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Data Everywhere:
Lessons From Big Data in the
Television Industry
By Susan Etlinger
with Rebecca Lieb and Jaimy Szymanski
Includes input from 18 ecosystem contributors
A Market Definition/Best Practices Report
July 10, 2014

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Drivers of Disruption and Insight ................................................................................................................................................................
Industry Drivers ....................................................................................................................................................................................................................
Consumer Behaviors ..........................................................................................................................................................................................................
Business Impacts ...............................................................................................................................................................................................................
Using Data to Drive Competitive Advantage ...............................................................................................................................
Programming ........................................................................................................................................................................................................................
Distribution ............................................................................................................................................................................................................................
Promotion ...............................................................................................................................................................................................................................
Ratings and Performance Evaluation ............................................................................................................................................................................
Data Sources and Implications ......................................................................................................................................................................
Best Practices and Recommendations ..............................................................................................................................................
Coming Up Next ...................................................................................................................................................................................................................
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Table of Contents
In 1951, Desi Arnaz of I Love Lucy fame made a decision that would signal the birth of
modern television. Rather than film the show with a single camera, as had been done up
to that point, he decided to use multiple cameras so he could shoot before a live audience,
ushering the “reaction shot” into television and creating a more vibrant, realistic, and
cinematic television experience.
While the television industry has changed dramatically since then, spurred by device
proliferation, changing distribution methods, and the increasing popularity of social media,
the rise of “TV Everywhere” and the resulting availability of new streams of digital data
represent a new resource for business models already in transition.
This report will examine four use cases for data to better understand this new technology
landscape and will lay out practical strategies that executives can use to address the
resulting opportunities and risks.
Executive Summary

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As a result of these new dynamics, the television industry is gaining access to a
broad range of signals that can be used to inform decisions from programming
to promotion to distribution to ratings. Following is a summary of the three
most prominent factors shaping the industry today: device proliferation, multiple
distribution methods and disparate social media platforms.
Industry Drivers
A recent episode of AMC’s Mad Men, featuring the 1969 moon landing, depicts
the pattern that dominated TV viewing until quite recently. Families, colleagues,
friends, and neighbors would gather around the set and communally watch an
event or a show, on a single device, at the time it was broadcast.
Today, the advent of multiple devices, distribution methods, and social media
platforms has shattered this model. Television viewing is multidimensional.
It’s multi-device, time-shifted, and often non-linear (or hyper-linear, e.g., binge
viewing). It’s no longer passive entertainment; television is characterized by
active viewer participation via social media sharing, commenting, and User-
Generated Content (UGC).
As a result, the industry is simultaneously grappling with a range of
dynamics. Audience fragmentation can be both a curse (lack of insight) and
a blessing (ability to personalize). Ratings methodologies and traditional KPIs
no longer reflect today’s reality. Content creation can be an organizational
burden, a competitive advantage, or both.
Data Everywhere: Lessons From Big Data
in the Television Industry
3
Drivers of Disruption and Insight
We’ve come a long way from the early days of television.
Today’s viewers watch Scandal with mobile device in hand
for a true second-screen experience, binge on Orange Is
the New Black, create memes and other user-generated
content from Game of Thrones and Breaking Bad, and chat
on Twitter with their favorite Being Mary Jane characters.
Family members watch their favorite programming
individually on their own devices.
Today, the advent
of multiple devices,
distribution
methods, and social
media platforms
has shattered this
model. Television
viewing is
multidimensional.
It’s multi-device,
time-shifted, and
often non-linear.

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Multiple disparate data streams may strain
organizational culture, providing a piecemeal view
of audience attitudes and patterns, or they can be
leveraged to better understand audience behaviors
and attitudes and to gain competitive advantage.
Following is a view of the primary industry trends
at play, their impact on consumer behavior, and the
resulting pressures and opportunities for business.
Device Proliferation
While in days past the “TV” referred to a single
device, today’s audiences have access to TV virtually
everywhere: on their computers, tablets, smartphones,
and even gaming consoles. This trend continues to
accelerate; a recent report by CMO.com states, “TV
Everywhere authenticated video from gaming consoles
and OTT devices grew 539% year-over-year.”1
Some of the biggest changes in the market result
from the fragmentation of audiences among these
devices, and the insights and blind spots this
fragmentation provides. Some organizations struggle
to make sense of disparate data streams, while
others see data as an opportunity to identify emerging
audience attitudes and behaviors. More than anything,
however, the availability of data at a device level
places a different lens on the TV viewing experience,
one that can provide insight in both directions.
Multiple Distribution Methods
While cable has been disrupting network television
for decades, and Web and mobile browsers aren’t
exactly new, the past few years have seen accelerated
fragmentation as streaming players, such as Apple
TV, Aereo, Roku, Redbox, Amazon Fire, Google TV,
and others, have gained popularity.2
CMO.com further
states, “Online video consumption across mobile
devices (smartphones and tablets) is at an all-time
high of 25%, with 57% year-over-year share growth in
the U.S. (Q1 2013 vs. Q1 2014).”3
While time shifting has been possible since the advent
of the VCR, what’s different now is that it’s delivered
via streaming, and therefore trackable. Now when
audiences time-shift and binge-view programming,
cable and satellite companies can detect and learn
from viewing patterns in a way that was previously
not possible. They can see how many minutes of a
show a viewer watches, whether they watch a single
episode in one sitting, or whether they run through
Source: Altimeter Group
Figure 1 Industry Drivers, Consumer Behaviors Spur Disruption and Insight

