Disentangling Technical and Content Attributes in Search Engine Ranking: A Comparative Study of Google and Bing

Disentangling Technical and Content Attributes in Search Engine Ranking: A Comparative Study of Google and Bing

Understanding why pages rank differently on Google versus Bing remains one of SEO's biggest mysteries — and a major competitive advantage for marketers. This project delivers a data-driven search engine ranking analysis system that decomposes ranking signals into two categories: technical SEO attributes (page speed, mobile-friendliness, schema markup, HTTPS, crawlability) and content attributes (keyword relevance, content depth, freshness, backlink quality, user engagement signals). The system collects search engine results pages (SERPs) for controlled query sets, crawls the top-ranking pages, extracts over 30 ranking features, and applies statistical analysis and machine learning models — including regression, random forests, and SHAP-based feature importance — to quantify each attribute's contribution to rank position on each engine. Findings reveal systematic differences in how Google and Bing weigh technical versus content factors. The output includes an interactive analytics dashboard, per-engine SEO scoring, and actionable optimization recommendations. Built with Python, Scrapy, BeautifulSoup, and Scikit-learn, this is a high-demand SEO data science project for digital marketing professionals, data analysts, and students exploring search engine ranking algorithms and comparative SEO analysis tools.

Components

Python 3.8+
Scrapy & BeautifulSoup
Scikit-learn
SHAP
Pandas
Plotly / Dash analytics dashboard

Key Features

• SERP collection and crawling for controlled query sets
• Extraction of 30+ technical and content ranking features
• Regression and random forest models with SHAP-based feature importance
• Per-engine SEO scoring revealing how Google and Bing weigh factors differently
• Actionable optimization recommendations dashboard

Applications

Digital marketing agencies, SEO professionals, data analysts and marketing data science students.