It is not news that marketers have access to more data today than ever before. The challenge, however, is knowing what data is useful and relevant and what is not. The right data can drive traffic to websites, increase engagement on Facebook and other social networks, gather reader preferences, and target click through’s on advertising. In short, marketing decisions flow easily and naturally with the right data and create better results. Our panelists will share their experiences and valuable insights into how they maximize budget, engage readers and drive book sales using data driven marketing.

1. Data Driven Marketing:
Engaging Readers &
Driving Sales
Book Expo America 2014 #BEAData
Fauzia Burke
Founder and President, FSB Associates
www.fsbassociates.com
@FauziaBurke

2. “You don’t have to
be good with
numbers
to love #Data”
@FauziaBurke
#BEAData

3. “Data is not about
numbers, it’s about
patterns.”
@FauziaBurke
#BEAData

4. @FauziaBurke
#BEADataBy Domo.com

5. Kate Rados
Tom Thompson
!
Elizabeth Dimarco
@FauziaBurke
#BEAData
Panelists

6. Data
1. Type
2. Method
3. Reason
4. Use
5. Surprises
@FauziaBurke
#BEAData

7. Kate Rados
Tom Thompson
!
Elizabeth Dimarco
@FauziaBurke
#BEAData
Panelists

8. Data Driven Marketing: Engaging
Readers & Driving Sales
@KateRados | #BEAData

9. #BEAData

10. #BEAData

11. #BEAData

12. #BEAData

13. #BEAData

14. #BEAData

15. #BEAData
Jersey City Moms Meetup Group

16. #BEAData
Jersey City Moms Meetup Group

17. #BEAData
Some Ways We Collect Reader Data
• Site Registration
• Sweepstakes
• Sales
• Surveys
• Email Activity
• Comments and Social Reactions

18. #BEAData
Some Ways We Use Reader Data
for Marketing Efforts
• Alert Readers to a Local Author Event
• Customize Email Messaging for New Releases
or Promotions
• Reach Readers Where they Read: Community
Sites, Social, Mobile, Email Newsletters, In-
Person Events
• New Online Products: Apps, Downloads,
Articles, Giveaways

19. #BEAData
• Year-Long Survey
• Questions:
– Who Makes Up the RIF Audience?
– Where Do They Get Book
Recommendations?
– How Do They Enter Our Weekly Sweeps?
– What Types of Books Do They Enjoy?

20. #BEAData

21. #BEAData

22. #BEAData

23. #BEAData

24. #BEAData
Data = Audience Compass
• Readers give us data with every interaction, informing us
how they want to communicate and learn about our
books.
• You can customize your conversation based on what a
reader reveals.
• Data is not the only information upon which to base
audience development strategy.
• Reader engagement is a balance of art and science.

25. Thanks!
@KateRados | #BEAData

26. THE
DATA-­‐DRIVEN
CAMPAIGN
RATIONAL
MARKETING
IN
A
MESSY
WORLD
#BEAData
VersoAdver@sing.com

27. RATIONAL
MARKETING
IN
A
MESSY
WORLD
THE
DATA-­‐DRIVEN
CAMPAIGN
VersoAdver@sing.com

28. RATIONAL
MARKETING
IN
A
MESSY
WORLD
Known
Knowns
Known
Unknowns
Unknown
Unknowns
Unknown
Knowns
THE
DATA-­‐DRIVEN
CAMPAIGN
VersoAdver@sing.com

29. • Impressions:
CPMs
for
networks
v.
niche
sites
v.
premium
sites
v.
super
premium
sites
• Clicks:
CTRs
for
web
v.
mobile
v.
network
v.
newslePers
• CPC:
Cost
per
click
• Engagements:
In-­‐ad
views,
tweets,
posts,
emails
• Conversions:
Email
sign-­‐ups,
downloads,
purchases,
etc.
KNOWN
KNOWNS
What
We
Measure
VersoAdver@sing.com

30. • Impressions:
CPMs
for
networks
v.
niche
sites
v.
premium
sites
v.
super
premium
sites
• Clicks:
CTRs
for
Web
v.
Mobile
v.
Network
v.
NewslePers
• CPC:
Cost
per
click
• Engagements:
Views,
tweets,
posts,
emails
• Conversions:
Email
sign-­‐ups,
downloads,
purchases,
etc.
Site
Reports,
3rd
Party
Server
Data
KNOWN
KNOWNS
How
We
Measure
VersoAdver@sing.com

31. • Frequency
to
conversion
• Path
length…
• Time
lag…
• Revenue
per
placement
KNOWN
KNOWNS
(Part
2)
What
We
Could
Measure:
Conversion
Metrics
VersoAdver@sing.com

