We are headed to the age of assistive task driven search where the user needs help to 'do' things as well as learn things. Smart speakers, mobile phones, assistive systems and conversational search and action devices are where the buck is headed for now. Where are we at in this wave? What are the challenges? What are the opportunities right now? Here we look at some of the ways we can start to prepare our tactics and strategy to be pioneering search marketers with conversation search and conversation action.

1. ”Voice
Search
SEO
&
Assistive
Systems...
Challenges
&
Opportunities”
“The
current
situation
for
voice
search
&
SEO
and
overcoming
challenges“
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:

3. So,
just
what
is
conversational
assistance
&
where’s
the
opportunity?

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

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

6. …
and
some
names
Subordinate
systems
Conducive
systems
Decisive
systems

7. A
Trio
of
assistive
systems

8. But…
we
will
look
at
only
2
types
today…
which
are
a
hybrid
within
the
assistive
systems
Provide
answers
/
search
Conversation
Search
Help
with
activities
/
tasks
Conversation
Actions

9. Ok
Google,
Hello
Siri,
Greetings
Cortana

10. Phones,
Desktop,
Laptops
&
Smart
Speakers
&
Watches

11. Joe
Public
is
in
awe
and
wonder
at
Smart
Speakers

12. Interest
over
time
for
Google
Home
&
Amazon
Alexa

14. Stepping
out
of
the
SEO
bubble
Source:
SISTRIX.
2018. Stepping
out
of
the
SEO
Bubble
-­‐ SISTRIX.
[ONLINE]
Available
at: https://www.sistrix.com/blog/stepping-­‐out-­‐of-­‐the-­‐seo-­‐bubble/.
26,700+
respondents

15. Why
do
people
use
voice
search?
Source:
Higher
visibility
study
on
2000
people
(opportunity
sampling)

16. When
do
people
use
voice
search?
Source:
Higher
visibility
study
on
2000
people
(opportunity
sampling)

17. What
do
people
do
when
using
voice
search?

18. We
are
still
on
‘Day
One’
with
this
stuff

19. We
are
like
prospecting
gold
miners…
future
facing

20. All
signals
point
to
the
future

21. By
2020
30%
of
web
browsing
sessions
will
be
done
without
a
screen
(Gartner,
2016)
30%
70%
Web
browser
sessions
Without
a
screen
With
a
screen

22. Search
engines
&
IR
researchers
compete
over
voice
recognition

23. How
can
we
maximise conversation
search
&
conversation
actions
opportunities?

24. Accuracy
of
results
is
more
important
than
quantity

25. Let
us
first
look
at
‘conversation
Search’

26. In
2017
we
asked
”How”
more
than
anything
else

28. Since
voice
search
is
mostly
on
mobile
devices
(including
phones)…
be
VERY
mobile
friendly

29. We
have
some
guidance

30. Google’s
human
quality
raters
guidelines

31. And
also
some
of
the
researchers
who
work
on
the
Conversational
Search
team
at
Google
Switzerland

33. Machine
Learning
Word2Vec
&
Concept2Vec

34. What
did
they
say?

35. Tip
1
– Meet
informational
needs…
in
the
right
context

36. What
are
the
questions?
Who? What?
Where? When?
Why?

37. Transactional
Navigational
Informational
A
Taxonomy
of
Web
Search
(Broder,
2002)

38. Map
Different
Question
Answering
Content
To
Informational
Needs
Informational
Navigational
Transactional
Guides,
FAQs,
Quick
Answers,
How
to,
Articles
Directions,
Branch
locations,
Meet
the
Team,
Avout
Case
Studies,
Product
Reviews,
Testimonials,
Product
videos,
360
images,
specs

39. Think
often
‘On
the
Go’
location
intent
focus

40. Tip
2
– Keep
answers
short
&
get
to
the
point
early
– prosody
modifications
&
sentence
stress

41. Keep
it
brief
&
concise

42. Tip
3
– Watch
out
for
grammar,
spelling
&
pronunciation

43. Soundex,
Metaphone,
Double
Metaphone
(or
similar)
Algorithms

44. Tip
4
– Watch
out
for
pesky
pronouns

45. Linguisitics are
Complicated
–
Watch
out
for
anaphora
&
cataphora
resolution

46. It’s
raining
pronouns
– many
types
of
pronouns

47. So
many
ways
to
misunderstand
natural
language
Intensive Reciprocal Reflexive Personal Relative Indefinite Demonstrativ
e
Possessive Interrogative
Myself Each
other Herself I Who Anything This Mine Who
Himself One
another
Himself You Whom Everybody That His Whom
Herself Myself He Whose few Those Theirs Which
Itself Ourselves She Which many These Hers What
Ourselves Yourself We That none Ours Whatever
Yourself They What some Yours Whichever
Me Whatever Whomever
Him Whoever
Her Whomever
Us whichever

48. Computer
programs
lose
track
of
who
is
who
easily
I’m
confused…
Here…
Have
some
flowers
instead
;P
;P

49. They
Us
She
He Them
I
You
Him Her
Me
E.g.
Minimize
these
personal
pronouns…

50. That Those
These This
Time
&
Space
confuses
things
further

51. Instead…
refer
to
entities
by
name
(where
possible)

52. Dis…
Ambiguate

53. Unstructured
data
(text)
Semi-­‐
structured
data
Relational
databases
Structured
data
XML
sitemaps Ordered
lists
Unordered
lists
Tabular
data
Data
Feeds
Turn
‘fluffy’
web
pages
into
machine-­‐
understandable
formats
– add
signals