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three or four (or more) episodes per night. They can
see whether audiences grow or shrink after the first
few episodes or from season to season and adjust
plans accordingly.
Social Media & Social Data
Social media — and the content and data it generates
— are having a profound impact on the television
industry. At the most basic level, phenomena such as
rating, sharing, liking, retweeting, and other forms of
structured and unstructured data sourced from social
media and proprietary platforms have created a dialog
among programmers, distributors, and networks —
and even between artists and the audiences they
desire to reach.
This represents a huge potential source for market
research, albeit one that is substantially unmediated
and requires intensive processing, analysis,
and integration with other data streams to yield
meaningful insight. Beyond likes and shares, however,
the emergence of user-generated content has
added a new dimension to the viewing experience.
In addition to consuming entertainment content,
audiences can be avid makers as well, editing,
mashing up, and otherwise recontextualizing the
shows that interest them, whether in video, photo, GIF,
fan fiction, or other form.
HBO’s Game of Thrones is a frequent recipient of
fans’ adoration and creative energy, some of which
can begin as true UGC and remain so and some of
which can be commissioned as branded content if
advertisers discover that the creator’s work resonates
with their audience. One example of this is a recent
video commissioned by Blinkbox, Tesco’s streaming
service, which was timed with the announcement of
the availability of Season Four of Game of Thrones.
The video, “The Pugs of Westeros,” features a group
of pugs dressed in Game of Thrones characters. It
garnered more than 1.3M views in its first three days.4
Beyond the use of UGC itself, the data it generates
with regard to views, reach, sharing behavior,
sentiment, and other attributes provides useful
insight into potential promotion strategies within a
fragmented and increasingly socially connected world.
For example, what topics and characters do people
tend to recreate most often? On what platforms?
In what medium? That could become an input to a
promotion strategy or to the next season’s trailer.
Consumer Behaviors
A recent Nielsen report entitled The Digital Consumer
reveals the extent to which digital technology has
permeated media industries. “As a result of the
explosion in digital and mobile device ownership,”
it reads, “American consumers are connected with
screens throughout the day and engage with media
content for more than 60 hours per week.”5
More than the sheer amount of screen time, however,
consumer behaviors have emerged that carry the
potential both for unprecedented insight and for
challenges in sourcing, processing, and interpreting
the data. Following are the most salient examples
of these new behaviors, as well as examples of their
impacts (see Figure 2).

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Figure 2 Emerging Consumer Behaviors Create Data Opportunities and Threats
Source: Altimeter Group
Behavior Description Data Impacts
“BYOD for the
Family”
Coined by Carri Bugbee, refers to the phenomenon
in which individual family members watch their
own programming on their own personal devices.
Enhanced information about individual family
members’ preferences and behaviors.
Binge Viewing Watching television for longer time spans than usual,
usually of a single television show. (Wikipedia)
Which programs are binge-worthy, suggesting
high engagement/preference.
Cord-Cutting/
Delaying
Canceling a cable or satellite TV subscription in
favor of other methods of accessing content.
Preferred devices, times, locations for viewing
content.
Over-the-Top
(OTT) Content
Delivery of audio, video, and other media
over the Internet without a multiple system
operator being involved in the control or
distribution of the content. (Wikipedia)
Browser-dependent. Multiple System Operator
(MSO), i.e., cable or satellite provider, loses direct
access to data and is dependent on other data
sources for consumer viewing habits.
Place-Shifting Recording video or audio programming to view
or hear it in another location. (ITV Dictionary)
Location: Where people watch particular shows;
at home, during likely commute hours, in multiple
locations. Experience: What shows they place-
shift versus others.
Second Screen
Viewing
The use of an additional monitor (e.g., tablet,
smartphone) while watching TV. It allows
the audience to interact with what they’re
consuming, whether it’s a TV show, video
game, or movie. (Mashable)
Which types of programming prompt
conversation during airtime. Scandal is an
example of a network show around which this
behavior is prevalent. Awards shows and sporting
events also prompt second-screen behavior.
Social Actions Liking, favoriting, retweeting, starring, or
otherwise showing preference for a social
post. Social actions require the use of code (a
button) that generates structured data.
Requires correlation with other data sources
(other social networks and viewer data, for
example) to demonstrate anything other than
momentum on a single channel.
Social Comments Commenting on a post or posts on a social
network. Unlike social actions, social
comments are expressed in natural language
(unstructured data).
Unstructured data requires strong text analytics
to interpret and may also require some human
involvement, but it is a direct, albeit, raw source
for consumer attitudes.
Social Sharing The practice of sharing content from a website
on a social media site or application. (Google)
A signal of advocacy, which requires analysis to
determine impact on audience acquisition.
Time-Shifting Recording video or audio programming to view or
hear it at another time. (ITV Dictionary)
When people watch particular shows: time of
day/week. What shows they time-shift.
TV “Super
Connectors”
TV Super Connectors must do any of the
following “several times a day”: follow TV shows
on social media; following actors/personalities
on social media; communicate about TV shows
and/or characters on social media. (CRE Talking
Social TV 2: September–October 2013)
In a word, influencers, but this is a specific
definition. Super Connectors may or may not
be popular, but network analysis can reveal
their impact on audience sentiment and/or
acquisition.
TV Everywhere An initiative to provide controlled access to pay
television (cable, satellite) customers across
multiple device platforms. The concept is based
on the capability of the content provider to verify
the end user’s identity and authorization to
access content. (Source: Akamai)
Multiple, disparate data streams from devices,
distribution channels, social media, third-party
sources, and others must be viewed in context to
provide real insight.
User-Generated
Content (UGC)
Any form of content, such as video, blogs,
discussion form posts, digital images, audio files,
and other forms of media, that was created by
consumers or end users of an online system
or service and is publicly available to other
consumers and end users. (Webopedia)
Shows prompt engagement that requires
commitment, such as videos, fan fiction, GIFs,
images, or others. The tone and topic of UGC can
also provide insight into sentiment related to the
show’s story or actors.

