Data Everywhere:Lessons From Big Data in the Television Industry

By Susan Etlingerwith Rebecca Lieb and Jaimy Szymanski

Includes input from 18 ecosystem contributors

A Market Definition/Best Practices Report

July 10, 2014

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

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

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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.

Using Data to Drive Competitive AdvantageData — 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.

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

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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

unique challenges faced by marketers and others whose brands are dependent on the visual web.

Data Sources and ImplicationsAs 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,

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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

TVMobile Device

ComputerConsole

etc.

Views

Completion (of Episode,

Season)

GeolocationDay Parting

Viewer-Supplied Ratings

VolumeReferrers

Clickthroughs Page Views

SurveysFocus Groups

VolumeReach

SentimentInfluencers

SalesSubscriptions

Ratings

Source: Altimeter Group

Figure 8 Primary Sources for TV-Related Data

Best Practices and RecommendationsA 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

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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

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.”

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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 NextAs 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:

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

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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.

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.

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MethodologyAltimeter 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

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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 ResearchThis 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.

PermissionsThe Creative Commons License is Attribution-Noncommercial-Share Alike 4.0 United States at http://creativecommons.org/licenses/by-nc-sa/4.0.

DisclaimerALTHOUGH 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 CONTRIBUTORS AND SHALL NOT BE USED FOR ADVERTISING OR PRODUCT ENDORSEMENT PURPOSES. THE OPINIONS EXPRESSED HEREIN ARE SUBJECT TO CHANGE WITHOUT NOTICE.

Authors

How to Work with Us Altimeter Group offers a number of ways to engage with us, either by project or on a more ongoing basis. One example is the Social Data Intelligence (SDI) Roadmap, a tool for business leaders who are using, or plan to use, social data to help guide business decisions. The SDI Roadmap is built on an Altimeter Group maturity model that is based upon detailed interviews with social data users and technologists. The model proposes a holistic approach to social data use across the enterprise — taking into account data gathered from multiple enterprise sources, such as Customer Relationship Management systems, Business Intelligence, and market research, and lays out a set of criteria for organizational maturity.

Deliverables from the SDI Roadmap include a Social Data Intelligence Scorecard and accompanying maturity model for social data strategy, as well as actionable recommendations for minimizing risk and improving overall business performance.

To learn more about the SDI Roadmap, contact Leslie Candy at leslie@altimetergroup.com or 617.448.4769.

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 corporations and agencies. Find her on Twitter at at her blog, Thought Experiments, at susanetlinger.com.

Altimeter is a research and consulting firm that helps companies understand and act on technology disruption. We give business leaders the insight and confidence to help their companies thrive in the face of disruption. In addition to publishing research, Altimeter Group analysts speak and provide strategy consulting on trends in leadership, digital transformation, social business, data disruption and content marketing strategy.

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Rebecca Lieb (@lieblink) is an analyst at Altimeter Group covering digital advertising and media, encompassing brands, publishers, agencies and technology vendors. In addition to her background as a marketing executive, she was VP and editor-in-chief of the ClickZ Network for over seven years. She’s written two books on digital marketing: The Truth About Search Engine Optimization (2009) and Content Marketing (2011). Rebecca blogs at http://www.rebeccalieb.com/blog.

Jaimy Szymanski (@jaimy_marie) is a Senior Researcher with Altimeter Group. She has assisted in the creation of multiple open research reports covering how disruptive technologies impact business. Jaimy has also worked with Altimeter analysts on varied 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.