Cracking the Code - How Actowiz Solved Glovo’s Data Volatility with Precision Glovo Data Scraping

Introduction

In the rapidly growing q-commerce industry, real-time, accurate data is the key to business success. Glovo, one of the world’s leading on-demand delivery platforms, poses a unique challenge—its store listings and pricing vary significantly based on time and user location. For businesses relying on Glovo Data Scraping to gain market insights, this inconsistency makes it nearly impossible to gather reliable data using traditional scraping methods. Actowiz Solutions stepped in to develop a powerful, multi-layered scraping strategy using Glovo API Scraping and geo-targeted requests to deliver complete and accurate datasets for our client’s real-time business needs.

The Client

A European market intelligence agency approached Actowiz Solutions seeking a custom-built Glovo Scraper to support data-driven insights for their retail clients. They required Web Scraping Glovo Delivery Data to monitor store availability, pricing dynamics, and brand competition across multiple cities in Spain, Portugal, and Italy. Their internal tools failed to capture the full scope of Glovo API data due to store-level volatility. The client needed a scalable solution to Scrape Glovo Data from all available locations with precise geospatial control and minimal duplication. They prioritized Glovo Scraper API efficiency, data completeness, and structured outputs for downstream analytics.

Key Challenges

Glovo's website dynamically displays stores based on the user's location and the current time, which resulted in up to 30% variance in store visibility depending on the hour or day. Relying solely on Glovo’s sitemap or basic city-based scraping produced incomplete data. Many stores with multiple branches (e.g., McDonald’s or KFC) were represented by a single URL, and store-level pricing fluctuated with delivery distance. Furthermore, the same store could appear in multiple polygon results, often with null values in metadata fields, requiring intelligent deduplication. These challenges made standard Glovo API Scraping methods unreliable. The client needed full market coverage, consistent data formats, and dynamic store metadata—all while avoiding duplication and performance issues. This made it clear that a traditional scraper wouldn’t suffice—what was needed was a smart, location-aware, and multi-phase Glovo Data Scraping strategy.

Key Solutions

Actowiz Solutions developed a three-phase Glovo Scraper strategy.

Phase 1: We captured an initial store base using Glovo’s public city listings and stored it as a cache (cache_type=1).

Phase 2: We deployed a polygon-based Glovo API Scraping process using 0.7km-radius geolocation grids across all cities. Each grid point made API calls to Scrape Glovo Data dynamically, identifying stores by delivery area.

Phase 3: Using advanced logic, we deduplicated stores by storeId and addressId, selecting entries with the most complete data and closest delivery fee match.

Finally, we fetched store-level metadata by calling the Glovo API using store_slug and location headers, creating enriched profiles with delivery fee info, operational status, geolocation, and pricing. Data was delivered monthly in structured CSV and JSON formats via a secure S3 bucket. This end-to-end Glovo Scraper API solution ensured 100% data accuracy with minimal noise.

Client Testimonial

“Actowiz Solutions delivered exactly what we needed—clean, precise Glovo data at scale. Their polygon-based strategy and attention to data integrity made a huge difference in the insights we could provide to our clients. The responsiveness of their team and custom delivery format helped us integrate the solution seamlessly into our analytics stack.”

– Data Science Lead, European Retail Analytics Firm.

Conclusion

By leveraging intelligent, geo-targeted, and multi-phase Glovo Data Scraping strategies, Actowiz Solutions empowered the client to overcome Glovo’s inherent data volatility. The enriched datasets helped them unlock real-time visibility into store availability, pricing trends, and competitor positioning across different cities. This case proves that precision Glovo Scraper architecture combined with smart deduplication and robust Glovo API Scraping can transform raw delivery data into actionable business intelligence.

Ready to Scrape Glovo Data with precision? Contact Actowiz Solutions today for enterprise-grade Glovo Scraper API solutions customized to your business goals!