Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

SERPs: From keyword to click. BrightonSEO (18th September 2015)

11,240 views

Published on

This deck analyses user behaviour in terms of search and was originally presented at BightonSEO on Friday, 18th September 2015, written and presented by Gerald Murphy.

Topics included and discussed:

Average number of keywords typed
Search query types and user behaviour (e.g. time spend on SERP)
Search engine click bias
Why heatmaps suck and why scanpaths rock my world
Attractive, clickable keywords for metadata
Why aggregated, universal, blended, SERPs exist
Presentation of results and scanning behaviour
Snippet length and information processing
Search behaviours (e.g. pearl growth)
Male vs female search behaviours
Age
Reading time
Mobile, the environment and search

Published in: Marketing
  • Be the first to comment

SERPs: From keyword to click. BrightonSEO (18th September 2015)

  1. 1. Gerald Murphy SERPs: From keyword to click
  2. 2. Anatomy of a search @GeraldSearchCopyright granted, reused, unmodified (Peter Morville)
  3. 3. Why do we search in the first instance? @GeraldSearch
  4. 4. Number of words @GeraldSearch
  5. 5. Types of search queries Informational (find) 48% to 70% (highest of 80% has been researched) of all queries Less time spent on SERP, compared to navigational queries More document time Navigational (get) 14% to 20% of all queries More time spent on SERP, compared to informational queries Less document time Transactional (buy) 20% to 30% of all queries Connectivity (calculate) Newest type of query, not queried (yet !) Google Knowledge Graph uses the theory behind this query type @GeraldSearch
  6. 6. [bbc] example of a navigational query Purposely non-capitalisated
  7. 7. Types of search queries Informational (find) 48% to 70% (highest of 80% has been researched) of all queries Less time spent on SERP, compared to navigational queries More document time Navigational (get) 14% to 20% of all queries More time spent on SERP, compared to informational queries Less document time Transactional (buy) 20% to 30% of all queries Connectivity (calculate) Newest type of query, not queried (yet !) Google Knowledge Graph uses the theory behind this query type (entities) @GeraldSearch
  8. 8. How engines classify queries in real-time? @GeraldSearch
  9. 9. …on highly ranked results? Why click…
  10. 10. Trust bias  Highly ranked results are trusted  Even if these abstracts are less relevant than other abstracts @GeraldSearchPhoto: Copyright granted, reused, unmodified
  11. 11. Quality bias  Searcher click decision is influenced by:  Relevance of clicked link  Overall quality of the other abstracts in the ranking @GeraldSearch
  12. 12. F-shaped pattern  We scan results in an F-shaped pattern @GeraldSearch
  13. 13. State of the art: Searcher behaviour @GeraldSearch
  14. 14. Heatmaps, suck ! @GeraldSearch
  15. 15. Scanpaths 1 2 3 45 6 7 8 9  16 scanpaths  5.8 compressed scanpaths  3.2 minimal scanpaths  We don’t always view in order of what the engine ranks  We click on what order the engine ranks  Trust and quality bias @GeraldSearch
  16. 16. Enhance CTRs: Attractive keywords CTRs: click-through rates @GeraldSearch
  17. 17. Aggregated, blended, or universal SERP @GeraldSearch
  18. 18. Presentation of results  There are two effective ways to display results  List format  Tubular interface @GeraldSearch
  19. 19. Snippet length of descriptions  If title, URL and description were on one line each, each would receive equal attention @GeraldSearch
  20. 20. Behaviour Patterns Cited from Peter Morville
  21. 21. Search behaviours Quit Source: Peter Morville (Search Patterns, Design for Discovery) Narrow Pogosticking Thrashing Expand Pearl Growing @GeraldSearch
  22. 22. Factors affecting a search @GeraldSearch
  23. 23. Offline and online behaviours
  24. 24. Income and educational level  Reflective of how we engage with technologies  More money, bigger choice of technology to choose from  But have no impact on search engine use or behaviour  Gender differences are mirrored offline and online @GeraldSearch
