Client Details
A global consumer goods brand required a scalable solution to monitor Minimum Advertised Price (MAP) compliance across hundreds of e-commerce platforms and retail aggregators.
Challenge
The client needed detailed visibility into how their products were listed and priced by third-party sellers. Information such as fulfillment methods (pickup, delivery, shipping), seller identity, and bundled promotions often impacted MAP compliance. Manual checks couldn’t keep pace with scale or frequency.
Solution
We deployed a high-scale, cloud-based web scraping framework to extract MAP-relevant data efficiently and accurately.
Key Technical Highlights
- Massive Scale Data Extraction: Our pipeline scrapes data from hundreds of websites weekly, processing extremely large volumes of records across regions and marketplaces.
- Granular Location-Wise Scraping: Captures product pricing and metadata across thousands of retail locations for each marketplace.
- Seller-Level Visibility: Extracts detailed seller-specific data, including fulfillment methods and promotional offers, enabling deeper MAP compliance analysis.
- Advanced Anti-Blocking System: Combines proxy rotation, human-like fingerprinting, CAPTCHA bypass, and the latest AI-assisted evasion methods to bypass leading bot-detection technologies.
- Centrally Managed Job Queue: Jobs are securely queued and intelligently distributed across a pool of dedicated scraping servers, based on resource availability and system load.
- Automated Data Validation: Post-scrape data flows through a QA pipeline built with Python, PySpark, and Databricks for structured and accurate delivery.
The Impact
✅ Scalable Compliance: Monitored 98.6% of global product listings within SLAs
✅ Precision Enforcement: Identified 37% more violations through location-specific analysis
✅ Infrastructure Reliability: Maintained 99.5% system availability despite anti-bot measures
✅ Streamlined Workflows: Reduced manual validation efforts by 82% through automated QA
