The Germany Fast Food Pricing Benchmark: McDonald's edition.
- 1,366 McDonald's restaurants
- 314 analyzed menu products
- 76,648 matched price pairs
- 16 German regions
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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 just a sample of the many we cleared. You open the file and start clean.
One item, few categories.
McDonald's shows one product in several spots at once - the main category, plus "Highlights" and "Extras." Pull the data and one restaurant counts the same burger two or three times. Your average is off before you start. We removed the repeats and kept each item once.
Categories you can't trust as they come.
The categories in the raw data don't match the ones on the menu. You can't rely on them, so every product has to be placed in the right category by hand. Skip it and your category numbers are wrong. We sorted every item ourselves, one by one.
Same product, two prices a day.
The same item can have a different ID and a different price depending on the time of day - a Cheeseburger costs one thing at breakfast, another later on. Mix the two and the price is meaningless. We separated them so every price means one clear thing.
Prices that are clearly wrong.
A few prices in the raw data make no sense - one venue listed a drink at 401 EUR. Leave them in and they drag every average with them. They're easy to miss in half a million rows. We found these outliers and took them out.
Venues matching across apps.
The same restaurant looks different on the app, on Wolt, and on Uber Eats - different names, slightly different locations. Linking them into one venue takes checking by hand, case by case. Get it wrong and you compare the wrong restaurants. We matched every venue across all three.
Six things you don't currently know about McDonald's pricing in Germany.
We matched 76,648 prices across every McDonald's in the country, and the data didn't say what we expected. These are six of the many findings in the report - the rest are inside.
Made for two sides of the same market.
One side sets the menu price. The other adds a markup on top. The same matched data shows both exactly what's happening across all 1,366 restaurants - where margin sits, where coverage is thin, and what the other side is doing about it.
For food delivery platforms
-> See how your markup compares to the market leader's, by product and by state, instead of guessing at one number.
-> Get a ready list of restaurants a rival already proves deliverable - your shortest path to more coverage.
-> See which items already carry a heavy markup and which stay close to the menu price.
-> Walk into franchise talks already knowing what the other app charges down the street.
For restaurant chains
-> See exactly what each app adds on top of your price, one restaurant at a time, before your next contract.
-> Identify which low-ticket items the platforms inflate hardest, so you know where to set contractual price floors and where it doesn't matter.
-> Know what 23% of your catalogue going dark on delivery actually costs you, or earns you, in margin protection.
-> Find your outlier restaurants before a customer posts the price gap for you.
This isn't a survey. Here's exactly how it was built.
Most "benchmarks" are repackaged surveys. This is raw price data, collected and cleaned by hand. Every step is below, and every step is in the report - so you can check the numbers instead of trusting them.
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."
What readers said about our previous reports.
Frequently Asked Questions
Want to talk it through first?
Questions about the data, the methodology, or which tier fits your team? Write to us and we'll answer - no sales script, just the person who built it.