Restaurant Profiles
Build structured records for restaurants, chains, brands, concepts, cuisines, and operating details.
- Name and brand
- Cuisine and category
- Hours and contact details
Home / Food & Restaurants
Restaurant market and menu intelligenceFood & Restaurants Data Scraping for Location-Level Market Intelligence
Collect and normalize restaurant locations, menus, item prices, modifiers, availability, delivery fees, ETAs, promotions, ratings, reviews, and platform visibility across eligible food websites and apps.
✓Store-level observations✓Menu and item matching✓Fee and modifier separation✓Historical menu signals
RESTAURANT MARKET MONITORLATEST OBSERVATION
DELIVERY LOCATIONMidtown Manhattan, New York
ORDER MODEDelivery
QSR
Example Burger KitchenAmerican · 4.6 rating · 25 to 35 min
LOCATION MATCHEDPlatform APlatform BDirect
CONFIGURED ITEM$13.74Required choices retained
DELIVERY FEE$2.49Address dependent
ESTIMATED TIME25 to 35 minObservation timestamped
01LocationStore and service area
02ChannelMarketplace or direct
03MenuCategory and item
04ConfigurationSize and modifiers
05OfferPrice, fee, promotion
06ObservationAvailability and time
Comparable restaurant data
A menu price is incomplete without its ordering context.
The same item can vary by store, platform, order mode, required selection, delivery address, promotion, and time. Kvetoiq retains that context so teams compare like with like.
01One restaurant brand can have many stores, virtual brands, and channel-specific listings.
02Required modifiers can change the minimum purchasable price of an item.
03Delivery fees, minimum orders, ETAs, and availability depend on the customer location.
04Promotions require conditions, dates, minimum spend, and eligibility to remain meaningful.
RAW LISTINGClassic Burger · $10.99
→
IDENTITYBrand + store + channel
MENU STRUCTURECategory + item + variant
→
CONFIGURATIONRequired and optional modifiers
COMMERCIAL CONTEXTPrice + fee + promotion
→
OBSERVATIONAvailability + ETA + timestamp
Food and restaurant data coverage
Capture the restaurant, menu, offer, and customer-facing signals that shape each market.
Field selection is aligned with your business decision, target geography, source feasibility, and required refresh cadence.
Locations & Coverage
Retain store identity, address, coordinates, service area, and channel presence.
- Store and branch IDs
- Address and coordinates
- Delivery and pickup coverage
Menus & Categories
Preserve menu hierarchy so items remain connected to the correct store, channel, and daypart.
- Menu and section names
- Breakfast and dayparts
- Item ordering and visibility
Items & Content
Collect item names, descriptions, images, dietary labels, and product attributes.
- Descriptions and images
- Dietary and allergen tags
- Nutrition when available
Variants & Modifiers
Model sizes, flavors, required selections, optional add-ons, limits, and incremental prices.
- Required choice groups
- Add-ons and exclusions
- Minimum and maximum rules
Prices & Promotions
Separate display prices, configured prices, bundles, coupons, discounts, and offer conditions.
- Item and combo prices
- Promotion conditions
- Platform markups
Delivery Experience
Observe delivery fees, service fees, minimum orders, ETA ranges, and order-mode availability.
- Delivery and service fees
- Minimum order
- ETA and distance context
Ratings & Reviews
Structure rating, review count, review text, date, language, and experience themes.
- Rating and review velocity
- Sentiment and topics
- Food, service, and value themes
Restaurant data entity modelDiscuss a Custom Restaurant Schema →
Keep every item connected to the restaurant context that produced it.
A durable restaurant dataset separates identity from changing observations. This supports matching, history, comparisons, and downstream analytics.
✓Stable entities for brands, restaurants, locations, menus, categories, and items
✓Channel-specific offers linked to the correct location and menu configuration
✓Timestamped observations for prices, availability, fees, ETAs, ratings, and promotions
RESTAURANT BRAND
LOCATIONAddress, geo, hours
CHANNELDirect or marketplace
MENUDaypart and category
ITEMName, content, image
VARIANTSize, flavor, format
MODIFIERChoice, rule, increment
OFFERPrice, fee, promotion
AVAILABILITYOrder mode and status
OBSERVATIONSource, location, time
Restaurant and store matching
Resolve the same location across direct sites, maps, and delivery platforms.