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What connects these new behaviors can best be characterized by the notion
of “TV Everywhere,” that the traditional, linear experience of communally
watching television beginning-to-end in a fixed location via a fixed medium
has now been completely up-ended. None of the traditional dimensions
— who, what, where, when, how — are stable or inherently predictable.
Conventional wisdom about the most basic tenets of entertainment
— programming, distribution, promotion, and ratings — are all open to
interpretation and generate unprecedented types of data.
Business Impacts
The combination of three key industry drivers: device proliferation, shifting
distribution channels, and the popularity of social media, have contributed
to dramatic changes in consumer behavior. These effects have rippled
throughout the TV industry and have affected nearly every aspect of the
business, from programming decisions to success metrics. Following are
some salient examples.
Audience Fragmentation
The fragmentation of distribution models, from linear network TV to
cable, satellite, and streaming, has brought with it both challenges to and
opportunities for insight about audience viewing habits. While Nielsen and
“Q” ratings used to be the alpha and omega of TV performance, device
proliferation makes a comprehensive view impossible.
On the social data side, Nielsen has added Nielsen Social Guides to capture
the impact of social conversation on Twitter, but it is currently of limited utility
as it excludes other social media platforms, thereby under-representing visual
content, such as images and GIFs, which are typically shared on platforms
like Instagram, Pinterest, Snapchat, Tumblr, or elsewhere. This can lead to
missed opportunities on platforms other than Twitter, as well as artificially
low KPIs for highly visual content — ironic given the highly visual nature of TV.
Interpretive Blind Spots
A June 23, 2014, blog post in the Wall Street Journal reported that “neither
comScore nor Nielsen — the two biggest companies in third-party audience
research for the Web — tracked the online audience” for the USA-Portugal
tie in this year’s World Cup. According to author Mike Shields, this means,
“If advertisers want an impartial estimate of how many people streamed the
game online, they’re out of luck.”6
Given the growing market share of tablets and smartphones during the past
few years, some site metrics may not always be the most reliable indicator of
network or franchise popularity. Chad Parizman, director of convergent media,
Scripps Networks, takes this into account when his team analyzes mobile site
performance for a given show or shows. “Look at a whole year [of mobile site
usage], and then look at December,” he says. “There’s a really clear impact of
holiday shopping.”
What connects
these new
behaviors
can best be
characterized by
the notion of “TV
Everywhere,” that
the traditional,
linear experience
of communally
watching
television
beginning-to-end
in a fixed location
via a fixed
medium has now
been completely
up-ended.

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Delayed Decision Making, Risks, and Opportunities
The Netflix revival of Fox’s Arrested Development is
widely cited as an example of the risks and opportunities
inherent in audience fragmentation. The show, which
premiered on Fox, ran for three seasons until Fox canceled
it amid declining ratings. But when Arrested Development
became available on Netflix, Netflix saw the audience
began to grow. According to Jenny McCabe, Director of
Global Media Relations at Netflix, “we could see that more
people were finding Arrested Development and starting to
watch it, which is why we thought it would be a good bet
for us to commission Season 4.”
The nature of the show – dialog-rich, with rapid-fire
jokes that viewers want to rewind and watch again – is
one possible explanation for the disparity between its
performance on linear (Fox) and non-linear (Netflix)
TV. It also raises larger questions about what insights
we can glean from data about linear versus non-linear
viewing experiences and how programmers might factor
those into future decisions. Says McCabe, “Netflix is an
Internet TV network, and every paradigm is different.”
While there was a good deal of speculation about the
ratings of the Arrested Development revival (Netflix will
not release viewing figures), the fourth season received
mixed critical reviews. Consider this in contrast to Orange
Is the New Black, which burst on the scene and became
an instant phenomenon. The OITNB craze also highlights
another factor that can strongly influence audiences: the
actors’ participation (or not) in social media, which can
generate signals (and therefore insight) into the drivers of
a particular character’s popularity, or lack thereof. These
findings reinforce the impact of industry drivers discussed
previously: New devices and distribution methods can
reveal new audiences and viewing patterns, while social
data illustrates attitudinal patterns that can inform future
decision making.
Organizational Strain
The convergence of multiple data streams brings
with it the convergence of multiple departments
and stakeholders within an organization, from IT to
market research, marketing, analysis, show-runners,
executives, and even, in some cases, the actors
and writers themselves, all with specific questions and
vested interests in how the organization communicates,
shares, and acts on the information. This can lead
to organizational strain as departments negotiate
on reporting standards, data and tool access, and
interpretation of results.
UsingDatatoDriveCompetitiveAdvantage
Data — derived from social media, viewing behavior,
metadata, search, ratings, geo-location, or from third-
party sources — is increasingly being used to make a
range of business decisions in the television industry. In
our research, Altimeter Group identified four primary use
cases for data analysis:
• Programming: ideation or validation of a
programming decision;
• Distribution: where to distribute content, whether it
is syndicated entertainment or other types of owned
media;
• Promotion: How and where to identify influencers
and develop, time, promote, and target content; and
• Ratings and Performance Evaluation: New and
augmented performance insight for TV shows, news
stories, or marketing initiatives.
Source: Altimeter Group
Figure 3 Four Primary Use Cases for Data in the
TV Industry

