Scrape API Data Scraping Cases
Amazon | Walmart Cross-Border E-commerce Data Collection Solutions – Empowering Data-Driven Business Growth
Below are our carefully selected real customer cases, showcasing the outstanding performance of Scrape API in various business scenarios. Each case has been authorized by customers for sharing, with authentic and reliable data.
Scrape API Application Case
Scrape API Success Story: Real-time High Success Rate Collection Drives Refined Upgrade of 30K SKU Selection
$2.2B Apparel Seller Leverages Pangolinfo Scrape API to Build SKU Insight Data Foundation
Client Overview
The client specializes in footwear and apparel categories, deploying across European and American markets through Amazon, with annual GMV of approximately $2.2 billion, over 30,000 SKUs, and products covering sports, outdoor, leisure and other scenarios. As business scale continues to expand, the client gradually transitioned from experience-driven product selection to data-based decision processes.
Challenges Faced
- Limited Product Selection Dimensions, Difficulty Quantifying Bestseller Potential: Existing tools had single data structures, lacking fine-grained collection and aggregation of fields like product category rankings, rating fluctuations, and listing timeliness. Product selection relied heavily on manual judgment with low decision efficiency.
- Large SKU Volume Causing Collection Success Rate Fluctuations: With over 30,000 SKUs that update frequently, traditional collection solutions experienced scraping failures and incomplete data under high concurrency conditions.
- Data Silo Issues: While having an in-house ERP system, third-party SaaS tool data was difficult to integrate, requiring manual import/export without forming a data closed loop.
Solution
- High-Fidelity Collection Supporting Multi-Market SKU Tracking:Scrape API supports over 10 Amazon marketplaces including US, UK, Germany, Japan, France, Spain, stably collecting key fields like product titles, prices, ratings, review counts, category rankings, and listing times.
- High Concurrency Scraping with 99%+ Collection Success Rate Ensuring Data Continuity: Leveraging Pangolin's global real IP network pool technology, maintains over 99% data collection success rate even under high concurrency environments with 30,000 SKUs collected multiple times daily.
- <bRapid Internal System Integration: Scrape API provides standardized data structures and configurable field interfaces, allowing the technical team to complete integration with the in-house ERP system in just 2 days.
Outstanding Results
DataPilot Application Case
Data Collection API Client Case: Structured Parsing Assists Product Selection, Pangolin Supports Multi-Category Expansion
$3B GMV Multi-Category Seller Achieves Data-Driven Follow-Sell Strategy Through DataPilot
Client Overview
The client has been deeply involved in cross-border e-commerce for many years, operating over 200 stores on Amazon with more than 10,000 SKUs, covering consumer electronics, home goods, outdoor equipment, tools and other categories. Their team adopts a dual-drive strategy of "rapid testing + follow-sell incubation".
Challenges Faced
- Insufficient Product Selection Filtering Conditions: Traditional tools mostly use fixed templates, unable to flexibly set combined filtering conditions like "first listing time" and "rating fluctuations", resulting in coarse product selection granularity and low hit rates.
- Missing Structured Information from Detail Pages:Information on product pages such as materials, dimensions, functional descriptions, and buyer Q&A contains consumer preferences and product pain points, but third-party tools cannot collect or structure this information.
- Overly Long Decision Cycles:Follow-sell windows are extremely brief, previously taking an average of 5-7 days from data collection to product selection decisions, making it difficult to respond to market changes in real-time.
Solution
- Personalized Field Configuration for Precise High-Potential SKU Filtering:： DataPilot allows users to flexibly configure fields according to business needs, filtering bestseller signals by price range, listing time, rating changes and other conditions.
- Multi-Page Structured Collection for Cross-Category Trend Insights: Supports cross-page scraping of keyword ranking pages and product detail page data, automatically parsing advertising blocks, functional descriptions, rating keywords, Q&A and other fields.
- Scheduled Refresh and Quick Table Generation to Improve Product Selection Decision Pace: Platform supports daily/hourly scheduled refresh of rankings and keyword data, quickly generating structured product selection tables.
