The document details insights from the 'Big Ideas from Big (or Small) Data' summit held in Canada on June 19, 2014, focusing on the importance of utilizing various data sources and methodologies for consumer research. It discusses the trends in social media usage, predictive data accuracy, and the necessity for data-driven marketing strategies to understand and engage audiences effectively. Additionally, it highlights the challenges and opportunities in the Canadian market, specifically regarding the success of U.S. authors in Canada.

1. Big
Ideas
from
Big
(or
Small)
Data
Book
Summit
Canada
Pete
McCarthy
The
Logical
Marketing
Agency

2. Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
June
19,
2014
2
Who
am
I
and
why
am
I
here?

3. Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
June
19,
2014
3
What
are
we
talking
about
and
why
are
we
talking
about
it
(now)?

4. We
are
talking
about
big
ideas.
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
4
Really,
a
process
which
may
yield
big
ideas.
Discussion
of
data
is
highly
probable.
It
is
a
capital
mistake
to
theorize
before
one
has
data.
Insensibly
one
begins
to
twist
facts
to
suit
theories,
instead
of
theories
to
suit
facts.
–
Sherlock
Holmes,
A
Scandal
in
Bohemia

5. This
is
a
big
idea!
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
5
94%
accuracy
of
opening
weekend
box
office
up
to
4
weeks
pre-­‐release…
2013

6. So
was
this
and
seems
to
still
be.
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
6
97%
correlation
between
“Twitter
chatter”
and
opening
weekend
box
office.
2010

7. Especially
when
combined
with
this
work.
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
7
Which
adds
(a
little)
more
(seemingly
correct)
data
to
eliminate
bias.
2012

8. This
might
be
part
of
a
big
idea…
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
8
77%
“predictive.”
Backward-­‐looking.
Reliability
of
data?
2012

9. 2013
1983
These
were
big
ideas…and
some
still
are…
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
9
Most
big
ideas
build
on
prior
big
ideas
–
successful
or
not.
2010
2010
2002
2000
1994

10. Why
we
are
here.
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
10
Because
of
what
Google
(and
others)
do.
Because
we
can
do
similar
things.
ü What
ü When
ü Where
ü Which
ü Who
ü How
ü Even
a
plausible
why!

11. Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
June
19,
2014
11
What
we
talk
about
when
we
talk
about
consumer
data

12. In
essence,
we
are
talking
about
useful
research.
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
12
Some
“types”
of
consumer
research
and
the
methods
used.
Secondary
Industry-­‐specific
Qualitative
Non-­‐transactional
Snapshot
in
time
Bricks
&
Mortar
Unknown
People
Unknown
Person
Primary
“Whole
World”
Quantitative
Transactional
Trended
“Digital/Online”
Known
People
Known
Person
|
|
|
|
|
|
|
|
Types
of
Research/Data
Methods
of
acquiring
research
data
1. By
surveying
people
2. By
observing
them

13. Research
that
yields
data
on
audiences
to
solve
below.
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
13
Big
data,
little
data
–generally
pretty
similar
data.
Just
scale
and
use
differ.
Aware
&
Will
Buy.
Aware
&
Will
Not.
Unaware
&
Just
Might!
Unaware
&
Just
Fine.
This
is
the
gold
mine
of
readers.
It
is
the
crossover
hit.
Especially
true
for
niche
and
vertical
publishers.
A
must.

14. Content
created/consumed
by
consumers.
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
14
Mary
Meeker
referred
to
the
“data-­‐creating
consumer”
as
a
top
2014
trend.

15. Major
social
platforms
total
registered
users.
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
15
0
200
400
600
800
1,000
1,200
1,400
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
Millions
Facebook
Twittter
Google+
(Gmail)
Pinterest
Instagram
Registered
users
as
of
May
2013.
Reported.
Several,
culled
by
Search
Engine
Journal

16. US
social
network
penetration
by
age
+
mobile.
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
16
As
of
May
2013.
Via
survey.
Pew
Research:
Social
Media
Update
2013
via
Search
Engine
Journal

17. Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
June
19,
2014
17
Canada-­‐specific
data.
Search
Market
Share
June
2014
opt-­‐in
panel.
June
2014.
Top
Social
Media
Sites
Used
in
Last
Month
Canada
“Digital”
Snapshot
Data
Source:
Experian
Hitwise
Canada
§ 86%
internet
penetration
§ 76%
mobile
internet
penetration
§ 56%
smartphone
penetration
§ 77%
of
owners
research
products
on
phone,
27%
buy
on
phone
§ 82%
Social
Media
penetration
§ 55%
Facebook
penetration
§ <2
hours/day
social
media
use
0%
10%
20%
30%
40%
50%
60%
Pinterest
LinkedIn
Google+
Twitter
Facebook

18. Canada
and
the
U.S.
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
18
Sources:
PWC
Global
Media
Outlook,
Census
Data,
Global
Web
Index
Wave
60
7
0
20
40
60
80
U.S.
Canada
137
17
0
50
100
150
U.S.
Canada
254
30
0
100
200
300
U.S.
Canada
315
35
0
100
200
300
400
U.S.
Canada
Population
(M)
Ratio:
1:9
Internet
Users
(M)
Ratio:
1:8.5
Facebook
Users:
Last
Month
(M)
Ratio:
1:8
Twitter
Users:
Last
Month
(M)
Ratio:
1:8.5
Trade
Book
Sale
Ratios
Range
from
1:15
to
1:10…
No
“apples-­‐to-­‐apples”
data
but
directionally
these
provide
a
sense.
A
sense
of
proportion.

19. Canadian
book
consumers
and
retail.
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
19
2012−2013.
Primarily
via
survey.
(I’ve
focused
on
the
Business
category.)
• 68%
Business
book
buyers
=
male
! >
50%
awareness
=
online
! Only
20%
purchase
impulsively.
BookNet
Canada,
“The
Canadian
Book
Consumer
2013”

20. June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
20
Some
really
useful
places
to
gather
consumer
data.
§ Social
Graph
They
know
consumers.
Online
and
offline.
360-­‐degree
view.
§ Ad
Platform
Open
(APIs,
Tools),
app
development,
Oauth
site
sign
on.
§ Constant
A/B
testing
Fail
fast,
fix.
§ Result:
Happy
Users/Advertisers
Despite
incredible
concerns
over
privacy.
Relevance
trumps
it.
§ Search
(&
lots
else)
Massive
share.
YouTube.
§ Ad
Platform
Targeted
inventory
at
an
all
time
high.
§ Literally
Building
a
Brain
Yes.
All
products
data-­‐driven.
Predictive.
.
§ Open
APIs
and
tools.
Oauth
site
sign
on.
§ Massive
growth
Wild
adoption
and
usage.
§ Ad
Platform
Targeting.
§ Timely
Almost
“now.”
Predictive.
§ Open
(for
now)
Can
get
at
the
data.
Oauth
site
sign
on.

21. A
sampling
of
useful
tools.
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
21
Social
Analytics
§ Simply
Measured
§ SproutSocial
§ Social
Bakers
§ Followerwonk
§ Commmun.it
§ Bit.ly
§ Topsy
§ Social
Mention
§ Facebook
Ad
Interface
§ Facebook
PowerEditor
§ EdgeRank
Checker
§ SimplyMeasured
§ Twitter
Ad
Interface
§ Radian
6/Crimson
Hexagon
§ HootSuite
§ Facebook
Insights
§ LinkedIn
Analytics
§ Instagram
Analytics
§ Etc.
Web/Email
Analytics
Web/SEO
§ Raven
§ Compete
§ Quantcast
§ SEO
Quake
§ SEM
Rush
§ Google
universal
analytics
§ WordTracker
§ WordStream
§ Amazon
comp
authors
§ Librarything
tags/
comps
§ Etc.
§ Google
Analytics
§ Omniture
§ ExactTarget
§ MailChimp
Mostly
not
huge,
costly
a
la
Adobe
or
Salesforce
§ Optimizely
§ Etc.
And
many,
many
more
to
fit
nearly
any
use
case
§ Google
Trends
§ Google
AdWords
§ Moz
§ Soovle
(autocompletes
in
general)
§ Seorch

22. I
like
how
this
guy
talks
about
research
and
data.
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
22
Nate
Silver.
(I
like
others,
also).
…if
the
quantity
of
information
is
increasing
by
2.5
quintillion
bytes
per
day,
the
amount
of
useful
information
almost
certainly
isn't.
Most
of
it
is
just
noise,
and
the
noise
is
increasing
faster
than
the
signal.
There
are
so
many
hypotheses
to
test,
so
many
data
sets
to
mine—but
a
relatively
constant
amount
of
objective
truth.
Photo:
Marius
Bugge
Bayes’ Theorem

23. Foxes
gather
“big
ideas”…quickly.
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
23
Photo:
Marius
Bugge
“The
fox
knows
many
little
things,
but
the
hedgehog
knows
one
big
thing.”
Hedgehogs
are
Type
A
personalities
who
believe
in
Big
Ideas—in
governing
principles
about
the
world
that
behave
as
though
they
were
physical
laws
and
undergird
virtually
every
interaction
in
society.
Foxes,
on
the
other
hand,
are
scrappy
creatures
who
believe
in
a
plethora
of
little
ideas
and
in
taking
a
multitude
of
approaches
toward
a
problem.
They
tend
to
be
more
tolerant
of
nuance,
uncertainty
,
complexity,
and
dissenting
opinion.
If
hedgehogs
are
hunters,
always
looking
out
for
the
big
kill,
then
foxes
are
gatherers.

24. One
second
on
Bayesian
statistics.
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
24
No
test
(I
wouldn’t
pass).
The
governing
principle
is
the
thing.
» Bayesian
statistics
is
a
subset
of
the
field
of
statistics
in
which
the
evidence
about
the
true
state
of
the
world
is
expressed
in
terms
of
degrees
of
belief
or,
more
specifically,
Bayesian
probabilities.
» Bayesian
statistics
(if
only
practiced
in
spirit)
sets
one
up
to:
§ Statistical
inferences
§ Statistical
modeling
§ Design
of
experiments
§ Statistical
graphics
§ Be
human
(encouraged)
§ Move
quickly,
get
lots
of
data
§ Admit
bias
but
try
to
verify
§ Change
tack
as
indicated
§ Becoming
“less
wrong”
(testing)
§ Becoming
even
less
“less
wrong,”
over
time
§ Demonstrating/validating
We
verify
or
discover
the
big
ideas,
as
opposed
to
just
having
them.

25. Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
June
19,
2014
25
Identifying
and
understanding
audiences
using
data

26. I
wonder
how
The
Signal
and
the
Noise
is
doing?
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
26
#1
Bestseller.
In
Statistics
Textbooks….
#989
overall.
Without
being
able
to
see
POS,
I
don’t
know
if
that
signifies…
I
might
throw
a
“Business
BISAC”
at
Amazon.
It’s
not
a
textbook.

27. Nate
Silver’s
audience.
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
27
Wonder
who
they
are.
I
have
guesses
but
that’d
be
bias.
Let’s
look.
720k
is
a
hefty
Twitter
following.
He’s
tweeted
often
and
“on
message.”
Recency.

28. Where
do
they
live?
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
28
Home
locations
of
unnamed
Silver
Twitter
followers
based
on
a
sample.
Directional.
New
York,
LA,
London.
Is
that
Canada
I
see?

29. Canada?
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
29
It
is
indeed.
But
those
followers
are
in
Seattle.
Drats!
Why
no
Canadian
followers?
Bug?
Opportunity?
(We
know
Canadians
use
Twitter.)

30. Google.ca
auto-­‐prompts
me
at
“s.”
That’s
good.
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
30
1
1b
Book
results
are
low
and
related.
Amazon
is
first
book
result.
Way
below
the
fold
on
any
device.

31. How
does
the
book
look
an
Amazon.ca,
Kobo,
Indigo.
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
31
There
a
book
audience
but
it
feels
small.
Two
reviews
feels
low…
Good
position.
Seem
like
more
consumer-­‐
aligned
categories
Would
have
expected
him
to
be
prompted
above
Nate
Southard…

32. What
is
the
search
interest
like?
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
32
Canada
–
Spikes
–
Volume
is
on
Him
Interest
falls
but
stays.
Book
present.
Google
Trends
Canada,
US.
January
2007
–
September
2012
September
2012
–
May
2014
Interest
falls
fast.
No
book.
January
2007
–
September
2012
US
–
Very
Similar

33. Comparing
raw
search
volume.
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
33
Canada
Brand
Search
Volumes
US
Brand
Search
Volume
1,400
reach
in
Facebook
CA
advertising
vs.
62,000
in
US
Ratios
feel
as
if
he
is
punching
below
weight.

34. More
data
on
interest
in
Canada
allows
inference…
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
34
Silver
does
not
enjoy
the
interest
here
that
he
does
in
the
states.
3%
is
too
small
number,
given
expected
ratios.
Canada
has
about
the
population
of
California.
Hypothesis:
he
is
under-­‐
indexing
in
CA.
Perhaps
there
is
room
for
sales
growth
–
in
and
using
social.

35. Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
June
19,
2014
35
Efficiently
growing
audiences
using
data

36. Mine
adjacencies.
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
36
Some
potential
adjacencies
for
Nate
Silver.

37. One
adjacent
audience:
Moneyball.
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
37
Google
Adwords
and
Facebook
confirm
connection
and
show
Canada
reach.
=
=
50,000
196,000

38. Ride
big
waves.
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
38
Google
Trends
Canada.
“538”
is
Silver’s
recently
re-­‐launched
site,
covering
things
from
sports
to
politics.
There
is
Canadian
search
interest
in
538.
He
is
predicting
the
World
Cup
winner
in
real
time.
15M
Tweets
on
World
Cup
in
past
month.
The
World
Cup
is
big
in
Canada
(I
did
verify).
Though
it
is
an
adjacency
that
is
further
away,
Silver
has
tied
himself
to
the
World
Cup
explicitly.
Hypothesis:
It
can
likely
be
capitalized
on
to
get
people
interested
in
him.

39. Reaching
“look-­‐alikes”
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
39
Some
characteristics
of
his
audience.

40. Regionality
gleaned
from
search.
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
40
Are
there
attributes
of
the
US
locales
that
“match”
Canadian
locales?
(DMAs)

41. Comp
authors:
adjacent
fans
and
look-­‐alikes.
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
41
Authors
whom
the
consumer
comps,
as
opposed
to
us.
Preferably
outside
book
spaces.
The
intersecting
folks
are
a
great
source
of
look-­‐alike
attributes.

42. Comp
authors:
adjacent
fans
and
look-­‐alikes.
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
42
We
can
use
the
Venn
to
find
people
to
target
who
look
exactly
like
the
shared
followers.

43. Thinking
in
terms
of
optimizing
“funnels.”
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
43
Goal:
sell
The
Signal
and
the
Noise
in
Canada.
One
potential
funnel
(to
test).
Segment
§ Male
§ Like
Moneyball
§ And
topics
related
directly
to
Moneyball
Platform
§ Facebook
§ Mobile
stream
Landing
§ Kobo
page
Creative
§ A:
Sports
§ B:
Business
This
is
funnel
A.
There
should
at
minimum
be
a
B,
testing
with
at
least
one
variable
changed.
Measure
costs
to
reach
fans
and
conversion
to
sale
(the
goal
here).
See
who
is
responding,
adjust
(more
hypotheses)
or
“get
out.”

44. This
may
not
be
a
“big
idea.”
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
44
But
if
it
were
to
be
successful
it
would
be
a
nice
one-­‐off
and
could
lead
to
learning
how
to
develop
a
process
of
outsizing
“American”
authors
in
Canada.
» One
could
systematically
identify
US
authors
with
works
on
sale
in
CA
§ Look
for
the
delta
in
unit
sales
between
US
and
CA.
IF
greater
than
norm,
examine.
» Do
the
same
with
authors
with
major
digital
presences
in
US
without
in
CA.
§ See
what
can
be
modeled
in
CA
from
the
US
presence
And
so
on…

45. Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
June
19,
2014
45
Suggestions
if
you’d
like
them
(along
with
2
warnings)

46. Suggestions
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
June
19,
2014
46
» Establish
goals
regarding
audience
identification.
§ What
outcome
would
be
ideal.
» Involve
organization
around
the
approach.
§ Marketing,
sales,
publicity,
IT
need
to
align
to
gain
maximum
value.
§ Affects
everything;
physical
distribution,
ad
creative,
PR
to
metadata,
etc.
» Recognize
that
it
is
a
process
of
testing
and
learning.
§ Failure
(of
a
reasonable
hypothesis)
is
not
a
bad
thing.
» Buy,
build,
find,
learn
the
systems
to
support
the
work.
§ Capture
learning
at
all
times.
§ Scale
when
the
value
is
there
(eg.
Big
Ideas
are
coming
and
are
repeatable).
May
prove
useful
if
data-­‐driven,
audience-­‐centric
marketing
is
of
interest.
See
warnings.

47. Two
warnings
1. This
is
relatively
technical
work
but
does
not
require
one
to
be
a
“data
scientist.”
Just
unafraid
of
technology,
curious,
and
able
to
employ
the
logic.
2. The
more
one
does
it,
the
faster
it
goes.
It
is
not
fast
at
first
but
is,
in
the
end,
likely
more
efficient
and
will
yield
big
ideas.
June
19,
2014
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
47
—Nate
Silver,
The
Signal
and
the
Noise

48. Thank
you
Big
Ideas
from
Big
(or
Small)
Data
|
Book
Summit
Canada
June
19,
2014
48