By industry
Pricing & Promotions
Brand & Intelligence
Digital Shelf & Search
Assortment
For Retailers
E-Commerce Dashboard
Flipkart Insights (Live) 
Grocery Intelligence.svg)
Grocery Price (U.S.).svg)
Quick Commerce (India) 
Food & Restaurant
Fashion Intelligence
Automotive
Travel & Hospitality
Real Estate
Food Delivery Intelligence 
OTT & Streaming 
Fuel & EV Charging Price Tracker 
By Use Case
Pricing Intelligence
Digital Shelf Analytics
MAP Monitoring 
Cross-Border Price Parity 
Share of Search
Review Sentiment
Kitchen Market Gaps 
Dynamic Pricing / AI Repricing 
Promotions & Deals Alerts 
B2B / POI & Lead Data 
AI Search Visibility Dashboard 
Baby Products on Quick Commerce 
Hotel & Flight Rate Parity 
By Region
Flipkart Real-Time Insights
Quick Commerce — Zepto · Blinkit
Pincode Price Tracker
GCC Q-Commerce — Talabat · Noon 
UK Grocery — Tesco, Sainsbury's, Asda
AU Grocery — Coles & Woolworths 
Marketplace Scrapers
Developer APIs
Universal APIs
Delivery & SDKs
Knowledge Center
Guides & Playbooks
Downloads & Tools
Trust & CompanyGirlfriend Collective Catalogue, Size Charts & Review Dataset
Girlfriend Collective built its following on recycled fabrics and an extended size range, and this file captures the catalogue size by size — 33 columns covering price, stock flag, colour, size, full size charts, customer review text, ratings and images.
Two fields set it apart: Size Chart with the measurements behind each size, and Reviews with the actual review text — both present on all 50 sampled rows.
The file already exists, because this pipeline runs once a month whether you buy it or not. Most vendors start collecting after you order — which is why they quote a lead time. Here the most recent file is in your account within minutes of payment, with an API key issued at the same time.
What you get, in plain terms
Four things. Fit and feedback, captured alongside price and stock.
One row per size and colour
The file is at variant level, not product level: every size and colourway of a style is its own row with its own SKU and stock flag, so you can see exactly which sizes are still buyable.
Size charts as structured data
Size Chart carries the measurement table behind XXS to 6XL, so an extended range can be compared with your own grading.
Review text, not just a score
Reviews holds review titles and bodies as JSON, so fit complaints and fabric praise can be mined per style.
However you want it
Direct download, REST API, Amazon S3, Google Cloud, Snowflake or SFTP. The API key comes with the dataset.
Fields included in this dataset
All 33 columns, in the exact order they appear in the file — taken straight from the sample, not from a brochure. The free sample ships with a data dictionary giving an example value for each one.
Sample rows from the real file
Real rows from the sample file, not an illustration. The supplied sample has 50 variant rows with all 33 columns.
Coverage
Captured monthly across girlfriend.com. One row is one size-colour variant, identified by its SKU, with the style in PID.
| Metric | In the supplied sample | What it tells you |
|---|---|---|
| Variant rows | 50 across 4 styles | Bike shorts, run shorts, bra, leggings |
| Price range | $68 – $108 | Core compressive range |
| Out of stock | 3 of 50 size rows | Well stocked in black |
| Size charts | On all 50 rows | Measurements behind each size |
| Review text | On all 50 rows | Titles and bodies as JSON |
| Ratings | On all 50 rows | Average 4.72 |
Only tracking leggings, a colour drop or the extended sizes? Send the list and we will price a narrower file.
Historical data
The last 30 days come with the dataset. Beyond that we hold Girlfriend Collective records from February 2025 onwards.
Ask about historical dataNeed more data points?
We can extend this dataset beyond the standard 33 columns — reviews split into one row each with date and rating is the most requested change, along with a category path.
Request custom fields“Their reviews told us exactly which fit problems customers hit. That is product development input, not just competitive data.”
What people use this dataset for
Retail and brand pricing teams
Benchmark list prices, markdown depth and timing against a competitor's live catalogue.
Merchandising and planning
See assortment width, size depth and what is already out of stock, category by category.
Marketplace and resale sellers
Track which styles, colours and sizes disappear first to shape your own buys.
Data and AI teams
Train product matching, attribute extraction and pricing models on clean, structured catalogue data.
About Girlfriend Collective catalogue data
Girlfriend Collective sells activewear made from recycled materials, with a size range that runs well beyond the industry norm and a customer base that reviews heavily. Both of those show up in the data.
Size charts and extended sizing
Every row carries the measurement table behind its size. For a brand benchmarking size inclusivity, or a retailer mapping another brand's grading onto its own, that is the field that makes the comparison real rather than nominal.
Reviews as product feedback
Reviews holds the review title and body text — what customers say about compression, waistband roll and fabric feel. Mined across styles, it is a structured read on fit problems in your own category.
Two things to plan for
The sampled colour was black on all 50 rows, so a fuller sample is worth asking for before you judge colour coverage. Review text includes the public display names customers chose; ask for the pseudonymised version if you do not need them. Category, Discount and UPC or EAN are empty throughout.
Is collecting this data legal?
Collecting publicly visible product and price information is generally lawful in most jurisdictions. Actowiz collects only public pages, respects robots.txt and platform terms, holds no personal data, and aligns with GDPR and CCPA. We are ISO 9001 and ISO 27001 certified, and this dataset carries documented provenance so your legal team can review the source before you buy.
Frequently asked questions
Size Chart gives the measurements behind it.
