We extract appliance listings, pricing signals, rebate intelligence, local inventory, and reviews from PC Richard. 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 Appliance Listings objects from pcrichard.com. All fields typed and schema-versioned.
"sku": "RF28R7351SR", "title": "Samsung 28 cu. ft. 4-Door French Door Refrigerator", "brand": "Samsung", "model_number": "RF28R7351SR", "price": 2499.99, "colour": "Stainless Steel", "energy_rating": "Energy Star Certified", "dimensions_width": "35.75 inches"
| # | sku | title | brand | model_number | category | sub_category |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Complete list of extractable fields for Pricing & Rebates objects from pcrichard.com. All fields typed and schema-versioned.
"sku": "RF28R7351SR", "price": 2499.99, "list_price": 3199.99, "discount_pct": 21, "rebate_available": true, "rebate_amount": 150.0, "open_box_available": true, "open_box_price": 2199.99
| # | sku | price | list_price | discount_pct | rebate_available | rebate_amount |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from pcrichard.com. All fields typed and schema-versioned.
"review_id": "REV-8849201", "sku": "RF28R7351SR", "star_rating": 4, "verified_purchase": true, "review_title": "Spacious and quiet", "review_date": "2026-03-14", "helpful_votes": 12, "syndicated_source": "samsung.com"
| # | review_id | sku | reviewer_name | verified_purchase | star_rating | review_title |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Store Inventory objects from pcrichard.com. All fields typed and schema-versioned.
"sku": "RF28R7351SR", "store_id": "NY-104", "zip_code": "10001", "in_stock": true, "stock_level": "Low Stock", "display_model_available": false, "pickup_eligible": true, "next_day_delivery": true
| # | sku | store_id | zip_code | in_stock | stock_level | display_model_available |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Search Results objects from pcrichard.com. All fields typed and schema-versioned.
"keyword": "french door refrigerator", "position": 3, "sku": "RF28R7351SR", "sponsored": false, "price": 2499.99, "rating": 4.5, "review_count": 842, "badge_text": "Top Seller"
| # | keyword | position | sku | title | price | sponsored |
|---|---|---|---|---|---|---|
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Our PC Richard scraper handles complex appliance data models: rebate tracking, zip-code dependent inventory, open-box pricing, and syndicated reviews. Built with session management to bypass bot protection.
Extract deep specification tables including dimensions, energy ratings, voltage requirements, and included accessories.
Capture mail-in rebates, instant savings, bundle discounts, and promotional end dates for accurate net-price calculation.
Simulate zip code entries to scrape store-level availability, display model status, and delivery timeframes.
Track discounted open-box inventory levels and condition grades across different retail locations.
Pull native and brand-syndicated reviews, capturing star ratings, verified purchase flags, and helpful votes.
Extract add-on costs for haul-away services, professional installation, and extended warranty tiers.
Monitor organic positioning for high-value keywords across major appliance and electronics categories.
Extract estimated yearly energy cost and kWh usage figures directly from product listing metadata.
Run pipelines daily or hourly. Receive only the records that changed, optimising your ingestion costs.
Brief in. Clean data out.
Provide category URLs, brand filters, or SKU lists. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, session management, and zip code handling for pcrichard.com.
Schema validation, null-rate checks, price-outlier detection, and inventory sampling before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Retailers protect their pricing and inventory data. Here is how we maintain stable extraction across localized storefronts.
PC Richard alters pricing, availability, and delivery estimates based on the user's location. Our crawlers manage persistent cookie sessions injected with specific target zip codes to extract localized data accurately.
Retail sites block datacenter IPs aggressively to stop competitor scraping. We route requests through US-based residential ISP proxies with realistic browser headers to maintain access.
Rebate details, open-box availability, and syndicated reviews often load asynchronously. We use Playwright to execute JavaScript and wait for network idle states, capturing data that static HTML parsers miss.
We maintain a hash index of last-seen values per SKU. Subsequent runs only push diffs, reducing compute cost and downstream processing load. You get a clean changelog rather than full re-dumps.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, category layout changes, and coverage drops. We respond before you notice.
Regional appliance retailers track PC Richard pricing, open-box discounts, and bundle offers to maintain competitive positioning.
Appliance manufacturers monitor listings for Minimum Advertised Price violations, tracking both visible prices and in-cart rebate structures.
Retail strategists analyze brand share, model availability, and category depth to optimise their own merchandising strategies.
Marketing teams track the frequency, duration, and depth of rebate programs and holiday sales events.
Analysts monitor out-of-stock rates and delivery timeframes across zip codes to infer regional inventory constraints.
Product managers aggregate review text to identify common appliance defects or positive features for future product iterations.
"Appliance pricing is complex. Without tracking installation fees, haul-away costs, and mail-in rebates, your competitive intelligence is fundamentally flawed."
Extracting clean data from PC Richard requires managing localized sessions, parsing asynchronous rebate structures, and standardising complex specification tables. DataFlirt handles the extraction and normalisation, delivering query-ready datasets so your team can focus on pricing strategy.
Everything supported by our pcrichard.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 pcrichard.com scraping, legality, and pipeline operations.
Ask us directly →Yes. We configure pipelines to inject specific zip codes into the session state, allowing us to extract localized pricing, availability, and delivery estimates for multiple regions concurrently.
We extract the raw specification tables and normalise the keys. Dimensions, energy ratings, and capacities are parsed into typed numeric fields rather than raw text strings.
Yes. We track open-box availability, condition grades, and discounted prices, mapped to specific store locations where the inventory resides.
For targeted SKU lists, we can run intra-day pipelines. For full catalogue sweeps, we typically configure daily runs. Change detection ensures you only process updates.
Yes. We parse the promotional text and structured metadata to extract rebate amounts, types (instant vs mail-in), and validity windows.
Yes. We provide a sample run of up to 500 SKUs or specific category pages during the scoping phase, allowing you to validate the schema and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or continuous price monitoring across 50 zip codes, we scope, build, and operate the pipeline. Tell us what you need.
