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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.
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Data Driven Marketing:Engaging Readers &
Driving SalesBook Expo America 2014 #BEAData
Fauzia Burke Founder and President, FSB Associates
www.fsbassociates.com @FauziaBurke
“You don’t have to be good with
numbersto love #Data”
@FauziaBurke
#BEAData
“Data is not about numbers, it’s about
patterns.”
@FauziaBurke
#BEAData
@FauziaBurke
#BEAData By Domo.com
Kate Rados
Tom Thompson !
Elizabeth Dimarco
@FauziaBurke
#BEAData
Panelists
Data1. Type
2. Method
3. Reason
4. Use
5. Surprises
@FauziaBurke
#BEAData
Kate Rados
Tom Thompson !
Elizabeth Dimarco
@FauziaBurke
#BEAData
Panelists
Data Driven Marketing: Engaging Readers & Driving Sales
@KateRados | #BEAData
#BEAData
#BEAData
#BEAData
#BEAData
#BEAData
#BEAData
#BEAData
Jersey City Moms Meetup Group
#BEAData
Jersey City Moms Meetup Group
#BEAData
Some Ways We Collect Reader Data
• Site Registration • Sweepstakes • Sales • Surveys • Email Activity • Comments and Social Reactions
#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
#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?
#BEAData
#BEAData
#BEAData
#BEAData
#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.
Thanks!
@KateRados | #BEAData
THE DATA-‐DRIVEN CAMPAIGN
RATIONAL MARKETING IN A MESSY WORLD
#BEAData VersoAdver@sing.com
RATIONAL MARKETING IN A MESSY WORLD
THE DATA-‐DRIVEN CAMPAIGN
VersoAdver@sing.com
RATIONAL MARKETING IN A MESSY WORLD
Known Knowns
Known Unknowns
Unknown Unknowns
Unknown Knowns
THE DATA-‐DRIVEN CAMPAIGN
VersoAdver@sing.com
• 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
• 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
• Frequency to conversion • Path length… • Time lag… • Revenue per placement
KNOWN KNOWNS (Part 2) What We Could Measure: Conversion Metrics
VersoAdver@sing.com
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
KNOWN KNOWNS How We Measure Conversion
& Customer Profile Data
VersoAdver@sing.com
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
• 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
• French Economists • Bots and Bad Guys • Unicorns and Sea Monsters • Amazon
UNKNOWN UNKNOWNS
VersoAdver@sing.com
UNKNOWN KNOWNS
VersoAdver@sing.com
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
Thank you
Tom Thompson
tom@versoadver@sing.com @tomthompson
Thank you!
#BEAData VersoAdver@sing.com
BooksILove
The mobile place for conversations between friends
about books.
© 2014, BooksILove™ www.booksilove.com
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
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
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
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
What has surprised us so far?
© 2014, BooksILove™ www.booksilove.com
We expected:
Clever Page-turning Suspenseful Thrilling
© 2014, BooksILove™ www.booksilove.com
What We Got
Reader 1 Reader 2 Reader 3
Suspenseful
Provocative
Thrilling
Authentic
Believable
Poignant
Surprising
Clever
Plausible
Thoughtful
© 2014, BooksILove™ www.booksilove.com
We expected:
Passionate Inspiring Heart-Breaking Authentic
© 2014, BooksILove™ www.booksilove.com
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
We expected:
Provocative Technical Plausible Hair-raising
© 2014, BooksILove™ www.booksilove.com
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
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
How the data has surprised us
Readers repeatedly select the same tiles across the
books they love
© 2014, BooksILove™ www.booksilove.com
BooksILove
© 2014, BooksILove™ www.booksilove.com
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