We extract verified merchant ratings, product reviews, and customer feedback from Feefo. 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 Merchant Profiles objects from feefo.com. All fields typed and schema-versioned.
"merchant_id": "M-847291", "name": "Acme Insurance", "domain": "acme-insurance.co.uk", "total_reviews": 14290, "average_rating": 4.6, "trust_badge_tier": "Platinum Trusted Service Award", "response_rate": 0.94
| # | merchant_id | name | domain | total_reviews | average_rating | trust_badge_tier |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Service Reviews objects from feefo.com. All fields typed and schema-versioned.
"review_id": "SRV-99214", "merchant_id": "M-847291", "rating": 5, "review_title": "Excellent customer service", "author_name": "John D.", "date_created": "2023-10-14T08:30:00Z", "verified_purchase": true
| # | review_id | merchant_id | rating | review_title | review_body | author_name |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Product Reviews objects from feefo.com. All fields typed and schema-versioned.
"review_id": "PRD-11029", "product_name": "Ergonomic Office Chair", "sku": "EOC-2023-BLK", "rating": 4, "review_title": "Good support, tricky assembly", "date_created": "2023-11-02T14:15:00Z", "author_name": "Sarah W."
| # | review_id | product_name | sku | rating | review_title | review_body |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Merchant Replies objects from feefo.com. All fields typed and schema-versioned.
"reply_id": "REP-44821", "review_id": "SRV-99214", "merchant_id": "M-847291", "reply_body": "Thank you for the kind words, John. We are glad we could help.", "date_replied": "2023-10-14T10:45:00Z", "response_time_hours": 2.25
| # | reply_id | review_id | merchant_id | reply_body | date_replied | response_time_hours |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviewer Data objects from feefo.com. All fields typed and schema-versioned.
"author_name": "John D.", "location": "London, UK", "total_reviews_left": 4, "verified_status": "Verified Buyer", "purchase_date": "2023-10-01", "review_date": "2023-10-14"
| # | author_name | location | total_reviews_left | verified_status | purchase_date | review_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Feefo scraper handles paginated review feeds, dynamic merchant profiles, and product-specific feedback loops with full JavaScript rendering and anti-bot circumvention built in.
Capture total review counts, average star ratings, trust badge tiers, and response rates across thousands of merchant domains.
Extract core service feedback including star rating, review text, author details, and verified purchase timestamps.
Isolate reviews tied to specific SKUs and product pages, distinct from general merchant service ratings.
Monitor how brands respond to negative feedback by extracting merchant reply text, timestamps, and staff names.
Log historical changes to Feefo Trusted Service Awards (Gold, Platinum) to track merchant reputation over time.
Extract author names, stated locations, and verified buyer status to build demographic sentiment profiles.
Convert relative time formats into structured ISO 8601 timestamps for precise time-series analysis.
Traverse thousands of review pages per merchant without dropping records or triggering rate limits.
Configure continuous pipelines at daily or weekly cadences to capture new reviews as they are published.
Brief in. Clean data out.
Provide merchant domains, Feefo profile URLs, or category lists. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, session management, and pagination logic for feefo.com.
Schema validation, null-rate checks, and sample data reviews before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Feefo employs standard rate limiting and dynamic content loading. Here is how we build resilient extraction pipelines.
We route requests through residential ISP proxies with realistic browser fingerprints, preventing IP bans and rate-limit triggers during deep pagination of high-volume merchant profiles.
Feefo relies on JavaScript to load review feeds and interactive elements. We run Playwright browser sessions to trigger lazy-loaded content and capture data that basic HTTP requests miss.
We use fallback chains for all target fields. If a CSS class changes, the pipeline falls back to XPath or text-pattern matching, ensuring continuous data flow.
For daily monitoring, we maintain an index of previously scraped review IDs. Subsequent runs only extract new reviews and updated merchant replies, reducing processing overhead.
Every run emits structured logs. We alert on null-rate spikes or coverage drops, fixing selector drift before it impacts your downstream data warehouse.
Brands track competitor review volumes, average ratings, and common complaints to identify market opportunities.
Data science teams ingest raw review text to train NLP models and track customer sentiment trends over time.
Agencies analyse merchant response times and reply text to audit client customer service operations.
Retailers extract SKU-level feedback to detect manufacturing defects or sizing issues across their catalogue.
B2B service providers target merchants with declining service ratings as prime prospects for operational software.
Private equity analysts use historical review velocity and rating trends as proxy metrics for company health.
"Feefo provides authenticated proof of customer satisfaction, but extracting that trust signal at scale requires dedicated pipeline infrastructure."
Most teams underestimate the investment required: reliable Feefo scraping requires residential proxies, full JavaScript rendering, rate-limit management, daily selector maintenance, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our feefo.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 UK and EU 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 feefo.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available data from Feefo is generally permissible under applicable law. DataFlirt targets only public merchant profiles and published reviews. We do not extract personal data beyond the public author name, circumvent authentication walls, or violate GDPR. Clients should review Feefo terms and consult legal counsel for specific use cases.
We use residential ISP proxies and request timing modelled on human behaviour. Our infrastructure automatically detects rate-limit responses (HTTP 429) and triggers proxy rotation or exponential backoff routines to ensure uninterrupted extraction.
Yes. We extract general merchant service reviews as well as SKU-specific product feedback, maintaining the relational link between the review, the merchant, and the specific product URL.
Pipelines can be configured for daily or weekly execution. For specific high-value merchant profiles, we can configure hourly delta runs to capture new reviews almost immediately after publication.
Yes. Every review record includes associated merchant reply text, the timestamp of the response, and the staff name if provided on the public profile.
Our smallest packages start at a defined list of merchant domains with weekly delivery. For larger scale continuous monitoring across thousands of profiles, we price based on volume and delivery frequency. Contact us with your target list for a scoped quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off merchant dump or a continuous review monitoring feed across 5,000 domains, we scope, build, and operate the pipeline. Tell us what you need.
