Restaurant White Space Analysis: How to Find Delivery Coverage Gaps Before Opening a New Site

Restaurant white space analysis helps a chain find places where a new restaurant could add useful delivery reach without simply copying the coverage it already has. It combines five views: demand, competition, live delivery coverage, predicted reach and overlap with the existing network.

In one Manchester validation, Getplace predicted catchments for 39 McDonald's restaurant addresses and compared them with observed Deliveroo zones. The published test reported a 90% match. That result shows why catchment modelling can help before opening, but the site decision still needs more than a predicted boundary.

A blank area on a map is only the starting point. It may be an attractive opportunity. It may also have weak demand, difficult delivery economics or strong competition just beyond the boundary. The decision comes from reading the layers together.

Key points

• White space is not the same as empty space. A useful opportunity needs demand as well as limited effective supply.
• Store locations do not show who can actually deliver to a customer. Coverage must be checked by platform, location and time.
• A proposed site should be assessed on net-new reach, not only the size of its predicted delivery area.
• Some overlap can improve delivery times or resilience. Too much may redistribute orders between the chain's own restaurants.
• A delivery map supports the site decision. It does not replace rent, labour, kitchen capacity, access and first-party sales forecasts.

What is restaurant white space analysis?

Restaurant white space analysis is a structured way to find areas where customer demand may not be served well by the current restaurant and delivery network. For delivery-led expansion, it compares estimated demand with competitor supply, real service areas, a candidate site's likely catchment and overlap with existing restaurants.

The word "white space" can be misleading. It sounds like the task is to find an empty patch. In practice, the strongest opportunity may sit between several busy areas. The important question is not whether the map looks empty. It is whether a new site can reach enough incremental demand on workable terms.

That distinction matters for multi-location chains. A large candidate delivery zone can look attractive while covering many of the same households as nearby restaurants. The new restaurant may perform well on its own, yet add less to the network than expected.

A delivery coverage gap is not automatically an opportunity

Coverage answers one question: where can a customer order from a restaurant under the observed platform conditions?

It does not answer how many people want the product, how often they order, what competitors offer or whether a new kitchen can serve the area profitably. This is why coverage and demand must remain separate in the analysis.

The opposite problem also appears. An area may show strong total delivery demand, but that demand may be divided among hundreds of active restaurants. A busy market is not always an easy market for one more location.

This is the useful contrast in Denis Chernobaev's delivery-spending work: market size and likely performance per restaurant are different questions. A site team needs both before it treats a high-demand area as white space.

Five layers turn a blank map into a site decision

1. Delivery demand shows where customers already order

Start with a consistent unit such as a postcode, neighbourhood or grid cell. Estimate delivery demand for each area over the same period.

Total orders or spending show market size. Demand per active restaurant adds competitive context. Neither measure should be treated as a sales forecast for the proposed site, but together they show where further investigation is worthwhile.

2. Competitive supply shows who is already serving that demand

Count restaurants that can actually serve the area, not only those with a nearby street address. Separate direct category competitors from the wider marketplace.

A restaurant three kilometres away may be a strong delivery competitor on one platform and absent on another. A physical-store map cannot show that difference.

3. Live delivery coverage shows the market customers can access

Delivery areas can differ by store, platform, customer location and time. Measure them under comparable conditions.

Getplace's restaurant delivery coverage and in-app visibility tools compare delivery areas across platforms and track where boundaries change. For a site decision, the important outputs are the households or areas served, the gaps between the chain and its competitors, and the boundaries that move between observation windows.

4. Predicted catchment shows what the candidate site could add

Before the restaurant opens, the team needs a defensible estimate of its likely delivery area. This estimate can be compared with the chain's current coverage and with competitor reach.

The prediction is not the final platform zone. It is a planning input. Its value comes from making alternative sites comparable before the business signs a lease.

5. Overlap shows whether the reach is really new

Measure the part of the predicted catchment that is unique and the part already served by the chain. If possible, weight overlap by households or demand rather than land area alone.

Overlap is not automatically bad. A new site may shorten delivery times, add kitchen capacity or protect service during peak periods. But the benefit should be explicit. Otherwise, a large delivery zone can hide a small net addition to the network.

A practical white-space analysis workflow

Step 1: Write the decision before opening the map

Define the decision in one sentence. For example: "Which of these three candidate sites adds the most useful delivery reach without creating excessive overlap?"

Set the market, platforms, category, time window and planning horizon. If these change between candidates, the comparison will not be like-for-like.

Step 2: Build demand and competition on the same geography

Use the same postcodes, neighbourhoods or grid cells for both layers. Compare total demand with the number and type of restaurants competing for it.

