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Case study 02Restaurant intelligenceManaged data collection

42 restaurant brands. One connected data workflow.

A restaurant data programme covering 42 brands, bringing location, menu and pricing information into a structured workflow for market and competitor analysis.

Restaurant brands
42
Pricing context
Location
Collection workflow
Recurring

01 / The challenge

A menu price needs its context.

Restaurant comparisons become misleading when the data loses its detail. An item name alone does not identify a comparable offer: the restaurant location, menu category, portion or size, and price all affect what the record means.

The programme covered 42 restaurant brands on DoorDash. The requirement was to organise the collected information into a recurring analytical workflow, rather than leave the client to reconcile separate brand and menu exports.

The difficult part was preserving useful differences while making the data consistent enough to work with. A shared structure should not erase a size variant, confuse a location-level price with a national price, or imply that every brand exposes the same information.

02 / Our approach

What we built around the brief.

  1. 01

    Connect brands, locations and menus

    We organised the data so menu records retained their restaurant and brand context. This made it possible to examine item-level information without losing where it came from.

  2. 02

    Normalise without flattening the detail

    Menu categories, product names, sizes and prices were brought into a consistent structure. Source-specific detail was preserved where available, rather than assuming that similarly named items were identical.

  3. 03

    Support recurring collection

    The collection and processing workflow was maintained as menus and source formats changed. Validation and ongoing upkeep were part of the service, alongside the initial data collection.

03 / Inside the work

From brand coverage to item-level detail.

The data model connects three levels: the brand, its restaurant locations and the menu items offered there. Additional information depends on what the source exposes.

From brand coverage to item-level detail.
Data groupWhy it matters
Brand & restaurantBrand and store identity keep menu observations tied to the right business and location.
Menu structureCategories and product names preserve how an offer is organised on the source.
Size & formatSize or variant descriptions distinguish offers that share a product name.
Item pricingPrices stay connected to the item, its variant and the restaurant context.
Ratings & reviewsSource-available rating and review information adds context where included in the collection scope.

42 is the number of brands in the programme, not the number of restaurant locations. Field availability varies by brand and location.

04 / The outcome

A foundation for meaningful comparisons.

Restaurant intelligence / data structure
42brands in the programme
  1. BrandIdentity & coverage
  2. RestaurantLocation context
  3. Menu itemSize, format & price

Connected detail, not just a list of menu prices.

The programme brought data from 42 brands into a maintained collection workflow. Brand-level coverage was combined with the restaurant and menu context needed to interpret individual records.

This gave the client structured inputs for menu, pricing and competitor analysis. It did not turn every item into a perfect like-for-like match; it kept the distinctions needed to make those comparisons responsibly.

How to read these results

Coverage describes the agreed 42-brand programme. It does not imply every restaurant location, every menu item or every field is available at all times. Source availability and project scope determine the delivered records.

05 / The takeaway

Consistency is not the same as making every record look identical. Useful restaurant intelligence preserves the differences that affect a price comparison.

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