Platform · HungerStation

HungerStation Data Scraping

Q: Do you know why a store was closed?

No, and we do not assert it. likely_prayer_window is derived from the observation time and labelled as derived — it says the observation sits in a window where closures are common, not that this store closed for that reason. Asserting a cause we cannot observe would put an inference in a column that looks like a fact.

The established KSA player. Its demand pattern is shaped by prayer times and Ramadan, and a schedule that ignores both produces a series full of artefacts.

HungerStation data scraping collects restaurant menus, item and modifier pricing, delivery fees and availability across Saudi cities. The distinctive collection problem is timing: prayer-time closures and the Ramadan cycle change availability and promotional behaviour in patterns that a naive daily schedule records as random noise.

An inquiry last quarter asked for biweekly competitor price tracking on HungerStation in the KSA market. The biweekly part is fine. The part that needed discussing was when in the day to observe.

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hungerstation.jsonl LIVE FEED

{"store_id":"hs-44120","city":"Jeddah", "item_price":32.00,"currency":"SAR", "min_realisable_price":41.00, "store_open":true,"serviceable":true, "observed_at":"2026-08-25T13:10:00+03:00", "daypart":"midday", "likely_prayer_window":false, "ramadan_period":false} {"store_id":"hs-44120", "store_open":false, "observed_at":"2026-08-25T15:35:00+03:00", "likely_prayer_window":true, "note":"DERIVED flag — we do not assert the reason it closed"} {"store_id":"hs-88012", "ramadan_period":true,"days_from_ramadan_start":12, "note":"phase-aligned, because Ramadan dates move each year"}

3 of 2,204,880 store-item rows 4 daypartsdaypart schedule is the design decision · schema v1.0

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

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HungerStation at a glance

How we handle HungerStation specifically

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

Platform: HungerStation — the established KSA delivery player
The timing problem: Prayer times and Ramadan shape availability and promotions
Consequence: A single daily observation at a fixed hour produces artefacts, not signal
Unit: Store level, with area serviceability recorded separately
Language: Arabic and English retained, neither translated into the record
Modifiers: Priced independently. Minimum realisable price computed on a stated basis
Fees: Delivery, service and small-order fees as separate fields
Refresh: Multiple dayparts; the schedule is the design decision

Platform specifics

Why the observation schedule is the main design decision

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

Prayer times close stores, and a fixed daily sample lands wherever it lands

Restaurants across Saudi Arabia close around prayer times. Those times shift daily and vary by city.

A daily observation at a fixed clock hour will sometimes land during a closure and sometimes not, with no field in the data explaining which. The result is an availability series that looks volatile and is mostly measuring the sampling schedule.

Store closed for prayer is not the same as a store having stopped trading.
Menu availability during a closure is frequently unchanged, so a closure does not indicate a delisting.
Delivery estimates lengthen around closures in ways that are not a service degradation.

We sample across dayparts rather than at a fixed hour, record daypart and observed_at with the local offset, and flag likely_prayer_window where the observation falls in one. That last field is derived and labelled as derived — we are not asserting a store closed for a particular reason, only that the observation sits in a window where closures are common.

Ramadan is a different market, not a seasonal adjustment

During Ramadan the entire demand pattern inverts: ordering concentrates around iftar and suhoor, menus change, promotional intensity rises sharply, and delivery capacity behaves differently from any other period.

Pooling Ramadan with the rest of the year produces averages that describe neither.
Year-over-year comparison breaks unless it aligns on the Ramadan cycle rather than the calendar, because the dates move.
Menu items appear and disappear for the period, which a range analysis will read as launches and delistings.

Every record carries ramadan_period and days_from_ramadan_start, so a series can be analysed by phase rather than by date. This is the same structure our Ramadan playbook sets out, applied to delivery rather than grocery.

If you are commissioning collection for a Ramadan analysis, the collection needs to be running before the period starts. A baseline captured afterwards is not a baseline.

Modifiers and the price a customer actually pays

As on any delivery platform, the item price is frequently not the entry price. Required modifier groups — size, base, protein choice — carry prices that are unavoidable.

