Peter McCarthy - The Consumer Data that Matters Now

The Consumer Data that Matters Now
Publishers Launch Conference, BookExpo America
May 29, 2013
Peter McCarthy

Executive Summary
There are known knowns. These are things we know that
we know. There are known unknowns. That is to say,
there are things that we know we don't know. But there
are also unknown unknowns. There are things we don't
know we don't know.
May 29, 2013 Consumer Data - PubLaunch BEA 3

Contents
»  Baseline
»  The types of things we know well
»  What we don’t know
»  Why what we know isn’t enough
»  What we can know much better
§  How: a hypothetical use case
»  What it may mean
4May 29, 2013 Consumer Data - PubLaunch BEA

Baseline
(who I am, what I talk about when I talk about data, today’s goal)

Who am I

Consumer marketing data today (and yesterday)
»  Demographics
§  Gender, age group, income level,
education level, etc.
§  Note: I include geographic region here
»  Psychographics
§  Beliefs, values, attitudes, opinions,
“lifestyles”
»  Behaviors
§  What people have done, are doing, and
are most likely to do next

Consumer marketing data
Consumer-Focused
1.  Who
2.  What
3.  Why
§  Product
§  Place
4.  Where
5.  When
6.  How
Results-Oriented
§  Price
§  Promotion
Measured
§  What’s working
§  What’s not
Optimization
?

My goal
»  Offering the suggestion that we understand our business well
enough today
»  That we understand core book buyers pretty well
»  Advancing these hypotheses:
§  That we reach many readers with books they do not want
§  That there are far more potential readers for each book we
publish than we do reach
§  That this happens because of the percentage of time we spend
looking at different types of data
»  Offering a suggestion on what we might do, how, and with
what results for earned, paid, and owned marketing efforts

The types of things we know well
(A lot about books, the book marketplace, reader personae)

US trade book sales continue shift online…
Source: 2013 Bookstats
Brick & Mortar
8.03
7.47
2011 2012
Net Sales ($B)
5.72
6.93
2011 2012
Net Sales ($B)

…and to eBooks, though more slowly this year
64
291
869
2,109
3,042
2008 2009 2010 2011 2012
+199%
+143%
+43%
eBook Net Sales ($M)
Logarithmic regression: R2=0.7853
+355%

eBook sales vary by category
46
198
585
216
484
1,291
469
592
1,831
2010 2011 2012
eBook Net Sales ($M)
Adult
Fiction
Adult
Nonfiction
Juvenile
Fiction

This data is required to manage our businesses
»  And there is much more than just the Bookstats data I’ve gone over
here
»  We also know a great deal based on our own internal data
»  We also watch aggregate consumer trends and use digital platforms
to derive insights
»  Result:
§  Informs strategy, aiding primarily in macro, strategic decision-making
§  We still can’t predict the future: all eBook projections way off…
Etc.

The book buyer
»  Peter Hildick-Smith of Codex
took a look at 30k past 12
month book buyers and
asked:
»  “Where did find out about the
book you last bought?”
31%
14%
10%
5%
6%
Physical Retailer
Reccomendations
Analog Publicity
4th Online Media
Online Booksellers
Source: Challenges to Book Discovery, DBW 2013
»  13% of readers recommend to 38%A Very Nice Find

§  Has created the personae of the
“power buyer”
§  17% of whom report acquiring “at
least weekly”
§  Skews female (it’s a not a he or a
she, it’s a blend)
§  Cluster in the 18 – 55 range
§  Are well off, professionals, many
clerical workers, and homeworkers
§  Favor eBooks over physical
§  Many shifting toward tablets and, it
seems, reading less
§  Preferred tablet becoming iPad
§  They as for top acquisition source
(results to right)
BISG’s “Power Buyer”
The eBook Reader and “Power Buyer”
How Acquired?
0% 50% 100%
Google
eBooks.com
Library or Library
Site
Apple iBooks
Barnes & Noble
Amazon
Power
Reader
Standard
Reader
Source: 2013 BISG Consumer Attitudes toward E-Books

