Dynamic e-commerce data monitoring workflow
Address the three core needs of e-commerce operations:
- Competitor Tracking: Input competing store URL, set depth 2 to crawl product list + detail page, auto update price/review count daily
- Explosion analysis: Extract "titles and main images of products with monthly sales >1000" through natural language commands to generate structured comparison tables.
- Public Opinion Monitoring: crawl social media API data to structure keyword clusters in user reviews in real time
Implementation steps: create a "618 Competitor Monitoring" project → add 10 store links → set "Price/Inventory/Rating" as the monitoring field → automatically synchronize the data at 8:00 every day → push reminders of abnormal changes via Webhook. Note that it is recommended to enable the "Anti-Crawler Simulation" option for batch crawling.
This answer comes from the articleSupametas.AI: Extracting Unstructured Data into LLM Highly Available DataThe
May not be reproduced without permission:AI productivity tools " How to apply Supametas.AI in e-commerce scenarios for competitive data monitoring?
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