We extract fashion resale listings, pricing signals, seller profiles, likes and demand signals, brand and style tags, and keyword rankings from Depop. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Active Listings objects from depop.com. All fields typed and schema-versioned.
"listing_id": "dep_184729301", "title": "Vintage Levi's 501 High Waisted Mom Jeans W28 L30", "seller_username": "vintagevaultldn", "brand": "Levi's", "size": "W28 L30", "condition": "Good", "price": 42.00, "currency": "GBP", "likes_count": 284, "style_tags": ["Y2K", "Vintage", "Denim"]
| # | listing_id | title | description | seller_id | seller_username | seller_followers |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Sold Listings objects from depop.com. All fields typed and schema-versioned.
"listing_id": "dep_184729301", "title": "Vintage Levi's 501 High Waisted Mom Jeans W28 L30", "sold_price": 38.00, "original_ask_price": 42.00, "discount_from_ask": 9.5, "likes_at_sale": 284, "condition": "Good", "sold_date": "2026-05-08"
| # | listing_id | title | brand | category | size | condition |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Seller Profiles objects from depop.com. All fields typed and schema-versioned.
"seller_id": "vintagevaultldn", "username": "vintagevaultldn", "followers_count": 18420, "listings_count": 412, "sold_count": 2841, "rating": 4.9, "top_brands": ["Levi's", "Nike", "Carhartt"], "member_since": "2019-03-12"
| # | seller_id | username | display_name | followers_count | following_count | listings_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search & Trending objects from depop.com. All fields typed and schema-versioned.
"keyword": "vintage levi's jeans", "position": 1, "listing_id": "dep_184729301", "likes_count": 284, "seller_followers": 18420, "style_tags": ["Y2K", "Vintage"], "is_sold": false, "scraped_at": "2026-05-12T08:30:00Z"
| # | keyword | position | listing_id | title | brand | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Depop is a social-first fashion resale platform where trend signals live in likes, aesthetic tags, and seller follower counts — not just price. Our scraper captures all of it: active and sold listings, brand intelligence, style taxonomy, and seller follower dynamics.
Capture likes counts per listing — Depop's primary demand-proxy metric. High-like, unsold listings reveal price resistance; high-like, sold listings reveal true market-clearing prices.
Scrape sold listing prices, original ask prices, and the spread between ask and sell — giving you real secondary market transaction prices, not just listing aspirations.
Extract brand tags, style tags (Y2K, Vintage, Cottagecore, Streetwear), and aesthetic labels per listing — the trend taxonomy that defines Depop's fashion data.
Full size label, condition grade (New, Like New, Good, Fair), and colour per listing — enabling size-curve and condition-pricing analysis at scale.
Follower count, following count, total listings, total sold count, review rating, and top brands per seller — the influence-layer data that drives Depop purchase behaviour.
Monitor listing position for any brand, style, or keyword search on Depop — capturing likes, condition, size, and price in each result record.
Capture listing date and correlate with likes velocity — identifying items accumulating demand rapidly, a leading indicator of sell-through before the algorithm surfaces them.
Depop listings span GBP, USD, EUR, and AUD sellers. We normalise currency per listing and apply FX conversion to a target currency on delivery.
One-off resale market snapshots or continuous trending-style monitoring pipelines at daily cadences with change-detection diffing.
Brief in. Clean data out.
Provide brand terms, style keywords, category paths, or seller usernames. We design the extraction schema together — active listings, sold listings, or both.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and Depop-specific pagination handling.
Likes count null-rate audits, sold price completeness checks, brand tag coverage validation, and sample records before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Depop's social-first architecture, infinite scroll feeds, and likes-as-signal data model require scraping logic that goes well beyond standard e-commerce extraction.
Depop's likes count per listing is the single most valuable demand signal on the platform — more predictive of sell-through than price alone. It's not exposed via any public API. Our web scraping layer captures likes counts directly from listing pages, enabling likes-velocity analysis and demand modelling that API-only approaches can't provide.
Sold listings reveal what buyers actually paid — not just what sellers asked. Depop marks sold items but doesn't remove them. Our pipeline identifies and separately extracts sold listings with their final price, original ask, and likes-at-sale — giving you true secondary market clearing prices per brand, size, and condition.
