In a mobile-first world, with time-poor users, research crazy and mainly browsing with one eye and one hand, we need to consider a few new approaches to SEO. Information overload is all around. We need to think about how we can maintain relevance with less in some cases. We need to consider the Iceberg Theory and simultaneously consider the Iceberg Syndrome as well as placement position in user view for content and links

1. ”Power
from
what
lies
beneath...
The
Iceberg
Approach
to
SEO”
“Task-­‐driven
search
for
the
time
and
attention
poor
overwhelmed,
mobile-­‐first
surfer“
Dawn
Anderson
@DawnieAndo from
@MoveItMarketing

2. WHO
IS
DAWN
ANDERSON
@dawnieando
dawn.anderson@@move-it-
marketing.co.uk
move-it-marketing.co.uk
linkedin.com/in/msdawnanderson
11+ years SEO & Digital Marketing Consultant
& Pracademic:
Contributor:
Speaker & Trainer:
Fellow of:

4. A
perfect
storm
is
building

5. Evolution
of
Information
Retrieval
Classic
Information
Retrieval
(IR)
Interactive
Information
Retrieval
(IIR)
Mobile
Information
Retrieval
(MIR)
Machine
Learning

6. Mizzaro’s
Framework
of
Relevance
• Information
resources
• User
problem
• Time
• Components

7. Evolution
of
Information
Retrieval
Classic
Information
Retrieval
(IR)
Traditional
10
blue
links

8. Evolution
of
Information
Retrieval
Interactive
Information
Retrieval
(IIR)
Machine
Learning
Human
is
in
the
loop
refining
with
feedback
until
the
informational
need
is
met
appropriately

9. Evolution
of
Information
Retrieval
Mobile
Interactive
Information
Retrieval
(MIIR)
Machine
Learning

10. Time
dimension
of
framework
of
relevance
extended
by
Cappala to
time-­‐
space
dimension
in
mobile
information
retrieval…
Because
the
user
is
on
the
move

11. Accelerometer
And…
the
speed
at
which
they
are
moving
impacts
relevance

12. "Mobile
IR
is
growing
much
faster
than
IR"
(Crestani et
al,
2017)

14. Something’s
brewing…‘A
Call
to
Arms’
in
‘Assistive
AI’

15. …
and
some
names
Subordinate
systems
Conducive
systems
Decisive
systems

16. These
are
the
names
of
the
‘Assistive
systems
categories’
given
by
Broder,
2018

17. When
Andrei
Broder
rocks
up
and
starts
giving
things
names…

18. It’s time to
wake up…
and smell
the coffee

19. You
might
know
some
things
he
named
already
in
2002
A
Taxonomy
of
Web
Search
Informational
Transactional
Navigational
Broder,
A.,
2002,
September.
A
taxonomy
of
web
search.
In ACM
Sigir
forum (Vol.
36,
No.
2,
pp.
3-­‐10).
ACM.

20. “Formally,
the
assistive
systems
can
be
viewed
as
a
selection
process
within
a
base
set
of
alternatives
driven
by
some
user
input.”
(Broder,
2018)

21. Layman’s
terms:
10
blue
links
&
a
search
box
is
just
now
one
of
very
many
ways
to
be
assisted
via
search
systems

22. Ubiquitous,
pervasive
computing

23. So…
back
to
exploring
these
systems

24. Conducive
Systems
(Broder,
2018)
This image cannot currently be displayed.
Ten
blue
links
&
a
search
box

25. DECISIVE
SYSTEMS
(Broder,
2018)
• All
decisions
are
automatically
made
• Ambiguities
are
resolved
• No
further
input
(refinement)
is
needed
by
the
user
• EXAMPLE:
Translation
systems,
self-­‐
driving
cars

26. SUBORDINATE
SYSTEMS

27. • Push
IR• Pull
IR
• Pull
Marketing
• Push
Marketing
Sales
/
outbound
advertising
Draw
customers
via
SEO
/
Content
/
inbound
Recommending
/
assistive
proactive
query
suggestions
/
task-­‐helping
Waiting
for
Search
Engine
User
to
enter
a
query
(re-­‐active)

28. Enter
>
Recommender
Systems

29. What
does
the
modern
assistive
search
system
user
look
like?

30. They
are
time
poor
and
research
crazy

31. Mostly
searching
with
one
eyeball
and
one
hand

32. Multi-­‐devicing (even
hugely
so
at
home)

33. Their
attention
span
is
not
that
short,
but
we
are
in
an
‘Attention
Economy’
I
can
concentrate
for
longer
than
you…
NO
YOU
CAN’T
MYTH

