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GeoIp2\Model\City Object ( [raw:protected] => Array ( [city] => Array ( [geoname_id] => 4509177 [names] => Array ( [de] => Columbus [en] => Columbus [es] => Columbus [fr] => Columbus [ja] => コロンバス [pt-BR] => Columbus [ru] => Колумбус [zh-CN] => 哥伦布 ) ) [continent] => Array ( [code] => NA [geoname_id] => 6255149 [names] => Array ( [de] => Nordamerika [en] => North America [es] => Norteamérica [fr] => Amérique du Nord [ja] => 北アメリカ [pt-BR] => América do Norte [ru] => Северная Америка [zh-CN] => 北美洲 ) ) [country] => Array ( [geoname_id] => 6252001 [iso_code] => US [names] => Array ( [de] => USA [en] => United States [es] => Estados Unidos [fr] => États Unis [ja] => アメリカ [pt-BR] => EUA [ru] => США [zh-CN] => 美国 ) ) [location] => Array ( [accuracy_radius] => 20 [latitude] => 39.9625 [longitude] => -83.0061 [metro_code] => 535 [time_zone] => America/New_York ) [postal] => Array ( [code] => 43215 ) [registered_country] => Array ( [geoname_id] => 6252001 [iso_code] => US [names] => Array ( [de] => USA [en] => United States [es] => Estados Unidos [fr] => États Unis [ja] => アメリカ [pt-BR] => EUA [ru] => США [zh-CN] => 美国 ) ) [subdivisions] => Array ( [0] => Array ( [geoname_id] => 5165418 [iso_code] => OH [names] => Array ( [de] => Ohio [en] => Ohio [es] => Ohio [fr] => Ohio [ja] => オハイオ州 [pt-BR] => Ohio [ru] => Огайо [zh-CN] => 俄亥俄州 ) ) ) [traits] => Array ( [ip_address] => 216.73.216.24 [prefix_len] => 22 ) ) [continent:protected] => GeoIp2\Record\Continent Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [code] => NA [geoname_id] => 6255149 [names] => Array ( [de] => Nordamerika [en] => North America [es] => Norteamérica [fr] => Amérique du Nord [ja] => 北アメリカ [pt-BR] => América do Norte [ru] => Северная Америка [zh-CN] => 北美洲 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => code [1] => geonameId [2] => names ) ) [country:protected] => GeoIp2\Record\Country Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [geoname_id] => 6252001 [iso_code] => US [names] => Array ( [de] => USA [en] => United States [es] => Estados Unidos [fr] => États Unis [ja] => アメリカ [pt-BR] => EUA [ru] => США [zh-CN] => 美国 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => isInEuropeanUnion [3] => isoCode [4] => names ) ) [locales:protected] => Array ( [0] => en ) [maxmind:protected] => GeoIp2\Record\MaxMind Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( ) [validAttributes:protected] => Array ( [0] => queriesRemaining ) ) [registeredCountry:protected] => GeoIp2\Record\Country Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [geoname_id] => 6252001 [iso_code] => US [names] => Array ( [de] => USA [en] => United States [es] => Estados Unidos [fr] => États Unis [ja] => アメリカ [pt-BR] => EUA [ru] => США [zh-CN] => 美国 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => isInEuropeanUnion [3] => isoCode [4] => names ) ) [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => isInEuropeanUnion [3] => isoCode [4] => names [5] => type ) ) [traits:protected] => GeoIp2\Record\Traits Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [ip_address] => 216.73.216.24 [prefix_len] => 22 [network] => 216.73.216.0/22 ) [validAttributes:protected] => Array ( [0] => autonomousSystemNumber [1] => autonomousSystemOrganization [2] => connectionType [3] => domain [4] => ipAddress [5] => isAnonymous [6] => isAnonymousProxy [7] => isAnonymousVpn [8] => isHostingProvider [9] => isLegitimateProxy [10] => isp [11] => isPublicProxy [12] => isResidentialProxy [13] => isSatelliteProvider [14] => isTorExitNode [15] => mobileCountryCode [16] => mobileNetworkCode [17] => network [18] => organization [19] => staticIpScore [20] => userCount [21] => userType ) ) [city:protected] => GeoIp2\Record\City Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [geoname_id] => 4509177 [names] => Array ( [de] => Columbus [en] => Columbus [es] => Columbus [fr] => Columbus [ja] => コロンバス [pt-BR] => Columbus [ru] => Колумбус [zh-CN] => 哥伦布 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => names ) ) [location:protected] => GeoIp2\Record\Location Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [accuracy_radius] => 20 [latitude] => 39.9625 [longitude] => -83.0061 [metro_code] => 535 [time_zone] => America/New_York ) [validAttributes:protected] => Array ( [0] => averageIncome [1] => accuracyRadius [2] => latitude [3] => longitude [4] => metroCode [5] => populationDensity [6] => postalCode [7] => postalConfidence [8] => timeZone ) ) [postal:protected] => GeoIp2\Record\Postal Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [code] => 43215 ) [validAttributes:protected] => Array ( [0] => code [1] => confidence ) ) [subdivisions:protected] => Array ( [0] => GeoIp2\Record\Subdivision Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [geoname_id] => 5165418 [iso_code] => OH [names] => Array ( [de] => Ohio [en] => Ohio [es] => Ohio [fr] => Ohio [ja] => オハイオ州 [pt-BR] => Ohio [ru] => Огайо [zh-CN] => 俄亥俄州 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => isoCode [3] => names ) ) ) )
country : United States
city : Columbus
US
Array ( [as_domain] => amazon.com [as_name] => Amazon.com, Inc. [asn] => AS16509 [continent] => North America [continent_code] => NA [country] => United States [country_code] => US )
In today’s data-driven world, extracting actionable insights from on-demand delivery platforms like Glovo is crucial for businesses that rely on real-time visibility into delivery operations, restaurant availability, and pricing strategies. However, Scraping Glovo Data comes with unique technical and strategic challenges that can distort data quality if not addressed properly.
