Scrape Restaurant Data Across Spain And Portugal

By Author: Actowiz Metrics
Total Articles: 305
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Client Overview
A leading food delivery brand wanted to improve its understanding of restaurant offerings, pricing, promotions, availability, and competitive positioning across the Iberian food delivery market. The client was operating across multiple locations and needed a consistent way to monitor restaurant information from major digital delivery platforms without relying on repetitive manual research.
Actowiz Metrics implemented Scrape Restaurant Data Across Spain and Portugal to create a structured restaurant intelligence framework covering menus, products, prices, ratings, delivery information, restaurant categories, promotional offers, and availability. The solution helped organize fragmented platform information into standardized datasets that could be compared across cities, restaurant types, and competitors. The project also strengthened Price & promotion intelligence by tracking changes in menu pricing, discounts, promotional offers, and product availability. This enabled the client to better understand market positioning, identify competitive pricing patterns, and support data-driven decisions around ...
... restaurant partnerships, assortment, and commercial strategy.

Objective
The project was designed to provide the client with reliable restaurant and menu intelligence across Spain and Portugal while reducing the effort required for recurring competitive research. The key objectives included:
Expand market visibility: Establish comprehensive Spain and Portugal restaurant data collection across relevant cities, restaurant categories, and delivery platforms.
Monitor restaurant listings: Capture restaurant names, locations, cuisines, ratings, delivery information, operating status, and other accessible listing attributes.
Track menu pricing: Collect product-level menu prices and identify changes across restaurants and locations.
Analyze promotions: Monitor discounts, special offers, meal deals, promotional labels, and other pricing incentives.
Compare competitors: Enable restaurant-level and product-level comparisons across different delivery platforms.
Track availability: Identify available, unavailable, newly listed, and removed menu products.
Standardize datasets: Normalize restaurant, menu, pricing, category, and location information into a consistent analytical structure.
Support recurring intelligence: Build a scalable framework capable of repeated monitoring across multiple cities and restaurant categories.
Improve decision-making: Provide structured data that could support pricing strategy, competitor research, restaurant selection, and commercial planning.
The overall objective was to convert fragmented restaurant-platform information into a centralized and actionable competitive intelligence resource.
Data Extraction Scope
Platforms Monitored
The project monitored major food delivery platforms relevant to the client's competitive landscape, including Glovo, Uber Eats, and Just Eat. Restaurant listings and menu pages were analyzed to capture available restaurant information, cuisine categories, menu products, pricing, ratings, promotions, delivery details, and availability. Platform-specific extraction logic helped accommodate differences in page structures and listing formats.
The collected records were subsequently standardized to support restaurant-to-restaurant and product-to-product comparisons.
Time Duration
The monitoring framework was designed for recurring collection rather than a one-time snapshot. Each extraction cycle generated timestamped records that could be compared against previous observations. Historical data helped identify changes in menu prices, newly introduced products, removed items, restaurant availability, ratings, promotions, and other market signals. Maintaining historical records also allowed the client to distinguish temporary promotional changes from longer-term pricing movements.
Number of SKUs / Categories
The extraction scope covered multiple restaurant categories, cuisines, menu sections, and food products. Menu items were organized using attributes such as restaurant name, product name, category, product description, price, portion size, and availability where accessible. This approach allowed the client to compare similar products across restaurants while reducing inconsistencies caused by different naming conventions.
Additional cities, restaurants, categories, and menu items could be added as the monitoring requirement expanded.
Frequency of Tracking
Data collection was scheduled according to the client's monitoring priorities. High-value restaurants, categories, and products could be tracked more frequently, while broader market coverage could follow a periodic schedule. Each cycle captured current restaurant and menu information and compared it against historical records. Automated scheduling reduced manual effort and helped identify price changes, availability shifts, new promotions, and assortment updates more efficiently.
The overall scope created a scalable framework for collecting restaurant intelligence while supporting market-level, restaurant-level, and product-level analysis.
Data Points Collected
The project captured ten core restaurant and menu attributes to create a structured competitive intelligence dataset:
Restaurant Name - Identified the restaurant or food-service business being monitored.
Restaurant Location - Recorded city, area, or location information where available.
Cuisine Type - Classified restaurants according to cuisine and food category.
Menu Product – Captured individual dishes, meals, beverages, and other listed products.
Menu Category - Organized products into sections such as mains, sides, desserts, and beverages.
Product Price - Recorded the listed selling price for each menu item.
Discount Price - Captured promotional or reduced prices when available.
Restaurant Rating - Recorded customer rating information displayed on the platform.
Availability Status - Identified whether restaurants or menu products were available.
Promotion Details - Captured visible offers, discounts, bundles, and promotional indicators.
The structured Uber Eats restaurant data for market analysis provided a consistent foundation for analyzing restaurant offerings, pricing, and promotions across monitored markets.
Business Impact Delivered
1. Improved Competitive Visibility
The project gave the food delivery brand a structured view of restaurant listings, menus, prices, promotions, ratings, and availability across monitored markets. Instead of relying on manual platform checks, teams could access standardized information and compare restaurants more efficiently. This created stronger visibility into competitor activity and market positioning.