The “book and eBook reader”
»  Reading and eReading habits
§  75% of the US population 16 and over reads
§  33% of that group had ether an eReader or a Tablet
§  67% of book readers said they had read one in the past 12
months
§  23% 16 and over had read a book
§  A lot on demographics
67% of US book readers age >16 report they
Have read a book in the past 12 months
=
~157,754,850 potential consumers
Source: Pew

What we don’t know
(Most of which won’t hurt us)

Precise eBook market dynamics…
64%
22%
10%
3%
“Conventional Wisdom”
Amazon Nook
Apple iBooks Other
eBook Market Share Market Dynamics
•  Conclusive Data on
Pricing
Lots of energy. smart thinking,
and facts, but…
•  The Effects of Self-
publishing
Huge. ISBN registrations
through Bowker + unknown
masses of KDP authors. Strong
belief they drive the average
bestseller price down…strong
•  Much at all about Apple
Surprising, given all the fuss.

“Real” consumers
»  That consumer for that book
»  Enough of an understanding and approach on the spectrum
of consumer relationships and how to have them
»  We don’t know most of those 150M potential consumers
Well-
known
Lightly
touched,
slightly
known
Currently
Unknown but
Interesting

Why it isn’t enough
(hypothesis: it is what we need, just not all of what we need)

The industry is making a fairly smooth shift…
Source: BISG Trends; Bookstats
Total US Trade Revenue with eBook Revenue Nested ($M)
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
2010 2011 2012
eBooks Print
But we’ve mostly managed to stay afloat while others have thrived…

Unfair comp 1: Amazon share price: May ‘08 – May ‘13
$78.45 / SHARE
$269.07 / SHARE
2008 2009 2010 2011 2012
100
200
300

Unfair comp #2: iPad growth 2010 – Q1 2013

Unfair comp #3: Apple profits, same period

In the end, here’s why I think what we know isn’t enough
»  We need to be demonstrably the best at connecting authors and
their titles to the most, most right readers – efficiently and repeatedly
»  What we know today allows us to run our businesses and manage a
shift. That alone is no small feat…but…
»  Basically, we’re surrounded…
Except if we can better reach those …
157,754,850 potential consumers
May9, 2013 Consumer Data - PubLaunch BEA 27

What we can know much better
(hypothesis: it’s easier than we think)

More about every potential reader
»  Demographics
»  Psychographics
»  Behaviors
§  By using the consumer data that we do have
§  But vastly increasing our efforts around
augmenting that with “raw” consumer data
§  Using tools to systematize and, if possible,
scale the knowledge and efforts

Some (really useful) sources of consumer data
§  Social Graph
They know consumers.
Now tying to offline sources.
§  Ad Platform
Open (APIs, Tools) and
Optimized.
§  Constant A/B testing
Fail fast, fix.
§  Result: Happy Users/Advertisers
Despite incredible concerns over
privacy. Relevance trumps it.
§  Search (& lots else)
Massive share, joyous
users.
Still the of ad inventory at an
all time high.
§  Literally Building a Brain
Yes. All products data-driven
.
§  Open
APIs and tools
§  Massive growth
Wild adoption and usage.
Targeting getting there but
they know what they need to
know.
§  Timely
Almost “now.”
§  Open (for now)
Can get at the data.

Obviously great tools for outbound marketing
»  And outbound best practices must be understood to best
execute
»  However, it is when we
§  Use them in specific public-facing ways…
§  turn them around and extract consumer data…
§  and mash that data with other sources of data…
»  That we can triangulate a potential consumer-set vis-à-vis the
three attributes we need to market most effectively…
»  Then we’ve got who, what, when, where, how, and why we
need to put the right message in front of the right person at
the right place at the right time

How: Big data, little data…right data, right time…
Big Data
§  Enterprise-scale tools
§  Batch data extraction via
APIs
§  Used for listening, real-
time platform monitoring
§  Marketing automation
§  Business intelligence
dashboards and mash-ups
§  Decision support,
exceptions reporting
§  Data-warehousing
§  Report generation
§  More…
§  Lighter-weight tools to
support same tactics
§  Require “cobbling” to
triangulate multiple data
points
§  But…
§  Are readily accessible,
easy-to-use, require less
organizational change…
§  And they work
Are not mutually exclusive – in fact, employing both is a best practice

