How Delivery Platforms Cost Londoners Money and the Case for a Civic Data-Supported Platform Cooperative
A Search Sciences™ Research Paper
Verified Baseline: 24 January 2026
Scientific Lead: Mohammed Younis, Chief Scientist, Search Sciences™
Abstract
As of 2026, the proliferation of digital delivery platforms has fundamentally changed urban commercial ecosystems. While these services purport to support convenience and market access, they generate systemic economic leakage from local businesses and communities. This white paper applies the Search Sciences™ framework to explain how structural information barriers and data poverty create dependency on extractive intermediaries such as Deliveroo, Uber Eats and Just Eat. These platforms consolidate demand data, maintain information asymmetry and impose disproportionately high commission costs. We argue that the architecture of local business information, when unstructured and fragmented, restricts alternative discovery pathways for both consumers and autonomous systems. Leveraging the new Data for London Library, we propose a model for a platform cooperative grounded in a high-fidelity civic data commons. Our analysis suggests that by restructuring local discovery information, London could reduce revenue leakage and promote economic inclusion. Early audits reveal the potential for significant increases in direct recommendation probability when machine readability is improved across local data. This paper sets the stage for a broader research programme on the role of structured civic data in restoring economic sovereignty to city economies.
Introduction
In London’s dynamic urban economy, digital delivery platforms such as Deliveroo, Uber Eats and Just Eat have become central to how residents order food, groceries and everyday goods. These services are marketed on convenience and reach, yet beneath this promise lies a significant economic transfer away from local businesses and households into platform revenue and marketing fees. The models underlying these platforms are not only economic phenomena but also problems of how information about businesses and services is structured, accessed and interpreted by machines.
By early 2026, the cumulative effect of delivery platform economics and the associated information asymmetry has reached a noticeable tipping point. The price of convenience is increasingly paid in the form of data poverty for local communities. Local restaurants and small enterprises are left with high commission fees and poor direct visibility while consumers often pay more for identical items through apps compared with direct transactions.
This paper applies the principles of Information Science and Search Sciences™ to this issue. It demonstrates that solving the economic and social challenges posed by delivery platforms requires rethinking how local business information is made legible, structured and shared. A structured civic database could provide the foundation for a platform cooperative that retains economic value locally and improves how machines interpret London’s business ecosystem.
The Economics of Delivery Platforms
Commission Structure and Cost to Restaurants
Delivery platforms typically extract significant revenue from every order processed through their systems. In London, restaurants partnering with platforms usually pay between 25 per cent and 35 per cent commission on each order. Many businesses additionally incur onboarding fees for equipment and marketing tools. On average, restaurants must raise their menu prices on platform listings by roughly 20 per cent to offset these costs.
For a sector where net profit margins often sit in single digits after rent, staff and ingredient costs, this so-called platform tax dramatically reduces profitability. In some instances, the cumulative effect of commissions, value added tax and indirect operating costs can mean that restaurants effectively surrender over 40 per cent of an order’s value to platform providers before any other costs are considered. This level of extraction weakens the financial position of local businesses and distorts local price structures.
Consumer Impact
The costs to consumers using delivery apps are equally stark. Research by a respected consumer organisation shows that the same meal can cost significantly more when ordered through an app compared with ordering directly from the restaurant. The difference arises because restaurants pass on some of their commission burden into menu pricing on the platforms, and customers also pay platform service fees. In many cases, ordering through an app can increase the cost by 20 per cent to 40 per cent or more for the same items.
A survey of UK customers found that a large proportion were unaware of how much more they were paying through delivery apps. Three quarters of respondents expected to pay less or the same when ordering via an app compared with dining in or ordering direct, yet the reality was that prices were often significantly higher.
These price differences are not limited to restaurant meals. Investigations comparing supermarket grocery items purchased through rapid delivery services with prices in stores have found that some grocery items cost significantly more when ordered via apps, reinforcing that the cost burden affects a broad range of household spending.
Information Poverty and Discovery Barriers
The Information Monopoly
The dominance of delivery platforms is not purely an economic outcome. It is also a consequence of how local business information is structured, accessed and interpreted by modern discovery systems. Many local restaurants and services suffer from data poverty. Their business credentials, real-time availability, pricing, hygiene ratings and service specifications often exist in fragmented, unstructured formats that are difficult to interpret automatically by search engines, AI assistants or autonomous agents.
In this environment, delivery platforms function as the sole reliable aggregators of local transactional data. They maintain rich, proprietary records of demand patterns, consumer preferences and merchant performance that are invisible to other discovery systems. This gives them a form of information asymmetry in which restaurants must pay for exposure and visibility without having equivalent access to the aggregated insights. Paying for featured listings and promotions becomes a way for businesses to regain a fraction of the visibility they would otherwise have by earning high referrals and recommendations through algorithmic systems.
