The Germany Fast Food Pricing Benchmark: Subway edition.
- 649 Subway restaurants
- 132 analyzed menu products
- 40,860 matched price pairs
- 16 German federal states
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If you've tried to analyze this data yourself, you've hit these walls.
Anyone who pulls fast food pricing data runs into the same problems, and each one quietly breaks the numbers if you miss it. These five are a sample of the many we cleared on Subway. You open the report and start clean.
One name, three products
On Uber Eats the name "Ham" covers the Ham sub, the Ham salad and the Ham wrap. 18 of the 149 product names on Uber Eats cover more than one product. Match by name and you compare a salad with a sandwich. We matched every item by product, never by name.
A €99 price that means "unavailable"
64 party platters are listed at €99 to €100 on the delivery apps, against €29 to €47 in the Subway app. All of them sit on round numbers. They are not prices, they mark an item as unavailable. Leave them in and two states gain several points of price premium they never had. We took them out and the report shows what they would have changed.
Blank is not zero
274 Uber Eats rows carry a Subway app price and nothing on the app side. Count them as zero and the price premium in Bayern, Baden-Württemberg and Berlin drops by four to five points. A missing price means a missing product, not a free one. None of these rows enters a comparison.
A €1.20 salad
One restaurant in Bonn lists a salad at €1.20, while its other 46 products run €3.99 to €7.89. A single record like that sets the national minimum for the whole product. We checked every extreme price against the rest of its own restaurant's menu. The Bonn record went out. A €12.99 salad in Monheim stayed in, because that restaurant prices 15 of its 16 salads at exactly €12.99.
One restaurant, three addresses
The same Subway has a different name and a slightly different location on each app. One restaurant in Augsburg sits 195 metres from its own address on Uber Eats and 440 km away on Wolt, because of a wrong coordinate. We matched all 649 restaurants by address and position, and checked by hand every match more than a kilometre away.
What 40,860 Subway prices actually show
We expected a chain story. The data told a franchise story, restaurant by restaurant. Here are six findings, each with the page where the full answer sits. There are many more in the report.
One dataset, both sides of the counter
A franchise sets the menu price. A platform lists it with a premium. Both are looking at the same 649 restaurants from opposite ends, and both need the same three answers: how big the premium is, where the apps don't reach, and what the other app charges at the same address.
For food delivery platforms
-> Put your price premium next to the other app's, on the same products in the same restaurants, state by state.
-> Get the 150 Subway restaurants that are on Uber Eats and not on Wolt, with addresses, and the 17 that go the other way.
-> See exactly where coverage drops off: 92% of restaurants in cities above half a million people are on at least one app, 19% in towns below 50,000.
-> Know, before the next franchise meeting, what the other app lists at that exact restaurant, to the cent.
For restaurant chains
-> Read what each app lists on top of your price, restaurant by restaurant, with the address next to every number.
-> See which 7 subs and which whole category never reach either app, and what that does to a family order.
-> See the 14 restaurants that hold every highest price in the country, street by street.
-> See how far your own prices already move before any app is involved: 56 prices on one sandwich, and 19% between the cheapest and the most expensive state.
Built from prices, not from opinions
No panel, no questionnaire, no estimates. Every number comes from a price that was listed in a real app for a real restaurant. The six steps below are the same six steps on page 2 of the report, so you can check each one yourself.
What readers said about our previous reports.
"I read the report and found it very insightful. What caught my attention the most was the mapping of areas with limited restaurant coverage. I think we could leverage a similar approach for lead generation campaigns- identifying underserved areas before competitors do and reaching out to potential prospects first. This could give us a meaningful first-mover advantage in those markets."
"Interesting selection analysis for UE Selection"
"The report is an absolute gold mine. It don't just show data, but technically tell new market entrants exactly what to do and where the hidden opportunities are."
"The work you did with the Uber report was great, really strong quality, and a very solid output."
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