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.
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?
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.
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