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Competition Benchmarking via Python-Powered Content Scraping
- •Designed and executed a bulk URL scraping and content extraction exercise to benchmark Tata 1MG's health content against Mayo Clinic, WebMD, Apollo Pharmacy, and other competitors.
- •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-based 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
Three new content attributes added based on competitive gap analysis.
Depth of five existing attributes increased based on benchmark findings.
Competitive benchmarking pipeline established as a repeatable process.
Tech Stack
PythonBeautifulSoup4PandasExcelGoogle Search Console