Data and Dark Kitchens

Tom Cross investigates the cryptic rise of dark kitchens by understanding customer data

An order for a Shake Shack burger goes through on the Deliveroo app. A rider is assigned an order and then sent to collect it. But he doesn’t go to a high street. He heads to an industrial estate Battersea. Why? The burger’s being cooked there, in a repurposed shipping container; it’s being cooked in a dark kitchen.

Dark kitchens are kitchens located inside containers, or similar structures, often on industrial sights which only provide takeaway food. Officially they are not restaurants but light industry. On Deliveroo’s interface, they are labeled “Deliveroo Editions”, since they have an exclusive contract with Deliveroo. Dark kitchens are changing the practices of cooking, a change possible due to Deliveroo’s digital interface; the customer data it gathers, and how it changes the “place” of restaurants.

Deliveroo’s operation system is one of “dual value production”: ‘the monetary value produced by the service provided is augmented by the use and speculative value of the data produced before, during and after service provision’ (van Doorn & Badger 2020: 1476). For Deliveroo, the monetary value is its 30% commission on each restaurant order, and speculative value is customer data. “Crude” data collected in real-time has no real value, but “refining” it through algorithms enables it to confer knowledge, and thus value and power (Kitchen 2014). Deliveroo constantly accumulates data because of its potential value.

Image courtesy of Johara Meyer

For this constant data collection, food delivery is deconstructed and modeled by studying basic entities, relations between them, and each entities’ function, to create an ontology (van Doorn & Badger; Agre 1994). A closed blueprint model of food delivery is then made.  Data on each stage of food delivery is collected and stored, subsequently merged with other records before being subjected to data analytics to create value (ibid).

Deliveroo thus creates data assets, with speculative value, out of customer’s orders by tracking users’ movement through digital space (Rose 2016). Specifically, Deliveroo captures data on how customers engage with the restaurant ranking presented to them when they go onto the app or website. Deliveroo’s data analysts try to “optimize” customers’ individualised rankings. “Optimal” is determined by where the restaurant the customer orders from is on the ranking; optimal is a customer ordering from the restaurant at the top (PyData 2019). Deliveroo refines this data to identify “customer demand for missing cuisines and hand-pick a brand” an area (Weiss in Walters & Crouch 2020: n/p).  Customer data enables Deliveroo to identify a market gap and fill it with Deliveroo Editions.

Dark kitchens are affecting restaurant labour, built urban space, and potentially domestic practices. Increasingly restaurants are only “virtual”, with no bricks-and-mortar high street counterpart. Deliveroo’s virtual interface enables this. The interface is the customer’s only contact point with the restaurant; restaurants’ physical locations do not matter for the customer if it’s in the order radius. A restaurant’s “place” is a virtual icon.

Additionally, dark kitchens provide kitchen equipment and rental contracts, making the overheads less than for brick-and-mortar restaurants, and do not require waiting staff. Meals from dark kitchens are thus cheaper, and more convenient for customers. Accordingly, there are widespread fears dark kitchens could further drain embattled high streets and force restaurant staff into worse working environments. Dark kitchens are literally ‘dark’ with no natural light. Indeed Franco Manca ended its dark kitchen experiment after staff complained they were ‘cooped up like battery hens’ (Meddings 2020: 3). There is also speculation of automation of food preparation due to its repetitive nature. The convenience, and cost-cutting, offered by dark kitchens underpins Deliveroo CEO Will Shu’s vision of a future where “cooking is purely a hobby” (Bloomberg 2019: 9.50-9.53).

The application of Deliveroo’s data extraction is changing restaurant, and high-street, geographies, paid-labour practices, and potentially domestic ones. With the Covid-19 pandemic fuelling a rise in takeaway orders, the use of dark kitchens will likely grow. But this future is not determined, after-all Deliveroo has yet to turn a profit


Agre, E (1994) Surveillance and capture: Two models of privacy. The Information Society, 10, (2), pp.101– 127.

Bloomberg Live (2019) Deliveroo CEO on the Global Food Delivery Business. [online video], available at: ,DOI: 18/11/2021

Meddings, S (2020) The dark kitchens cooking up a revolution; While restaurants struggle, the takeaway market is booming – and it’s feeding a new industry. The Sunday Times, August 23rd, pp.2-3.

PyData (2019) Jonny Brooks-Bartlett – How Deliveroo improved the ranking of restaurants – PyData London 2019. [online video], Available at:, doi: 18/11/2021.

Rose, G (2016) Rethinking the geographies of cultural ‘objects’ through digital technologies: Interface, network and friction. Progress in Human Geography, 40, (3), pp.334-351.

Walters, G & Crouch, G (2020) Will Deliveroo’s dark kitchens kill off your favourite restaurant? Investigation reveals meals from famous brands are being cooked in CAR PARKS and windowless ‘SHEDS’ – while delivery giant charges small family eateries sky-high commissions. The Mail on Sunday, 4th October, available at:, doi: 20/11/2021.

van Doorn, N & Badger, A (2020) Platform Capitalism’s Hidden Abode: Producing Data Assets in the Gig Economy. Antipode, 52, (5), pp.1475-1495.

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