Location-aware Glovo data for pricing, menu and market intelligence
Turn agreed Glovo restaurant listings, menus, grocery catalogs, displayed prices and delivery-related observations into structured datasets for business analysis.
NenoData offers a sample-first approach to evaluating Glovo extraction requirements. Define your target countries, delivery areas, merchant types, required fields and refresh expectations before production scope is confirmed.
Glovo-specific access, geographic coverage, field availability and collection cadence require feasibility and permissions review.
A displayed delivery-marketplace price is not always a universal product price.
The same restaurant brand or grocery product may be presented differently depending on the selected delivery address, merchant outlet, availability, promotional conditions and observation time.
A comparison that ignores those differences can produce misleading conclusions.
For example, two observations showing different prices for a grocery product may represent different store branches, pack sizes, delivery areas or promotion conditions rather than a straightforward price change.
Restaurant menus introduce another layer of complexity. Categories, items, variants and optional modifiers need to remain connected to the merchant and menu from which they were observed.
For pricing analysts, restaurant groups and market researchers, a useful Glovo dataset therefore needs three things:
Consistent structure
A consistent merchant-to-product or restaurant-to-menu structure.
Observation context
Sufficient geographic and commercial context to interpret each observation.
Repeatable comparisons
Timestamps and source references that support repeatable comparisons.
NenoData's existing managed extraction services use source scoping, structured records, validation and agreed delivery to address these requirements.
A Glovo-specific engagement should begin with feasibility review and a representative sample rather than an assumption that every marketplace field is accessible.
Glovo data scraping is the collection of permitted information from agreed Glovo sources and its transformation into structured records for analysis.
Depending on approved access and source visibility, a proposed dataset may contain restaurant or supermarket listings, menu categories, products, displayed prices, promotions, availability indicators and delivery-related information.
Collection may be performed once or repeated according to an agreed schedule where technically supported.
The objective is not simply to retrieve webpage content. It is to preserve the relationships and observation context needed for reliable downstream analysis.
The following are candidate extraction fields. They should not be interpreted as a guarantee of availability across every Glovo country, merchant or source type.
| Data category | Candidate fields | Availability boundary |
|---|---|---|
| Merchant information | Name, category, merchant identifier, source URL | Where visible or authorized |
| Restaurant menus | Category, item, description, base price | Depends on menu visibility |
| Modifiers | Option group, selection, additional charge | Where exposed |
| Grocery catalogs | Product name, brand, size, unit, category | Depends on store catalog |
| Pricing | Displayed current price, reference price, currency | Preserve offer context |
| Promotions | Discount label, displayed promotional price, offer conditions | Where visible |
| Delivery information | Fee, estimated time, serviceability | Location-dependent |
| Availability | Visible item or store status | Observation-specific |
| Merchant reputation | Displayed rating or review count | Where publicly visible |
| Source metadata | URL, market, location, timestamp, collection status | Defined in output schema |
Glovo's official Partner API documentation establishes that product catalog, price, availability and promotion information form part of its partner integration ecosystem. It does not establish that all these fields are publicly extractable for competitor research.
A proposed Glovo restaurant scraper should retain the relationship between the restaurant, its menu categories and the items displayed within each category.
Where available, relevant information includes item descriptions, base prices, size variants, modifiers, availability indicators and promotional labels.
For broader menu-data requirements, see NenoData Restaurant Menu Data Scraping.
Delivery-related observations can provide additional commercial context when the source displays them.
These may include delivery charges, estimated delivery windows, merchant serviceability and availability indicators.
Because these values may depend on the selected delivery location or time, they should be recorded as observations rather than permanent merchant attributes.
Promotional information may include visible discount labels, displayed reference prices, promotional prices or eligibility conditions.
Subscription-related offers should only be included where their terms and visibility can be confirmed.
A promotion shown to one visitor or delivery context must not automatically be treated as available to every customer.
Glovo supports a broader marketplace experience than restaurant-menu browsing alone. Its consumer website includes store-based discovery across international markets.
For business intelligence projects, restaurant and grocery records should share essential observation metadata while retaining their distinct product structures.
Restaurant datasets can be designed around menu hierarchy, item descriptions, base prices, option groups and customization charges.
Grocery datasets require additional attention to brand, pack size, quantity, unit of measure and product variants.
A 500 g product should not be compared directly with a 1 kg listing simply because both share a similar name.
Where product identifiers are unavailable, matching rules should be agreed rather than assuming identical products can always be recognized automatically.
Preserve country, city, delivery area, currency, source reference and observation timestamp.
Glovo's consumer experience is organized around identifying the stores available to a customer in a relevant delivery area.
Its official website presents store discovery and country-specific marketplace entry points.
Consequently, the geographic context used for a collection is an important part of the proposed dataset.
For each market, the project specification should identify:
A single delivery-area sample cannot establish complete coverage of an entire city or country.
A proposed Glovo price monitoring workflow records the displayed price alongside the merchant, product, location, currency and collection timestamp.