We deliver modifier groups with is_required, option prices and selection limits, and compute min_realisable_price as item plus cheapest required options, with the basis recorded.

Why this matters more in a competitor-tracking use

Two restaurants can list the same headline price and differ substantially once required modifiers are applied. A competitor price index built on item price alone will rank them as equivalent when a customer experiences them as 20–30% apart.

The inquiry behind this page was for competitor price tracking specifically, which is exactly the use where item price alone gives the wrong answer.

Scope

What we collect on HungerStation, 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

Store-level menu and item pricing, with area serviceability separate
Modifier groups with required flags, option prices and selection limits
Minimum realisable price computed at a stated basis
Arabic and English item names retained as published
Daypart and local-offset timestamp on every observation
likely_prayer_window flag, labelled as derived
ramadan_period and days_from_ramadan_start for phase-aligned analysis
Delivery, service and small-order fees as separate fields
Store open state distinct from area serviceability

❌ What we do not, and why

A single fixed-hour daily sample presented as a clean series
A store closure attributed to a specific cause by us
Sales, order volumes or restaurant revenue
Rider, customer or order data
Machine translation written into item name fields

Core HungerStation fields

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

Field	What it is on this platform
store_id / brand_id / city / area	Store-level identity and geography
item_id / item_name_ar / item_name_en	Both scripts as published
item_price / currency	Headline price
modifier_groups	Array with is_required, option prices and selection limits
min_realisable_price / price_basis	Item plus cheapest required options, basis stated
delivery_fee / service_fee / small_order_fee	Each separately
store_open / serviceable	Two distinct states
observed_at / daypart	Local offset retained, daypart derived
likely_prayer_window	Derived and labelled as such, never asserted as a cause
ramadan_period / days_from_ramadan_start	For phase-aligned comparison
promo_type / promo_funder	Platform-funded and merchant-funded kept apart

Use cases

What teams do with HungerStation data

Competitor price tracking that reflects what customers pay

Minimum realisable price rather than headline item price, which is the difference between two restaurants ranking as equivalent and ranking 20-30% apart.

Ramadan performance analysis, phase-aligned

ramadan_period and days_from_ramadan_start on every record, so year-over-year comparison aligns on the cycle rather than the calendar, since the dates move.

Availability that is not an artefact of the sampling hour

Multi-daypart observation with prayer-window flagging, so an availability series measures the market rather than the schedule.

Delivery fee and promotional intensity by area

Fees and promotions as their own series by area and funder, since both move independently of menu price.

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

Send us a HungerStation 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.

HungerStation is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. HungerStation 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 HungerStation-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

HungerStation data scraping: frequently asked questions

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

You can, and the series will be noisy for reasons that have nothing to do with the market. Restaurants close around prayer times, which shift daily and vary by city, so a fixed-hour sample lands inside a closure sometimes and not others with no field explaining which.

We sample across dayparts, record the daypart and local offset, and flag observations that fall in a window where closures are common.

No, and we do not assert it. likely_prayer_window is derived from the observation time and labelled as derived — it says the observation sits in a window where closures are common, not that this store closed for that reason.

Asserting a cause we cannot observe would put an inference in a column that looks like a fact.

As a distinct market phase rather than a seasonal adjustment. Demand inverts around iftar and suhoor, menus change and promotional intensity rises, so pooling it with the rest of the year produces averages describing neither.

Every record carries ramadan_period and days_from_ramadan_start so analysis aligns on the cycle rather than the calendar, since the dates move each year.

Because required modifiers are unavoidable. Two restaurants can list the same headline price and differ 20 to 30 percent once required size or base selections are applied.

For competitor price tracking specifically — which is the use this page was built for — item price alone gives the wrong ranking.

Yes, and it is usually the right scope. GCC delivery competition is between platforms as much as between restaurants, so a single-platform view misses the comparison that matters.

Both use the same schema and the same area panel, so records are comparable by construction.

We quote individually. Drivers are city and area count, store count, menu depth and observations per day — the observation count matters more here than on most platforms because the daypart schedule is the design decision.

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

See real HungerStation 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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