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

1. Gerald Murphy
SERPs: From keyword to click

2. Anatomy of a search
@GeraldSearchCopyright granted, reused, unmodified (Peter Morville)

3. Why do we search in the first instance?
@GeraldSearch

4. Number of words
@GeraldSearch

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. [bbc] example of a navigational query
Purposely non-capitalisated

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. How engines classify queries in real-time?
@GeraldSearch

9. …on highly ranked results?
Why click…

10. Trust bias
 Highly ranked results are trusted
 Even if these abstracts are less relevant than other
abstracts
@GeraldSearchPhoto: Copyright granted, reused, unmodified

11. Quality bias
 Searcher click decision is influenced by:
 Relevance of clicked link
 Overall quality of the other abstracts in the ranking
@GeraldSearch

12. F-shaped pattern
 We scan results in an F-shaped pattern
@GeraldSearch

13. State of the art: Searcher behaviour
@GeraldSearch

14. Heatmaps, suck !
@GeraldSearch

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. Enhance CTRs: Attractive keywords
CTRs: click-through rates @GeraldSearch

17. Aggregated, blended, or universal SERP
@GeraldSearch

18. Presentation of results
 There are two effective ways to display results
 List format
 Tubular interface
@GeraldSearch

19. Snippet length of descriptions
 If title, URL and description were on one line each,
each would receive equal attention
@GeraldSearch

20. Behaviour Patterns
Cited from Peter Morville

21. Search behaviours
Quit
Source: Peter Morville (Search Patterns, Design for Discovery)
Narrow
Pogosticking Thrashing
Expand
Pearl Growing
@GeraldSearch

22. Factors affecting a search
@GeraldSearch

23. Offline and online behaviours

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Summary

35. You rock my world !
Thank you