Delivery time: 26 minutes. Delivery fee: 7.0 PLN. That’s what a customer sees when ordering from your restaurant on Bolt at 10 AM on a Tuesday. By 4 PM, the same customer, ordering from the same restaurant on the same platform, sees: 34 minutes and 9.0 PLN. Different time slot, same platform – conditions already changed significantly.
Now add five platforms, a dozen competitor chains, and ten cities across Europe. The picture becomes complex fast.
Delivery Conditions Are Not Static
Most restaurant chains still treat delivery conditions as a background variable. They know their own fees, they’ve agreed on terms with platforms, and they assume competitors operate in roughly the same way. That assumption is increasingly wrong.
Food delivery platforms adjust conditions dynamically. Delivery time estimates change based on courier availability, traffic, order volume, and weather. Fees shift depending on distance, peak demand, and platform-side promotions. Minimum order thresholds vary by location. And when courier availability drops or weather deteriorates, platforms will throttle or cut a restaurant’s visibility entirely – removing it from search results or marking it as unavailable, regardless of whether the restaurant itself is open.
The result: at any given moment, the competitive delivery landscape in a single city looks different than it did two hours ago.
For a restaurant chain managing dozens or hundreds of locations, this creates a real intelligence gap. You know your own conditions. You don’t know what a customer sees when they compare you to Burger King, KFC, or the local pizza chain on the same platform at the same time.
What Hourly Scraping Actually Captures
Hourly scraping of food delivery platforms means simulating what a real customer sees, systematically and at scale. The data collected includes:
Estimated delivery time (ETA). Not the theoretical estimate, but the actual time displayed to a customer at a specific location, at a specific time of day. This varies significantly across platforms and time slots. In our data, a single chain’s ETA on Wolt during a dinner rush (6 PM) was consistently 15-20 minutes longer than on Bolt at the same hour.
Delivery fees. These differ not just between platforms but within the same platform depending on distance configuration and active promotions. A customer 1.5 km from your restaurant may see a different fee than one 2.5 km away. Tracking this across competitors gives you a genuine pricing benchmark.
Availability. Is a competitor’s restaurant open on a given platform at 10 PM? At midnight? Availability gaps create opportunities that most chains miss because they don’t know they exist.
Promotional positioning. Platforms surface certain restaurants higher in search results or mark them with badges (free delivery, top seller, Prime discount). Monitoring this over time reveals how competitors are investing in platform visibility and when promotions are active.
Minimum order values. A small difference in minimum order thresholds can meaningfully shift conversion, especially in lower-price categories.
The Business Value: Three Practical Applications
1. Real-time competitive benchmarking
When you can compare delivery conditions across chains and platforms on an hourly basis, benchmarking stops being a quarterly exercise. You start seeing patterns: which competitors consistently offer faster ETAs, whether that’s a function of platform partnerships or courier density. You see when delivery fees spike for everyone (signaling platform-wide changes) versus when only one competitor’s fees change (signaling targeted promotions or renegotiated terms).
This kind of data allows for faster, more grounded responses. If a competitor runs a free-delivery promotion on Glovo every Thursday evening, you can decide deliberately whether to match it, counter it, or let it pass.
2. Identifying platform-specific performance gaps
One consistent finding from monitoring across European markets: a chain’s performance varies significantly between platforms, even for the same location. A restaurant that ranks prominently on Uber Eats can be effectively invisible on Wolt if delivery conditions are unfavorable. Hourly data reveals these gaps systematically, not by accident.
For chains operating across multiple countries, the variation is even more pronounced. The competitive dynamics on Glovo in Warsaw look different from those in Madrid or Bucharest. Having the data to map those differences is the first step to acting on them.
3. Alert-based reaction to competitive moves
Static weekly reports are useful. Automated alerts are more useful. When you’re monitoring delivery conditions hourly, you can configure triggers: if a competitor’s ETA on platform X drops below a threshold in a given city, flag it. If a new chain appears in a location with aggressive delivery fee positioning, flag it. The response cycle compresses from weeks to hours.
The Methodology: How It Actually Works
The technical approach involves simulating customer sessions from multiple geographic points within each city. Rather than scraping from a single IP or location, the system deploys agents across different locations within a metropolitan area, capturing the conditions a real customer would see from their specific address.
This matters because delivery conditions are often distance-dependent. A customer 800m from a restaurant sees different terms than one 3km away. Monitoring only one data point per city gives an incomplete picture.
Data is collected across all major platforms: Glovo, Uber Eats, Wolt, Bolt, Just Eat / Pyszne.pl, Deliveroo, and Delivery Hero, depending on the market. The output is structured CSV files or dashboard-ready data, updated every hour, covering all tracked locations and competitors.
No IT integration on the client side is required. The data lands in agreed formats, ready to load directly into existing BI tools or reporting workflows.
What the Data Reveals Over Time
When you accumulate weeks and months of hourly delivery data, patterns emerge that aren’t visible in any single snapshot.
You can see whether a competitor’s peak-hour ETAs are improving or deteriorating over time, which suggests changes in their courier network or platform investment. You can identify seasonal patterns in delivery fee behavior across the market. You can map which platforms are gaining traction with your competitor set in specific cities, before that shift shows up in any industry report.
This is what turns delivery monitoring from a tactical tool into a strategic one. The hour-by-hour data is useful for immediate decisions. The accumulated dataset is useful for understanding where the market is going.
A Practical Example
Consider a QSR chain operating in five Polish cities. They track delivery conditions for themselves and four competitors across three platforms, every hour, seven days a week.
Over three months, the data shows: on Wolt, one competitor consistently achieves 5-8 minute lower ETAs during Friday evening peaks. The chain’s own ETA is acceptable during off-peak hours but degrades under volume. Cross-referencing with the platform’s own restaurant ranking data, they can see this competitor has invested in Wolt’s priority courier program.
That’s an insight a quarterly review would miss. The response, whether it’s a platform negotiation, a staffing adjustment during peak hours, or a promotional counter-move, becomes grounded in actual observed market behavior rather than intuition.
The Competitive Context
Food delivery in Europe is maturing. The platforms are increasingly sophisticated in how they manage their restaurant ecosystems, and the conditions they offer are part of a dynamic negotiation. Chains that understand the real-time delivery landscape have a structural advantage in those negotiations. Chains that rely on static benchmarks are working with a map that’s already out of date.
Hourly scraping of delivery conditions isn’t a niche analytical exercise. For any chain with meaningful delivery volume and competitive exposure across multiple platforms and cities, it’s becoming a baseline operational capability.
The data exists. The question is whether you’re collecting it.
