that the platforms are identical or independent
Order volumes, revenue, courier, customer or reviewer data

Core Postmates fields

The full dictionary is agreed during scoping. These are the fields specific to this platform.

Field	What it is on this platform
platform / brand	The consumer brand, on shared infrastructure
merchant_id / merchant_name / city	A commercial entity, and where
item_id / item_name / item_price	As listed
overlap_with_uber_eats	Measured per market in the pilot
also_on_uber_eats	Per merchant, where both are in scope
price_differs_from_uber_eats	From paired records only
brand_exclusive_promo	Where a promotion runs on one brand only
modifier_groups / min_realisable_price	The number a customer can pay
delivery_fee / service_fee	Each separately
panel_schedule_id / observation_interval_minutes	So divergence is not a timing artefact
observed_at	Timestamp

Use cases

What teams do with Postmates data

Deciding whether to collect both

Overlap measured on your own markets in the pilot, with a recommendation against commissioning both where the share is very high — since buying the same data under two brand names benefits the vendor and not the client.

Brand-exclusive promotion tracking

Promotions appearing on one brand only, which is the part of this platform that genuinely differs from Uber Eats and is far cheaper to collect than a full second panel.

Price divergence between brands

Differences computed from paired records on a shared schedule, so a gap is an observation rather than a timing artefact.

Western US coverage depth

Where the platform's historical footprint is strongest, which is where its merchant set is most likely to differ.

The 24-hour sample — run on your sources, not ours

Send us a Postmates item or category list. We run real collection against it and return the output within 24 hours, with the platform-specific fields populated so you can check them yourself rather than take our word for it.

Real extraction from your actual sources
Returned within 24 hours
Coverage and QA note included
You keep the data either way
No card, no trial clock
Named engineer on the call

Get my free sample Book a 20-min scoping call Reply within one business day. Reference calls available under NDA.

How we engage

Three ways to engage us

Same collection pipeline and QA underneath. The difference is who holds the schedule and how the data reaches you.

Managed service (most common)

We own the collection, the QA and the delivery. You receive clean data on a schedule and never touch a scraper.

Dedicated engineer assigned to your account
Site changes fixed by us, not reported to you
Scheduled delivery to your warehouse or S3
Named contact on Slack or email

Best fit: Teams who need the data, not the infrastructure.

API access

The same collection pipeline exposed as an authenticated REST endpoint your systems query directly.

On-demand and scheduled endpoints
Rate limits agreed to your load profile
Sandbox keys for integration testing
Versioned schema with deprecation notice

Best fit: Product and engineering teams building on live data.

One-time or project extraction

A defined pull for a specific question — market sizing, diligence, a pitch, a one-off audit.

Fixed scope agreed in writing upfront
Single delivery with full QA report
Methodology documented for your records
Converts to managed if you want continuity

Best fit: Research, strategy and diligence work with a deadline.

Pricing

Every engagement is quoted individually, because the honest answer depends on your scope: how many sources, how many records, how often, and how the data reaches you. We scope it with you, run a free pilot on your own sources, and then quote a fixed monthly figure — no per-request metering and no overage billing when volumes move. Request a quote and you will have a number after one call.

Postmates is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Postmates data becomes useful when it sits next to the competitor set on one schema, refreshed on one schedule, so a price index or availability comparison is genuinely like-for-like.

That is what food & restaurant data covers, and a Postmates-only engagement can be expanded into it without rebuilding. If you already know you need several platforms, start there instead — it is the same pipeline and usually the better scoping conversation.

FAQ

Postmates data scraping: frequently asked questions

Platform-specific questions, including what cannot be collected here.

No. It was acquired by Uber in 2020 and continues as a consumer-facing brand with its own app and marketplace.

What changed is underneath: merchant and delivery networks merged, and orders fulfil through the Uber Eats system. So it is neither shut down nor an independent competitor.

That is measurable and we measure it first. The pilot returns overlap_with_uber_eats — the share of merchants and items appearing on both at the same price, on your own markets.

If that share is very high, we will tell you not to commission both. Buying the same data under two brand names is the kind of thing a vendor benefits from and a client does not.

Brand-level promotions, fee structures, merchant presentation and geographic strength — the platform's historical footprint in the western US is stronger.

Where overlap is high, the interesting collection is the difference rather than the whole, and a targeted programme capturing divergence is far cheaper than a full second panel.

From paired records — the same merchant and item on both brands, observed on the same schedule. Never asserted from one side.

The shared schedule matters because prices and availability move within the day on delivery platforms, so records taken hours apart contain a timing artefact that can exceed the gap being measured.

No. Take rate, order volumes and merchant revenue are not published, and inferring them would require cost data nobody outside the platform holds.

Usually less than a full platform engagement, because where overlap is high the right programme captures divergence rather than the whole catalogue.

One scoping call, a free pilot within 24 hours including the overlap measurement, then a fixed monthly quote. Request a quote.

See real Postmates data before you commit to anything

Send us an item or category list. We return the output within 24 hours with the platform-specific fields populated.

Get a free sample Talk to an engineer

Free pilot, no card, no obligation. If we cannot collect a field you need on this platform, the sample shows you that too.

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