Data Everywhere:
Lessons From Big Data in the
Television Industry
By Susan Etlinger
with Rebecca Lieb and Jaimy Szymanski
...
Drivers of Disruption and Insight ...........................................................................................
As a result of these new dynamics, the television industry is gaining access to a
broad range of signals that can be used ...
4
Multiple disparate data streams may strain
organizational culture, providing a piecemeal view
of audience attitudes and ...
5
three or four (or more) episodes per night. They can
see whether audiences grow or shrink after the first
few episodes o...
6
Figure 2 Emerging Consumer Behaviors Create Data Opportunities and Threats
Source: Altimeter Group
Behavior Description ...
Preview Only
To download this report in full at no cost, please visit our website at:
http://bit.ly/altimeter-report-big-d...
Authors
How to Work with Us
Altimeter Group offers a number of ways to engage with us, either by project or on a more ongo...
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[Report] Data Everywhere: Lessons from Big Data in the Television Industry, by Altimeter Group

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Device proliferation, shifting distribution channels, and the popularity of social media are driving meaningful changes in consumer behavior that affect nearly every aspect of the TV business.

This report explores the phenomenon of “TV Everywhere," and includes research on the drivers of this new disruption, four use cases, and actionable strategies to address the challenges and opportunities.

Download the full report at: http://bit.ly/altimeter-report-big-data-tv

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[Report] Data Everywhere: Lessons from Big Data in the Television Industry, by Altimeter Group

  1. 1. 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 Preview Only
  2. 2. 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 ................................................................................................................................................................................................................... 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 Preview Only 3 3 5 7 8 9 9 10 13 14 14 16
  3. 3. 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. Preview Only
  4. 4. 4 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 Preview Only
  5. 5. 5 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). Preview Only
  6. 6. 6 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. Preview Only
  7. 7. Preview Only To download this report in full at no cost, please visit our website at: http://bit.ly/altimeter-report-big-data-tv
  8. 8. 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. Altimeter Group 1875 S Grant St #680 San Mateo, CA 94402 info@altimetergroup.com www.altimetergroup.com @altimetergroup 650.212.2272 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. Preview Only

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