The problem is not lack of local data, but lack of structured, interconnected and machine-readable local data that can be integrated into broader discovery ecosystems outside of individual proprietary platforms.
The Role of Civic Data Infrastructure in London
Data for London Library
In 2025, the Greater London Authority launched the Data for London Library, a platform designed to make city data more accessible, discoverable and usable. The Library aggregates thousands of datasets from public bodies, councils, transport authorities and statistical sources, creating a shared resource for policymakers, researchers, entrepreneurs and community organisations.
This initiative recognises that high-quality, structured data is a form of infrastructure that underpins public services and economic innovation. Importantly, the platform is built to support discovery — helping users find useful datasets whether they are open or governed under secure sharing agreements. Its growth is meant to support smarter, fairer, AI-enabled services by ensuring that representative data is discoverable and trustworthy.
The emergence of this type of civic data infrastructure in London provides the foundational building block required to address information poverty at scale. It demonstrates a commitment to opening and structuring local data so machines and humans alike can interpret it accurately and consistently.
The Case for a Platform Cooperative Supported by Civic Data
Eliminating the Discovery Premium
A platform cooperative owned by participating businesses, consumers and potentially civic stakeholders could use a shared, verified database of local entities — drawn from a structured civic data hub — to power discovery and routing. If AI assistants and search systems can match consumer intent directly with local business offerings through a verified data source, the need to rely on extractive intermediaries is significantly reduced.
By eliminating the discovery premium that currently accrues to commercial platforms, a cooperative model could facilitate direct connections between buyers and sellers without imposing disproportionate costs.
Reducing Commissions
Prototype cooperative models and alternative delivery services have shown that when data is shared instead of siloed, commission rates can be drastically reduced. By agreeing to common infrastructural costs and using shared logistics, cooperatives can set administrative fees that are a fraction of the levels seen in commercial delivery platforms. Without a heavy need to extract value for distant shareholders, more of the transaction value stays within local businesses and communities.
Restoring Price Transparency
A shared data model that includes up-to-date pricing and availability removes much of the hidden mark-ups that currently frustrate app users. When users can access verified pricing and stock information through machine-readable databases, they can make informed choices and are less likely to pay inflated prices that result from opaque secondary charges.
Such transparency also supports civic goals by helping residents understand economic choices in the context of the broader cost of living.
Conclusion: London as a Benchmark for Information Sovereignty
London is uniquely positioned to lead a shift from extractive digital platforms to a system rooted in a semantic commons. The development of structured, machine-readable civic business data enables a new generation of discovery systems that serve local communities fairly. When high-quality data is available and discoverable, artificial intelligence and advanced search systems can produce accurate, context-rich results — not only improving discovery but also enhancing economic fairness and local inclusion.
This approach aligns with the core philosophy of Search Sciences™: that improving the quality, structure and accessibility of information does not merely optimise search results but improves the environments in which economies, services and communities function.
Technical Addendum: Metadata Standards for a London Civic Data Commons
To enable a platform cooperative to compete with global delivery algorithms, the civic database must adopt a high-fidelity entity framework that ensures local businesses are machine interpretable by AI assistants and autonomous procurement systems.
Core Entity Standards
| Metadata Layer | Standard Adopted | Purpose |
|---|
| Identity and Trust | GS1 GTIN and GLN | Provides globally unique identification for products and physical business locations |
| Service Context | Schema.org/Restaurant | Enables structured capture of menus, opening hours and cuisine types |
| Economic Logic | Schema.org/Offer | Defines pricing, availability, and cooperative-specific incentives |
| Civic Compliance | Local Borough Data | Includes verified licensing information, hygiene ratings and sustainability certifications |
Structural Interoperability
The database must use faceted taxonomies so that entities are represented across multiple semantic axes:
- Temporal: Real-time capacity and lead times
- Geospatial: Hyperlocal delivery zones defined by spatial polygons instead of simple postcodes
- Relational: Explicit links between producer, cooperative rider, and consumer
Chief Scientist Oversight
Scientific Lead: Mohammed Younis, Chief Scientist, Search Sciences™
Methodological Basis: This paper utilises the Structural Information Integrity Framework to analyse local economic leakage.
Data Verification: Current as of 24 January 2026.
References
- London Data Commission. (2025). Data for London Library: A Framework for Civic Digital Infrastructure. Greater London Authority.
- Which? (2025). The Hidden Costs of the Food Delivery Revolution. Which? research on delivery app pricing disparities.
- Best In London. (2025). Food-Ordering Platforms Guide for London Restaurateurs. Analysis of commission structures.
- Younis, M. (2026). Search Sciences™: Resolving Semantic Ambiguity in Local Urban Discovery. Younis Group Research Series.