Quick Commerce Dataset: Product, Pricing & Review Data for Enterprise Intelligence
Structured, continuously refreshed quick commerce data across leading q-commerce platforms, dark store networks, and instant delivery apps — built for enterprise teams that need hyperlocal, decision-grade quick commerce market intelligence at scale.
- GDPR-Compliant Sourcing
- Hyperlocal Accuracy
- Deduplicated & Normalized
- Enterprise SLA
- AI & ML Ready
300M+
Q-commerce records indexed
40+
Q-commerce platforms covered
25+
Countries & metro markets
Hourly
Pricing & availability refresh
What Is the WebDataInsights Quick Commerce Dataset?
The WebDataInsights Quick Commerce Dataset is a comprehensive, production-ready collection of structured data sourced from the world’s leading quick commerce platforms, instant delivery apps, dark store operators, and rapid grocery delivery services. It captures the full quick commerce product, pricing, and customer experience landscape across major urban markets globally.
Quick commerce — often called q-commerce — is the fastest-evolving segment of digital retail, defined by sub-60-minute delivery windows, hyperlocal dark store fulfilment, and real-time inventory management. The data that governs it is equally dynamic. Our quick commerce dataset is engineered to match that velocity: refreshed hourly, entity-resolved, and normalized across platforms before delivery to enterprise teams.
Enterprise teams use this quick commerce dataset to power hyperlocal competitive pricing engines, monitor SKU availability across dark store networks, train AI demand forecasting and recommendation models, track consumer sentiment on q-commerce platforms, and conduct rapid grocery market sizing research across metropolitan markets.
The dataset is structured around three specialized sub-datasets — each independently licensable or available as a unified quick commerce intelligence package — designed to serve distinct operational, analytical, and AI-driven use cases across product intelligence, hyperlocal pricing, and customer review analytics.
Quick Commerce Product Dataset
Structured product catalog data across q-commerce platforms: SKUs, categories, brand data, pack sizes, nutritional metadata, availability signals, and dark store assortment coverage by geography.
Quick Commerce Pricing Dataset
Hyperlocal pricing intelligence: real-time and historical prices, surge pricing signals, delivery fee structures, platform-level discount patterns, and multi-platform price comparison across q-commerce apps.
Quick Commerce Review Dataset
Structured customer review data from q-commerce platforms: product ratings, delivery experience scores, sentiment analysis, aspect-level tags, and verified purchase flags across all covered apps.
Three Specialized Quick Commerce Sub-Datasets
Each sub-dataset is purpose-built for specific q-commerce industry use cases, delivered in normalized, schema-consistent format for immediate integration into your analytics, pricing, or AI infrastructure.
Q-Commerce Product Catalog Intelligence
Comprehensive product-level data across all major quick commerce categories — groceries, fresh produce, household essentials, personal care, pet supplies, and ready-to-eat meals — structured for competitive catalog benchmarking, AI training, and assortment analysis.
- Product titles, descriptions & brand data
- Category hierarchy (up to 5 levels: grocery, FMCG, etc.)
- SKU identifiers: EAN, UPC, GTIN, platform ID
- Pack size, weight, unit type & quantity
- Nutritional information & dietary tags
- Product images & asset URLs
- Availability status & dark store coverage signals
- Seller / brand & platform source attribution
Hyperlocal Pricing & Discount Intelligence
Multi-dimensional quick commerce pricing data — from real-time platform snapshots to 12-month historical archives — built for hyperlocal competitive pricing engines, FMCG brand intelligence, and q-commerce market research workflows.
- Current price, MRP & effective consumer price
- Platform discount percentage & absolute value
- Delivery fee, surge fee & service charge data
- Promotional tag & deal type classification
- Historical price time-series (up to 12 months)
- Multi-platform price comparison by SKU & city
- Price change frequency & velocity metrics
- Subscription / membership pricing variants
Customer Review & Delivery Sentiment Data
Structured customer review records from q-commerce platforms enriched with NLP-derived sentiment analysis — covering both product quality and delivery experience signals — purpose-built for brand monitoring, LLM fine-tuning, and consumer intelligence workflows.
- Star ratings (1–5) & total review count per SKU
- Review title & full review body text
- Product quality sentiment score & label
- Delivery experience sentiment score & label
- Verified purchase / verified delivery flag
- Aspect-level sentiment tags (freshness, packaging, speed)
- Helpful vote count & review recency classification
- Review date, platform source & reviewer city
Data Schema & Field Reference
Every quick commerce sub-dataset ships with a standardized schema and comprehensive field documentation. Below are the core fields across all three q-commerce datasets — delivered in your chosen format, ready for immediate pipeline integration.