2. Better Pricing Intelligence
Historical menu records enabled the client to identify price increases, reductions, discounts, and category-level pricing differences. Commercial teams could evaluate whether pricing movements were isolated to specific restaurants or represented broader market patterns. This supported more informed pricing reviews and competitive assessments.
3. Stronger Promotional Monitoring
The solution captured promotional indicators, discounts, meal deals, and other visible offers. By tracking these elements over time, the client could understand how competitors used promotions to attract customers. This helped commercial teams evaluate competitive promotional pressure and identify categories where promotional activity was particularly strong.
4. Enhanced Restaurant Benchmarking
Standardized restaurant and menu datasets enabled comparisons across cities, cuisines, restaurants, and platforms. Teams could review restaurant assortment, pricing, ratings, and availability using consistent attributes. This made it easier to identify strong competitors, pricing outliers, assortment differences, and market opportunities.
5. More Efficient Market Research
Automated data collection significantly reduced the need for repetitive manual research. Rather than checking numerous restaurant pages individually, teams could work with structured datasets containing the required information. This improved research efficiency and allowed analysts to focus more time on interpretation and strategy.
6. Stronger Data-Driven Planning
The collected information supported broader food & beverage analytics by connecting restaurant pricing, menu assortment, promotions, availability, and competitive activity. These insights could contribute to restaurant partnership strategies, market expansion planning, assortment reviews, pricing decisions, and ongoing competitive intelligence initiatives.
Overall, the solution transformed fragmented food delivery platform information into a scalable intelligence resource that supported faster and more informed commercial decisions.
Tools & Technology Used
Custom Scraper
Actowiz Metrics developed customized scraping workflows capable of handling restaurant listings, menu pages, product attributes, prices, promotions, ratings, and availability information. Platform-specific parsing logic helped accommodate differences in website structures and data presentation. Extraction rules were designed to maintain consistency across large numbers of restaurant and menu records.
API Data Feed
Where appropriate data access mechanisms were available, API-based feeds supported structured information delivery. API integration allowed collected records to move efficiently into downstream processing environments. Data fields were mapped against standardized schemas to ensure consistent restaurant, product, pricing, and category information.
Dashboards
The collected datasets could be connected to dashboards for interactive analysis. Users could filter information by country, city, restaurant, cuisine, category, platform, date, and other relevant attributes. Dashboard views made it easier to identify price differences, restaurant availability changes, promotional patterns, and competitive movements without manually reviewing raw datasets.
Automation Workflows
Automated workflows handled recurring extraction, validation, transformation, and delivery processes. Scheduling enabled the system to collect updated information at predefined intervals. Validation checks helped identify duplicate records, missing values, structural changes, and unexpected data patterns before the information was delivered for analysis.
Analytics & Visualization
Analytical workflows supported price comparisons, percentage-change calculations, restaurant benchmarking, promotion analysis, category comparisons, and historical trend evaluation. Visualization tools converted large volumes of restaurant information into accessible charts, tables, and dashboards. The overall technology framework supported Scrape Restaurant Data Across Spain and Portugal by connecting automated extraction with structured processing and business-focused reporting.
Client Testimonial
"The Scrape Restaurant Data Across Spain and Portugal solution has given our team a much clearer understanding of restaurant pricing, menus, promotions, and competitive positioning. The structured datasets have significantly reduced the effort required for recurring market research.
We particularly value the ability to compare restaurant and menu information across multiple delivery platforms and markets. The automated monitoring framework has helped our commercial teams work with more current and actionable intelligence."
Director of Market Intelligence, Food Delivery Brand
Final Outcome
The project delivered a structured and scalable framework for monitoring restaurant and menu intelligence across Spain and Portugal. Instead of depending on fragmented manual research, the client gained standardized datasets covering restaurant information, menu products, pricing, promotions, ratings, availability, and other relevant attributes.
Through Scrape Restaurant Data Across Spain and Portugal, the food delivery brand could compare restaurant offerings and pricing across major digital delivery channels more consistently. Historical records provided additional context for understanding changes in menu prices, product availability, restaurant assortment, and promotional activity.
The automated framework also improved the efficiency of recurring market intelligence activities. Scheduled extraction reduced repetitive manual checks, while validation and normalization processes helped maintain consistent data quality. Dashboards and analytical outputs made the collected information more accessible to pricing, commercial, category, and strategy teams.
The framework was designed to scale as the client's requirements evolved. Additional restaurants, cities, cuisines, categories, platforms, and monitoring frequencies could be incorporated into the existing workflow. This provided a foundation for continuous restaurant intelligence rather than a one-time research exercise.
Ultimately, the project helped the client turn large volumes of food delivery platform information into practical competitive intelligence. By combining automated collection, historical tracking, structured datasets, and visualization, the solution supported more informed pricing, promotional, assortment, restaurant partnership, and market expansion decisions.

Source : https://www.actowizmetrics.com/scrape-restaurant-data-spain-portugal.php
Original: https://www.actowizmetrics.com

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