How Delivery Platforms Can Find Merchant Coverage Gaps by City and Category

Delivery platforms find merchant coverage gaps by measuring what customers can actually order at fixed locations and times, then comparing that supply by city, neighbourhood, category and competitor. A useful gap is not simply a district with few restaurant pins. It is an address-and-time segment where the platform’s own demand evidence indicates a need, but relevant, available or distinctive merchant choice is weak.

This turns merchant acquisition from citywide list building into a more precise marketplace decision: which merchant, in which category and area, would add the most useful customer choice?

Key points

• Citywide merchant totals can hide neighbourhoods with weak choice.
• A listed restaurant does not always mean an available restaurant. Coverage must be observed from customer locations and at relevant times.
• Category presence and category depth are different. One available option may technically fill a category without creating meaningful choice.
• Competitor supply can reveal acquisition candidates, but a popular rival-platform merchant is not automatically incremental for your marketplace.
• The best acquisition target improves access, category choice or availability in specific address-and-time segments.
• Coverage is not demand. Platform teams should combine the outside marketplace view with their own search, session and order signals.

What is a merchant coverage gap?

A merchant coverage gap is a part of a delivery marketplace where available restaurant supply falls short of a defined customer or commercial need. The gap may affect a whole district, one category, a daypart or a set of customer addresses.

This definition is narrower than “we need more restaurants”. More merchant records do not always create a better marketplace. Ten similar restaurants added to a well-served centre may change the supply count while doing little for customers in an outer neighbourhood with limited dinner options.

For platform teams, the useful unit is not the city alone. It is the combination of customer location, observation time, category and available merchant.

Six merchant coverage gaps that platform teams should separate

1. Platform footprint gap

The platform does not serve a city, district or customer location at all. This is the broadest gap and usually requires a market or service-area decision before merchant acquisition can help.

2. Address-level reach gap

The platform operates in the city, but customers at particular addresses cannot access enough relevant merchants. Store pins may sit nearby while their live delivery areas stop short of those customers.

3. Category gap

A category that matters to local customers is absent or materially under-represented. The category definition must be consistent. “Asian”, “Japanese” and “sushi” cannot be compared reliably if each platform classifies the same restaurants differently.

4. Category-depth gap

The category exists, but choice is thin against the platform’s own target or a relevant competitor benchmark. One burger restaurant and ten burger restaurants both count as category presence, but they create different marketplace experiences.

There is no universal number that defines enough choice. The threshold should reflect the market, category, customer demand and service standard being assessed.

5. Availability gap

Merchants are listed but unavailable when customers want to order. A category may look healthy in a static merchant database and weak during breakfast, late evening or a weekend peak.

6. Competitive supply gap

A competing platform offers accessible merchants or category choice that the platform does not. This can create a focused acquisition list, but it still needs an incremental-value check. Signing a competitor-only merchant may strengthen the offer, or it may mostly add more supply to an already crowded category.

Why citywide merchant counts hide the real supply problem

A city total combines strong and weak areas into one number. It also mixes merchants that are available to many customers with merchants whose delivery reach is narrow or inconsistent.

Getplace’s published posts have examined new venue additions by London district and category, city footprint growth in Poland and platform-specific delivery areas in London. The underlying datasets were not supplied for this guide, so their figures and market conclusions are not reproduced here.

The useful method is to compare the structure of supply, not only its total size.

How to run a merchant coverage-gap analysis

Step 1: Define the acquisition decision

Write the decision before collecting data. For example: “Which dinner merchants should we acquire to improve choice in the north of this city?”

Set the city, customer areas, platforms, categories, observation times and review period. A broad request to “find supply gaps” will otherwise produce a broad list with no clear priority.

Step 2: Build a stable customer-location grid

Choose a repeatable set of customer locations across the city. These may be postcodes, neighbourhood centres or smaller grid cells, depending on the decision.

Use the same locations for every platform and observation period. A restaurant address is not enough because delivery availability depends on where the customer is ordering from.

Step 3: Observe the live customer-facing marketplace

For each location and time, record which merchants are available, their categories and the service details needed for the decision. Keep unavailable listings separate from merchants that can accept an order.

Repeat observations across relevant dayparts. A lunch-only view should not be used to describe late-evening category coverage.

Getplace’s delivery coverage and in-app visibility work applies this customer-location view across platforms and observation times.

Step 4: Normalise merchants and categories

Match the same restaurant across repeated observations and competing platforms. Separate individual outlets from brands, and flag virtual brands or multiple listings that may share one kitchen.