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These use cases range in maturity from ideation
(what could be done, given available data), to ad
hoc (done in certain circumstances), to formalized
(programmatic; part of standard procedure). In many
cases, however, lack of integration among data
sources remains a challenge.
Programming
Arguably the most famous example of using big data as
an input to programming decisions is the Netflix TV show
House of Cards. A February 2013 New York Times story,
“Giving Viewers What They Want,” rather breathlessly
chronicled how Netflix “is commissioning original content
because it knows what viewers want before they do.”7
The Netflix example is one of validation (predicting
that there would likely be an audience for House of
Cards). Data can also be used for ideation, from finding
ideas that would otherwise be hidden to increasing the
airtime of beloved characters. While this is still a fairly
rare example, it is a natural extension of time-honored
market research practices. Says J.P. Lespinasse, Senior
Director of Social Media at BET Networks, “It’s not a
stretch to say we talked to five guys in Chicago, and they
didn’t like the show, so we re-cut it.”
Distribution
Data can be used to make distribution decisions
or evaluate the impact of new distribution models
on audience growth or other factors. Cable and
satellite set-top boxes and streaming devices, such
as Roku, Apple TV, Redbox, and Amazon Fire, all
have the potential to collect data about what users
watch, how much they watch, and when and even
where they watch.
Viewed individually, all of these distribution methods
have “blind spots.” In aggregate, however, they can
reveal viewing patterns that can be used to make
decisions regarding programming or promotion.
One of the more salient examples is HBO. While the
channel was originally available only via the cable or
satellite set-top box, HBO added “HBO Go” to extend
the viewing experience to mobile or desktop devices.
While HBO Go is only available to subscribers, it’s
an example of how networks are using multiple
Big Data in the Media Industry
In the media industry, data-driven journalism is
becoming more widely adopted. “We lead meetings
with numbers,” says Mary Nahorniak, Social Media
Editor, USA Today.8
USA Today has a data team that is core to the
business. “Our data team is regularly finding stories
buried in data, and that’s super interesting.” One
recent example was a USA Today series on private
airplane (aka “general aviation”) crashes. For this
series, the data team, by analyzing NTSB reports
and FAA regulations, was able to discover that the
NTSB is frequently unable to adequately investigate
the causes of general aviation crashes, with a
resulting death toll that numbers roughly nine times
those of commercial airlines.9
While media shares some characteristics with
television, one salient difference is in the volume
and speed of content. Rather than one TV movie or
15-episode series, a newspaper may publish many
stories per day, albeit requiring varying levels of
reporting and therefore resource. But the lesson is
similar: Data can provide valuable clues to untold
stories or audience attitudes.
methods to reach their desired audience. In line with
this strategy, the company recently made some older
shows available on Amazon. This not only has the
potential to spur audience growth, it also provides a
more granular level of detail on viewing patterns within
a subscriber account.
All of these dynamics are changing so quickly
that current behaviors may not always be the best
predictors of future ones. Says Social TV Analyst
Carri Bugbee, “Young people — kids and teens — have
a personal relationship with their devices, so even if
a family has a TV, they’d rather watch on their own
device. Will they even watch TV on their computer
in five years? We don’t really know.” While we can’t
predict these eventualities today, the best method
available to us is to watch the trends and look for
anomalies that may signify a shift in behavior.
Promotion
Data can be used as an input to promotion planning
overall, as well as to granular strategies. “At HBO,” says
Sabrina Caluori, Vice President of Digital and Social Media,
“we use data to help us optimize our plans. Particularly

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Figure 4 HGTV Handmade on YouTube
with a new show, we can make a lot of assumptions
about what the fan community might look like and how
viewers may respond to the show and the storylines.
But those are just assumptions until the show airs
and we see what fans are saying and how they’re
growing. That allows us to tweak digital activations and
strategies, particularly for shows in their first season or
between seasons one and two.”
Audience Acquisition
HGTV’s “HG Handmade” is a good example of how
data can inform an audience acquisition strategy (see
Figure 4). While HGTV has a passionate audience via
its cable network, the company wanted to find a way
to reach “cord-cutters”: millennials and others who
might be interested in HG content but who are not
current subscribers to cable or satellite TV. This was a
particularly salient opportunity, given that the popularity
of the maker movement during the past several years
has shifted perceptions of the crafting industry.
Says Scripps’ Chad Parizman, “Part of the strategy
was, ‘can we build a YouTube business around
content that falls squarely inside the HG brand but
view it through a different lens?’” Understanding that
every group has its own unique sociology, the idea
was to find existing talent and craft a strategy to
group them together via the HGTV brand, evaluating
content performance to determine the most effective
ratio for content to engagement and audience growth.
With not quite seven months under its belt, the “HG
Handmade” channel on YouTube has nearly twice
the number of subscribers as the HGTV YouTube
account. With regard to ratings impact, Parizman
admits, “The jury is out on that. The sense is that
there is a minimum threshold that it takes for social
to affect ratings. Beating benchmarks and year-over-
year growth is awesome. But no one knows what the
minimum threshold of volume is.”
Even if there is not yet a guaranteed way to detect impact
on ratings, there is value in introducing the brand to a new
generation of viewers and thus creating the opportunity
for crossover between channels.
Ad Targeting
“One of the opportunities of big data in the television
industry is the ability to think about audiences at
a deeper level than was possible in the past,” says
Simulmedia CMO David Cooperstein. Simulmedia, a
company that sells data-driven television advertising
campaigns, takes an audience-based, data-driven
approach to using television advertising inventory.
Rather than traditional demographic data that may, for
example, identify a segment as “women aged 18-49,”
Simulmedia, according to Cooperstein, “goes deeper on
the definition of the audience, and sells against that target
audience across networks.” Example inputs include:
• Set-top-box data (customer data and viewing
behavior)
• Third-party data (for example, MRI and credit card data)
• When possible, proprietary sales data provided by
the customer
The next step is to analyze the data deeply to develop a
hypothesis on a commercial placement’s potential impact.
Then, the team performs a closed-loop analysis using set-
top-box data and tune-in data to identify promotions that
actually worked: whether someone saw a spot and watched
the show, made a purchase, or visited a retail location that
corresponded to an advertising spot. Says Cooperstein, “This
gives TV a level of measurability that it hasn’t had before.”
Real-Time Marketing
Altimeter Group analyst Rebecca Lieb defines “real-time
marketing” as “the strategy and practice of responding with
immediacy to external events and triggers. It’s arguably the
most relevant form of marketing, achieved by listening to
and/or anticipating consumer interests and needs.”10

