We extract brand profiles, verified buyer reviews, resolution rates, and star ratings from ConsumerAffairs. 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 Brand Profiles objects from consumeraffairs.com. All fields typed and schema-versioned.
"brand_id": "CA-98421", "brand_name": "American Home Shield", "overall_rating": 4.1, "total_reviews": 34219, "verified_buyer_count": 28410, "resolution_rate": 0.89, "category": "Home Warranty", "website_url": "ahs.com"
| # | brand_id | brand_name | category | overall_rating | total_reviews | verified_buyer_count |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Reviews & Ratings objects from consumeraffairs.com. All fields typed and schema-versioned.
"review_id": "REV-774921", "brand_id": "CA-98421", "star_rating": 1, "verified_buyer": true, "review_text": "Contractor never showed up after charging the service fee.", "review_date": "2026-03-14", "brand_response": true, "resolution_status": "Pending"
| # | review_id | brand_id | reviewer_name | reviewer_location | star_rating | review_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Complaint Resolutions objects from consumeraffairs.com. All fields typed and schema-versioned.
"resolution_id": "RES-44192", "review_id": "REV-774921", "issue_category": "Service Delay", "brand_response_text": "We apologise for the delay. A new contractor has been dispatched.", "resolution_outcome": "Resolved", "time_to_resolution_days": 4, "customer_satisfaction_update": "Positive"
| # | resolution_id | review_id | brand_id | issue_category | initial_complaint_date | brand_response_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Category Rankings objects from consumeraffairs.com. All fields typed and schema-versioned.
"category_slug": "home-warranty", "category_name": "Home Warranty Companies", "rank_position": 2, "brand_name": "American Home Shield", "score_index": 8.4, "trending_status": "stable", "top_rated_flag": true
| # | category_slug | category_name | rank_position | brand_id | brand_name | score_index |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Reviewer Data objects from consumeraffairs.com. All fields typed and schema-versioned.
"reviewer_id": "USR-99214", "display_name": "John D.", "state": "Texas", "city": "Austin", "total_reviews_submitted": 4, "verified_identity_flag": true, "helpful_votes_received": 12
| # | reviewer_id | display_name | state | city | total_reviews_submitted | helpful_votes_received |
|---|---|---|---|---|---|---|
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Our pipeline navigates ConsumerAffairs directory structures, paginates through millions of reviews, and structures unstructured complaint data into clean, queryable formats.
Capture overall star ratings, total review counts, verified buyer ratios, and contact metadata for any listed company.
Extract complete review narratives, star ratings, and helpful vote counts across all paginated endpoints.
Isolate reviews marked as verified buyers to filter out unverified sentiment and spam.
Track brand response times, resolution outcomes, and customer satisfaction updates on initial complaints.
Monitor brand positions within specific ConsumerAffairs categories like Home Warranty or Auto Insurance.
Extract reviewer state and city data to build regional sentiment maps for national brands.
Run pipelines daily or weekly to capture only new reviews and updated resolution statuses.
Navigate Cloudflare and rate limits using residential proxy rotation and automated CAPTCHA solvers.
Extract the complete review history for a brand from its first listing date on the platform.
Brief in. Clean data out.
Provide specific brand URLs, category slugs, or keyword sets. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, session management, and CAPTCHA handling for consumeraffairs.com.
Schema validation, null-rate checks, and sample review data verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
ConsumerAffairs protects its data with rate limits and dynamic rendering. Here is how we maintain reliable extraction.
We bypass rate limits and IP bans using US-based residential ISP proxies with realistic browser fingerprints and randomised request timing.
Major brands have tens of thousands of reviews spread across thousands of pages. Our crawlers manage state and retries to ensure zero dropped records deep in the pagination tree.
Certain brand response threads and resolution modals require JavaScript execution. We run headless Playwright sessions to hydrate the DOM and extract nested text.
We maintain a hash index of existing reviews. Subsequent runs only push new reviews or updates to resolution statuses, reducing processing overhead.
We use multiple fallback chains per field, including CSS, XPath, and JSON-LD extraction, ensuring layout updates do not break the data feed.
Enterprise brands monitor new complaints and resolution times to maintain their overall platform rating.
Marketing teams track competitor review velocity, common complaint themes, and category rankings to refine positioning.
Data science teams ingest review text to train NLP models on consumer sentiment and product feedback.
Investors evaluate target company customer satisfaction and churn risk by auditing historical complaint volumes.
Operations teams compare their brand response times and resolution rates against category averages.
Agencies analyse verified buyer demographics and geographic distribution to understand brand reach.
"ConsumerAffairs holds the definitive record of verified buyer complaints and brand resolution metrics, but extracting this requires traversing millions of paginated review endpoints."
Most teams underestimate the investment required: reliable ConsumerAffairs scraping requires residential proxies, full JavaScript rendering, CAPTCHA handling, 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 consumeraffairs.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 and retry logic. Playwright handles JavaScript rendering and interaction flows.
We maintain pools of US residential ISP proxies. Rotation happens per-request to prevent rate limiting.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management.
Data delivered to where your team already works — no new tooling required.
About consumeraffairs.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available reviews and brand profiles is generally permissible. DataFlirt targets only public, non-authenticated data. We do not extract personal data beyond public display names or circumvent authentication walls.
We use US-based residential ISP proxies and request timing modelled on human behaviour to avoid triggering anti-bot protections.
Yes. We can run a full backfill to extract all historical reviews for specific brands before transitioning to a daily or weekly incremental feed.
Our change detection system monitors previously scraped reviews for status changes. If a pending complaint is marked as resolved, we emit an updated record.
We typically start with a defined list of target brands or specific category slugs. Contact us with your target volume for a scoped quote.
Yes. We provide a sample extraction of up to 500 reviews for your target brands to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off historical backfill or a continuous sentiment monitoring feed, we build and operate the infrastructure. Tell us your requirements.