34. Information
overload
madness

35. Remember
the
‘Jam
Study’?

36. THE
ATTENTION
ECONOMY
Information
overload
Too
much
content
We
are
all
researchers
Agile
task-­‐
driven
searchers
‘Noisy’
information
systems

37. SEO
IN
A
CORNER
NOBODY
PUTS

38. You
noticed
mobile
phone
screens
were
small…
right?

39. We
need
to
think
about
icebergs
&
iceberg
approaches
in
modern
mobile-­‐first
and
assistive
SEO

40. 57%
We
need
to
think
about
…Iceberg
Syndrome

41. Iceberg
Syndrome
&
‘Reasonable
Surfer’
combined

42. And
…Iceberg
Theory

43. Less
is
sometimes
so
much
more
-­‐
Do
the
Chunky
Monkey,
Do
the
Swan,
Do
the
Bear

44. Thinking
about
information
overload
and
using
mental
heuristics
to
‘chunk’
Do
the
chunky
monkey

45. Clustering
and
chunking

46. INFORMATIONAL
CLUSTERS
&
WORKING
MEMORY
‘CHUNKING’
https://www.nngroup.com/articles/chunking/

47. Concise
brevity
&
directional
heuristics
TL:DR
Exec
Summary
AbstractShort
Answer
Table
of
Contents

48. Page
Jumps
<center>
<a
href="#Top">Top</a>
&nbsp;|&nbsp;
<a
href="#Bot">Bottom</a>
</center>
<center>
<a
href="#Top">Top</a>
&nbsp;|&nbsp;
<a
href="#Bot">Bottom</a>
</center>
<a
name="Top"><a
name="Bot">

49. Think
Gestalt
Principles
&
Adaptive
Web
Design

50. Our
minds
are
thought
to
understand
patterns
and
fill
in
gaps

51. We
will
look
at
3
main
Gestalt
principles
Proximity
Similarity
Closure

52. Gestalt
Principle
-­‐
Proximity

53. Gestalt
Principle
-­‐ Similarity

54. Gestalt
Principle
– Closure
-­‐
Reification
– What
do
you
see?

55. The
‘Long
Form’
Content
Myth

56. Because…
it
depends
Task
dependent
‘BM25
Fails’
Theory
Page
Length
Normalization
-­‐
suppression
Probably
ignored
on
transactional
pages
Niche
dependent

57. “The
Brain
processes
visuals
60,000
times
faster
than
text”
– 3M
Corporation
Research

58. Their
attention
span
is
not
that
short,
but
we
are
in
an
‘Attention
Economy’
I
can
concentrate
for
longer
than
you…
NO
YOU
CAN’T
MYTH

59. Be
interactive…
be
adaptive…
think
Gestalt

60. Because…
Google
Is

61. Calm
on
the
surface…
Furiously
semantically
powered
from
beneath
Do
the
swan

62. Power
yo
unstructured
data
with
collection
co-­‐
occurring
semi-­‐
structured,
relational
databases
&
structured
data
markup

63. Unstructured
data
(text)
Semi-­‐
structured
data
Relational
databases
Structured
data
XML
sitemaps Ordered
lists
Unordered
lists
Tabular
data Data
Feeds

64. Database
wins
every
time

65. Build
a
ontological
lexicon

66. Jump
Pages

67. Navigation
is
King
for
humans
&
search
engines

68. Utilise the
power
of
rich
relevant
text
combined
with
structure
from
navigational
systems

69. Structured
&
semi-­‐structured
task-­‐driven
and
contextual
internal
links

70. Breadcrumbs
• Global
• Sectional

71. Types
of
Internal
links
• Audience
defined
• Chronological
• Alphabetical
• Step
navigation
• Task
driven
navigation
• Conceptual
navigation
• Hierarchical
categories
&
subcategories
• Breadcrumbs
EVERY
SINGLE
MENU
ON
YOUR
SITE
IS
AN
OPPORTUNITY
TO
ADD
STRUCTURE
&
POWER

72. Also
a
pretty
good
antedote
for
‘crawl
budget’
issues…
There…
I
said
the
words
”crawl
budget”…
aargh

73. Identify
as
many
navigational
assistive
routes
to
the
most
important
pages
as
possible
Too
many
near
duplicate
pages?
Antedote:
-­‐>
Switch

74. YOU
NEED
A
SITEMAP
STRATEGY
Image
credit:
Wikipedia
•Visual
sitemap
•XML
sitemap
•HTML
sitemap

75. XML
Sitemap(s)
Visual
Sitemap(s)
All
parties
informed
&
educated
on
importance
Front
facing
HTML
sitemap(s)
Trio
of
sitemaps BOTS
HUMAN
VISITORS
SEO
/
DEVS
/
CONTENT
TEAM

