← Back to Library
0:00 / 0:00

Day 11 X 365 | How Google Search Engine Works | what is SERP structure

Digi Guru
52 views · October 25, 2020
Subscribe

Space play/pause · ← → skip 5s · F full screen · M mute

Search engine results pages are web pages served to users when they search for something online using a search engine, such as Google. The user enters their search query (often using specific terms and phrases known as keywords), upon which the search engine presents them with a SERP
Every SERP is unique, even for search queries performed on the same search engine using the same keywords or search queries. This is because virtually all search engines customize the experience for their users by presenting results based on a wide range of factors beyond their search terms, such as the user’s physical location, browsing history, and social settings. Two SERPs may appear identical, and contain many of the same results, but will often feature subtle differences.

The appearance of search engine results pages is constantly in flux due to experiments conducted by Google, Bing, and other search engine providers to offer their users a more intuitive, responsive experience. This, combined with emerging and rapidly developing technologies in the search space, mean that the SERPs of today differ greatly in appearance from their older predecessors.
#365challenge #seo #digiguru

My notes

Your notes are saved in this browser. Log in to keep them on every device.

    LATEST CASES
    CASE NO. 01199 Why Did IBM Watson Oncology Strategy Fail? Case Study / CASE NO. 01141 IBM Watson Health Case Study: Scaling Failure in Oncology / CASE NO. 00819 Spotify AI DJ Case Study: Personalization at Scale / CASE NO. 00812 Shopify AI-first Hiring Case Study / CASE NO. 00809 Duolingo’s AI-Driven Personalization Strategy / CASE NO. 00802 Amazon’s AI‑Driven Warehouse Robotics / CASE NO. 00794 Klarna’s AI Service Rollout: 3 Workforce Impacts / CASE NO. 00792 JPMorgan’s AI Contract Review / CASE NO. 00789 Netflix’s Recommendation Algorithm Teaches Retention / CASE NO. 00785 H&M’s Fast-Fashion Localization Strategy / CASE NO. 00764 Jollibee’s International Expansion Strategy / CASE NO. 00762 Tesla’s Shanghai Gigafactory Market Entry / CASE NO. 00760 Danone’s Wahaha Joint Venture Dispute / CASE NO. 00758 Netflix’s Content Localization Strategy / CASE NO. 00756 Why Did Walmart Fail in Germany? / CASE NO. 00754 Uber’s China Exit: 3 Strategic Lessons / CASE NO. 00752 IKEA’s India Market Entry Challenge / CASE NO. 00750 Starbucks China Entry: 3 Moves That Won Millions / CASE NO. 00748 How Did McDonald’s Localize Its Menu in India? / CASE NO. 00733 Chick-fil-A’s Drive-Thru Operations / CASE NO. 01199 Why Did IBM Watson Oncology Strategy Fail? Case Study / CASE NO. 01141 IBM Watson Health Case Study: Scaling Failure in Oncology / CASE NO. 00819 Spotify AI DJ Case Study: Personalization at Scale / CASE NO. 00812 Shopify AI-first Hiring Case Study / CASE NO. 00809 Duolingo’s AI-Driven Personalization Strategy / CASE NO. 00802 Amazon’s AI‑Driven Warehouse Robotics / CASE NO. 00794 Klarna’s AI Service Rollout: 3 Workforce Impacts / CASE NO. 00792 JPMorgan’s AI Contract Review / CASE NO. 00789 Netflix’s Recommendation Algorithm Teaches Retention / CASE NO. 00785 H&M’s Fast-Fashion Localization Strategy / CASE NO. 00764 Jollibee’s International Expansion Strategy / CASE NO. 00762 Tesla’s Shanghai Gigafactory Market Entry / CASE NO. 00760 Danone’s Wahaha Joint Venture Dispute / CASE NO. 00758 Netflix’s Content Localization Strategy / CASE NO. 00756 Why Did Walmart Fail in Germany? / CASE NO. 00754 Uber’s China Exit: 3 Strategic Lessons / CASE NO. 00752 IKEA’s India Market Entry Challenge / CASE NO. 00750 Starbucks China Entry: 3 Moves That Won Millions / CASE NO. 00748 How Did McDonald’s Localize Its Menu in India? / CASE NO. 00733 Chick-fil-A’s Drive-Thru Operations /