The document summarizes key findings about consumer search behavior based on various studies and data analyses. It discusses that there are over 1 trillion annual Google searches conducted, and explores why people search and the different factors that influence search behavior, including goals, age, gender, location, and more. It also outlines the main search patterns observed, such as quitting, narrowing, expanding, and pogosticking. Mobile search behavior is discussed as well, noting how it has overtaken desktop searching and differs based on location and device.

1. Consumer
Search
Behaviour
THURSDAY 29TH OCTOBER 2015

2. Introductions
Gerald Murphy
Senior Search Manager
Sandra McDill
Chief Digital Officer

5. Search is huge
There are more than 1 trillion annual Google searches
– 137 searches, per person
Worldometers
Search Engine Land
There are over 1
trillion annual
Google searches

6. WHY DO WE SEARCH?

7. Problem

8. Goal

9. There is only so much we can predict

11. Income, Education and Health
• Reflective of how we engage with technologies
– More money, bigger choice of technology to choose from
• Will impact the types of products we will already have as a goal
• But have no impact on search engine use or behaviour

12. 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
Age development photo: Copyright granted, reused, unmodified (TheIRHistory)

13. Location
• Location is increasingly a huge factor for search as Google increases its
relevancy
• When a consumer adds location based search queries we have to listen to
the signal that is increasingly more important
• Yet where someone is from, isn’t going to impact their search behaviour

15. 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

16. 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

19. …on highly ranked results?
SO WHY DO WE CLICK?

20. Trust bias
• Highly ranked results are trusted
• Even if these abstracts are less relevant than other abstracts
Photo: Copyright granted, reused, unmodified

21. Quality bias
• Searcher click decision is influenced by:
– Relevance of clicked link
– Overall quality of the other abstracts in the ranking
0
10
20
30
40
50
60
70
Google Bing Yahoo
Comparison of relevant results vs
relevant descriptions
Descriptions
Results

22. F-shaped pattern
• We scan results in an F-shaped pattern

23. State of the art: Searcher behaviour
Query log analysis
‘Hold times’ of
documents
Number of clicked
results
Ranking of clicked
documents
Eye tracking
Evaluation of eye-
movement on a
computer screen
Areas of Interest
(AOI)
Heat maps
(don’t produce
these)
Gaze maps User studies
Mapping of gaze
data to web content
pages
Mapping of gaze
data to the results
page: ‘The Golden
Triangle’
Mapping of eye
movement to
Results and Content
pages: ‘The F-
shaped pattern’
Scanpaths

24. Heatmaps, suck !

25. 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

26. Enhance CTRs: Attractive keywords
Free Online Deal Affordable Price
Low Instant Local Fundamental Professional
Compare Today Find Official Shop
Great Site Qualified Save Secure
CTRs: click-through rates

27. Aggregated, blended, or universal SERP

28. Snippet length of descriptions
• If title, URL and description were on one line each, each would receive
equal attention
0 2 4 6 8 10
Short
Medium
Long
Navigational
URL
Description
Title
0 2 4 6 8
Short
Medium
Long
Informational
URL
Description
Title

29. Cited from Peter Morville
BEHAVIOUR PATTERNS

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

31. Quit
• We all quit at one point but why?
– Speed?
– Information seeking process successful?
– Information seeking process unsuccessful?
Bounce Rate
Pages viewed
per session
Time spent
on page
Click depth
Cart
abandonment

32. Narrow
• Second most common search pattern
– Refinement

33. Expand
• Uncommon
– Difficult problem
People also
searched for
Auto
completion
Auto
suggestions
Facets

34. Pearl Growing
• Expert searching
– Mining content and metadata
Competitor
insights
Facilitate
content ideas
‘Similar
searches’ for
keyword
research

35. Pogosticking
• Conversation-attribute search
• We want a rich selection of information
– Google Knowledge Graph
– PPC and SEO results
• Result sampling
– But too much is bad

36. Thrashing
• Anchor bias
– We think we carry out the perfect search

38. Mobile is huge
• Mobile browsers have increased 53% vs 2013
• Mobile Apps have increased 90% vs 2013
– This has contributed to a total increase of 77% of the total increase in
time spent
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Jan-13 Jan-14 Jan-15
476,553.00
480,967.00 550,522.00
409,847.00
621,410.00 778,954.00
77,081.00 97,440.00 118,299.00
Mobile Browser
Mobile App
Desktop
http://www.comscore.com/Insights/Presentations-and-Whitepapers/2015/2015-Global-Digital-Future-in-Focus

39. Mobile Search

40. 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

41. 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

42. Advanced Search
• 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

43. Get a responsive website
• Mobile search has overtaken desktop on Google (SEL, May 2015)
• Justify to senior management with data analysis
Search Engine Land
Data
analysis
CRO
and UX
Be SEO
ready,
launch!

44. Geo data
• Discover your hotspots with data
– Top regions and cities
• Store targeting
– Paid search
– Organic search
*physical addresses are required

45. And use the mobile behaviour

46. Summary
Searchers are
complex
people
Goals or
problems
Trust bias
Quality bias
Scan ~16
SERP points
Interface
design
influence
6 search
behaviours
7 broad factors
impacting a
search
Mobile
behaviours

47. fin.
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Copyright © 2015 7thingsmedia LTD.
To get a copy of the presentation:
Matthew.Wilkinson@7thingsmedia.com
0207 017 3199