54. Structured
data
explosion
2017
2014

55. Over
half
of
voice
search
results
hold
featured
snippets
(Dr
Pete
Myers,
Moz,
2017)
Work
on
building
out
the
Knowledge
Graph

56. Tip
5
– Cover
all
bases
due
to
paraphrasing
absence

57. • Well
structured
long
form
informational
content
(where
appropriate)
• Semantic
headings
• Write
for
a
featured
snippet
win
(few
exceptions)
• Cover
the
bases
because
of
extraction
&
compression
(no
paraphrasing)

58. Tip
6
-­‐ Avoid
Tables

59. You
may
need
a
dual
or
triple
content
strategy
2%

60. Now
let’s
look
at
‘conversation
actions’

62. Many
subtasks
towards
a
major
task

63. Google
Assistant
– Actions,
Entities,
Dialog
flows
&
Intents

64. Many
built-­‐in
intents
&
many
‘coming
soon’

65. Extend
Actions
on
Google
using
Machine
Learning

66. Extend
Actions
on
Google
using
Machine
Learning

67. Understand
your
customers
to
assist
with
AI
Customer
Service
Data
Customer
Panels
Email
questions
FAQs
Build
Assistant
App

68. Understand
your
customers
to
assist
with
AI
Perceived
Information
need
Micro-­‐task
Micro-­‐task Micro-­‐task Micro-­‐task Micro-­‐task Task
Micro-­‐task Micro-­‐task Micro-­‐task Micro-­‐task Task
Micro-­‐task Micro-­‐task Task
Micro-­‐task
Micro-­‐task
Micro-­‐task Task
Micro-­‐task Micro-­‐task Task
Micro-­‐task Task
We
can
identify
the
user’s
probable
top
tasks
&
subtasks
Identify
their
needs
&
what
info
they
need
along
the
way

69. Paraphrase
handling
on
‘Actions’
appears
to
be
programmable

70. Hotel
Booking
Dialogflow

71. Book
hotel
intent
When
do
you
want
to
stay?
dates
dates
How
many
nights?
3
nights 2
nights
Overnight A
week
Single
or
double
room?
Single
room Double
room
Programme your
own
expected
questions
and
answers

73. We
are
at
’Day
One’
but
the
future
is
’Assistive’

74. Thank
you
Keep
in
touch
@DawnieAndo
@MoveItMarketing

75. References,
Sources
&
Further
Reading

76. References
• Broder,
A.,
2002,
September.
A
taxonomy
of
web
search.
In ACM
Sigir forum (Vol.
36,
No.
2,
pp.
3-­‐10).
ACM.
• Chuklin,
A.,
Severyn,
A.,
Trippas,
J.,
Alfonseca,
E.,
Silen,
H.
and
Spina,
D.,
2018.
Prosody
Modifications
for
Question-­‐Answering
in
Voice-­‐Only
Settings. arXiv preprint
arXiv:1806.03957.
• HigherVisibility.
2018. How
Popular
is
Voice
Search?
|
HigherVisibility.
[ONLINE]
Available
at: https://www.highervisibility.com/blog/how-­‐popular-­‐is-­‐voice-­‐search/
• Filippova,
K.,
Alfonseca,
E.,
Colmenares,
C.A.,
Kaiser,
L.
and
Vinyals,
O.,
2015.
Sentence
compression
by
deletion
with
lstms.
In Proceedings
of
the
2015
Conference
on
Empirical
Methods
in
Natural
Language
Processing (pp.
360-­‐368).
• Filippova,
K.
and
Alfonseca,
E.,
2015.
Fast
k-­‐best
sentence
compression. arXiv preprint
arXiv:1510.08418.
• Google
Developers.
2018. Content-­‐based
Actions
| Actions
on
Google
| Google
Developers.
[ONLINE]
Available
at: https://developers.google.com/actions/content-­‐
actions/.
[Accessed
18
June
2018]

77. References
• Mitkov,
R.,
2014. Anaphora
resolution.
Routledge.
• NLP
Department
-­‐ Stanford
University
-­‐ Imran
Q
Sayed.
2018. Issues
in
Anaphora
Resolution.
[ONLINE]
Available
at: https://nlp.stanford.edu/courses/cs224n/2003/fp/iqsayed/project_report.pdf.
[Accessed
28
June
2018].
• Radlinski,
F.
and
Craswell,
N.,
2017,
March.
A
theoretical
framework
for
conversational
search.
In Proceedings
of
the
2017
Conference
on
Conference
Human
Information
Interaction
and
Retrieval (pp.
117-­‐126).
ACM.
• Schalkwyk,
J.,
Beeferman,
D.,
Beaufays,
F.,
Byrne,
B.,
Chelba,
C.,
Cohen,
M.,
Kamvar,
M.
and
Strope,
B.,
2010.
“Your
word
is
my
command”:
Google
search
by
voice:
a
case
study.
In Advances
in
speech
recognition (pp.
61-­‐90).
Springer,
Boston,
MA.
• SISTRIX.
2018. Stepping
out
of
the
SEO
Bubble
-­‐ SISTRIX.
[ONLINE]
Available
at: https://www.sistrix.com/blog/stepping-­‐out-­‐of-­‐the-­‐seo-­‐bubble/.
[Accessed
16
June
2018].

78. References
• The
Stanford
Question
Answering
Dataset.
2018. The
Stanford
Question
Answering
Dataset.
[ONLINE]
Available
at: https://rajpurkar.github.io/SQuAD-­‐explorer/.
• Trippas,
J.R.,
Spina,
D.,
Cavedon,
L.,
Joho,
H.
and
Sanderson,
M.,
2018.
Informing
the
Design
of
Spoken
Conversational
Search.