Outstanding Results
DataPilot Competitor Monitoring Case
E-commerce Data Scraping Solution Case: Intelligent Ad Targeting, Comprehensive Control from Competitor Rankings to Regional Performance
$1.8B GMV Mother & Baby Integrated Seller Builds Hourly Competitor Ad Monitoring System
Client Overview
The client is a major cross-border seller in the mother & baby category with annual GMV of approximately $1.8 billion. Products include baby strollers, car seats, baby care products, etc., adopting a dual model of factory direct control and overseas brand operations. They highly focus on ad ROI and specifically require real-time and precise competitor ranking monitoring and regional targeting performance analysis.
Challenges Faced
- Opaque Competitor Strategies:Core keywords are often occupied by competitors in ad positions, lacking real-time monitoring. Sudden loss of ad positions directly affects organic rankings and conversion rates.
- Significant Regional Placement Differences: The same ad shows significant differences in click-through rates and conversion performance between West Coast and East Coast, making it difficult for teams to quickly attribute and optimize regionally.
- Lagging Bidding Strategy Adjustments: Ad bidding decisions mainly rely on experience, unable to automatically adjust in real-time based on ranking changes, easily leading to dilemmas of "high price with no benefit" or "ranking drop with traffic plunge".
Solution
- Hourly Competitor Monitoring: System performs hourly updates on ad rankings for specified keywords of own and competitor products, supports historical trend backtracking, and quickly identifies position-grabbing risks.
- Intelligent Analysis by Postal Zones: Supports displaying ad rankings and performance data for different postal zones (regions), providing basis for regional ad configuration and placement strategies.
- Centralized Multi-keyword Monitoring: System tracks ad ranking changes for hundreds of core and long-tail keywords in parallel, automatically identifying traffic fluctuations, ranking drop risks, and competitor dynamics.
Outstanding Results
Scrape API Platform Application
Amazon Data Collection API Application Case: Keyword Analysis Platform Builds High-Precision Scraping Engine, Supporting Million-Level Traffic Models
Data Tool Platform Sif Adopts Pangolin to Achieve High-Concurrency, Multi-Postal Zone, High-Fidelity Page Data Collection
Client Overview
Sif.com is a SaaS platform providing data analysis services for cross-border e-commerce sellers. Core products include keyword ranking monitoring, traffic source attribution analysis, competitor ad tracking and other functions. The platform relies on high-frequency, high-dimensional Amazon public page data to generate traffic analysis reports, serving thousands of global cross-border sellers.
Challenges Faced
- Massive Collection Requests with Extremely High Concurrency and Stability Requirements: Sif needs to scrape millions of keyword search results daily, covering natural rankings, ad positions, brand zones and other modules, requiring the system to have high-concurrency scheduling capabilities.
- Complex Keyword Page Structure with High Restoration Accuracy Requirements: Keyword result pages contain various layouts including ads, brand zones, natural search, etc., with frequently changing structures. Sif's reporting engine is extremely dependent on position information and product zone markings.
- Support for Differentiated Scraping by Postal Zone Dimensions: Customers need to analyze ranking differences and ad distribution for the same keywords in different postal zones (such as East Coast vs West Coast), so collection capabilities need to support specified zip code triggered scraping and output results with regional tags.
Solution
- High-Concurrency Stable Collection Supporting Million-Level Daily Requests: Scrape API is built on Pangolin's global real IP scheduling architecture, supporting automatic task sharding and dynamic scheduling, ensuring 99%+ success rate even under massive keyword and high-frequency refresh demands.
- High-Fidelity Structured Parsing of Keyword Pages: Can completely scrape various module elements in keyword pages and structurally annotate ad blocks, natural rankings, brand display positions, etc., ensuring "ready-to-use" data for analysis systems.
- Support for Postal Zone Parameters and Regional Comparison Analysis: Provides flexible interface configuration, can set target zip codes and attach regional tags to returned data, helping customers establish ranking and traffic difference models at the regional level.
Outstanding Results
Related Products and Services
Scrape API
Core data collection services, supporting mainstream platforms such as Amazon and Walmart.
AMZ Data Tracker
The Data Pilot visualized data analysis platform can directly convert data into Excel, making data insight simpler.
Customized solutions
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