Depop's search results, seller shops, and explore feeds load via infinite scroll. Our Playwright pipeline triggers scroll events to load and capture the complete listing set — not just the first page that naive scrapers return.
Depop's style tags (Y2K, Vintage, Grunge, Coastal Grandmother) and aesthetic labels are a unique fashion trend taxonomy not available on any other resale platform. We extract the full tag set per listing — enabling style-trend analysis and aesthetic-demand modelling at catalogue scale.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, likes count outliers, sold price coverage drops, and schema drift — and respond before you notice.
Resale platforms, authentication services, and fashion brands use Depop sold prices to model secondary market valuations for specific brands, items, and condition grades.
Fashion forecasters and brand strategy teams use Depop style tags, aesthetic labels, and likes-velocity data as a leading indicator of Gen-Z trend cycles — often 6–12 months ahead of mainstream retail adoption.
Luxury and streetwear brands track their own secondary market prices, condition distribution, and likes signals on Depop to understand brand equity and resale desirability.
ML teams building fashion AI use Depop listing data — brand tags, style labels, condition grades, size data, and image URLs — as training data for fashion classification, recommendation, and resale price prediction models.
Resale aggregators and fashion apps map high-follower Depop sellers and their top brands — identifying influential curators driving demand in specific style niches.
Sustainability researchers and consultancies use Depop sold volume data, condition distribution, and category mix to quantify resale market activity and circular fashion flows.
"Depop's likes count is the fashion resale market's most honest demand signal — and its sold listing prices are the only source of real Gen-Z secondary market transaction data. Neither is available via API."
Extracting Depop data reliably requires infinite scroll handling, likes signal capture from listing pages, sold-vs-active listing classification, style tag extraction, and daily selector maintenance. DataFlirt absorbs that complexity so your trend research and brand strategy teams can focus on the insights — not the infrastructure.
Everything supported by our depop.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration, deduplication, and retry logic. Playwright handles Depop's React SPA rendering, infinite scroll triggering, and seller shop pagination.
We maintain pools of UK and US ISP residential proxies — the primary Depop user geographies. Rotation happens per-request with sticky sessions where required.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About depop.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available listing, pricing, and seller data from Depop is generally permissible under applicable law — reinforced by the hiQ v. LinkedIn ruling and similar precedents. DataFlirt targets only public, non-authenticated data. We do not extract personal data, private messages, or purchase history. We recommend clients review Depop's ToS independently and consult legal counsel for specific use cases.
Yes. This is one of the most valuable aspects of our Depop pipeline. Sold listings remain visible on Depop with a sold badge. We separately extract sold listings with their final price, original ask price, likes count at time of sale, and sold date — giving you real secondary market clearing prices, not just aspirational ask prices.
Yes. Likes count is scraped directly from listing pages. This is Depop's primary demand-proxy signal — high likes on an unsold listing indicates price resistance; high likes on a sold listing confirms demand. It's not available via any public API and is one of the most distinctive fields in our Depop dataset.
Style tags and aesthetic labels (Y2K, Vintage, Grunge, Cottagecore, Dark Academia, etc.) are Depop-native classification signals applied by sellers. They represent the fashion taxonomy that Gen-Z buyers actually use to discover items. Extracting them at scale enables style-trend analysis, aesthetic demand modelling, and brand-by-aesthetic segmentation that no other data source provides.
Yes. Brand tag is a structured field per listing. We can scope a pipeline to specific brand terms — extracting all active and sold listings for a defined brand set — giving you a comprehensive secondary market price and demand dataset for those brands.
Our smallest packages start at a defined brand or keyword set (typically 2,000–20,000 listings) with weekly delivery. For broader style taxonomy research or full seller-map programmes, we price based on volume and cadence.
Yes. Every pipeline run captures timestamped likes counts per listing. Likes velocity — the rate of likes accumulation over time — is computable from the resulting time-series, and is available from the date your pipeline starts.
Absolutely. We provide a sample run of up to 500 listings including active and sold records as part of the pre-engagement scoping process — so you can validate style tag coverage, likes completeness, and schema fit before signing any contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a brand resale valuation dataset, a Gen-Z fashion trend monitor, or a sold price history feed — we scope, build, and operate the pipeline.