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Figure 5 Arby’s and Pharrell:
Real-Time Marketing at the Grammys
Television is one of the most salient opportunities
for Real-Time Marketing (RTM) as it can — in the
case of events such as a presidential inauguration,
World Cup, Academy Awards, or even a Scandal
episode — provide an audience with four distinct
benefits: surprise and delight, brand relevance, the
right audience at the right time, and a reminder that
the brand is “always on.”11
One recent example of TV-related RTM featured a
play on the hat worn by music artist Pharrell at the
2014 Grammy Awards. Arby’s playful tweet to Pharrell
prompted a response by the artist, garnering many
thousands of tweets and retweets, as well as responses
by other brands, including Gain, Hyundai, and Pepsi.
Another recent example featured the Uruguay-Italy
FIFA World Cup game on June 24, 2014. Player Luis
Suarez, who had allegedly bitten Italian player Giorgio
Chiellini, was treated to a chorus of tweets from brands.
McDonald’s Uruguay was the first, chiding Suarez while
offering a brand-relevant alternative: “Hi @luis16suarez,
if you were hungry you could have taken a bite of a Big
Mac.” Within minutes, brands such as Trident Gum,
Whataburger, TGIFridays, and others got into the mix,
featuring their brands in humorous ways. One of the
cleverest: MLB, with this salient reminder: “There’s no
crying biting in baseball.”
While we don’t know the impact these tweets had in
aggregate, it would be fairly easy to perform a volume
and reach analysis to determine which traveled
furthest and fastest, and use that data to make
inferences about what types of responses are likely to
elicit the best response in the future.
Figure 6 Real-Time Marketing: McDonald’s
Uruguay

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Big Data in the Music Industry
Spalding Entertainment, based in Nashville, uses
data in a number of ways to benefit country
music artists such as Rascal Flatts and Jason
Aldean. One of the most interesting is their
in-venue use of social media. Spalding uses
Chirpify to build and nurture its fan base and
tweet-to-screen technology with hashtags and
content to activate audiences at shows while
they’re waiting for the show to start, changing
acts, or at other times. One example: offering seat
upgrades to people who tweet a specific hashtag,
and, assuming they have opted in, retargeting
them later for other offers. Before the show, they
leverage Jamplify to engage and reward fans
to help promote that the tour is coming to their
local city. In addition to helping cultivate fans and
sell tickets, the data gleaned from this initiative
can also be used to find adjacencies between a
specific artist’s audience and local brands that
may want to reach that audience.
To maintain trust and authenticity, Spalding is careful
to ensure its social data strategies align with the
artist’s fan base and recommendations. To a great
extent, social media is just an extension of the way
it has always done business. Says Amanda Cates,
Director of Web and Digital Marketing, “In country
music, we’ve always cultivated our fan base.”
Content Development
Scripps Networks uses digital data to help determine
the “golden ratio” of content to audience engagement.
Says Chad Parizman, “Right now we’re trying
to correlate volume of posts with the nature of
engagement. Our goal is to make sure everything we’re
doing is as efficient as possible. This year, we’re having
the best social year ever, by two to three times the
amount of traffic. We use social data to drive business
cases: Should we spend more on user-generated
content? Do we need more people? Our early numbers
say it’s worth it to create more content.”
At the same time, he says, “we’re still very early on in
our ability to correlate content with business outcomes.”
This is especially true given recent changes to Facebook
algorithms, which, much like the Google search
algorithm, continue to adapt over time and thus make ad
equivalency metrics, such as “reach,” nearly impossible
to predict. But Parizman is not troubled by that fact.
“At some point,” he says, “we’re going to have to treat
what we do on social the same way we’re treating our
websites: put the best content out there and hope the
algorithm accounts for that. We can’t always be chasing
the dragon.”
Talent Development
Another use case for social data that is starting to garner
attention is in talent development. BET Networks used
Adobe Social to discover that one character on the Being
Mary Jane show was more popular than expected and
that she was also highly quotable. This discovery led
the character, Avery, played by Robinne Lee, to be more
prominently featured in broadcast commercials and in
social content, as well as to the decision that she live-
tweet during episodes in which she appears. In addition,
said J.P. Lespinasse, “The day after I pulled our social
data, Robinne was on the front page of the website.”
Influence Mapping
For brands, one of the most compelling uses of
data is the ability to understand who within a
certain community is influencing the conversation;
specifically, whose content is being shared most
widely beyond the original community. The A&E show
Duck Dynasty provides a useful case in point. While
the official Twitter show account has 1.9M followers
and cast member Sadie Robertson has 1.24M, it’s
actually Sadie rather than the official account who
influences fans to share content.
Figure 7 shows a comparison performed by Tellagence of
the influence of the official show account, as it relates to
sharing #DuckDynasty content, versus organic mentions
of “Duck Dynasty” for Robertson’s individual account. The
fan structure on the left indicates that while the account
reaches a broad audience, its followers do not tend to
share that content with others in their network.

