We extract product specifications, historical pricing, retailer offers, and merchant ratings from PriceRunner. 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 Metadata objects from pricerunner.com. All fields typed and schema-versioned.
"product_id": "pr-3210984", "name": "Apple iPhone 15 Pro 128GB", "brand": "Apple", "category": "Mobile Phones", "ean": "0195949041234", "lowest_price": 999.0, "highest_price": 1099.0, "rating": 4.8
| # | product_id | name | brand | category | ean | lowest_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Retailer Offers objects from pricerunner.com. All fields typed and schema-versioned.
"product_id": "pr-3210984", "retailer_name": "Currys", "price": 999.0, "shipping_cost": 0.0, "total_price": 999.0, "stock_status": "In stock", "delivery_time": "1-3 days"
| # | product_id | retailer_name | retailer_id | price | shipping_cost | total_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Price History objects from pricerunner.com. All fields typed and schema-versioned.
"product_id": "pr-3210984", "date": "2026-05-12", "lowest_price": 999.0, "average_price": 1045.5, "price_drop_pct": 5.2, "retailer_count": 24, "currency": "GBP"
| # | product_id | date | lowest_price | average_price | price_drop_pct | retailer_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Merchant Ratings objects from pricerunner.com. All fields typed and schema-versioned.
"retailer_name": "Currys", "overall_rating": 4.2, "review_count": 45102, "positive_pct": 82, "neutral_pct": 10, "negative_pct": 8, "response_time": "24 hours"
| # | retailer_id | retailer_name | overall_rating | review_count | positive_pct | neutral_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Expert Reviews objects from pricerunner.com. All fields typed and schema-versioned.
"product_id": "pr-3210984", "publication": "TechRadar", "score": 4.5, "max_score": 5.0, "summary": "An excellent premium smartphone.", "pros": "['Great camera', 'Fast processor']", "cons": "['Expensive']"
| # | product_id | publication | score | max_score | summary | pros |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our PriceRunner scraper handles every layer of the platform: product specifications, retailer offers, historical pricing graphs, and merchant ratings, with JavaScript rendering and session management built in.
Extract EANs, brand details, technical specifications, and category taxonomy for accurate product matching.
Capture base price, shipping costs, total price, delivery estimates, and stock status across all listed merchants.
Scrape the underlying data from PriceRunner price history graphs to track lowest price trends over time.
Extract retailer review scores, review volumes, and sentiment distribution to evaluate seller reputation.
Track inventory availability and shipping cost variations to calculate true landed costs.
Crawl entire category trees to map the complete electronics and gadgets landscape on PriceRunner.
Collect aggregated professional review scores, summaries, pros, and cons linked to specific products.
Extract data from PriceRunner UK, SE, DK, and NO domains using a unified extraction schema.
Run one-off bulk exports or configure continuous pipelines at hourly or daily cadences with change-detection diffing.
Brief in. Clean data out.
Provide category URLs, EAN lists, or keyword sets. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, and session management for PriceRunner.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Aggregator sites heavily protect their pricing data. Here is how we stay resilient, and why teams choose managed infrastructure over DIY.
PriceRunner uses strict rate limiting and IP reputation checks. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management.
Price history graphs and interactive retailer offer lists require JavaScript execution. We run full Playwright browser sessions to hydrate dynamic widgets and capture data that headless HTTP clients miss entirely.
Aggregator layouts shift frequently. Our selector strategy uses multiple fallback chains per field, combining CSS selectors, XPath, and JSON-LD structured data extraction to ensure pipeline stability.
For large product catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing compute cost, storage bloat, and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, price outliers, schema drift, and coverage drops, responding before you notice.
Retailers monitor competitor pricing across the market to optimise their own pricing strategies and protect margins.
Brands track merchant performance, shipping costs, and stock availability across different retail partners.
Analysts track price drops, promotional periods, and category saturation trends to identify investment opportunities.
Machine learning teams use structured product specifications and pricing histories to train recommendation engines.
Supply chain teams correlate price elasticity and stock depth indicators to improve procurement models.
Distributors audit third-party sellers for MAP violations and unauthorised reselling activities.
"PriceRunner aggregates the most competitive retail pricing signals across Europe, but extracting that historical data requires purpose-built infrastructure."
Most teams underestimate the investment required. Reliable PriceRunner 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 pricerunner.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 European regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda for burst scaling and ECS for sustained loads. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed PostgreSQL.
Data delivered to where your team already works — no new tooling required.
About pricerunner.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available pricing and product information from PriceRunner is generally permissible under applicable law. DataFlirt targets only public, non-authenticated data. We do not extract personal 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 503 and CAPTCHA rate spikes in real time and trigger pool rotation automatically.
We support pricerunner.co.uk, pricerunner.se, pricerunner.dk, and pricerunner.no from a unified schema.
Yes. We execute JavaScript to render the price history charts and extract the underlying data points, providing you with a structured time-series of past pricing.
Full catalogue refreshes at daily cadence complete within a 6 to 12 hour window depending on size. Real-time streaming pipelines achieve lower latency for specific product sets.
Our smallest packages start at a defined product list, typically 1,000 to 50,000 items, with weekly delivery. For larger catalogues, we price based on volume and delivery frequency.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off product catalogue dump or a continuous price-monitoring feed across 1M products, we scope, build, and operate the pipeline. Tell us what you need.