This avoids a common mistake: choosing the largest market without checking how thinly its demand is distributed.

Step 3: Observe real delivery areas

Record where the chain and its competitors are available from representative customer locations. Repeat the observation across the platforms and time periods that matter to the decision.

A single lunchtime snapshot should not be used as a stable description of dinner coverage.

Step 4: Predict each candidate site's catchment

Create a predicted delivery area for every candidate. Use the same assumptions and model version for each site.

The goal is comparison. Which candidate reaches areas the chain does not already serve? Which one mainly sits inside the current network?

Step 5: Separate gross reach from net-new reach

For each candidate, report at least:

• total predicted area or households reached;
• unique area or households not currently served by the chain;
• overlap with existing restaurants;
• competitor coverage inside the candidate area;
• demand indicators for the unique and overlapping parts.

This is the point where a visually impressive zone can become a weak candidate.

Step 6: Add the operating constraints

The map is not the final decision. Add rent, labour access, kitchen capacity, road access, expected delivery time, opening hours and first-party unit economics.

A site with less theoretical reach may be the better choice if it serves the area more reliably or avoids putting pressure on nearby franchisees.

The Manchester test shows why catchment prediction matters

Getplace tested this approach using 39 McDonald's restaurant addresses in Manchester. The model predicted an individual delivery catchment for every address, then compared those predictions with the real McDonald's delivery map on Deliveroo.

The published Manchester case study reported a 90% match between the predicted and observed zones. The difficult part was not drawing the outer edge of the network. It was estimating the boundaries between neighbouring restaurants.

The result matters because a candidate can be assessed while it is still a pin on a map. A team can compare likely reach and overlap before the restaurant opens.

There is an important limitation. The public case study does not publish the complete calculation behind the 90% figure or the exact observation date. It should therefore be read as the result of that Manchester test, not as a universal accuracy guarantee for another city, brand or platform.

How to compare candidate restaurant sites

A useful comparison should answer five questions in plain language:

1. Demand: Is there enough delivery activity in the area to justify deeper review?
2. Competition: How many relevant restaurants can already serve the same customers?
3. Coverage: Which households can the chain and its competitors reach on each platform?
4. Overlap: How much of the candidate's predicted reach is genuinely new to the network?
5. Economics: Can the site serve that reach with workable delivery times, capacity and unit economics?

The winning site is not necessarily the one with the largest circle. It is the one that gives the network the strongest combination of incremental reach and operational fit.

Teams planning several openings should also compare candidates at network level. Two individually attractive sites may overlap heavily with each other. Evaluating them one at a time can overstate the total opportunity.

For a deeper explanation of that risk, see Getplace's guide to delivery-zone overlap and cannibalisation.

What white-space analysis cannot prove on its own

White-space analysis narrows the decision. It does not remove uncertainty.

• Coverage is not demand. Being able to order does not mean customers will order.
• Estimated demand is not a sales forecast for the proposed restaurant.
• A predicted catchment is not an observed platform boundary.
• Geographic overlap is not the same as transferred sales.
• A marketplace snapshot is not a stable trend.

The analysis becomes stronger when it is connected to first-party orders, conversion, delivery times, kitchen capacity and local operating costs. This is the same principle described in Getplace's broader guide to restaurant delivery analytics: outside-marketplace data helps teams test explanations, but it does not prove causation by itself.

White space is the reach the network can use

Restaurant white space analysis is not a search for empty map tiles. It is a way to find incremental reach that the chain can serve well.

Start with demand. Check the competition customers can actually access. Observe real delivery areas, predict the candidate catchment and measure how much of that reach is new. Then put the map beside the operating economics.

That process will not make the location decision automatically. It will make the trade-offs visible before the expensive part begins.

If your team is comparing candidate sites, Getplace can map the relevant demand, coverage and overlap for the cities and platforms in scope.

About the evidence

This is a method-led guide and does not introduce a new market dataset. Its central example uses Getplace's published Manchester validation, which tested predicted catchments for 39 McDonald's restaurant addresses against observed Deliveroo delivery zones and reported a 90% match. The public case study does not state the full match calculation or exact observation date. Product descriptions reflect the Getplace website and visibility page accessed on 21 August 2026.

Sources

1. Getplace. Know where a new restaurant will deliver before it opens. 4 August 2026.
2. Getplace. Delivery coverage and in-app visibility. Accessed 21 August 2026.
3. Getplace. What Is Restaurant Delivery Analytics? Pricing, Coverage and Visibility Explained. 16 August 2026.