Names alone are unreliable. Matching can combine address, coordinates, phone, brand, store ID, cuisine, hours, menu similarity, and other agreed signals.
01Normalize names, units, addresses, and geographic coordinates.
02Separate physical stores from virtual brands and shared kitchens.
03Retain source identifiers and matching evidence for review.
Cross-Platform Location MatchILLUSTRATIVE WORKFLOW
Example Burger Kitchen125 Market St · Platform A · Store 482
MATCH
Example Burger Kitchen Midtown125 Market Street · Direct · Unit 7
Example Wings125 Market St · Platform B · Delivery only
LINK
Shared Kitchen LocationSame coordinates · related menu signals
Matching criteria and review thresholds are configured for the project. Illustrative labels do not represent production accuracy claims.
Configured Item PriceORDER CONTEXT RETAINED
Displayed base price$10.99
Required size selection+$1.50
Selected add-on+$1.25
Item promotion-$0.00
Configured item total$13.74
Delivery fee$2.49
Service feeCaptured separately
Taxes and tipExcluded from item match
True menu price composition
Separate the displayed price from the amount a customer can actually configure.
Kvetoiq can retain base prices, required modifier increments, optional add-ons, bundle rules, promotions, and order-level fees as distinct fields.
01Compare base item prices independently from delivery and service charges.
02Calculate a minimum configured price when required choices apply.
03Preserve currency, order mode, delivery location, and observation time.
Cross-platform menu comparison
Compare matched items without flattening the menu details that explain the difference.
A comparison record should show what changed, where it changed, and whether the products are genuinely equivalent.
| Matched item | Direct | Platform A | Platform B | Required choices | Availability | Observed change |
|---|---|---|---|---|---|---|
| Classic Burger | $10.49 | $10.99 | $11.49 | Size required | All channels | Platform B increased |
| Chicken Combo | $13.99 | $14.99 | $14.49 | Drink and side | Direct, A, B | Modifier updated |
| Plant Burger | $12.49 | $12.99 | Not listed | Optional add-ons | Direct and A | Platform B removed |
| Family Meal | $32.00 | $34.00 | $35.00 | Four selections | All channels | Promotion ended |
Values are illustrative and demonstrate the structure of a normalized comparison record.
Delivery context intelligence
Track the customer-facing cost and convenience of ordering from each location.
Delivery availability, fees, ETAs, minimum orders, and promotions can change with the delivery address, order value, time, and platform.
✓Delivery and pickup availability by restaurant location
✓Delivery fee, service fee, minimum order, and ETA range
✓Delivery radius and customer-location context when observable
✓Promotion eligibility and platform-specific conditions
Store Delivery SnapshotAVAILABLE
DELIVERY FEE$2.49Observed for selected address
ETA RANGE25 to 35 minTimestamp retained
MINIMUM ORDER$15.00Platform condition
PICKUPAvailableSeparate ordering mode
Illustrative service-area position
Customer address is within the observed delivery area.
Food and restaurant intelligence use cases
Turn menus, locations, offers, and guest signals into practical market decisions.
Competitive Menu Monitoring
Compare matched items, configured prices, combos, and channel markups across selected competitors.
Explore Price Monitoring →Restaurant Location Mapping
Build location inventories by brand, cuisine, market, service area, ordering channel, and operating status.
Menu Change Detection
Identify item additions, removals, price movements, description changes, and modifier updates.
Promotion Intelligence
Monitor discounts, coupons, bundles, free-delivery offers, conditions, and promotion duration.
Delivery Experience Analysis
Compare availability, delivery fees, minimum orders, pickup options, and ETA ranges.
Guest Sentiment Analysis
Structure ratings and reviews around food quality, service, delivery, value, and recurring issues.
Cloud Kitchen Detection
Investigate shared addresses, overlapping menus, virtual brands, and delivery-only concepts.
Menu & Cuisine Gap Analysis
Compare cuisine coverage, item presence, dietary options, price bands, and assortment gaps.
Market Expansion Research
Assess restaurant density, competitive sets, cuisine mix, pricing, reviews, and platform coverage.
Restaurant Catalog Development
Build structured restaurant, location, menu, and item records for eligible food-tech applications.
Platform Visibility Monitoring
Observe restaurant and item presence in eligible search, category, cuisine, and location views.