When recurring collection is feasible, observations can be compared to identify changes in:
Reliable comparison requires consistent item matching and sufficient context.
A change between two observations does not establish the exact time a merchant changed its price. It establishes that different values were observed at the recorded collection times.
1 September, 09:00 UTC
4.50 EUR · Example Store A
Product X, 500 g, Zone 1
8 September, 09:00 UTC
4.80 EUR · Example Store A
Same product and zone
8 September, 09:00 UTC
4.20 EUR · Example Store B
Product X, 500 g, Zone 2
Synthetic demonstration — not actual Glovo extraction.
Synthetic demonstration — not actual Glovo extraction.
| Observation | Merchant | Price | Currency | Context |
|---|---|---|---|---|
| 1 September, 09:00 UTC | Example Store A | 4.50 | EUR | Product X, 500 g, Zone 1 |
| 8 September, 09:00 UTC | Example Store A | 4.80 | EUR | Same product and zone |
| 8 September, 09:00 UTC | Example Store B | 4.20 | EUR | Product X, 500 g, Zone 2 |
The first two records illustrate a comparable price change. The third is a separate merchant and location observation.
Historical datasets should preserve both the original observed value and the metadata required to explain a comparison.
Refresh frequency, history retention and change-detection rules must be agreed during scoping.
Glovo provides official partner integrations for businesses operating through its platform.
Its documented Partner API includes functions for managing orders, synchronizing product catalogs, updating pricing and availability, creating promotions and managing outlet operations.
These integrations are intended for authorized partner operations.
They should not be described as an unrestricted public API for collecting competing restaurants' or supermarkets' data.
| Consideration | Official Partner API | Proposed managed extraction |
|---|---|---|
| Primary purpose | Authorized business operations | Scoped research and analytical datasets |
| Access | Partner authorization | Approved source-specific access |
| Data scope | Permitted partner resources | Fields confirmed during feasibility review |
| Catalog operations | Documented partner functionality | Observation and structuring where feasible |
| Competitor coverage | Not established by API documentation | Requires separate source review |
| Output | API-specific structures | Agreed analytical schema |
| Maintenance | Partner integration responsibilities | Defined in managed project scope |
The term Glovo API scraping should therefore not be used to imply that NenoData possesses Glovo partner credentials or unrestricted API access.
Glovo's logistics integration documentation is also a separate operational integration category, not evidence of access to marketplace-wide restaurant or grocery data.
NenoData's existing restaurant and food-delivery services describe scoped extraction, normalization, validation and structured delivery. The following workflow applies that established approach to a proposed Glovo engagement, subject to feasibility approval.
Define markets, delivery locations, merchant types, source permissions, required fields and output expectations.
Following approval, collect agreed records and preserve merchant, product, geographic and observation relationships.
Apply the approved schema, field-type checks, currency handling, missing-value rules and record validation.
Supply the agreed dataset. Where recurring collection is approved, define scheduling, change comparison and maintenance expectations.
A representative Glovo sample should establish the field-level production specification before collection volume or recurring commitments are agreed.
A useful output separates merchant identity, product information, commercial observations and collection metadata.
The exact schema depends on whether the project covers restaurant menus, grocery catalogs or both.
| Field | Purpose |
|---|---|
| source_platform | Identifies Glovo |
| source_url | Preserves source reference |
| merchant_id | Links observations to a merchant where available |
| merchant_name | Displayed restaurant or store name |
| merchant_type | Restaurant, supermarket or other category |
| country / city | Market identification |
| delivery_location | Scoped geographic context |
| category_name | Menu or catalog category |
| product_name | Item or product identity |
| brand | Grocery brand where displayed |
| pack_size | Quantity or size context |
| base_price | Observed listed price |
| promotional_price | Observed offer price where visible |
| currency | Price currency |
| availability_status | Displayed status where available |
| collected_at | Observation timestamp |
| collection_status | Collection outcome |
These are proposed analytical fields, not Glovo's official API schema.
The following is entirely fictional and demonstrates a proposed output format.
{
"source_platform": "Glovo",
"source_url": "https://example.com/illustrative-source",
"merchant_id": "EXAMPLE-MERCHANT-001",
"merchant_name": "Example Grocery Store",
"merchant_type": "supermarket",
"country": "Example Market",
"city": "Example City",
"delivery_location": "Example Zone A",
"category_name": "Dairy",
"product_name": "Example Yogurt",
"brand": "Example Brand",
"pack_size": "500 g",
"base_price": 4.50,
"promotional_price": null,
"currency": "EUR",
"availability_status": "illustrative",
"collected_at": "2026-09-01T09:00:00Z",
"collection_status": "illustrative_only"
}A genuine Glovo feasibility sample must replace this illustration before any claim of demonstrated Glovo extraction is published.
Compare selected restaurant menus, categories, displayed prices and available customization options within agreed markets.
Monitor comparable menu items across selected restaurant locations and observation periods, preserving currency, product configuration and delivery context.
Study visible discount labels, displayed promotional prices and offer patterns where those fields are available through approved sources.
