Careem Data Scraping Services

Coverage is thinner, so coverage is the analysis

Careem's origins are in ride-hailing, and its delivery verticals operate alongside that rather than as the core business. In practice that means merchant coverage per zone is typically thinner than on delivery-first rivals in the same cities.

What that changes

• Presence is the first question. For a chain, whether it is listed in a zone matters more than a few percent of price difference.
• A competitor absent from this platform is a distribution fact worth knowing, not a data gap.
• Coverage changes are competitive events — merchants joining or leaving a platform in a zone.
• Price comparison on a thin merchant set risks generalising from a small sample.

We deliver merchant_present, serves_zone and merchants_in_zone, plus merchant_joined_at and merchant_left_at from continuous collection. We also report the merchant count per zone alongside any price aggregate, so nobody reads a category average built on four merchants as a market view.

Reporting the sample size beside the aggregate sounds obvious and is routinely omitted. On a thin platform it is the difference between a usable figure and a misleading one.

Same GCC discipline as our other regional platforms

The structural requirements here match our Talabat service, and we apply them identically.

• Market on every record with currency and VAT treatment, since GCC markets diverge enough that a combined figure is rarely actionable.
• Arabic and English listings linked to a canonical identity with confidence, since counted separately they inflate competitive density.
• Verticals kept separate, since food menus and packaged SKUs share no useful schema.
• Delivery zone per city, since coverage varies within cities.

Where clients collect both platforms, records join on merchant identity, market and zone. That join is the point: the useful analysis in this region is which merchants are on which platform in which zone, and at what price when they are on both.

Ramadan timing applies here too

As on every GCC delivery platform we collect, the trading day inverts during Ramadan. Demand concentrates around and after Iftar and runs late, while daytime volume falls.

A collection schedule built for daytime sampling misses the peak entirely, and delivery promise in particular needs capturing through the peak ordering window rather than at an arbitrary daytime point.

We weight collection to evening and overnight hours during the window and record observed_at with a derived daypart on fee and promise fields. Our Ramadan playbook sets out the full timing design.

What we cannot do is attribute a promise change to ride demand versus delivery demand. On a shared-fleet platform that attribution is not observable, and we report the timestamped observation rather than modelling a cause — the same position as on Grab.