32. Machine
learning
can
inform
ad
targe@ng
by
tes@ng
and
evolving
the
user
profile
with
demographic,
psychographic,
behavioral
data
KNOWN
KNOWNS
(Part
2)
What
We
Could
Measure:
User
Profile
VersoAdver@sing.com

33. KNOWN
KNOWNS
How
We
Measure
Conversion
&
Customer
Profile
Data
VersoAdver@sing.com

34. KNOWN
KNOWNS
How
We
Measure
Conversion
&
Customer
Profile
Data
Site
Tags
(“Cookies”)
3rd
Party
Data
Deep
Learning
Algorithms
Audience
Extension
“Look-­‐alike
modeling”
VersoAdver@sing.com

35. • Past
performance
is
no
guarantee
of
future
results.
• Why
do
they
(or
don’t
they)
click?
Product
v.
Placement
v.
Crea@ve
v.
Timing
• Display
effect:
ComScore
and
IAB
studies
• Which
part
of
the
markeLng
pie
got
the
sale?
Adver@sing,
PR,
reviews,
social,
or
all-­‐the-­‐above?
KNOWN
UNKNOWNS
VersoAdver@sing.com

36. • French
Economists
• Bots
and
Bad
Guys
• Unicorns
and
Sea
Monsters
• Amazon
UNKNOWN
UNKNOWNS
VersoAdver@sing.com

37. UNKNOWN
KNOWNS
VersoAdver@sing.com

38. DIGITAL
REPORTING
• CONFIRMS
site-­‐reported
data
• TRACKS
effec@ve
CTR,
CPM,
CPA
• MEASURES
campaign
performance
against
compe@@ve
set
• DELIVERS
ac@onable
data
• INFORMS
ongoing
and
future
campaigns
VersoAdver@sing.com

39. Thank
you
Tom
Thompson
tom@versoadver@sing.com
@tomthompson
Thank
you!
#BEAData
VersoAdver@sing.com

40. BooksILove
The mobile place for
conversations between friends
about books.
©
2014,
BooksILove™
www.booksilove.com

41. What types of data
do we collect?
Data from readers who are
talking about and
recommending books to
their friends.
©
2014,
BooksILove™
www.booksilove.com

42. How do we collect it?
• Readers use our mobile app.
• We get data directly from
readers about the books they
love.
• We’re creating the ultimate
focus group.
©
2014,
BooksILove™
www.booksilove.com

43. Why do we collect it?
• Create apples-to-apples comparative data
• See conversational trends ahead of buying
trends
• Gain insights about personal
recommendations
©
2014,
BooksILove™
www.booksilove.com

44. How can it be used to make
better marketing decisions?
Can help understand:
• Why readers love a book
• Which specific elements of a book are
most loved
• How reader perceptions of one book
compare to another
• What are reader trends
• Who are a book’s fans and evangelists
©
2014,
BooksILove™
www.booksilove.com

45. What has surprised
us so far?
©
2014,
BooksILove™
www.booksilove.com

46. We expected:
Clever Page-turning Suspenseful Thrilling
©
2014,
BooksILove™
www.booksilove.com

47. What We Got
Reader 1 Reader 2 Reader 3
Suspenseful
Provocative
Thrilling
Authentic
Believable
Poignant
Surprising
Clever
Plausible
Thoughtful
©
2014,
BooksILove™
www.booksilove.com

48. We expected:
Passionate Inspiring Heart-Breaking Authentic
©
2014,
BooksILove™
www.booksilove.com

49. What We Got
Reader 1 Reader 2 Reader 3
Suspenseful
Passionate
Thrilling
Breath-taking
Authentic
Inspiring
Heart-breaking
Page-turning
Intriguing
Thoughtful
©
2014,
BooksILove™
www.booksilove.com

50. We expected:
Provocative Technical Plausible Hair-raising
©
2014,
BooksILove™
www.booksilove.com

51. What We Got
Reader 1 Reader 2 Reader 3
Suspenseful
Audacious
Thrilling
Provocative
Authentic
Technical
Believable
Page-turning
Plausible
Clever
Surprising
©
2014,
BooksILove™
www.booksilove.com

52. Summary of "
readers’ book descriptors
Reader 1 Reader 2 Reader 3
Suspenseful (3)
Passionate
Audacious
Breath-taking
Thrilling (3)
Provocative (3)
Authentic (3)
Heart-breaking
Technical
Inspiring
Believable (2)
Poignant
Page-turning (3)
Plausible
Clever (2)
Surprising (2)
Intriguing
Thoughtful
©
2014,
BooksILove™
www.booksilove.com

53. How the data has
surprised us
Readers repeatedly select
the same tiles across the
books they love
©
2014,
BooksILove™
www.booksilove.com

54. BooksILove
©
2014,
BooksILove™
www.booksilove.com