76. Take
a
birds
eye
view
of
your
document
collection

77. Internal
link
visualisations

78. GREAT
TOOL:
Convertcsv.com
Image
credit:
Wikipedia
• URL
EXTRACTOR
TOOL
• EXTRACTS
TO
EXCEL
FOR
EASY
MANAGEMENT

79. Use
1st &
2nd Level
Relatedness

80. Machine
Learning
Word2Vec
&
Concept2Vec

82. Relatedness
for
Disambiguation
–
Co-­‐occurrence
Vector
Windows
First
Level
Relatedness
Co-­‐
Occurrence
Vectors
&
vector
window
Second
Level
Relatedness

83. Watch
out
for
scissor
happy
‘pruning’

84. IsRelatedTo Schema

85. Use
structured
data…
of
course

86. The
Web
of
Data
versus
The
Web
of
Document

87. “The
Web
as
I
envisaged
it,
we
have
not
seen
it
yet.”
(Sir
Tim
Berners-­‐Lee)
(2009)
Inventor
of
The
World
Wide
Web
&
‘Linked
Data’

88. Structured
data
explosion
2017
2014

89. SEOs
have
definitely
helped
to
start
to
turn
the
web
into
a
web
of
data

90. 25,471
88,467
56,307
148,960
2017 2018
STRUCTURED
DATA
IMPLEMENTATION
YoY
2018
versus
2017
– 3
x
Growth
JSON-­‐LD
MicroData

91. • Information
resources
• Can
be
a
descriptor
(not
necessary
to
have
a
document)
– a
‘thing’
may
well
suffice
• Sometimes
there
is
no
need
for
a
document
at
all
• Also
read
the
work
of
Cindy
Krum
on
this
point
Cappala extends
Mizzano’s ‘Framework
of
Relevance’
for
Interactive
Mobile

92. So…Schema
‘All
The
Things’
X
3
implementation
YoY

93. Growth
of
near
me
‘now’

94. AreaServed Schema
likely
useful
here

95. Test
your
structured
data

96. Work
on
‘Google
My
Business’
Maximise GMB

97. Sequentially
foraging
for
information
to
complete
tasks
and
sub-­‐tasks
Do
the
bear

98. Sequential
task-­‐driven
predictive
search
(not
overly
user-­‐modelled
or
overly
personalised)

99. Because…
for
example

100. It’s
not
very
much
about
you
any
more…
but
more
the
tasks
you
and
others
like
you
do

101. BUT…
HUMANS
ARE
INFORMAVORES
We
need,
and
thrive
off,
information

102. Predictive
task-­‐driven
search
is
like
an
RPG…
’
Tasks
are
pretty
predictable…
as
too
are
their
subtasks

103. TASK
PREDICTION
&
TIMELINES
&
SUBTASK
IDENTIFICATION
MOT
anyone?

104. Potentially
huge
search
engine
efficiencies…
prefetch
document?

105. Tasks
!=
sessions

106. Do
the
bear…
Using
hints,
scents
&
patches
– Optimal
foraging
theory
(Pirolli
&
card)

107. Understand
your
bears…Ask
customers,
staff
&
panels

108. TOP-­‐TASK
IDENTIFICATION
&
SEQUENCE
Gizza Job
Gizza Job

109. Sequence
signposting…
Where
to
next?

110. To
summarise…

111. A
search
UI
being
agilely
redesigned
over
time
with
adaptive
content
and
responsive
user
design
and
Gestalt
principles.
UI
for
the
overwhelmed
mobile-­‐first
user
in
an
attention
economy.

112. Attention
Economy
&
Information
Overload
– Chunking
&
task
assistive
recommending
(Push
IR)
Gestalt
Principles
Mobile
Search
UI
&
Sunburst
type
radial
data
visualisation
with
Adaptive
web
design
Berry
picking
-­‐ Human
information
foraging
theory
–
information
scents
&
information
patches
Relatedness
-­‐ Conceptual
1st &
2nd level
context
&
concept
disambiguation
Immediacy
/
Space
+
Time
dimension
(Geo
location
+
speed
of
movement)