  25. 25. Male searchers  Tend to spend more time examining SERP  5.4 times more likely than females to inspect lower ranked results  More linear  View on average 3 more pages than females  More time to enter queries  Scan and filter results on SERP
  26. 26. Female searchers  Do not scroll as much  Less linear  Open 2 more browser tabs for more complex searches  Tend to repeat views of old results  Fixate heavily on positions #2 and #3  Prefer to read target results on paper  Less time on SERPs  Browse websites more deeply
  27. 27. Age  Younger and middle-aged searchers are very similar  18 to 60s  Older spend double the amount of time on the SERP  On average an extra 4 seconds @GeraldSearchAge development photo: Copyright granted, reused, unmodified (TheIRHistory)
  28. 28. Reading time  Indicative of interest for news stories  Reading time and scrolling equals relevance browsing  For web information retrieval reading time is not indicative of document relevancy because reading time differs between subject and task  Difficult to interpret @GeraldSearchBook photo: Copyright granted, reused, unmodified (Simon Cocks)
  29. 29. Mobile search  Good abandonment is higher on mobile search  Where searchers do not click but are satisfied with results  30% reduction in performance tapping buttons when walking  When we walk our arms move vertically  This is why voice search exists @GeraldSearch
  30. 30. Mobile search  Our search behaviour changes on public transport  Stronger observation on public transport  Momentarily passes on the street  Not an issue  Continue walking  Unwanted attention quickly passes @GeraldSearch
  31. 31. Mobile  Engines will soon process mobile queries like PPC, whereby:  Location  Time of day  Day of week  Weather conditions  Current activity of user  Temporal patterns (i.e. weekday vs weekend)  Will be factored into a mobile search @GeraldSearch
  32. 32. Mobile in social situations  70% of searches are conducted at home or at work  Mobile search is a social activity  Often conducted in the presence of others  Device type impacts mobile search  High-end smartphones have similar patterns to desktop searchers @GeraldSearch
  33. 33. Key references • Ashkan, A. and Clarke, C.L.A. (2012) Modeling Browsing Behaviour for Click Analysis in Sponsored Search. CIKM • Belkin, N. J. (2000) Helping People Find What They Don't Know. The Human Element 43(8) • Buscher, G., White, R.W., Dumais, S.T. and Huang, J. (2012)Large-Scale Analysis of Individual and Task Differences in Search Result Page Examination Strategies. WSDM • Cole, M.J., Gwizdka, J., Liu, C., Bierig, R., Belkin, N.J., and Zhang, X. (2011) Task and user effects on reading patterns in information search. 23 23(2011) • Cutrell, E. and Guan, Z. (no date) Eye tracking in MSN Search: Investigating snippet length, target position and task types. -- • Hochstotter, N., and Lewandowski, D. (2009) What users see -- Structures in the search engine results page. Information Sciences 179(2009) • Practical Ecommerce (online) Why is Metadata important? • Lewandowski, D. (2008) The retrieval effectiveness of web search engines: considering results descriptions. Effectiveness of web search engines- • Nettleton, D.F. Gonzales-Caro, C. (2012) Successful Web Searches: What Makes the Difference? An Eye-Tracking Study. -- • Rafiei, D., Bharat, K., and Shukla, A. (2010) Diversifying Web Search Results. WWW Full Paper • Rele, R.S. and Duchowski, A.T. (no date) Using eye tracking to evaluate alternative search results interfaces. -- • Singer, G., Norbisrath, U., Lewandowski, D. (no date) Impact of Gender and Age on performing Search Tasks Online. -- • Sushmita, S. Joho, H. and Lalmas, M. (no date) A Task-Based Evaluation of an Aggregated Search Interface. -- • Wilson, T.D. (2000) Human Information Behaviour. Special Issue on Information Science Research 3(2) • Gerald Murphy Search (online) • Gerald Murphy LinkedIn (online)
  34. 34. Summary
  35. 35. You rock my world ! Thank you

×