Product Dataset Schema
Pricing Dataset Schema
Review Dataset Schema
See the Quick Commerce Data Before You Buy
A representative composite record from the Quick Commerce Product + Pricing Dataset — normalized, enriched, and schema-consistent across all covered q-commerce platforms and metro markets.
{
"product_id": "WDI-QC-IN-BLR-00847231",
"title": "Amul Taaza Full Cream Milk",
"brand": "Amul",
"ean": "8901063021983",
"category_path": ["Dairy", "Milk", "Full Cream"],
"pack_size": "1 Litre",
"weight_grams": 1030,
"city_code": "BLR",
"platform_source": "blinkit",
"mrp": 68.00,
"effective_price": 62.00,
"discount_pct": 8.82,
"delivery_fee": 9.00,
"surge_fee": 0.00,
"deal_type": "platform_discount",
"currency_code": "INR",
"availability": true,
"rating": 4.3,
"review_count": 2841,
"product_sentiment": 0.81,
"delivery_sentiment": 0.77,
"estimated_delivery": "8 minutes",
"snapshot_ts": "2025-06-15T09:30:00Z"
}* Sample records are illustrative. Full dataset records include all schema fields listed above plus product and city-level attributes. Request a full sample export →
Hyperlocal Quick Commerce Coverage at Platform Depth
Our quick commerce data collection infrastructure covers 40+ q-commerce platforms and instant delivery apps across 25+ countries and 100+ metropolitan markets — capturing product catalog, hyperlocal pricing, and customer review data at a frequency calibrated to the speed at which q-commerce inventory and pricing actually moves.
Unlike conventional ecommerce data, quick commerce data requires city-level and even dark-store-level resolution. Our collection methodology captures pricing and availability signals at the metro-market level, enabling city-by-city competitive benchmarking that national-level ecommerce data cannot support.
Coverage spans all major quick commerce categories: fresh produce and dairy, packaged groceries and FMCG, household and cleaning products, personal care and beauty, pet supplies, ready-to-eat and meal kits, over-the-counter health products, baby care, and stationery — with category-level filtering available on request.
Enterprise clients can configure coverage by specific platform, metropolitan market cluster, product category, or brand scope — with custom collection additions available for platforms not in the standard coverage list.
Quick Commerce Platforms & Sources We Cover
Our quick commerce dataset spans the world’s most commercially significant q-commerce apps, instant grocery delivery platforms, dark store operators, and rapid delivery networks — segmented by region and vertical for precision market intelligence.
Blinkit
Q-Commerce · India
Swiggy Instamart
Q-Commerce · India
Zepto
Q-Commerce · India
BigBasket Now
Instant Grocery · India
Dunzo Daily
Instant Delivery · India
JioMart Express
Q-Commerce · India
Gorillas
Dark Store · Europe
Getir
Q-Commerce · Europe & Turkey
Flink
Instant Grocery · Europe
Jiffy
Dark Store · Europe
Glovo
Instant Delivery · Europe
Rohlik
Rapid Grocery · Europe
Picnic
Rapid Grocery · Europe
Wolt Market
Q-Commerce · Europe
Talabat Mart
Q-Commerce · Middle East
Noon Minutes
Q-Commerce · UAE & KSA
Careem Now
Instant Delivery · MENA
GoPuff
Q-Commerce · US & UK
DoorDash Convenience
Instant Delivery · US
Grab Mart
Q-Commerce · Southeast Asia
Gojek GoMart
Q-Commerce · Southeast Asia
15+ More
Custom on request
Also covering: grocery chain rapid delivery arms (Tesco Whoosh, Walmart Express, Carrefour Flash), food delivery platforms with convenience verticals, and regional dark store networks. Request custom platform coverage →
How Enterprise Teams Use Quick Commerce Data
From hyperlocal pricing automation to FMCG brand intelligence and AI model training, the WebDataInsights Quick Commerce Dataset powers commercial and strategic decisions across the full q-commerce value chain.
Hyperlocal Competitive Pricing Intelligence
Monitor SKU-level price movements across q-commerce platforms city by city, detect platform discount patterns, identify surge pricing windows, and calibrate your own trade pricing strategy with real-time and historical quick commerce data — not delayed weekly reports.
