The term “traditional players” is a bit deceiving when referring to the entertainment industry. Most traditional players are market-leading companies that have achieved success (or at least endured successfully) in the creation and distribution of film and television content. They invest millions in innovation and new technology. Most are aggressively involved in streaming services. Many have their own direct-to-consumer offerings.
So, traditional doesn’t mean behind the times, technologically speaking.
Among US broadband households, Parks Associates finds that 72% subscribe to at least one over-the-top (OTT) video service, while 46% subscribe to two or more OTT services. Further, 25% subscribe tothree or more OTT services. Traditional content and service brands have a hand in many of these offerings.
Yet, traditional players in the pay TV industry are increasingly facing a new generation of companies, including online giants and connected device manufacturers, that use data in new ways to define and drive their entertainment-focused businesses. Despite their investments and innovations, many traditional companies now find themselves at a disadvantage, in terms of data analysis and use, to new companies that have entered the ecosystem. As a result, pay TV providers and their broadcast and network content partners are playing catch-up, seeking to gain data-oriented expertise and adjust their way of doing business in order to better perform, compete and attract a new generation of customers.
Importantly, all market players (new and traditional) leverage data in their business decisions. However, the new generation of data-centric companies use data in fundamentally different ways. Areas such as advertising, content-related decisions, user experience and feature design, and use of artificial intelligence/machine learning reveal some of the differences between these market players.
The broadcast and cable network television space was built with advertising at its core. TV-based advertising funds much of the programming that is produced today and commands the largest share of advertising budgets.
Advertising is also a core business model for new generation companies, including Facebook and Google, which focus on digital advertising. Without live, linear TV channels and related ad inventory, these companies emphasize ad relevance over ad reach. Their data collection includes habits and interests beyond content viewing. So, their consumer data along with their data expertise and tools gives them a notable advantage in addressable advertising, which leverages data on consumers’ characteristics and behaviors to target ads at likely buyers.
The new generation of entertainment and advertising companies continues to innovate around consumer targeting, leveraging extensive data to better tie advertisers to prospective customers. Two recent trends include predictive targeting and re-targeting. Both innovations require access to data that most pay TV providers do not have today. Yet, through interactive ads, more contextual data about consumers, and partnerships with advertisers or retailers, pay TV providers could find ways to adopt or approximate these new approaches in addressable advertising.
Deciding which shows or films will be approved for production, picked up for broadcast, and renewed for a new season is a complex mix of art and science. Content creators regularly seek ways to produce something novel and interesting, pushing the envelope for entertainment. Studio and network executives must rely on a combination of history, data and experience to predict which shows and films will be popular and profitable.
Multiple executives are involved in the project funding (or green-lighting) decision process, particularly for films that often have significant budgets and impact multiple areas of the business. The data used is often from each executive’s own area of responsibility, but comparative data to similar content that has been either successful or unsuccessful weighs heavily in the decision process.
Decisions to continue or renew TV series depends significantly on Nielsen viewership ratings, but also include DVR viewing, on-demand and streamed viewing, social media buzz, and the potential for additional sales via syndication or streaming platforms. As with green-lighting decisions, there are few defined rules to determine continuation or cancellation.
The new generation of entertainment companies have a more data-centric approach to content investment or licensing. Rather than funding a pilot or handful of episodes, Netflix invested $100 million up front for 26 episodes of “House of Cards,” one of its most popular original series. The company analyzed several factors based on their own viewers and viewers of the UK miniseries that the show was based upon, including consumption habits, actors, and director. Most importantly, Netflix executives trusted the findings of their data analysis and were willing to make a significant financial investment as a result.
User experience and feature design
Each year, operators and technology vendors spend millions of dollars in design or iteration of the set-top box or app features, with a key focus on subscribers and a compelling user experience (UX). Most pay TV providers rely on internal data regarding subscriber usage behavior as well as traditional consumer data from interviews, surveys, focus groups, and customer feedback. Product managers review this data to create a set of product requirements that are then vetted by senior managers before submission to an internal technical team or vendors, who will provide their own input. Some operators have adopted agile-based approaches to software development to better leverage data and accelerate the UX development cycle.
While new generation companies may also use traditional methodologies, they take a more hypothesis-based approach than many pay TV providers in iterating and refining their UX. In the online space, A/B testing evolved in web development and analytics as companies sought to optimize their website experience. Initial efforts were often manual, but companies today frequently use automated tools and sophisticated analysis solutions in A/B testing. While many companies test product offerings until deployment, new generation companies continuously perform A/B testing on all aspects of their service. This results in a continuously evolving product offering that quickly adapts to changing competition, technology, user habits, and subscriber interests.
A/B testing tools are emerging in the pay TV space, primarily related to apps and websites used for video, TV everywhere, or other services.
Use of artificial intelligence/machine learning
Though machine learning and other areas of artificial intelligence (AI) are seen as critical areas of expertise in new generation companies, these tools have only limited use today in the pay TV space. At present, use of AI in the pay TV space is primarily in applications such as content discovery, voice control, and metadata enhancement.
Importantly, each of these areas is application-specific, with the AI elements often licensed from other companies that specialize in AI technology. Among pay TV providers, machine learning and AI are seen as feature enablers rather than tools for use within the enterprise.
In contrast, new generation companies use machine learning tools daily throughout the organization in areas such as marketing, operations, IT, and product development. Google, Microsoft, Apple, Amazon, and Facebook all have developed AI-based technologies internally and acquired companies specializing in a variety of areas of AI over the past few years. As part of their cloud services division, Microsoft, Amazon, and Google offer cloud-based AI tools as a service, encouraging developers to experiment with and integrate AI into their products, development tools and platforms. These companies employ data scientists who continually enhance their AI and machine learning tools and data analysts who interpret the output of these tools to find actionable insights.
At established organizations such as traditional pay TV providers, broadcasters, and network groups, cultural changes are some of the most difficult to implement. Companies must begin with specific goals and objectives in mind with appropriate expectations of results and timing.
Feedback from companies that have made this transition suggest that impatience for quick results can potentially lead to invalid results, which can damage internal confidence. Ultimately, operators will need to begin now to adopt a new data-centric approach, knowing that changes may take years to accomplish.
As Senior Research Director and Principal Analyst, Brett Sappington leads Parks Associates research practice for entertainment, broadband access, and consumer electronics markets. His personal and custom research focuses on trends and technology innovations among major service providers, content producers, networks, and technology vendors and the market forces affecting their businesses. Brett is an internationally recognized thought leader in the television, broadband, and online video service industries.
Brett has spent over twenty years in the industry as an analyst, executive manager, and entrepreneur for companies specializing in cloud, communication, and IP-related technologies. He founded a successful networking technology startup, built new divisions of wireless networking and audio software products, and was involved in the development and marketing of early-market products for Wi-Fi, VoIP, video-over-IP and other technologies.
Brett holds an MBA from the University of Texas at Austin with a concentration in high-tech marketing and a BA in physics from Baylor University.
Industry Voices are opinion columns written by outside contributors—often industry experts or analysts—who are invited to the conversation by FierceVideo staff. They do not represent the opinions of FierceVideo.