Compare grocery catalog breadth, product categories, pack sizes and visible availability across selected stores and markets.
Organize merchant observations by country, city and delivery area to investigate visible marketplace presence.
The result represents the approved sampling scope, not necessarily a complete census of every Glovo merchant.
Use normalized records to support internal BI, pricing analysis, product matching or structured dataset enrichment.
NenoData's existing services document CSV, Excel, JSON, API-ready output and scoped recurring delivery. Its custom-pipeline service also describes delivery into agreed downstream systems. These are general company capabilities; their applicability to Glovo must be confirmed separately.
| Delivery option | Intended use | Glovo-specific status |
|---|---|---|
| CSV | Tabular analysis | Confirm during scoping |
| Excel | Business-user review | Confirm during scoping |
| JSON | Nested menu and catalog records | Confirm during scoping |
| API-ready output | Engineering workflows | Confirm during scoping |
| Database-ready files | Analytics ingestion | Separately scoped |
| Scheduled feeds | Recurring observations | Subject to feasibility |
| Webhook or destination integration | Automated downstream workflows | Separately scoped |
For recurring projects, the agreement should specify collection cadence, expected observation windows, history retention, exception handling and delivery responsibilities.
No universal real-time Glovo monitoring or guaranteed refresh interval is implied.
A production specification should establish what is included, what remains conditional and what is outside the collection scope.
The purpose of validation is to make collected records interpretable and suitable for the agreed business use case. It does not establish perfect accuracy, universal coverage or uninterrupted availability.
A self-service Glovo scraper places responsibility for source evaluation, collection scripts, schema design and maintenance with the buyer.
A proposed managed engagement defines those responsibilities through an agreed project specification.
| Consideration | Self-service scraper | Managed engagement |
|---|---|---|
| Source feasibility | Buyer evaluates | Reviewed during scoping |
| Location inputs | Buyer implements | Defined in project specification |
| Product relationships | Buyer structures | Included in approved schema |
| Validation | Buyer maintains | Agreed validation workflow |
| Price history | Buyer stores and compares | Available where scoped |
| Delivery | Tool-dependent | Agreed format and destination |
| Maintenance | Buyer-owned | Defined in engagement |
Neither approach guarantees access to restricted sources or unavailable fields.
A useful feasibility sample should demonstrate the exact records your team intends to analyze.
Include your target countries, cities, delivery areas, example merchants, required fields and preferred output format.
For recurring requirements, also specify the intended refresh cadence and whether historical price, promotion or availability comparisons are needed.
The sample review should confirm source feasibility, geographic handling, available fields, output structure and outstanding limitations before production commitments are made.
Glovo data scraping refers to collecting permitted information from agreed Glovo sources and organizing it into structured datasets. Depending on source visibility and approved access, the information may include merchant listings, restaurant menus, grocery products, prices, promotions and delivery-related observations.
NenoData provides general managed restaurant-menu, food-delivery and structured data-extraction services. Glovo-specific restaurant and grocery extraction requires feasibility review, an approved collection scope and representative sample validation before production capability can be confirmed.
Recurring collection can be proposed where technically feasible and approved. The collection schedule, item-matching rules, geographic inputs and historical retention requirements must be defined before production.
The selected delivery location provides context for the merchant and product information observed. Comparisons should preserve this context rather than assume that a listing or price observed in one area applies to an entire city or country.
Displayed promotions may be included where available through the approved source. Promotional conditions, subscription eligibility and other restrictions must be preserved where relevant and visible.
Yes. Glovo documents official Partner API integrations for orders, catalogs, pricing, promotions and outlet management. These integrations are designed for authorized partners and should not be confused with unrestricted public access to marketplace-wide competitor data.
Grocery catalog extraction can be evaluated for selected stores and markets. Product names, brands, categories, pack sizes, displayed prices and availability are candidate fields, subject to source visibility and feasibility confirmation.
Coverage must be evaluated against Glovo's current operating markets and the requested cities, delivery areas and merchant types. NenoData does not claim universal Glovo market coverage.
NenoData's general extraction services describe CSV, Excel, JSON and API-ready output, with additional delivery arrangements available through scoped engagements. Applicable Glovo-specific formats must be confirmed in the project specification.
Accuracy and freshness depend on source availability, agreed validation rules, collection frequency and observation context. A timestamp establishes when information was collected, not when Glovo or a merchant last updated it.
Pricing depends on source feasibility, requested markets, merchant volume, field depth, collection cadence, validation and delivery requirements. Review NenoData Pricing for general information and request a project-specific assessment.
General service information is on the NenoData Pricing page.
Tell us which Glovo markets, merchants and product categories matter to your business.
NenoData can review your requirements, evaluate the applicable source and permission boundaries, and define the next steps for a representative sample.
Whether your project focuses on restaurant menus, grocery assortment, price history or delivery-marketplace intelligence, begin with an agreed schema and clear geographic observation context.
Glovo-specific extraction, market coverage, refresh frequency and delivery commitments require technical feasibility and scope approval.