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

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State of the art: Searcher behaviour
@GeraldSearch

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Heatmaps, suck !
@GeraldSearch

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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

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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

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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