- Real-time SKU price monitoring by city & platform
- Platform discount depth & frequency analysis
- Delivery fee benchmarking across q-commerce apps
- MRP compliance & trade margin monitoring
FMCG Brand & Category Intelligence
Track your brand’s distribution footprint, availability rate, and pricing consistency across quick commerce platforms and dark store networks — and benchmark against competitor brands in real time across every metro market you operate in.
- Brand distribution & dark store coverage mapping
- SKU availability rate monitoring by city
- Competitor brand share-of-shelf tracking
- Category assortment depth benchmarking
AI, Machine Learning & Demand Forecasting
Large-scale structured quick commerce product, pricing, and review data is foundational for training demand forecasting models, NLP sentiment classifiers for q-commerce platforms, recommendation engines, and LLM fine-tuning pipelines for grocery and instant delivery applications.
- Dark store demand forecasting models
- Q-commerce sentiment classification (NLP)
- Product substitution recommendation engines
- LLM fine-tuning for grocery & FMCG domain
Quick Commerce Market Research & Sizing
Quantify q-commerce market penetration by city, benchmark platform assortment depth against competitors, identify category whitespace across metro markets, and size the addressable quick commerce opportunity before committing to platform or city expansion investment.
- City-level q-commerce market sizing
- Platform assortment depth comparison
- Category whitespace identification by market
- Dark store footprint & coverage gap analysis
Consumer Sentiment & Delivery Experience Analytics
Transform millions of q-commerce customer reviews into structured intelligence — covering both product quality signals and delivery experience feedback — revealing what drives loyalty, what causes churn, and where platform experience gaps exist across each city and platform.
- Product quality sentiment by SKU & category
- Delivery experience tracking (speed, packaging, accuracy)
- Platform NPS & reputation benchmarking
- Freshness & quality complaint pattern analysis
Investment & Due Diligence Research
Private equity, venture capital, and corporate strategy teams use quick commerce datasets to validate q-commerce platform traction, assess SKU assortment maturity, benchmark dark store operational performance, and monitor portfolio company market positioning across metro markets.
- Q-commerce platform traction & growth signals
- Assortment maturity & category depth assessment
- City-by-city market penetration analysis
- Portfolio company competitive positioning
Who Uses Quick Commerce Datasets?
Commercial, brand, technology, and investment teams across the quick commerce ecosystem rely on structured q-commerce data to sharpen competitive positioning, automate pricing decisions, and build AI-powered instant delivery experiences.
FMCG & CPG Manufacturers
Track brand distribution, monitor trade pricing compliance, benchmark competitor SKUs, and identify availability gaps across quick commerce platforms and cities in real time.
Quick Commerce Platforms
Benchmark assortment depth against rivals, monitor hyperlocal competitor pricing, and analyse customer review sentiment to optimize platform experience and retention.
Grocery Retailers & Superchains
Monitor how your products are priced and reviewed on q-commerce platforms versus in-store, and track competitor rapid delivery arms entering your markets.
AI & Retail Technology Companies
Train demand forecasting, product substitution, and NLP sentiment models using large-scale labeled q-commerce product and review data across multiple cities and platforms.
Management Consulting Firms
Deliver evidence-based q-commerce strategy engagements — market entry analysis, platform competitive benchmarking, and category optimization — backed by objective data.
Private Equity & Venture Capital
Validate q-commerce platform growth metrics, assess dark store network maturity, and benchmark portfolio company performance across metro markets during diligence and monitoring.
Market Research Firms
Augment primary research with objective q-commerce pricing signals, availability rates, and consumer sentiment data across cities, platforms, and product categories.
Logistics & Supply Chain Teams
Monitor q-commerce SKU availability patterns and demand spikes to anticipate last-mile fulfilment pressure and optimize dark store inventory replenishment cycles.
Quick Commerce Data at Scale
Access comprehensive quick commerce data across grocery delivery platforms, dark stores, retailers, brands, products, pricing, and promotions worldwide. Power better decisions with accurate, real-time, and continuously refreshed data.
Start Your ProjectFlexible Q-Commerce Data Delivery for Every Stack
Your quick commerce data arrives in the format your engineering and analytics teams already work with — no transformation overhead, no internal wrangling required on day one.
