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name: analyzing-search-algorithms
description: Analyzes how Google Search works including crawling, indexing, ranking algorithms, and user experience factors. Use when explaining search engine mechanics, SEO strategy, or information retrieval systems.
---

Quick Start

Google Search operates in three main phases:

1. Crawling: Googlebot discovers web pages via links and sitemaps
2. Indexing: Content is analyzed, processed, and stored in Google's index
3. Ranking: Algorithms determine which results to show for each query

Check if a page is indexed: site:example.com/page-url in Google Search.

Workflow

Progress:

• Query Processing: Parse user intent, correct spelling, identify entities
• Document Retrieval: Find relevant pages from 100+ billion page index
• Ranking: Apply 200+ ranking factors via machine learning models
• Result Assembly: Generate snippets, knowledge panels, rich results
• Personalization: Adjust based on location, search history, device
• Quality Assessment: Apply spam detection and content quality filters

Progress:

• URL Discovery: Find new pages via links, sitemaps, URL submissions
• Crawl Budget Allocation: Prioritize important pages for crawling
• Content Extraction: Parse HTML, JavaScript, CSS, images, videos
• Content Processing: Extract text, understand structure, identify topics
• Index Storage: Store processed content in distributed database
• Freshness Updates: Re-crawl pages based on update frequency

Examples

Example 1 - Ranking Factor Analysis: Input: "Why does Page A rank higher than Page B for 'best coffee maker'?" Output: Analyze: content quality (expertise, authority, trustworthiness), relevance to query intent, page experience signals (Core Web Vitals), backlink profile, content freshness, user engagement metrics, mobile-friendliness.

Example 2 - Query Processing: Input: User searches "weather tomorrow" Output: Google identifies location intent, determines user's location, retrieves weather data from trusted sources, displays rich weather card with forecast rather than just web page links.

Example 3 - SERP Feature Selection: Input: Query "how to tie a tie" Output: Google shows: featured snippet with step-by-step text, video results carousel, related questions ("People also ask"), image results, standard web results - based on query type indicating instructional intent.

Best Practices

• Focus on user intent over keyword matching - Google prioritizes satisfying search intent
• Consider the full user journey from query to task completion
• Remember Google uses machine learning models (RankBrain, BERT, MUM) for semantic understanding
• Page experience signals (loading speed, mobile-friendliness, safe browsing) increasingly important

• Create content that demonstrates E-A-T (Expertise, Authoritativeness, Trustworthiness)
• Optimize for featured snippets by providing clear, concise answers
• Structure content with proper heading hierarchy and schema markup
• Monitor Core Web Vitals and technical SEO fundamentals
• Build topic authority through comprehensive, interlinked content clusters

• Use structured data markup for rich results eligibility
• Implement proper internal linking architecture
• Ensure crawlable site structure with XML sitemaps
• Optimize for mobile-first indexing
• Monitor Google Search Console for crawling and indexing issues

Common Pitfalls

• Keyword stuffing: Google's algorithms detect and penalize unnatural keyword usage
• Ignoring search intent: Optimizing for keywords without understanding user intent behind queries
• Focusing only on desktop: Google uses mobile-first indexing for most websites
• Neglecting page experience: Poor Core Web Vitals can hurt rankings despite good content
• Link schemes: Artificial link building tactics violate quality guidelines
• Duplicate content: Multiple pages with similar content can cannibalize rankings
• Ignoring freshness: Some topics require regular content updates to maintain relevance
• Over-optimization: Making content too SEO-focused at expense of user experience
• Misunderstanding ranking factors: Correlation doesn't equal causation in SEO analysis