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SEO in the Age of Artificial Intelligence | How AI influences Search

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SEO hast changes over the past decade. Understand how classical ranking factors become less important, while user experience dominates the top rankings.

As seen live on stage at @ProjectAcom #PakCon2018 in Berlin.

Published in: Marketing

SEO in the Age of Artificial Intelligence | How AI influences Search

  1. 1. #pakcon2018 Philipp Klöckner @pip_net SEO in the Age of Artificial Intelligence
  2. 2. Philipp Klöckner Angel Investor & Advisor @pip_net 2005 2010 2015 2018 #pakcon2018
  3. 3. AI Already Omnipresent in Google Products
  4. 4. Search Engine Optimization Artificial Intelligence #SEO #AI 1. Status | 2. Functionality | 3. Implications
  5. 5. Classical Search Engine Optimisation Framework SEO Content Popularity Technical SEO • Inventory • Text • Rich Media • Video • Advice • Structured Data • Tools & Apps • Interactive Content • LINKS • Mentions • Brand Search • Comp. Brand Search • Direct Type-Ins • Sharing • All available signals • Internal Linking • URL Design • Indexation • Heading Tags • Href Lang Setup • Structured Data • HTTPS/HTTP2
  6. 6. Search Engine Optimisation Post-Panda (2011) SEO Content Popularity Technical SEO • Inventory • Text • Rich Media • Video • Advice • Structured Data • Tools & Apps • Interactive Content • … • Links • Mentions • Brand Search • Comp. Brand Search • Direct Type-Ins • Sharing • All available signals • Internal Linking • URL Design • Indexation • Heading Tags • Href Lang Setup • Structured Data • HTTPS/HTTP2 User Experience • Bounce Rate • Back To SERP • Dwell Time • Retention • Trust • Search Journey • Satisfaction of Intent PageSpeed
  7. 7. Search Engine Optimisation Today (2018) SEO Content Popularity Tech SEO User Experience
  8. 8. The Future of Search Engine Optimisation SEO C P T User Experience
  9. 9. Caffeine Update June 2010 February 2011 Google Panda Update Hummingbird August 2013 March 2015 RankBrain Amit Singhal leaves February 2016 May 2017 “AI first” Company Timeline: How AI entered Google Search Artificial Intelligence gradually changes how search works
  10. 10. 200 Ranking Factors
  11. 11. Training Machine Learning on User Signals Refined Search Fast & Slow Clicks Task Completion Quality Raters & Guidelines Browsing & Supplemental Data More Background: https://www.cnbc.com/2018/09/17/google-tests-changes-to-its-search-algorithm-how-search-works.html
  12. 12. Search Engine Optimisation Today (2018) SEO Content Popularity Tech SEO User Experience
  13. 13. The Future of Search Engine Optimisation SEO C P T User Experience
  14. 14. The Future of Search Engine Optimisation: Implications SEO C P T User Experience Hygiene Factors Defining the Top Rankings Ranking Factors: Good Proxy Signals for Website Quality • Direct Measurement of User Experience and Satisfaction • Extensive Testing and Incremental Learning • Training AI based on Immediate User Feedback • Data Aggregation, „Reverse Aging“, Even Stronger Network Effects and Moats
  15. 15. Implications of an AI-centric Search Environment User Focus More than ever: Relentless Focus on the User • No compromise on user experience • Understanding Intent, Needs and Objections • Strong Focus on Mobile and Speed • Embrace Voice and Conversational Search KPIs & Metrics How to Measure a Great Product and User Experience? • Conversion? (sparse data) • Bounce Rate? (unreliable) • Session Time? • Pages/Session? • Retention? • Secondary Metrics: • Supply, Inventory • Liquidity • PageSpeed Organisation Challenging the classical “SEO Team” Approach • SEO has interfaces with Dev, Product, PR, Design, Content/Editorial, etc • Ideal World: SEO would be a frontend PM skill • Install tech. SEO as a PM • Create independent x- functional SEO dev team Tools & Insights From Ranking-Tracking to Pattern Recognition • „Why have I lost rankigns?“ - „Who won and why?“ • Typical SEO Analysis: Data Aggregation & Pattern Recognition • New Tools should identify patterns and less obvious correlations automatically
  16. 16. The Only SEO Metric I Measure Daily… What matters most • Conversion (indexed) • Impressions/Session • Time On Site • Bounce Rate • Returning Visitors • Ultimately: Monthly Activity
  17. 17. Implications of an AI-centric Search Environment User Focus More than ever: Relentless Focus on the User • No compromise on user experience • Understanding Intent, Needs and Objections • No more “SEO text”, useful content or no text at all • Strong Focus on Mobile and Speed • Paywalls/Signup Layer might incur high indirect costs • Embrace Voice and Conversational Search • “Panda Diet” – Trimming the weak sports of your website KPIs & Metrics How to Measure a Great Product and User Experience? • Conversion? (sparse data) • Bounce Rate? (unreliable) • Session Time? • Pages/Session? • Retention? • Secondary Metrics: • Supply, Inventory • Liquidity • PageSpeed Organisation Challenging the classical “SEO Team” Approach • SEO has interfaces with Dev, Product, PR, Design, Content/Editorial, etc • Ideal World: SEO would be a frontend PM skill • Install tech. SEO as a PM • Create independent x- functional SEO dev team Tools & Insights From Ranking-Tracking to Pattern Recognition • „Why have I lost rankigns?“ - „Who won and why?“ • Typical SEO Analysis: Data Aggregation & Pattern Recognition • New Tools should identify patterns and less obvious correlations automatically
  18. 18. Takeaways: For Your Kind Consideration…. 1. Google Search heavily relies on AI already 2. Previous ranking factors are gradually declining in relevance 3. Classical SEO remains to be a hygiene factor instead 4. The best user experience will win position #1 in Google 5. Currently, Time on Site might be the best metric to measure UX 6. PMs have to learn SEO or SEOs have to merge into PMs 7. Equality of arms: Machines are also better at analysing ranking changes
  19. 19. THANK YOU! /kloeckner /in/kloeckner @pip_net mail@pip.net
  20. 20. The Panda Diet Addendum
  21. 21. Theory: Typical Page Quality (Qp) over Number of Pages (np) np Qp Homepage Category Category+Brand Facetted Search Thin Catalogue (low inventory) Dupe Content page „no results“ page highestlowestmediorceuseful 400.000200.000 300.000100.000 Page Quality (Qp) can be defined as content richness, engagement, ultimateley how useful the page is to the user. But also its revenue potential. PROBLEM: Since Panda (2011) this structure has become toxic.
  22. 22. TIME FOR A PANDA DIET!
  23. 23. Theory: Typical Page Quality (Qp) over Number of Pages (np) np Qp highestlowestmediorceuseful 400.000200.000 300.000100.000 Average Quality 😞 Quality Threshold 80% (mediocre and better) NOINDEX (320.000) INDEX (80.000) New Average Quality QTYINCREASE Target = 80% Quality Threshold RANKINGS Page Quality (Qp) can be defined as content richness, engagement, ultimateley how useful the page is to the user. But also its revenue potential.

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