Diese Präsentation wurde erfolgreich gemeldet.
Wir verwenden Ihre LinkedIn Profilangaben und Informationen zu Ihren Aktivitäten, um Anzeigen zu personalisieren und Ihnen relevantere Inhalte anzuzeigen. Sie können Ihre Anzeigeneinstellungen jederzeit ändern.

The confluence of SEO & CRO

1.049 Aufrufe

Veröffentlicht am

Insights Analyst, Angus Carbarns, explores the rising role of machine learning and the confluence of SEO and CRO in creating truly effective user-first search experiences. He delivered this talk at the ManyMinds conference in London.

Veröffentlicht in: Daten & Analysen
  • Als Erste(r) kommentieren

The confluence of SEO & CRO

  1. 1. @dogdigital Optimising for task completion in a Machine Learning world.
  2. 2. • I work at Dog Digital, a full-service design and digital marketing agency • My formative ‘digital’ years were spent doing outreach at a big agency, followed by stints in content and site strategy at some small agencies • I now help clients use analytics, personalisation and CRO to solve their customers’ problems • As a psychology and sociology graduate, I’m interested in the ‘what’(quant) and the ‘why’ (qual) of human behaviour @dogdigital
  3. 3. @dogdigital
  4. 4. @dogdigital
  5. 5. @dogdigital Very low authority Thousands of authoritative links Even lower authority Thousands of links, high Page/Domain Authority
  6. 6. Primarily link-based, relatively static, predictable ranking model. Multiplicity of factors (including links), resulting in a more personalised search experience. Driven totally by machine learning? Likely adaptive to personal needs and intents. @dogdigital
  7. 7. 9 Image: Gianluca Fiorelli, Moz “It (RankBrain) works when people make ambiguous searches or use colloquial terms, trying to solve a classic breakdown computers have because they don’t understand those queries or never saw them before.” Greg Corrado, Principal Scientist Google
  8. 8. 10 • In-store sales measurement • Data-Driven Attribution modelling • Cross-device Attribution @dogdigital
  9. 9. 11 @dogdigital Source: How SOASTA & Google used machine learning to predict bounce rate and conversions
  10. 10. More complex, personalised search algorithm. @dogdigital _ Edmond Lau, Wired 2016
  11. 11. @dogdigital
  12. 12. 18 • Identify the purpose of the landing page. • Identify main content, supplementary content and advertisements. Is it easy to identify the main content immediately? • Review main content with regard to the purpose of the page. • Determine the amount of useful main content. • Determine the benefit of the supplementary content.
  13. 13. 19 @dogdigital “Google could see how satisfied users were... The best sign of happiness was ‘the long click’… That meant Google had successfully fulfilled the query. But unhappy users were unhappy in their own ways, most telling were the ‘short clicks’” Steven Levy, In the Plex Source: Cyrus Shpard, M0z
  14. 14. 20 @dogdigital Source: Larry Kim, WordStream
  15. 15. 21 @dogdigital Source: SEMrush Ranking Factors Study 2017
  16. 16. 22 @dogdigital Source: SEMrush Ranking Factors Study 2017
  17. 17. 23 @dogdigital “…When you add in more images and other elements that make pages more complex, those sessions converted less. Why? The culprit might be the cumulative performance impact of all those pages elements. The more elements on a page, the greater the page’s weight”. “Bounced sessions had median full page load times that were 53% slower than non-bounced sessions” Tammy Everts, SOASTA
  18. 18. @dogdigital
  19. 19. 26 @dogdigital
  20. 20. 27 @dogdigital - Identifying & addressing usability issues - Improving funnel efficiency - Investigating Bounce Rate causes - Split testing - Multivariate testing - Personalisation - User testing - Website surveys - Journey mapping
  21. 21. 28 @dogdigital - Identifying & addressing usability issues - Improving funnel efficiency - Investigating Bounce Rate causes - Split testing - Multivariate testing - Personalisation - User testing - Website surveys - Journey mapping Irrespective of specifics, it’s always about finding out why visitors aren’t completing an action, and fixing it.
  22. 22. More complex, personalised search algorithm. @dogdigital
  23. 23. 32
  24. 24. 33 • What are we trying to improve? • What issues are we aware of (to guide step 2)? • What does ‘good’ look like? • Create a framework • Traffic breakdown • Device insights • Analysis against key Goals • Qualitative analysis • Synthesise key findings • How can we improve Goal X? • Hypotheses informed by data • Prioritise based on projected impact, difficulty, resource • Test hypotheses • Implement if successful • Continually iterate and deploy if not • Challenge biases!
  25. 25. 35 @dogdigital
  26. 26. 36 @dogdigital • • • • • • •
  27. 27. @dogdigital
  28. 28. @dogdigital
  29. 29. @dogdigital
  30. 30. 42 @dogdigital • • • •
  31. 31. More complex, personalised search algorithm. @dogdigital
  32. 32. 44 @dogdigital
  33. 33. 45 @dogdigital
  34. 34. More complex, personalised search algorithm. @dogdigital
  35. 35. @dogdigital
  36. 36. @dogdigital
  37. 37. @dogdigital
  38. 38. 52 @dogdigital
  39. 39. 53 @dogdigital
  40. 40. 54 @dogdigital
  41. 41. 55 @dogdigital 3% uplift in Organic entries into booking engine
  42. 42. 56 @dogdigital
  43. 43. 57
  44. 44. Find me over at: www.dogdigital.com/thoughts @dogdigital @anguscarbarns

×