This in-depth guide is designed for ambitious Amazon sellers and data analysts aiming to solve core pain points in traffic source analysis and ad spend ROI. The article begins by deconstructing the complex composition of Amazon’s Search Engine Results Page (SERP), introducing advanced analytical concepts like “Digital Share of Voice” (SOV) to demonstrate the limitations of traditional methods. After a thorough comparison of the deep pros and cons of data scraping solutions—manual checks, generic tools, and in-house scrapers—the guide highlights the core technical advantages of the Pangolin Scrape API as an enterprise-grade solution, such as its industry-leading ad collection rate and precise geolocation simulation capabilities. Furthermore, it provides an enterprise-level code example using Python and Pandas to demonstrate how to automatically scrape, analyze SERP data, and generate a CSV report. Finally, the article pioneers a framework for advanced strategic applications based on raw data, including building a dynamic SOV monitoring system, reverse-engineering competitor ad strategies, mining market opportunities from review data, and preparing high-quality training datasets for AI models. The goal is to help users ultimately transform their data scraping capabilities into an unbeatable business insight and competitive edge.