Then apply a consistent category taxonomy. Without this step, apparent category gaps may come from naming differences rather than real customer choice.

Step 5: Measure supply at the address-and-time level

For each location, category and observation time, calculate the measures that match the decision. Useful measures include:

• number of available outlets;
• number of distinct brands;
• share of eligible observations with no available option in the category;
• availability consistency across dayparts;
• merchants accessible on a competitor but not on the platform;
• customer locations that a proposed merchant could serve, based on an explicitly defined coverage estimate.

Report the denominator behind every share. “Twenty per cent of areas have a gap” is not useful unless the team knows which areas, times and category rules were included.

Step 6: Add demand and marketplace context

Coverage shows what customers could order. It does not show what they wanted to order.

Platform teams can add first-party evidence such as category searches, sessions with little relevant choice, abandoned journeys, order frequency and customer requests. External demand estimates may add context, but they should not be presented as observed platform demand.

This step prevents a visually empty map from becoming an automatic acquisition priority.

The same distinction is central to restaurant delivery analytics: coverage describes what was accessible under the observed conditions, while demand and performance need their own evidence.

Step 7: Rank merchants by incremental marketplace value

A useful acquisition score should reflect what the merchant changes, not only its brand size. Review:

• customer locations that could gain a new relevant option;
• category or daypart gaps the merchant could reduce;
• competitor-only coverage it could address;
• expected availability and operational fit;
• overlap with similar merchants already on the platform;
• commercial effort required to sign and activate the merchant.

The result should be a short, explainable list. A city lead should be able to see why merchant A could improve dinner choice in weak neighbourhoods while merchant B mainly duplicates an already dense central category.

This incremental-value check is related to Getplace’s guide to incremental versus overlapping supply, but the platform decision here begins with customer choice and merchant acquisition.

Step 8: Measure the result after launch

Repeat the same location-and-time observations after the merchant becomes active. Check whether customer access, category depth and availability improved in the intended areas.

Then use first-party performance data to test whether the change improved customer behaviour and marketplace economics. A coverage analysis can show that choice expanded. It cannot prove incremental orders or revenue on its own.

Merchant coverage analysis is not restaurant white-space analysis

The two methods use some of the same maps, but they answer different questions.

Restaurant white-space analysis asks where a chain might open a site that adds useful delivery reach without excessive overlap. Merchant coverage analysis asks where a delivery platform lacks enough relevant supply and which acquisition target could improve the marketplace.

The buyer, decision and success measure are different. Keeping them separate prevents this article from competing with site-selection content and helps teams choose the right analysis.

Common mistakes in platform coverage-gap analysis

• Counting listings instead of live availability: A catalogue can overstate the choice a customer sees.
• Using one city total: Strong central supply can hide weak outer areas.
• Treating all categories as equal: The right depth depends on customer demand and the commercial role of the category.
• Ignoring time: Breakfast, lunch, dinner and late-night coverage may tell different stories.
• Skipping merchant matching: Duplicate or inconsistent listings distort platform comparisons.
• Equating supply with demand: An uncovered area is not automatically a valuable market.
• Rewarding gross additions: A new merchant may increase the count without improving access or distinctiveness.

Better merchant acquisition starts with a specific gap

The practical question is not “How many merchants does this city have?” It is “Where, when and in which category does the customer lack relevant choice?”

Answer that at customer-location level, compare the result with competing platforms, add demand evidence and rank merchants by the access or choice they could add. That makes acquisition priorities easier to explain and the post-launch result easier to measure.

Getplace can help delivery-platform teams compare merchant supply, delivery coverage and availability across the cities, categories, customer locations and times that matter to the decision.

About the evidence

This is a method-led guide and does not introduce a new market dataset. It draws on Getplace’s public delivery-coverage capability and Denis Chernobaev’s posts about merchant growth, category mix and platform-specific coverage. The posts were used as editorial starting points only. Their figures were excluded because the underlying datasets, samples and collection methods were not supplied for this article.

Sources

1. Getplace. Delivery coverage and in-app visibility. Accessed 24 August 2026.
2. Getplace. What Is Restaurant Delivery Analytics? Pricing, Coverage and Visibility Explained. 16 August 2026.
3. Getplace. Cannibalization in Delivery Apps: What It Is and How to Measure It. 29 June 2026.
4. Denis Chernobaev. Deliveroo venue additions by London district and category. LinkedIn, 27 May 2026.
5. Denis Chernobaev. Wolt and Glovo venue growth in Poland. LinkedIn, 21 May 2026.