JSON / JSONL
API integration & streaming pipeline ready
CSV / TSV
Direct BI tool & spreadsheet compatibility
Parquet
Columnar storage for big data & ML workflows
REST API
Real-time query access with city & SKU filters
Cloud Storage
S3, GCS, or Azure Blob scheduled delivery
Custom Pipeline
SFTP, webhook, or data warehouse push
Delivery cadence: one-time export · hourly · daily · weekly. All formats ship with full schema documentation, field dictionaries, and sample records across multiple cities.
Enterprise Quick Commerce Data Quality You Can Depend On
We operate as a specialized q-commerce data engineering partner — not a generic scraping service. Built for teams where hyperlocal accuracy, delivery freshness, and compliance are non-negotiable.
Hyperlocal Data Resolution
Unlike broad ecommerce datasets, our quick commerce data is captured and delivered at city-level and dark-store-level resolution. Pricing, availability, and assortment data is attributed to specific metro markets — enabling the hyperlocal competitive intelligence that q-commerce strategy demands.
Hourly Refresh for Q-Commerce Velocity
Q-commerce pricing and availability changes faster than any other retail segment. Our collection infrastructure is calibrated to match it — with hourly pricing snapshots, real-time availability signals, and sub-daily refresh options for platforms where price velocity is highest.
Dual Sentiment: Product + Delivery
Our Quick Commerce Review Dataset is uniquely structured with separate NLP sentiment scores for product quality and delivery experience — a distinction critical to q-commerce brand and platform intelligence that generic review datasets do not capture.
Legal & Compliance Framework
All quick commerce data is collected from publicly available sources under a defensible legal framework aligned with GDPR, CCPA, and applicable data protection regulations. Customer review data is anonymized throughout. Full source provenance documentation available on request.
Production-Grade Data Quality
Every q-commerce record passes through SKU-level entity resolution, deduplication across platforms and cities, pack-size normalization, and schema validation before delivery. You receive clean, decision-grade quick commerce intelligence — not raw outputs requiring internal engineering.
Custom Platform & City Scope
Standard coverage doesn’t match your market footprint? Enterprise clients can configure custom collection scope — specific q-commerce platforms, city clusters, product categories, or brand-level monitoring perimeters — with agreed delivery timelines and SLA commitments from day one.
Frequently Asked Questions
Answers to the questions enterprise buyers, FMCG brand managers, data engineers, and procurement teams ask most often about our quick commerce datasets.
A quick commerce dataset is a structured collection of data extracted from q-commerce platforms, instant grocery delivery apps, dark store operators, and rapid delivery networks. It typically covers three core data types: quick commerce product data (SKUs, categories, pack sizes, availability, nutritional metadata), quick commerce pricing data (real-time prices, discounts, delivery fees, historical price trends), and quick commerce review data (customer ratings, review text, product sentiment, delivery experience scores). Enterprise teams use quick commerce datasets for hyperlocal competitive intelligence, FMCG brand monitoring, AI model training, dark store demand forecasting, and q-commerce market research.
The Quick Commerce Product Dataset includes structured product records covering: product titles and descriptions, category hierarchy (up to 5 levels including grocery, FMCG, personal care), brand and manufacturer data, product identifiers (EAN, UPC, GTIN, platform-specific ID), pack size, weight and unit type, nutritional information and dietary tags (vegan, gluten-free, organic), product image URLs, real-time availability status, dark store coverage signals by city, and platform source attribution. All records are deduplicated and normalized across all covered q-commerce platforms and metro markets.
The Quick Commerce Pricing Dataset provides hyperlocal pricing intelligence at the city and platform level including: current MRP (maximum retail price), effective consumer price after discounts, discount percentage and absolute value, delivery fee, surge pricing fee, service charge, deal type classification (platform discount, brand promo, flash sale), subscription and membership pricing variants, historical price time-series data (up to 12 months), multi-platform SKU price comparisons by city, price change frequency and velocity metrics, and currency-normalized values. Data is captured at metro-market resolution — not national averages.
The Quick Commerce Review Dataset contains structured customer review records uniquely providing separate NLP-derived sentiment scores for both product quality and delivery experience — a critical distinction for q-commerce intelligence. Fields include: overall star rating (1–5) and review count per SKU, review title and full body text, product sentiment score and label, delivery experience sentiment score and label, verified purchase and verified delivery flags, aspect-level sentiment tags (freshness, packaging quality, delivery speed, order accuracy), helpful vote count, reviewer city, review date, and platform source. All records are anonymized.
