We extract product listings, sold histories, pricing signals, seller intelligence, and condition metadata from Mercari. 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 Product Listings objects from mercari.com. All fields typed and schema-versioned.
"item_id": "m12345678901", "title": "Vintage Levi's 501 Jeans", "brand": "Levi's", "price": 45.0, "currency": "USD", "condition": "Good", "likes_count": 24, "status": "on_sale", "shipping_payer": "buyer"
| # | item_id | title | brand | category | sub_category | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Sold History objects from mercari.com. All fields typed and schema-versioned.
"item_id": "m98765432109", "title": "Nike Air Force 1 White Size 10", "brand": "Nike", "sold_price": 85.0, "currency": "USD", "condition": "Like New", "sold_date": "2023-10-15T14:30:00Z", "shipping_payer": "seller"
| # | item_id | title | brand | category | sold_price | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Seller Profiles objects from mercari.com. All fields typed and schema-versioned.
"seller_id": "u555444333", "username": "vintage_finds_99", "rating_score": 4.9, "rating_count": 842, "items_for_sale": 156, "items_sold": 1204, "badges": "['Quick Shipper', 'Reliable']", "verification_status": "verified"
| # | seller_id | username | profile_url | rating_score | rating_count | items_for_sale |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from mercari.com. All fields typed and schema-versioned.
"keyword": "y2k fashion", "position": 3, "item_id": "m1122334455", "title": "Y2K Rhinestone Baby Tee", "price": 25.0, "condition": "Fair", "status": "on_sale", "scraped_at": "2023-10-20T09:15:00Z"
| # | keyword | position | item_id | title | brand | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Category Data objects from mercari.com. All fields typed and schema-versioned.
"category_id": "c123", "category_name": "Women's Tops", "parent_category_id": "c10", "level": 2, "item_count": 450000, "top_brands": "['Shein', 'Zara', 'H&M']", "scraped_at": "2023-10-20T10:00:00Z"
| # | category_id | category_name | parent_category_id | level | item_count | top_brands |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Mercari scraper handles the complexities of a fast-moving peer-to-peer marketplace: tracking sold listings, seller verification badges, condition metadata, and dynamic search results — all with robust anti-bot circumvention.
Title, description, brand, category, condition, price, and images — scraped at the item level with full metadata.
Capture the actual sold price, not just the listing price, to build accurate valuation models for secondhand goods.
Extract seller ratings, sold counts, verification status, and badges to evaluate seller reliability and volume.
Track item condition tags and Mercari Authenticate badges for high-value items like sneakers and designer bags.
Monitor search results for specific keywords or brands to track inventory velocity and market saturation.
Extract who pays for shipping (buyer vs seller) and shipping fees to calculate total landed costs.
Track 'likes' count on listings to gauge consumer interest and demand before an item sells.
Run pipelines at regular intervals to detect status changes from 'on_sale' to 'sold' in near real-time.
Extract top-level category metrics and brand dominance within specific resale verticals.
Brief in. Clean data out.
Provide keywords, brands, categories, or seller IDs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for mercari.com.
Schema validation, null-rate checks, price-outlier detection, and sample listings before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Mercari employs strict rate limiting and bot detection to protect its C2C ecosystem. Here is how we ensure reliable data delivery.
Mercari aggressively blocks datacenter IPs. We utilize US-based residential proxies with realistic browser fingerprints and randomized request intervals to mimic genuine user browsing behavior.
Mercari is a Single Page Application. We deploy Playwright to handle JavaScript rendering, ensuring we capture data loaded asynchronously, such as infinite scroll results and dynamic pricing.
To capture accurate sold data, our pipelines frequently poll active listings, detecting the exact moment an item transitions from 'available' to 'sold', capturing the final transaction price.
Where possible, we interface directly with Mercari's underlying GraphQL APIs, bypassing HTML parsing for faster, more reliable data extraction while maintaining strict rate-limit compliance.
C2C listings are inherently messy. We apply post-processing to normalize brand names, categorize conditions, and parse unstructured descriptions into clean, queryable fields.
Authentication platforms and bulk resellers use sold history data to build accurate pricing algorithms for secondhand goods.
Luxury brands monitor listings for counterfeit items and unauthorized gray-market sales.
Fashion analysts track keyword velocity and sold volume to identify emerging vintage and Y2K trends.
Other C2C marketplaces analyze seller overlap, fee structures, and category dominance.
Professional flippers use real-time alerts for underpriced items from specific brands or categories.
Retailers analyze the secondary market volume of their products to gauge oversupply and brand dilution.
"Mercari holds the ground truth for what consumers are actually willing to pay for secondhand goods — a critical signal for the circular economy."
Extracting data from C2C marketplaces is notoriously difficult due to unstructured user inputs, rapid inventory turnover, and aggressive bot mitigation. DataFlirt manages the residential proxies, JavaScript rendering, and schema normalization required to turn Mercari's chaotic listings into structured, reliable intelligence.
Everything supported by our mercari.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 JavaScript rendering, cookie sessions, and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across US regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
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 mercari.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Mercari is generally permissible under applicable law. DataFlirt targets only public, non-authenticated listing, pricing, and seller profile data. We do not extract personal buyer data or circumvent authentication walls. Clients should review Mercari's ToS and consult legal counsel for specific use cases.
We use US-based residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and randomized request intervals. We also interface with their GraphQL APIs where possible to minimize HTML scraping footprint.
Yes. We can extract data from listings marked as 'sold', capturing the final transaction price, date of sale, and item condition, which is critical for valuation models.
Yes. We apply normalization routines to standardize brand names, categorize conditions, and parse key attributes from user-generated descriptions.
For targeted monitoring of specific listings or search queries, our pipelines can run at high frequencies (e.g., every 15-30 minutes) to detect status changes near real-time.
Absolutely. We provide a sample run of up to 500 listings or 50 search result pages as part of the pre-engagement scoping process — so you can validate schema fit, field completeness, and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a bulk export of sold history or continuous monitoring of specific brands — we scope, build, and operate the pipeline. Tell us what you need.