In this blog, we’ll explore a comprehensive strategy for robust Web Scraping Glovo Delivery Data, including challenges, technical implementation, scheduling strategies, and use cases. Whether you're a retail analyst, data scientist, or enterprise looking to gain a competitive edge, this guide will equip you with everything you need for effective Glovo API Scraping.
Glovo is a leading on-demand delivery platform headquartered in Barcelona, Spain, operating in over 25 countries across Europe, Africa, and Latin America. Launched in 2015, Glovo connects users with local businesses, enabling them to order everything from food and groceries to pharmacy products and retail items through a single mobile app. Known for its distinctive yellow branding and courier backpacks, Glovo has positioned itself as more than just a food delivery service—offering a versatile urban logistics solution. With a strong focus on innovation, Glovo has integrated features like live order tracking, contactless delivery, and in-app payments to enhance user experience. For businesses, Glovo provides extended reach and visibility, allowing local stores to tap into a broader customer base. Its API and digital ecosystem also offer valuable data that can be leveraged for business intelligence. As Glovo continues to expand, it remains a vital player in the evolving landscape of q-commerce and hyperlocal delivery.
Problem Statement: Glovo only displays stores that are online at the time of access. This leads to high volatility (between 10–30%) in store visibility depending on the time of day and day of the week.
Suggested Fix: Perform multiple API requests across various time windows throughout the month, across all cities. These periodic scrapes will populate a centralized cache collection, used later for comprehensive store crawling.
Real-Time Store Availability Fluctuation (A Coruña Example):
Problem Statement: Glovo’s city-based URLs show only brand-level data, not individual store locations. For instance, all McDonald's branches in Barcelona share the same listing URL and the specific branch is dynamically chosen based on delivery location.
Suggested Fix: Run API requests from different delivery locations within each city using a polygon grid with center points at 0.7km radius to improve spatial data coverage.
Inputs:
Goal: Use known stores and polygons to start building an enriched store cache.
Why Polygons?
Glovo changes availability and pricing dynamically based on user location. To capture this variability:
https://api.glovoapp.com/v3/feeds/categories/1?limit=5000&offset=0
Fields Extracted:
Storage Path Example:
To avoid redundant requests and prioritize the freshest listings:
1. Deduplicate polygon entries:
2. Deduplicate full cache:
3. Fetch detailed store metadata:
https://api.glovoapp.com/v3/stores/{store_slug}?includeClosed=true&includeDisabled=true
store name, opening hours, menu, location coordinates, pricing, availability, etc.
<details> <summary>Interpretation:</summary> This volatility proves that any single request is insufficient for Glovo Data Scraping. Strategic time-based scrapes across polygon zones are necessary to capture the full picture of store availability. </details>
By using Glovo API Scraping, businesses can monitor dynamic pricing models for identical products across multiple delivery zones. This allows brands to understand how pricing fluctuates based on consumer location, time, and demand spikes. With Web Scraping Glovo Delivery Data, you can benchmark against competitors like McDonald's or KFC and adjust your delivery fee models accordingly.
Using a detailed Glovo Scraper, businesses can identify under-served regions by tracking store availability, coverage gaps, and delivery footprints. Polygon-based analysis reveals hyperlocal insights that are not available through traditional sitemaps. Whether launching new cloud kitchens or setting up franchise stores, this Glovo Data Scraping method helps in pinpointing viable entry zones with sufficient demand.
