We extract merchant profiles, aggregated scores, verified buyer reviews, and seller responses from ResellerRatings. 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 Store Profiles objects from resellerratings.com. All fields typed and schema-versioned.
"store_id": "RS-84729", "store_name": "TechGadgets Direct", "overall_rating": 4.2, "total_reviews": 1428, "five_star_count": 892, "category": "Electronics"
| # | store_id | store_name | store_url | overall_rating | total_reviews | five_star_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews objects from resellerratings.com. All fields typed and schema-versioned.
"review_id": "REV-993821", "store_id": "RS-84729", "star_rating": 5, "verified_buyer": true, "review_date": "2026-03-14", "has_seller_response": true
| # | review_id | store_id | reviewer_username | star_rating | review_date | review_title |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviewer Profiles objects from resellerratings.com. All fields typed and schema-versioned.
"reviewer_id": "USR-44219", "username": "TechEnthusiast99", "join_date": "2024-11-02", "total_reviews_written": 14, "location": "Chicago, IL", "average_rating_given": 3.8
| # | reviewer_id | username | join_date | total_reviews_written | helpful_votes_received | location |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Seller Responses objects from resellerratings.com. All fields typed and schema-versioned.
"response_id": "RSP-11029", "review_id": "REV-993821", "responder_name": "Customer Success Team", "response_date": "2026-03-15", "resolution_status": "Resolved", "time_to_respond_days": 1
| # | response_id | review_id | store_id | responder_name | response_body | response_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Category Rankings objects from resellerratings.com. All fields typed and schema-versioned.
"category_name": "Computer Hardware", "store_name": "TechGadgets Direct", "rank_position": 12, "overall_score": 8.4, "total_category_reviews": 45000, "scraped_at": "2026-05-12T10:15:00Z"
| # | category_id | category_name | store_id | store_name | rank_position | overall_score |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our ResellerRatings scraper navigates directory structures, handles review pagination, and extracts complete merchant profiles with built-in anti-bot circumvention.
Capture merchant names, contact details, overall ratings, review distributions, and category assignments across the entire directory.
Extract review text, star ratings, submission dates, and helpful vote counts. Paginate through thousands of historical reviews per store.
Isolate reviews marked as verified buyers to filter out unverified sentiment and focus on actual customer experiences.
Monitor how merchants reply to feedback. Extract response text, responder names, and calculate time-to-resolution metrics.
Compile reviewer profiles including join dates, total reviews written, and average ratings given to identify serial complainers or brand advocates.
Track store positions within specific industry categories to measure competitive standing over time.
Maintain a hash index of last-seen values. Subsequent runs only push new reviews or updated store metrics, reducing processing load.
Bypass rate limits and CAPTCHAs using rotating residential proxies and realistic browser fingerprinting.
Run bulk exports or configure continuous pipelines at daily or weekly cadences to keep your warehouse updated.
Brief in. Clean data out.
Provide store URLs, category names, or keyword sets. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for resellerratings.com.
Schema validation, null-rate checks, and sample review data verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Review platforms protect their data aggressively. Here is how our infrastructure maintains stable extraction.
Directory sites use strict rate limiting and IP reputation checks. Our crawlers use residential ISP proxies with realistic browser fingerprints and randomised request timing to avoid blocks.
Stores with tens of thousands of reviews require careful pagination logic. We manage state across deep page traversals, ensuring complete data capture without triggering session resets.
DOM structures change. Our selector strategy uses multiple fallback chains per field, including CSS selectors and XPath, so a layout update does not break your data pipeline.
For large directories, we maintain a hash index of last-seen values. Subsequent runs only push new reviews or updated store aggregates, reducing downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes or coverage drops and respond immediately.
Retailers monitor competitor ratings, review volume, and customer complaints to identify service gaps and market opportunities.
Agencies aggregate sentiment data across directories to track brand health and measure the impact of customer service initiatives.
B2B service providers identify merchants with poor ratings as targets for customer experience software or consulting services.
Data science teams use review text to train NLP models, extracting common themes in customer dissatisfaction or praise.
Comparison shopping engines pull aggregated scores to display trust badges and merchant ratings alongside product listings.
Private equity firms analyze historical review trends to assess the operational health of target e-commerce acquisitions.
"ResellerRatings holds critical verified buyer sentiment data, but extracting it requires navigating aggressive rate limits and complex pagination structures."
Most teams underestimate the investment required for reliable directory scraping. ResellerRatings requires rotating residential proxies, JavaScript rendering for dynamic review loads, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our resellerratings.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. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda and ECS. 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 resellerratings.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information is generally permissible under applicable law. DataFlirt targets only public, non-authenticated store profiles and reviews. We do not extract private user data or circumvent authentication walls. Clients should review terms of service and consult legal counsel for specific use cases.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for rate limit spikes in real time and trigger pool rotation automatically.
Yes. Our pipeline paginates through the entire review history for specified merchants, capturing data back to the first submitted review.
Pipelines can be configured to run daily or weekly. For specific high-priority merchants, we can configure hourly polling for new reviews.
Yes. If a merchant has replied to a review, we extract the response text, responder name, and timestamp, linking it directly to the original review record.
Our smallest packages start at a defined list of merchants (typically 500-5,000 stores) with weekly delivery. For full category extraction, we price based on volume and delivery frequency.
Absolutely. We provide a sample run of up to 50 merchants as part of the pre-engagement scoping process so you can validate schema fit and data quality before signing any contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off directory dump or a continuous review-monitoring feed across 10,000 stores, we scope, build, and operate the pipeline. Tell us what you need.
