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Data AnalyticsContent StrategySEO/GEOCompetitive Intelligence
Competition Benchmarking via n8n & Python Content Scraping
- •Designed and executed a bulk URL scraping and content extraction exercise using an n8n and Python pipeline to benchmark content against Mayo Clinic, WebMD, and Apollo Pharmacy.
- •Analysis covered both quantitative dimensions (content volume, attribute coverage) and qualitative dimensions (depth, accuracy, user-friendliness).
- •Findings were used to prioritise content gaps and inform attribute expansion decisions.
The Challenge
- No systematic view existed of how Tata 1mg's content compared to international health information standards.
- Scraping and structuring content at scale across multiple competitor platforms required a robust, repeatable technical pipeline.
The Approach
- Built a Python and n8n-assisted scraping pipeline using BeautifulSoup4 and Pandas to extract and structure content from competitor URLs at scale.
- Designed a comparison framework mapping content attributes across platforms.
- Synthesised findings into a prioritised roadmap for content improvements.
Results
Surfaced 3 new attributes and deepened 5 existing ones based on competitive gap analysis.
Competitive benchmarking pipeline established as a repeatable n8n-assisted process.
Tech Stack
Pythonn8nBeautifulSoup4PandasExcelGoogle Search Console