113. Thank
you
Keep
in
touch
@DawnieAndo
@MoveItMarketing

114. References
• https://www.seroundtable.com/google-­‐search-­‐tab-­‐bar-­‐navigation-­‐25824.html
• Nielsen
Norman
Group.
2018. How
Chunking
Helps
Content
Processing.
[ONLINE]
Available
at: https://www.nngroup.com/articles/chunking/.
[Accessed
02
June
2018].
• Nogueira,
R.
and
Cho,
K.,
2017.
Task-­‐oriented
query
reformulation
with
reinforcement
learning. arXiv preprint
arXiv:1704.04572.
• Dumais,
S.,
2013,
March.
Task-­‐based
search:
a
search
engine
perspective.
In NSF
Workshop
on
Task-­‐Based
Search (Vol.
1).
• Park,
D.,
Kim,
S.,
Lee,
J.,
Choo,
J.,
Diakopoulos,
N.
and
Elmqvist,
N.,
2018.
ConceptVector:
text
visual
analytics
via
interactive
lexicon
building
using
word
embedding. IEEE
transactions
on
visualization
and
computer
graphics, 24(1),
pp.361-­‐370.
• Image
attribution
-­‐ By
JasonHise at
English
Wikipedia
-­‐ Transferred
from
en.wikipedia to
Commons.,
Public
Domain,
https://commons.wikimedia.org/w/index.php?curid=1724044

115. References
• De
Oliveira,
R.
and
Pentoney,
C.,
Google
LLC,
2018. Methods,
systems,
and
media
for
presenting
a
user
interface
customized
for
a
predicted
user
activity.
U.S.
Patent
Application
15/234,446.
• Medium.
2018. Hamburger
menu
alternatives
for
mobile
navigation
–
Zoltan
Kollin – Medium.
[ONLINE]
Available
at: https://medium.com/@kollinz/hamburger-­‐menu-­‐alternatives-­‐for-­‐
mobile-­‐navigation-­‐a3a3beb555b8.
[Accessed
02
June
2018].
• Verschure,
P.F.,
Pennartz,
C.M.
and
Pezzulo,
G.,
2014.
The
why,
what,
where,
when
and
how
of
goal-­‐directed
choice:
neuronal
and
computational
principles. Phil.
Trans.
R.
Soc.
B, 369(1655),
p.20130483.

116. References
• Maxwell,
D.
and
Azzopardi,
L.,
2018,
March.
Information
Scent,
Searching
and
Stopping.
In European
Conference
on
Information
Retrieval (pp.
210-­‐222).
Springer,
Cham.
• Hua,
W.,
Song,
Y.,
Wang,
H.
and
Zhou,
X.,
2013,
February.
Identifying
users'
topical
tasks
in
web
search.
In Proceedings
of
the
sixth
ACM
international
conference
on
Web
search
and
data
mining (pp.
93-­‐
102).
ACM.
• Richardson,
M.,
2008.
Learning
about
the
world
through
long-­‐term
query
logs. ACM
Transactions
on
the
Web
(TWEB), 2(4),
p.21.

117. References
• Singhal,
A.,
Buckley,
C.
and
Mitra,
M.,
2017,
August.
Pivoted
document
length
normalization.
In ACM
SIGIR
Forum (Vol.
51,
No.
2,
pp.
176-­‐184).
ACM.
• Singhal,
A.,
2001.
Modern
information
retrieval:
A
brief
overview. IEEE
Data
Eng.
Bull., 24(4),
pp.35-­‐43.
• Ong,
K.,
Järvelin,
K.,
Sanderson,
M.
and
Scholer,
F.,
2017,
August.
Using
information
scent
to
understand
mobile
and
desktop
web
search
behavior.
In Proceedings
of
the
40th
International
ACM
SIGIR
Conference
on
Research
and
Development
in
Information
Retrieval (pp.
295-­‐304).
ACM.

118. References
• Broder,
A.,
2002,
September.
A
taxonomy
of
web
search.
In ACM
Sigir
forum (Vol.
36,
No.
2,
pp.
3-­‐10).
ACM.
• Broder,
A.,
2018,
February.
A
Call
to
Arms:
Embrace
Assistive
AI
Systems!.
In Proceedings
of
the
Eleventh
ACM
International
Conference
on
Web
Search
and
Data
Mining (pp.
1-­‐1).
ACM.
• Hua,
W.,
Song,
Y.,
Wang,
H.
and
Zhou,
X.,
2013,
February.
Identifying
users'
topical
tasks
in
web
search.
In Proceedings
of
the
sixth
ACM
international
conference
on
Web
search
and
data
mining (pp.
93-­‐
102).
ACM.
• Nogueira,
R.
and
Cho,
K.,
2017.
Task-­‐oriented
query
reformulation
with
reinforcement
learning. arXiv preprint
arXiv:1704.04572.

119. Wang
Mizzaro Scagnetto
Church Chen
Crestani
Vassena
Follow
the
work
of: