Tried something new, doing a night-before data-driven marketing dive on an author with whom I was entirely unfamiliar to form and test hypotheses on the off chance that there may remain efficient ways of keeping this book on the bestseller list. So, this deck finds me marketing a niche romance title, Parable, by Terri Blackstock. In real time. I can honestly say it was a ball.

1. Using
Data
to
Identify,
Target,
and
Connect
with
Audiences
Peter
McCarthy
McCarthy
Digital
|
The
Logical
Marketing
Agency
Presented
April
30,
2014
Leadership
Summit
and
ECPA
Annual
Member
Meeting

2. Contents
» Who
am
I
and
why
am
I
here?
» Audience-­‐centricity
is
key
» Data
and
tools,
applied
» What’s
next?
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
2

3. Contents
» Who
am
I
and
why
am
I
here?
» Audience-­‐centricity
is
key
» Data
and
tools,
applied
» What’s
next?
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
3

4. Who
am
I?
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
4

5. Who
am
I?
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
5

6. Or
did
you
mean
this?
» Demographics
§ My
gender,
age,
income
level,
education
level,
marital
status,
etc.
§ Note:
I
include
geographic
region
here
» Psychographics
§ My
beliefs,
values,
attitudes,
opinions,
favorite
things,
places,
activities
» Behaviors
§ What
I
have
done,
am
doing
(and,
perhaps,
most
likely
to
do
next)
A
person’s
future
behavior
is
impossible
to
predict.
But
is
easier
the
nearer
it
is
to
happening
and
the
more
we
know
about
the
person.
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
6

7. Why
am
I
here?
» United
States
Reading
and
eReading
habits
§ 76%
of
the
US
population
18
and
over
read
a
book
in
the
past
year
§ 28%
of
that
group
do
on
either
an
eReader
or
a
Tablet
§ Demographically
diverse
as
could
be
76%
of
US
book
readers
age
>
18
report
they
have
read
a
book
in
the
past
12
months?!
=
~183,797,307
potential
consumers
(that
we
don’t
know
very
well)
Source:
Pew
Center
for
Internet
Research
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
7

8. Contents
» Who
am
I
and
why
am
I
here?
» Audience-­‐centricity
is
key
» Data
and
tools,
applied
» What’s
next?
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
8

9. Audience-­‐centricity
Consumer-­‐first
1. Who
2. What
3. Why
Results-­‐oriented
§ Product
§ Placement
4. Where
5. When
6. How
§ Price
§ Promotion
Measured
Optimization
§ What’s
working
§ What’s
not
§ With
nuance
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
9

10. The
universe
of
potential
readers
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.
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
10

11. So
how
do
we
find
them?
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
.
It
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
11

12. Contents
» Who
am
I
and
why
am
I
here?
» Audience-­‐centricity
» Data
and
tools,
applied
» What’s
next?
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
12

13. What
I
talk
about
when
I
talk
about
data
“Universe”
Publishing
Industry
(Truly)
Consumer
§ Macro
data
§ Facts,
trends,
projections
§ May
be
commercial
in
nature,
may
not
§ Likely
Sources
§ Nonprofits
like
Pew
Research
§ Trade
groups
§ Corporate
annual
reports
§ Data
Corporations
(eg.
ComScore,
Gallup,
Nielsen,
etc.)
§ Consultancies
(McKinsey,
Forrester,
Gartner)
§ News
outlets
§ Macro
and
micro
data
§ For
industry
as
a
whole
§ Or
one
publisher
(our
own)
§ Or
one
book/author
(our
own)
§ Facts,
trends,
projections
§ Generally
commercial
in
nature
§ Likely
Sources
§ Retailers
to
POS
systems
§ Some
CRM(ish)
activity
§ Smaller
set
of
Data
Corporations
(eg.
Nielsen,
BISG,
Bowker)
§ Etc.
§ News
outlets
§ Macro
and
micro
data
§ Consumer
behavior
in
aggregate
§ But
also
specific
groups
of
consumers
“in
the
wild”
§ Facts,
trends,
projections
§ Often
not
commercial
in
nature
§ Likely
Sources
§ Bespoke
surveys
(Nielsen)
§ Real
online
usage
§ Search
§ Social
§ Commerce
§ Other
§ Analytic
tools
§ Etc.
I
prefer
talking
about
this
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
13

14. “Universe”
All
relevant
and
useful
when
pulling
out
the
crystal
ball
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
14

15. Example
data:
“universe”
Global
tablet
market
share
by
platform
Source:
Business
Insider
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
15

16. Example:
“smaller
universe”
Time
per
day
Americans
spend
on
different
digital
activities
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
16

17. Example:
“smaller
universe
still”
Demographic
makeup
of
major
social
networks
2013
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
17

18. Example:
“universe
not
so
far
away”
Amazon
share
price
trailing
5
years
Source:
Google
Finance
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
18

19. Industry
Well-­‐understood,
(should
be)
easier,
useful
for
running
our
businesses
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
19

20. Example:
industry
6.93
Net
Sales
($B)
2011 2012
Source:
2013
Bookstats
US
trade
book
sales
Brick
&
Mortar
8.03
Net
Sales
($B)
7.47
2011 2012
5.72
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
20

21. Example:
industry
US
eBook
sales
2008
-­‐
2012
64
Net
Sales
($M
Logarithmic
regression:
R2=0.7853
291
869
2,109
3,042
+199%
+143%
+43%
+355%
2008
2009
2010
2011
2012
Source:
2013
Bookstats
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
21

22. (Truly)
Consumer
Crazy
and
unbelievably
useful
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
22

23. Some
(really
useful)
sources
of
(US)
consumer
data
The
irrefutable
ROI
of
social
is
in
the
analytics
and
BI
on
the
back
end
§ The
Social
Graph
They
know
consumers.
Now
tying
to
offline
sources.
§ Ad
Platform
Inventory,
Open
(APIs),
demographics,
beliefs,
behaviors
§ Constant
A/B
testing
Fail
fast,
fix
§ Result:
Happy
Users/Advertisers
Despite
incredible
concerns
over
privacy.
Relevance
trumps
it
§ Search,
ads
(&
lots
of
data)
Massive
share
of
almost
everything
they
do
§ Ad
Platform
Inventory,
highly
behavioral
§ Basically
Building
a
Brain
Yes.
All
products
data-­‐driven
§ Open
APIs
and
tools
§ Smaller
but…
Wild
adoption,
solid
usage
§ Ad
Platform
Targeting
has
arrived
as
has
the
front-­‐end
to
allow
for
goals
§ Timely
Basically
“now”
§ Very
Open
(for
now)
Can
get
at
the
data
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
23

24. The
graph
is
simply
so
rich
and
so
aware
Of
course,
the
“currency”
is
personal
privacy…which
73%
of
us
(gladly?)
pay
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
24

25. One
can
capture
actual
usage
as
it
occurs
“in
the
wild”
A
sampling
of
tools…mostly
not
huge,
costly
a
la
Adobe
or
Salesforce
Social
Analytics
§ Simply
Measured
§ SproutSocial
§ Social
Bakers
§ Followerwonk
§ Commmun.it
§ Bit.ly
§ Topy
§ Social
Mention
§ Facebook
Insights
§ Facebook
Ad
Interface
§ Facebook
PowerEditor
§ EdgeRank
Checker
§ SimplyMeasured
§ Twitter
Ad
Interface
§ Argyle
§ LinkedIn
Analytics
§ Pinterest
Analytics
§ Tumblr
Analytics
§ Instagram….
Web/SEO
Web/Email
Analytics
§ Google
Trends
§ Google
AdWords
§ Moz
§ Soovle
(autocompletes)
§ Raven
§ Compete
§ Quantcast
§ SEO
Quake
§ Google
universal
analytics
§ WordTracker
§ WordStream
§ Amazon
comp
authors
§ Librarything
tags/
comps
§ Etc.
§ Google
Analytics
§ Omniture
§ Constant
Contact
§ MailChimp
§ Optimizely
–
landing
pages
And
many,
many
more
to
suit
the
myriad
use-­‐cases,
but…
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
25

26. But
it
isn’t
really
about
the
tools
Yes,
one
must
have
a
toolkit.
But
it’s
about
triangulation
and
application
Goals
direct
research
Launch
Hypothesis,
test,
hypothesis,
test,
hypothesis,
test,
hypothesis,
test
Measure
&
Optimize
Communicate,
Prune,
change,
apply
apply
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
26

27. So
let’s
triangulate
and
see
what
happens
I
chose
a
title
from
the
CBE
Bestseller
List,
which
seemed
to
be
doing
well
but
maybe
had
a
little
more
in
it.
I
tried
to
“work
it”
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
27

28. LibraryThing,
GoodReads,
Amazon…
Establish
true
comp
authors
and
titles
as
well
as
reader
and
buyer
verbiage
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
28

29. Multiple
auto-­‐fills
(not
logged
in,
cookies
cleared)
Hypothesis:
no
one
searches
for
Terri
Blackstock?
Seems
they
do
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
29

30. And,
indeed,
it
is
definitely
true
Raven
Tools
(with
assist
from
Moz,
Majestic,
SEM
Rush
assuage
that
concern
That
volume
is
on
her
name
alone
and
only
in
Google.
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
30

31. Google
Trends:
how
does
she
stack
up
then?
Waning
but
not
bad.
Interesting
differences
in
regionality
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
31

32. Try
a
Google
search
(cached
cleared,
logged
out)
Tremendous
results
for
author
and
title
+
author
queries
Fine
on
the
“branded”
terms.
People
who
Seek
shall
find.
But
what
about
others?
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
32

33. So,
where
are
the
fans?
What
are
they
saying?
Google’s
related
searches,
some
“listening”
in
on
Twitter…
Again,
seems
okay.
Now
I
want
to
know
who
is
looking.
Who
are
her
fans?
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
33

34. The
graph
Here
rolled
up
under
Twitter
followers
with
data
appended
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
34

35. Her
Twitter
map
looks
a
lot
like
her
search
map
Is
she
posting
at
the
right
time?
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
35
Using
Data
to
Identify,
Target,
Connect
|
ECPA

36. This
is
just
fine.
A
deeper
dive
not
worth
it
at
this
time
Her
Followers
Her
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
36

37. So,
Terri’s
followers
are
mostly
married
white
Christian
women.
40-­‐49
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
37

38. Who
else
do
they
follow?
Some
patterns
emerge
but
really
not
that
much
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
38

39. New
hypothesis:
she’s
undersized
for
some
reason
right
now
And
I
don’t
need
to
know
why
(I
always
forget
that!).
Just
remedy
it…
topsy
author
comp
Start
to
look
at
two
comp
authors
who
seem
“larger”
or
“hotter”
right
now.
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
39

40. I
decide
to
ride
the
Francine
Rivers
wave
(so
to
type)
Solely
because
of
Dee
Henderson’s
lack
of
real
online
footprint
People
Talking
About
This
437
4,817
Terri
Blackstock
Francine
Rivers
A
check
on
Facebook
causes
me
a
moment
of
pause
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
40

41. Grab
a
tool
to
compare
the
two
authors
in
that
space…
Her
fans
are
there
but
it
feels
like
there’s
a
somewhat
quiet
echo
chamber
going
on.
§ Especially
with
people
who
don’t
already
know
her
or
her
work.
§ Hypothesis:
she
isn’t
garnering
new
demand
from
additional
affinities
§ Hypothesis:
she’s
presenting
as
a
devout
Christian
writer
of
suspenseful
mysteries.
Which
is
great
for
reaching
the
people
who
know
and
will
buy.
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
41

42. A
quick
stop
at
her
site
to
check
that
is
in
working
order
First
time
we’ve
turned
away
from
the
audience.
Ran
WooRank.
Site
needs
some
touch
up
but
it
will
do.
So,
right
into
some
narrow-­‐casted,
inexpensive
Facebook
ads
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
42

43. Let’s
find
new
fans
on
Facebook!
Our
targeting:
Women
(no
age
as
people
don’t
enter
it)
who
live
in
the
US,
are
married,
enjoy
family
life,
and
as
our
first
interest…Francine
Rivers.
Then
we
just
take
it
from
there
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
43

44. Then
some
Twitter
followers
of
Francine
Rivers
Under
100
to
be
exact.
Strong
ones
with
the
characteristics
of
Terri’s
followers
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
44

45. Lastly
we
go
looking
for
some
keywords
she
can
own
Very
dependent
on
her
name.
So,
Christian
suspense
and
variants
jump
out.
We
make
sure
she
will
be
able
to
“own
them.”
There’s
competition
but
she’ll
get
in
the
mix.
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
45

46. A
tool
called
Seorch
gives
us
some
good
generic
ones
Christian
suspense
books
Christian
suspense
fiction
Christian
suspense
novels
Christian
suspense
romance
Christian
suspense
Christian
suspense
romance
Christian
suspense
writers
Christian
suspense
romance
novels
Christian
suspense
author
Christian
suspense
movies
Christian
suspense
series
Christian
suspense
thrillers
Christian
suspense
fiction
books
Christian
suspense
book
reviews
Christian
suspense
authors
list
Christian
suspense
audiobooks
Christian
suspense
fiction
authors
Top
Christian
suspense
authors
Christian
suspense
publishers
Top
Christian
suspense
books
Good
Christian
suspense
books
Best
Christian
suspense
novels
Christian
suspense
authors
fiction
Christian
suspense
romance
books
Christian
fiction
suspense
series
Popular
Christian
suspense
authors
And
we
knew
the
brand
terms
well
already.
Should
be
no
issue
to
optimize
her.
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
46

47. Lastly,
we’ll
need
to
optimize
for
the
incoming
traffic
Would
have
tightened
the
middle
and
bottom
of
the
funnel
first
but
the
book
is
out
» We’ll
need
to
overhaul
her
Amazon
presence
to
include
some
new
terminology,
without
overdoing
it
» Speed
up
her
site
and
get
some
of
that
same
verbiage
there
» Add
Google
Analytics
to
her
site
so
that
we
can
set
up
a
formal
funnel
to
measure
to
our
goal,
which
I
would
advocate
should
be
a
newsletter
sign
up…
» It
makes
life
easier
when
you’ve
got
a
bigger
group
of
people
who
know
and
will
buy!
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
47

48. Contents
» Who
am
I
and
why
am
I
here?
» Audience-­‐centricity
» Data
and
tools,
applied
» What’s
next?
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
48

49. Constant
triangulation
–
before
anything
happens
And,
of
course,
more
often
than
not
it
isn’t
the
chase
I
just
described
The
smart
business
of
the
future
will
correlate
and
compute
a
mix
of
data
including
demographics,
psychographics,
web
analytics,
social
analytics
and
business
intelligence
to
create
predictive
scenarios
that
can
be
delivered
in
real
time
at
the
point
of
need.
–
Paul
Simbeck-­‐Hampson
Marketing
Consultant
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
49

50. These
guys
predicted
the
future!
Opening
weekend
movie
revenues.
With
greater
than
90%
accuracy.
Source
HP
Labs:
http://www.hpl.hp.com/research/scl/papers/socialmedia/socialmedia.pdf
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
50

51. Thank
you
very
much.
pete@mccarthy-­‐digital.com
@petermccarthy
April
30,
2014
Using
Data
to
Identify,
Target,
Connect
|
ECPA
51