We extract furniture listings, appliance specifications, dynamic pricing, store inventory levels, and financing terms from conns.com. 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 conns.com. All fields typed and schema-versioned.
"sku": "892341", "title": "Samsung 27.4 cu. ft. Side-by-Side Refrigerator", "brand": "Samsung", "category": "Appliances", "sub_category": "Refrigerators", "price": 1299.99, "list_price": 1599.99, "rating": 4.6
| # | sku | title | brand | category | sub_category | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Financing objects from conns.com. All fields typed and schema-versioned.
"sku": "892341", "price": 1299.99, "retail_price": 1599.99, "discount_pct": 18.7, "financing_available": true, "monthly_payment": 54.16, "apr_estimate": 29.99, "clearance_flag": false
| # | sku | price | retail_price | discount_pct | financing_available | monthly_payment |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Inventory & Store objects from conns.com. All fields typed and schema-versioned.
"sku": "892341", "online_stock": true, "store_id": "TX-104", "store_stock": 3, "pickup_available": true, "delivery_available": true, "zip_code": "77002"
| # | sku | online_stock | store_id | store_stock | pickup_available | delivery_available |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Specifications objects from conns.com. All fields typed and schema-versioned.
"sku": "892341", "brand": "Samsung", "model_number": "RS27T5200SR", "colour": "Stainless Steel", "energy_star": true, "warranty_parts": "1 Year", "warranty_labor": "1 Year"
| # | sku | brand | model_number | colour | material | energy_star |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from conns.com. All fields typed and schema-versioned.
"review_id": "REV-99214", "sku": "892341", "rating": 5, "title": "Great fridge for the price", "date": "2023-11-14", "verified_buyer": true, "helpful_votes": 12
| # | review_id | sku | author | rating | title | body |
|---|---|---|---|---|---|---|
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Our conns.com scraper handles category pagination, dynamic pricing updates, location-based inventory checks, and financing terms extraction. We bypass bot protection and deliver structured data directly to your warehouse.
Extract titles, descriptions, dimensions, weights, and high-resolution image URLs for every furniture piece and appliance on the site.
Capture Conn's specific financing offers, estimated monthly payments, APR ranges, and promo text for credit-based pricing analysis.
Check stock levels, display model availability, and pickup options across specific zip codes and retail store IDs.
Monitor clearance flags, retail prices, active discounts, and promotional pricing changes on a daily or hourly schedule.
Parse structured specification tables including model numbers, energy ratings, warranty details, materials, and exact dimensions.
Extract customer sentiment, verified buyer status, star ratings, and review text across all product categories.
Maintain exact category hierarchies and brand associations to normalise conns.com data against your existing product taxonomy.
Reduce processing overhead by receiving only the records that have changed since the previous extraction run.
We manage proxy rotation, session headers, and CAPTCHA solving to ensure uninterrupted data flow from conns.com.
Brief in. Clean data out.
Select target categories, specific brands, or store locations. We map the extraction schema to your exact requirements.
We configure Playwright crawlers, proxy rotation, and session management tailored specifically for conns.com architecture.
We run schema validation, null-rate checks, and price anomaly detection on a sample dataset before production launch.
Structured JSON, CSV, or Parquet files are pushed to your S3 bucket, BigQuery dataset, or delivered via Webhook.
Retail sites deploy aggressive rate limiting and dynamic content loading. Here is how we maintain stable extraction pipelines.
Conn's heavily relies on client-side rendering to calculate monthly payments and display location-specific pricing. We execute full Playwright browser sessions to hydrate these widgets and capture the true price.
Inventory data requires setting a specific store context or zip code. Our pipeline manages distinct cookie sessions for multiple geographic regions to extract accurate local stock levels concurrently.
To avoid IP bans during deep category crawls, we route requests through US-based residential proxies, pacing request velocity to mimic legitimate user browsing patterns.
Retailers frequently update their frontend frameworks. We use multi-layered selector strategies including XPath, CSS, and regex fallbacks to ensure structural changes do not break the data feed.
Instead of re-scraping the entire catalogue daily, we index known URLs and only extract pages where HTTP cache headers or sitemap timestamps indicate a modification, optimising delivery speed.
Retailers track Conn's pricing, clearance events, and financing offers to adjust their own pricing strategies and remain competitive.
Brands analyse category depth, brand representation, and new product introductions to identify gaps in the market.
Logistics teams monitor out-of-stock rates and restock timelines across specific regions to gauge appliance supply chain health.
Financial analysts track in-house financing terms, APR changes, and promotional credit offers to understand consumer lending trends.
Manufacturers aggregate review data across multiple retailers, including Conn's, to guide product development and QA.
Appliance and electronics brands monitor advertised prices to ensure retail partners comply with Minimum Advertised Price agreements.
"Extracting retail data requires more than a simple HTTP GET. You need a pipeline that handles JavaScript hydration, regional contexts, and daily schema shifts."
Most internal teams abandon retail scraping projects when they hit the maintenance wall. Managing proxies, updating selectors, and handling CAPTCHAs consumes valuable engineering hours. DataFlirt provides the infrastructure and the operational oversight so your team receives clean, structured data without the operational burden.
Everything supported by our conns.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.
We utilise Scrapy deployed on Kubernetes to distribute extraction tasks across hundreds of nodes, ensuring rapid catalogue coverage.
Our middleware handles cookie persistence, header rotation, and location context injection to bypass basic anti-scraping measures.
Airflow orchestrates post-extraction validation tasks, checking for null values, price anomalies, and schema compliance before delivery.
Data delivered to where your team already works — no new tooling required.
About conns.com scraping, legality, and pipeline operations.
Ask us directly →Yes. We configure the pipeline to inject target zip codes or store IDs during the session initiation, allowing us to extract localised pricing and inventory availability across multiple regions concurrently.
We support daily, weekly, or custom schedules. For critical pricing intelligence, we can configure high-frequency pipelines targeting a specific subset of SKUs.
Yes. We capture the advertised monthly payment amounts, APR estimates, and promotional financing text displayed on the product pages.
We map parent-child relationships, extracting unique SKUs, prices, and specifications for each colour, size, or configuration option available on the listing.
We deliver structured data matching the agreed schema. Dates, prices, and numerical fields are cast to appropriate data types, and text fields are stripped of extraneous HTML and whitespace.
Our automated monitoring detects schema drift immediately. Our engineering team updates the extraction logic, typically resolving selector issues within hours to prevent data disruption.
20-minute scoping call. Pilot dataset within the week. Production within two. Stop managing proxies and writing brittle scraping scripts. Define your data requirements and let DataFlirt deliver production-grade datasets directly to your infrastructure.