FWIW: I use a subset of ~100 tools to triangulate, plan & execute
Social Analytics
§  Simply Measured
§  Peek Anaytics
§  SproutSocial
§  Trackur
§  Tweriod
§  Tweepi
§  Buffer + Bit.ly
§  Twitter Ad Interface
§  Etc.
§  Facebook Insights
§  Facebook Ad
Interface
§  Facebook
PowerEditor to
Create Audiences
§  Facebook Lookalike
Audiences
§  EdgeRank Checker
§  LinkedIn Ad Editor
§  Pinterest Analytics
§  Google Alerts
§  Goodreads comp
authors
§  Etc.
Web/SEO
Support Tools
§  Google Trends
§  Google AdWords
§  Keyword / Placement
Suggestion Tool
§  Amazon search autofill
§  Google search autofill
§  Compete
§  Quantcast
§  SEO Moz
§  SEO Quake
§  Google universal analytics
§  Amazon search auto-fill
§  Amazon comp authors
§  Librarything tags
§  Google search auto-fill
§  Etc.
§  Excel Plugins
§  Various Web Us
§  IFTT
§  Lots of Chrome Extensions/Apps
§  Others

Some Use Cases
(hypothesis: sample triangulating)

Google Trends

Amazon auto-fill (not logged in, cookies cleared)

What it may mean
(hypothesis: quick burst of pain, then good stuff)
SEO Quake

Google keyword suggestion tool

Facebook Ad Interface

Facebook Insights > Power Editor > Custom/Lookalike

Peek Analytics

Simply Measured

Then a bunch more till there’s “enough to go on” then..
»  Map to internal data (sales, etc.)
»  Ensure goals understood
»  Align everything with attributes
§  Meta-data through to creative
»  Put all measurement in place
§  Google goals, affiliate tracking, etc.
»  Create light execution plan
§  Four Phases: foundation, tactical growth,
pruning, communicate
»  Launch
»  Optimize/Prune
Specifics differ but works
for inbound/outbound,
earned, paid, “rented”, etc.

What it May Mean
(hypothesis: all in the eye of the beholder…)

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
Constant triangulation – however you can do it

These guys predicted the future!

Some will have a harder time than others
§  C-Level misalignment
§  Over-emphasis on attribution
direct ROI out of the gates
§  Over emphasis on “owning the
relationship”
§  There is a range of
relationships and knowing
them (and that they know
you is pretty good)
§  Over-emphasis on creative
planning
§  Overly concerned with beginning
thinking with the “what” not the
“who”
§  Allow for marketing budgets that
can “move”

Skillsets, experience, orientation, or willingness key
Source: The Soda Report, 2013
Challenge
Data Collection 36%
Data entry 9%
Data storage 7%
Data search 14%
Data sharing 16%
Data analysis 54%
Data cleansing 30%
Creating value/insights 49%
Other 6%
Q. What challenges does your company face with data management?

Publishers as best connector of book to reader results from:
»  Being marketing scientists, marketing quants, or getting some
§  They are very useful.
§  Understand that search and social are consumer activities/tools but
business ones as well. Heavy duty ones. Like coop. Remember coop?
»  Obtaining true, live consumer insights
§  In the wild, as they’re behaving, living, speaking up.
»  Taking action based on their own knowledge + data
»  Optimizing
»  Lather, rinse, repeat
»  Market the marketing all along the way (because they know what
happened)
Expanded reach beyond core book buyers

And if you can scale it…
»  The Ever-Learning Marketer or Marketing Organization
§  Puts the right book in front of the right consumer at the right time
§  Optimizes marketing spend
§  Improves stakeholder relations through rich communication, clear
accountability, and an abundance of creativity (there’s a lot in the data!)
§  Does this in partnership with other areas and eventually improves the
entire organization's capabilities, cross-pollinating learning

Smarter, ever-improving marketing at scale
To become a chess grandmaster also
seems to take about ten years. (Only
the legendary Bobby Fisher got to that
elite level in less than that amount of
time: it took him nine years.) And what’s
ten years? Well, it’s roughly how long it
takes to put in ten thousand hours of
hard practice. Ten thousand hours is
the magic number of greatness.” —
p. 41, Outliers

Thank you very much
(hypothesis: probably one or two too many slides…)

Peter McCarthy - The Consumer Data that Matters Now

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