Aggregating store visibility from multiple time-stamped API requests lets you evaluate how often certain restaurants are available and how long they remain online. This helps in ranking stores based on uptime, menu consistency, and responsiveness—key metrics for food delivery operators and aggregators to optimize their service models with Glovo API.
Using Scrape Glovo Data techniques, analysts can cross-reference store data with consumer delivery hotspots. This reveals demand density, preferred cuisines per region, and store delivery radius effectiveness. With this, logistics teams can optimize delivery routes, reduce ETAs, and improve customer satisfaction.
Franchise owners with multi-location presence can utilize Glovo API Scraping to compare pricing, discounts, ratings, and delivery efficiency across their branches. This empowers better control over brand uniformity and identifies operational bottlenecks through centralized Glovo Scraper API insights.
At Actowiz Solutions, we specialize in Glovo Data Scraping with robust, scalable, and intelligent scraping infrastructure tailored to tackle Glovo’s dynamic environment. Our advanced solutions use geo-targeted polygon grids, real-time API parsing, and smart deduplication methods to ensure 100% visibility across all store listings and pricing changes. Whether you need raw data, structured JSON outputs, or monthly performance dashboards, we deliver clean, accurate, and actionable datasets. With deep expertise in Glovo API Scraping, we help businesses optimize pricing strategies, monitor competitors, and make informed decisions. Partner with us for seamless, efficient, and fully customizable Glovo data extraction solutions.
In a fast-moving q-commerce landscape, data accuracy is everything. With Glovo’s store availability and pricing constantly changing based on time and location, businesses need more than basic scraping—they need a smart, strategic approach. Glovo Data Scraping using polygon-based requests, deduplication, and metadata extraction ensures complete coverage and real-time insights. This empowers brands to stay competitive, make data-driven decisions, and understand hyperlocal market dynamics. At Actowiz Solutions, we deliver powerful Glovo Scraper API solutions tailored to your business goals.
Contact Actowiz Solutions today to unlock the full potential of Glovo data for your business! Contact Us Now for custom scrapers, detailed dashboards, and scalable API solutions! You can also reach us for all your mobile app scraping, data collection, web scraping , and instant data scraper service requirements!
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Industry:
Coffee / Beverage / D2C
Result
2x Faster
Smarter product targeting
“Actowiz Solutions has been instrumental in optimizing our data scraping processes. Their services have provided us with valuable insights into our customer preferences, helping us stay ahead of the competition.”
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Real Estate
Real-time RERA insights for 20+ states
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Data Analyst, Aditya Birla Group
✓ Boosted data acquisition speed by 3×
Organic Grocery / FMCG
Improved
competitive benchmarking
“With Actowiz Solutions' data scraping, we’ve gained a clear edge in tracking product availability and pricing across various platforms. Their service has been a key to improving our market intelligence.”
Product Manager, 24Mantra Organic
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Quick Commerce
Inventory Decisions
“Actowiz Solutions has greatly helped us monitor product availability from top three Quick Commerce brands. Their real-time data and accurate insights have streamlined our inventory management and decision-making process. Highly recommended!”
Aarav Shah, Senior Data Analyst, Mensa Brands
✓ 28% product availability accuracy
✓ Reduced OOS by 34% in 3 weeks
3x Faster
improvement in operational efficiency
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Business Development Lead,Organic Tattva
✓ Weekly competitor pricing feeds
Beverage / D2C
Faster
Trend Detection
“The data scraping services offered by Actowiz Solutions have been crucial in refining our strategies. They have significantly improved our ability to analyze and respond to market trends quickly.”
Marketing Director, Sleepyowl Coffee
Boosted marketing responsiveness
Enhanced
stock tracking across SKUs
“Actowiz Solutions provided accurate Product Availability and Ranking Data Collection from 3 Quick Commerce Applications, improving our product visibility and stock management.”
Growth Analyst, TheBakersDozen.in
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Real results from real businesses using Actowiz Solutions
In Stock₹524
Price Drop + 12 minin 6 hrs across Lel.6
Price Drop −12 thr
Improved inventoryvisibility & planning
Actowiz's real-time scraping dashboard helps you monitor stock levels, delivery times, and price drops across Blinkit, Amazon: Zepto & more.
✔ Scraped Data: Price Insights Top-selling SKUs
"Actowiz's helped us reduce out of stock incidents by 23% within 6 weeks"
✔ Scraped Data, SKU availability, delivery time
With hourly price monitoring, we aligned promotions with competitors, drove 17%
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