Dietary & Nutrition Intelligence
Organize observable nutrition, allergen, vegan, vegetarian, halal, kosher, and other dietary labels.
Team-to-data mapping
Give each food and restaurant team the market context behind its decisions.
Revenue & Pricing
Items, variants, modifiers, prices, promotions, channel markupsCompetitive price and value decisions
Menu & Culinary
Categories, items, ingredients, dietary labels, menu changesMenu development and gap analysis
Digital Commerce
Availability, fees, ETA, content, ratings, platform visibilityOrdering experience improvement
Growth & Expansion
Locations, cuisines, restaurant density, reviews, price bandsMarket and whitespace assessment
Brand & Marketing
Promotions, menu content, reviews, sentiment, search presenceCampaign and positioning intelligence
Supply & Operations
Item availability, hours, delivery status, menu volatilityOperational and assortment signals
Food-Tech Product
Restaurant, location, menu, item, offer, and review recordsCatalog, discovery, and analytics applications
Research & Investment
Supply, pricing, locations, ratings, concepts, market changesCompetitive and market assessment
Historical restaurant timeline
Retain how menus, prices, availability, offers, and reputation changed over time.
Timestamped observations create a market history instead of preserving only the latest restaurant record.
01
First observedIdentity and context stored02
Menu changedCategory or item updated03
Price changedPrevious and current retained04
Offer changedPromotion or condition revised05
Status changedAvailability or hours moved06
Delivery changedFee or ETA updated07
Review changedRating or volume movedRestaurant and food platform coverage
Assess the channels that shape each restaurant market.
Every source is evaluated for required fields, geography, location context, update behavior, responsible collection, and technical feasibility.
DoorDashUber EatsGrubhubSeamlessPostmatesDelivery.comChowNowToastSliceYelpGoogle MapsTripadvisorOpenTableRestaurant WebsitesChain WebsitesOrdering PagesZomatoSwiggyDeliverooJust EatTalabatFoodpandaCareemOther eligible sources
Restaurant data workflow01 02 03 04 05 06
Validate locations, menus, order scenarios, matching rules, and fields before scaling.
Define Decisions
Confirm markets, channels, restaurants, fields, and business outcomes.
Validate Sources
Test representative stores, menus, locations, and ordering scenarios.
Design Model
Map brands, locations, menus, items, modifiers, offers, and observations.
Build & Normalize
Collect, match, structure, separate costs, and preserve context.
Review Sample
Confirm fields, matching, configured prices, quality rules, and output.
Launch & Maintain
Monitor collection, refresh records, and manage source changes.
Restaurant data quality
Validate identity, menu structure, commercial context, and freshness.
Location Validation
Check brand, store, address, coordinates, identifiers, and channel relationships.
Menu Hierarchy
Keep menus, categories, items, variants, and modifiers in the correct structure.
Item Matching
Retain matching evidence and distinguish exact, equivalent, and uncertain candidates.
Cost Separation
Separate base price, modifier increments, promotions, delivery fees, and service fees.
Location Context
Preserve delivery address, store, order mode, geography, and service-area assumptions.
Freshness Controls
Timestamp observations and assess cadence against the volatility of each field.
Duplicate Controls
Identify duplicate listings while protecting distinct stores, menus, and virtual brands.
Source Monitoring
Detect structural changes, extraction anomalies, unexpected gaps, and schema drift.
Data delivery
Receive structured restaurant data where your team already works.
Delivery can be aligned with your schema, refresh cadence, validation rules, and downstream environment.
CSVExcelJSONJSONLParquetAPIWebhookS3AzureGoogle CloudSnowflakeBigQueryDatabaseCustom Integration
Related Kvetoiq services
Build the restaurant data workflow around the source, cadence, and decision.
App Scraping
Assess eligible Android and iOS restaurant and delivery sources.
Explore App Scraping →Live Crawler
Support scheduled observations, changes, alerts, and fresh restaurant data.
Explore Live Crawler →AI-Powered Scraping
Assist menu extraction, classification, matching, and review analysis.
Explore AI-Powered Scraping →Web Scraping API
Connect structured web data to applications and internal workflows.
Explore Web Scraping API →Responsible collection
Scope restaurant data collection around legitimate business use.
Kvetoiq evaluates public accessibility, requested fields, source conditions, personal-data considerations, request controls, retention, intended use, and delivery requirements. Project-specific legal questions should be reviewed by qualified counsel.