13.
Conversely, Robertson’s account shows multiple
clusters denoting a high degree of sharing behavior
several degrees removed from the original post. The
conclusion: The @DuckDynasty account is useful for
broadcasting information, but if the show wants to
communicate beyond the core audience, Robertson is
the more effective messenger.
This also is useful when crafting promotional
strategies around specific actors or characters and
for those actors as they put their own deals together.
All things being equal, an actor with a significant and
active social presence will bring measurable value to a
show. In the future, will contracts specify social media
participation in addition to the usual press junkets? And
will talent be engaged and compensated for promoting
the shows on which they appear? This poses a new set
of considerations, both for actors and producers.
Ratings and Performance Evaluation
For decades, television show performance was
dominated by two ratings: the Nielsen rating, traditionally
the standard for determining the size and demographics
of TV audiences, and the “Q” rating, which measures the
familiarity and appeal of brands and individuals. Today,
with the advent of multiple devices and distribution
13
Source: Tellagence
Figure 7 Fan Conversation Trumps Brand Conversation
channels, not to mention the availability of sentiment,
reach, and volume data on social media, traditional ratings
methodologies no longer tell the entire story.
Because viewing data are now decentralized, these
methodologies may be unable to account for web viewers
(FIFA World Cup), Netflix and Amazon viewers, tablet and
smartphone viewers, and those active on other platforms.
This is also true in respect to the impact of social data
on decisions related to programming, competitive
positioning, and brand health. While Nielsen Social
Guides focuses on Twitter, for example, it does not
account for other social platforms. As a result, shows
with highly visual content or social media-friendly stars
(Girls, The Mindy Project, Scandal) must seek out other
ways to interpret visual media, such as photos, GIFs, or
video on Instagram, Tumblr, Pinterest, or Snapchat.
This creates both an organizational challenge (in
terms of scale) and also a challenge to insight, partly
because the ratings methodologies have not been able
to keep pace with the changes in the industry, but also
because interpreting visual data is still a relatively new
science, at least in its commercial application. Tools
such as Ditto and Piquora, which provide analytics on
photographic images, are beginning to address the

14.
unique challenges faced by marketers and others
whose brands are dependent on the visual web.
Data Sources and Implications
As the industry ecosystem has become increasingly
complex and interdependent, so has the data
ecosystem that holds the threat of missed
opportunities, as well as the promise of insight.
Today, the television industry uses the following
data sources in varying combinations to garner
insight into viewer habits and preferences (see
Figure 8).
While it’s one thing to have access to these data
streams, it’s another to make sense of them from
an audience point of view. One basic approach is to
organize the data points (which come from multiple,
disparate sources) into a simple storyline, identifying
who is watching, what they’re watching, at what time,
in what location, and, to the extent possible, their
expressed motivations.
The first step is to align the available metrics with these
categories. While social identity is still a challenge,
aggregating the data by trend (focusing on the what,
14
where, and how) and correlating it can surface previously
unseen relationships that can yield actionable insight.
The following page features a list of common television
metrics, organized by the simplest framework possible:
who, what, where, when, and why (see Figure 9: TV
Metrics Offer Insight Into Viewer Attitudes, Behaviors).
Data Sources
TV
Mobile Device
Computer
Console
etc.
Views
Completion
(of Episode,
Season)
Geolocation
Day Parting
Viewer-Supplied
Ratings
Volume
Referrers
Clickthroughs
Page Views
Surveys
Focus Groups
Volume
Reach
Sentiment
Influencers
Sales
Subscriptions
Ratings
Source: Altimeter Group
Figure 8 Primary Sources for TV-Related Data

15.
Best Practices and Recommendations
A characteristic that defines TV data pioneers is they
embrace rather than resist market changes. Multiple
devices, distribution methods, and social data present
challenges to be sure, but also offer unprecedented
opportunities for insight and innovation. Following
are some of the strategies we have identified that
distinguish these early leaders.
They Value Curiosity and Scientific Method
In addition to basic performance reporting (reach,
volumes, and the like), the most successful teams
are looking for relationships between data sets that
illuminate trends, opportunities, and risk. They are
collaborating with stakeholders and other analyst teams
and documenting the data available through multiple
15
sources. They’re willing to try structured experiments to
detect unknown relationships that may reveal insights that
can be used to serve multiple aspects of the business.
They Seek Ways to Scale
Many of the people we spoke with voiced a mix of
frustration and excitement with the state of analytics.
Their frustration comes from the sheer time and effort
needed to gain access to and analyze so many new and
disparate data sets, while the excitement comes from
their belief that, if they can source, process, and analyze
their data more efficiently, it will free them to deliver more
insight and value to the business. On the vendor side,
the most interesting solutions offer ways to automate
processes, whether they are related to classification,
tagging, integration, visualization, alerting, or other areas.
Source: Altimeter Group
Figure 9 TV Metrics Offer Insight Into Viewer Attitudes, Behaviors
Who What When Where How Why
• Subscriber Info
• Social Profile
• Inferred
Demographics
• Influencers
• Topics
• Networks
• Reach
• Sentiment
Viewing History
• Single Episode
• Full Season
• Pause, rewind
Purchase
History
Social Sharing
Trends
• Shares
• Retweets
UGC Trends
• GIFs
• Video, Vine
• Images
• Blog Posts
Social Action
Trends
• Likes
• +1s
• Favorites
• Follows
• Pins
Searches
Ratings
• Nielsen
• Viewer-
supplied
Viewing data
trends
•Time of Day
(Day Parting)
• Air time vs.
time shift
Geoocation Data
and History
• Zip Code
Device
Information
• Tablet
• Phone
• TV
• Computer
• Gaming
Console
• Other
Distribution
Method
• Satellite
• Cable
• Streaming
Sentiment
Analysis of
social data