Coverage spans 40+ quick commerce platforms globally. In India: Blinkit, Swiggy Instamart, Zepto, BigBasket Now, Dunzo Daily, and JioMart Express. In Europe: Gorillas, Getir, Flink, Jiffy, Glovo, Rohlik, Picnic, and Wolt Market. In the Middle East: Talabat Mart, Noon Minutes, and Careem Now. In the US and UK: GoPuff and DoorDash Convenience. In Southeast Asia: Grab Mart and Gojek GoMart. Also covered: grocery chain rapid delivery arms (Tesco Whoosh, Walmart Express, Carrefour Flash) and regional dark store networks. Custom platform additions are available for enterprise accounts on request.
Quick commerce data differs from standard ecommerce data in three fundamental ways. First, it requires hyperlocal resolution — pricing, availability, and assortment data must be captured at the city and dark store level, not just the national marketplace level. Second, it requires much higher refresh frequency — q-commerce platforms change prices and availability multiple times per day, requiring hourly or near-real-time data collection rather than daily or weekly snapshots. Third, the data schema is unique to q-commerce: fields like estimated delivery time, dark store coverage signals, surge pricing, delivery fees, pack size normalization, nutritional metadata, and separate product-vs-delivery sentiment scores are specific to the q-commerce context and are not captured in standard ecommerce datasets.
Refresh cadence is fully configurable based on your use case:
- Hourly / near-real-time: For live pricing monitoring, surge pricing detection, and real-time availability tracking across dark stores
- Daily snapshots: For competitive intelligence dashboards, FMCG brand tracking, and sentiment trend monitoring
- Weekly batches: For market research workflows, assortment benchmarking, and strategic reporting
- One-time historical export: For ML training datasets, investment due diligence, and retrospective market analysis
Yes. City-level data resolution is a core feature of the WebDataInsights Quick Commerce Dataset. Pricing, availability, and assortment data is captured and attributed at the metro-market level, enabling city-by-city competitive benchmarking across 100+ cities. This makes it possible to compare, for example, how the same SKU is priced across Blinkit, Swiggy Instamart, and Zepto in Mumbai versus Bengaluru versus Delhi — which is not possible with national-level ecommerce data. Enterprise clients can also filter delivery to specific city clusters or metro markets relevant to their business footprint.
Data is delivered in your preferred format: JSON, JSONL, CSV, TSV, or Parquet for batch delivery. Real-time access is available via REST API with city-level, platform-level, SKU-level, and category-level filtering. Cloud delivery is supported to Amazon S3, Google Cloud Storage, and Azure Blob Storage on configurable schedules. Custom delivery pipelines including SFTP, webhook push, and direct data warehouse ingestion (Snowflake, BigQuery, Redshift) are available for enterprise accounts. All formats ship with schema documentation and field dictionaries.
Yes. All quick commerce data is collected from publicly available sources under a defensible legal framework aligned with GDPR, CCPA, and applicable data protection regulations. Customer review data is fully anonymized — no personally identifiable information (PII) is included in any delivery. Full source provenance documentation is available for enterprise data governance and legal review. We recommend clients conduct their own legal assessment for jurisdiction-specific use cases and downstream AI training applications.
Quick commerce datasets power several specialized AI and ML applications:
- Training dark store demand forecasting and inventory replenishment models
- Building product substitution recommendation engines for out-of-stock scenarios
- Fine-tuning LLMs on q-commerce and grocery domain language
- Building NLP sentiment classifiers trained on real customer review text — with separate product and delivery experience labels
- Developing surge pricing prediction and dynamic discount optimization models
- Building hyperlocal search relevance and product ranking models for q-commerce apps
- Training SKU availability prediction models for dark store operations
Yes. Enterprise clients can request fully custom dataset configurations including specific q-commerce platform coverage, city or metro market clusters, product category inclusions or exclusions, brand-level monitoring scope, custom field mappings, and tailored delivery schemas. Representative sample datasets — matching your target platforms, cities, categories, and delivery format — are provided during the evaluation process before any commercial commitment. Contact our data team to scope your exact requirements and receive a sample within 48 hours.
Ready to Evaluate the Quick Commerce Dataset?
Request a sample export tailored to your target q-commerce platforms, cities, categories, and delivery format — at no cost. Our data team will scope your requirements and configure a representative sample within 48 hours.
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