Read the Privacy Policy →✓Public-source and field assessment
✓Purpose and data-minimization review
✓Request-rate and source-impact controls
✓Personal-data and retention considerations
✓Documented schema and delivery scope
Food and restaurant data FAQ
What teams ask before starting.
What is food and restaurant data scraping?
It is the managed collection and structuring of eligible public restaurant information from websites, apps, directories, maps, ordering pages, review platforms, and food-delivery marketplaces.
What restaurant data can Kvetoiq collect?
Potential fields include restaurant profiles, brands, locations, coordinates, cuisines, hours, menus, categories, items, descriptions, prices, variants, modifiers, promotions, availability, delivery fees, ETAs, ratings, reviews, images, dietary labels, and observable nutrition information.
Can you collect complete restaurant menus?
Eligible menus can be structured into menu, category, item, variant, modifier group, modifier, price, availability, image, dietary label, and observation records.
Can sizes, modifiers, and add-ons be collected?
Yes, when observable. Data can include required and optional groups, available choices, incremental prices, minimum and maximum selections, defaults, exclusions, and item relationships.
Can restaurant locations be matched across platforms?
Matching can combine normalized name, brand, address, coordinates, phone, source identifiers, hours, cuisine, menu similarity, and other agreed evidence.
Can menu items be matched across delivery apps?
Yes. Matching rules can evaluate item names, descriptions, images, sizes, variants, modifiers, category context, and price while retaining uncertain candidates for review.
Can delivery and service fees be separated from item prices?
Yes, when sources expose the components. Base item prices, modifier increments, discounts, delivery fees, service fees, small-order fees, taxes, and other charges can be modeled separately.
Can pickup and delivery prices be compared?
Yes. Each observation should retain the order mode, location, platform, store, menu configuration, currency, and timestamp so comparisons remain meaningful.
Can restaurant availability and delivery ETAs be monitored?
Potential observations include open or closed status, item availability, delivery eligibility, pickup availability, ETA range, minimum order, and selected customer-location context.
Can promotions and coupon conditions be collected?
Observable offer details can include discount type, value, eligible items, minimum spend, delivery conditions, validity dates, customer eligibility, and promotion text.
Can menu additions, removals, and changes be detected?
Yes. Timestamped observations can identify category, item, description, price, modifier, availability, promotion, and image changes.
Can ratings and reviews be collected?
Eligible public data may include rating, review count, text, date, language, reviewer context when appropriate, restaurant location, ordering channel, and source.
Can restaurant sentiment be analyzed?
AI-assisted enrichment can classify sentiment and topics such as food quality, service, delivery, value, packaging, portions, and recurring complaints under agreed validation rules.
Can cloud and ghost kitchens be identified?
Research can examine shared addresses, coordinates, menu overlap, brand relationships, phone details, operating hours, and delivery-only patterns. Results should retain evidence and confidence classifications.
Can dietary, nutrition, and allergen data be collected?
Observable labels may include calories, ingredients, allergens, vegan, vegetarian, gluten-free, halal, kosher, and other dietary attributes. Availability and definitions vary by source.
Can restaurant data be delivered through an API?
Yes. Delivery options may include API, webhook, CSV, Excel, JSON, JSONL, Parquet, cloud storage, databases, warehouses, and custom integrations.
How frequently can restaurant data be refreshed?
Cadence depends on source behavior, location volume, menu volatility, field requirements, rendering, validation, intended use, and responsible request controls.
How is food and restaurant data validated?
Validation may cover identity, location, menu hierarchy, duplicates, prices, currencies, modifiers, fees, availability, timestamps, expected fields, anomalies, and source changes.
Can we review a sample before production?
In many cases, representative restaurants, menus, locations, and order scenarios can confirm the schema, matching rules, configured-price logic, quality checks, and delivery format.
How does Kvetoiq approach responsible restaurant data collection?
Kvetoiq scopes public sources, necessary fields, source conditions, request controls, privacy considerations, retention, intended use, and delivery requirements. Qualified counsel should review project-specific legal questions.
Start with representative restaurants
See what a reliable restaurant dataset could unlock.
Share your target markets, restaurants, platforms, delivery locations, fields, refresh requirements, matching goals, and delivery destination. Kvetoiq will help shape a practical collection and validation plan.