16.
They Know Their Data Sources
Leaders are disciplined about inventorying,
assessing, and measuring the many inputs that can
provide insight and competitive advantage. They
understand the nuances among data sets and how
they may affect results. On the social side, they’re
looking at emerging platforms, such as Snapchat,
and factoring retail trends into their device data.
More than anything, they understand that data
provide a map of a much larger ecosystem, rather
than an end in itself.
They Think From the Viewer’s POV
To tell a coherent story, one based not upon the
pragmatic realities of disparate data streams but on
the viewer herself, organizations must pull together
their primary data sources into one “source of truth”
that takes these trends — individual and aggregate
— and displays them in a way that surfaces real
insights. Rather than show- or network-centric
television, this is a first step toward a real viewer-
centric experience.
They’re Practical for the Short Game, Visionary
for the Long Game
Another aspect that defines data leaders in this
industry is that they are practical about what can
be done today, given available tools and resources,
but they continue to push the boundaries of what
is possible. Sabrina Caluori of HBO views it as a
challenge of storytelling.
“One of the challenges we face right now,” she
says, “is the attempt to tie digital data with our
traditional metrics. Not only do we bring together
Facebook, Twitter, and YouTube data to tell a story
about Game of Thrones, but how do we overlay
that with our traditional data from Nielsen to tell
a more complete story? We are in the really early
stages of that, and we have a long way to go to find
true correlations and true causality. The industry
is wishing we were at causality, but the models are
just not that mature yet.”
16
Other best practices that Caluori and others interviewed
are using include:
• Organizational alignment (bringing new groups
together);
• Using what they’ve learned to inform strategy: future
campaigns, programming, or other decisions;
• Accounting for multiple forms of expression —
sound, image, emoticon, video — in their strategies;
• Looking to understand behavior rather than relying on
traditional demographics-based assumptions; and
• Thinking beyond the bare facts of the data to
questions about the possibilities of TV itself.
They’re Unafraid to Lead by Creating New
Experiences
The juncture we have reached with television and
technology is in many ways not that different from
what Desi Arnaz faced more than 60 years ago when
he made a decision that would change the narrative
structure of television. Says Altimeter Analyst Brian
Solis, “For TV to survive, or at least prolong the
experience as we know it, networks must treat TV
Everywhere with haste.”
To do so, he says, requires leadership. “Simply
extending content is ordinary. Leadership takes
the vision to create new experiences that cater to
the digital attention span and are also native and
optimized to the device.”
Fulfilling the promise of digital transformation — whether
for television or other industries — ultimately requires
a strategic approach to data. But, beyond data, digital
transformation “starts with a desire to innovate and the
courage to break new ground. That part is human,” says
Solis. “Data is the compass.”
Coming Up Next
As TV Everywhere becomes more prevalent, the industry will
need to examine its assumptions about this medium from
almost every angle. Here are some of the most salient issues:

17.
TV Everywhere = Data Everywhere
As TV becomes available through more devices
and channels, and as methods of expression in
social media continue to evolve, the industry will
need to contend with an ever-shifting mass of data
points and even data types. This will drive a need for
organizational alignment and data as a service within
the organization.
As technology continues to mature, the scaling issues
of existing data will be replaced by new challenges
in sourcing, processing, and analysis. This requires
individuals and organizations to think ahead of the
game, particularly analysts and data scientists who are
closest to the data sets themselves.
The Visual Web
As we have seen, particularly in the past two years,
the web is becoming far more visual, and visual data
types — emoticons, GIFs, images, and video itself —
are sometimes challenging to interpret. Expect more
disruption in this area as technology advances to
interpret visual, aural, and other unstructured and/or
otherwise challenging data types.
Data at Scale
The days in which organizations can hire ever-growing
teams of analysts are numbered. Technology will
continue to improve its ability to address analysis
issues (sentiment/image analysis), tagging and
attribution, integration, and other ways of normalizing
vast and disparate data sets.
As technologies such as IBM’s Watson (which can ingest
data, pose hypotheses, and communicate confidence
levels) become more commercially available, analysts
will be freed to spend more time on strategic rather than
brute-force analysis — the “likely why,” in addition to the
“likely what.” But these advances are dependent on the
increasing sophistication and commercial viability of this
technology.
Behavior Trumps Demographics
As the industry becomes more skilled at understanding
actual consumer behavior, demographics — long the
proxy for insight — will become less important. The
17
ability to discern individual consumer preferences will
make personalization more practical and traditional
demographics less relevant. This will enable marketers
and advertisers to build profiles based not on inferred
attributes but on actual behavior.
Emotion Drives Decisions
While behavioral data can tell us what consumers are
actually doing, social data holds clues to the consumer
attitudes and emotions that influence behavior. Jesse
Redniss, Chief Strategy Officer of Spredfast, says, “With
universal transparency by the consumer, I really do think
there’s something to the idea that the data can tell us
about attachment and emotion and that can be used to
some degree for the purpose of real-time marketing.”
While not every network will make these choices, and
while they will nevertheless have to navigate new and
complex privacy implications in how new data streams
are used, the increasing availability of high-quality data
will nevertheless bring these issues to the forefront and
force networks to make conscious decisions about the
relationship they want to have — and are willing to work
for — with viewers.

18.
Endnotes
1
CMO.com, U.S. Digital Video Benchmark, Adobe Digital Index
Q1 2014.
2
As of this writing and based on the June 25, 2014, Supreme
Court decision, Aereo has paused its operations. See Scotus
blog: http://www.scotusblog.com/case-files/cases/american-
broadcasting-companies-inc-v-aereo-inc/.
3
Ibid.
4
YouTube, “The Pugs of Westeros,” https://www.youtube.
com/watch?v=2EoQCtPR2-I.
5
Nielsen, The Digital Consumer, February 2014.
6
Wall Street Journal, Mike Shields, June 23, 2014. http://
mobile.blogs.wsj.com/cmo/2014/06/23/nielsen-and-
comscore-cant-tell-you-how-many-people-streamed-usas-
world-cup-tie-with-portugal/.
7
New York Times, David Carr, “Giving Viewers What
They Want,” February 24, 2013. http://www.nytimes.
com/2013/02/25/business/media/for-house-of-cards-using-
big-data-to-guarantee-its-popularity.html?pagewanted=all_
r=0.
8
For example, during and after the 2014 Academy Awards,
Nahorniak says, “We saw that people wanted to talk about
the Oscars all day. They still wanted to see photos three, six,
even 24 hours later, so we tried to find ways to sustain that
interest.” At the same time, USA Today is careful to balance
sustained audience interest with the availability of news pegs
that justify continued coverage. “There will be some kind of
natural drop-off point when news is waning without new
developments, and we’re trying to identify that point.”
9
USA Today, “Unfit For Flight,” Thomas Frank, June 16, 2014.
http://www.usatoday.com/longform/news/nation/2014/06/12/
lies-coverups-mask-roots-small-aircraft-carnage-unfit-for-
flight-part-1/10405323/.
10
Real-Time Marketing: The Ability to Leverage Now,
Rebecca Lieb, (Altimeter Group: December 2013). http://www.
slideshare.net/Altimeter/report-realtime-marketing-the-agility-
to-leverage-now-by-rebecca-lieb-jessica-groopman.
11
Ibid.
18
Methodology
Altimeter Group conducted qualitative research and analyses
for this report, using both interviews and briefings on the use
of big data and its use in digital entertainment. This included:
• Interviews with 7 brands
• Interviews with 9 technology companies
• Interviews with 2 thought leaders
Ecosystem Input
This report includes input from market influencers, vendors,
and end users who were interviewed by or briefed Altimeter
Group for the purposes of this research. Input into this
document does not represent a complete endorsement of the
report by the individuals or the companies listed below.
Media and Entertainment Brands (7)
BET, JP Lespinasse, Senior Director, Social Media
HBO, Sabrina Caluori, Vice President, Digital and Social Media
Netflix, Jenny McCabe, Director of Global Media Relations
Scripps Networks, Chad Parizman, Director, Convergent Media
Spalding Entertainment, Amanda Cates, Director, Web and
Digital Marketing
Turner Broadcasting, Jeff Eddings, Senior Director of Product
Management, Emerging Technologies (former)
USA Today, Mary Nahorniak, Social Media Editor
Technology Vendors (9)
Bitly, Mark Josephson, CEO
Chirpify, Kevin Tate, Chief Revenue Officer
Ditto, David Rose, CEO
LittleBird, Marshall Kirkpatrick, CEO
Mashwork, Jared Feldman, CEO and Founder
Networked Insights, Howard Ballon, GM, Media and
Entertainment
Simulmedia, David Cooperstein, CMO
Spredfast, Jesse Redniss, Chief Strategy Officer
Tellagence, Matt Hixson, CEO and Nitin Mayande, Chief
Scientist
Industry Thought Leaders (2)
Carri Bugbee, Social Media Marketing and Social TV Strategist
Dayna Chatman, USC Annenberg School for Communication
and Journalism

19.
19
Acknowledgements
First and foremost, our gratitude to the executives and
industry experts who gave so generously of their time and
knowledge by consenting to be interviewed for this research.
Additional thanks due to insights and/or support from Pernille
Bruun-Jensen, Catriona Churman, Kevin Driscoll, Andrew
Jones, Charlene Li, Rebecca Lieb, Vladimir Mirkovic, Brian
Solis, Christine Tran, Julie Viola, and Ming Wu. Additional
thanks to industry experts who spoke with me on background
for this report. You may be unsung, but you’re very much
appreciated. Finally, any errors are mine alone.
Open Research
This independent research report was 100% funded by
Altimeter Group. This report is published under the principle of
Open Research and is intended to advance the industry at no
cost. This report is intended for you to read, utilize, and share
with others; if you do so, please provide attribution to Altimeter
Group.
Permissions
The Creative Commons License is Attribution-
Noncommercial-Share Alike 4.0 United States at http://
creativecommons.org/licenses/by-nc-sa/4.0.
Disclaimer
ALTHOUGH THE INFORMATION AND DATA USED IN THIS REPORT HAVE BEEN
PRODUCED AND PROCESSED FROM SOURCES BELIEVED TO BE RELIABLE, NO
WARRANTY EXPRESSED OR IMPLIED IS MADE REGARDING THE COMPLETENESS,
ACCURACY, ADEQUACY, OR USE OF THE INFORMATION. THE AUTHORS AND
CONTRIBUTORS OF THE INFORMATION AND DATA SHALL HAVE NO LIABILITY
FOR ERRORS OR OMISSIONS CONTAINED HEREIN OR FOR INTERPRETATIONS
THEREOF. REFERENCE HEREIN TO ANY SPECIFIC PRODUCT OR VENDOR BY
TRADE NAME, TRADEMARK, OR OTHERWISE DOES NOT CONSTITUTE OR IMPLY
ITS ENDORSEMENT, RECOMMENDATION, OR FAVORING BY THE AUTHORS OR
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ENDORSEMENT PURPOSES. THE OPINIONS EXPRESSED HEREIN ARE SUBJECT
TO CHANGE WITHOUT NOTICE.

20.
Authors
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Susan Etlinger (@setlinger) is an Industry analyst at Altimeter Group,
where she works with global organizations to develop big data and
analytics strategies that support their business objectives. Susan has
a diverse background in marketing and strategic planning within both
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Experiments, at susanetlinger.com.
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executive, she was VP and editor-in-chief of the ClickZ Network for
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Jaimy Szymanski (@jaimy_marie) is a Senior Researcher with
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research and advisory projects for Fortune 500 companies in
the telecomm, travel, pharmaceutical, financial, and technology
industries. Her research interests lie in social TV, gamification, digital
influence, and consumer mobile.