Data Analytics core to Pay-TV corporate strategy
Big Data has taken center stage in Media strategy. It is being widely used by Pay-TV operators and OTT players to enhance customer service, marketing, advertising, content recommendation and technical operations. Big data is also playing a highly influential role in the way content is created and how original programming is put together. The growth of Pay-TV analytics market can be evinced from the fact that it is expected to more than double from USD1.8bn in 2018 to USD3.7bn by 2022.
Data is created every second a customer uses (or even scrolls through) the content, however, whether it is captured and how it is used can make a world of a difference between champions and laggards of the industry. Even where there’s a will to collect data, most multichannel networks face a herculean task to overcome legacy organizational barriers.
Data collection can be done through two-way set-top boxes which permit viewers to change channels, switch to the Internet, and order paid programming, among other functions and generate micro-level data from consumer viewing behavior. Clicks and channel-changing behavior can be captured through the remote control of the set-top box. Besides, set top box data analysis, operators dig out information about the behavior pattern of users along with comment feature behaviors to better categorize user information.
While Pay-TV operators have been slow to respond to change and use big data, companies like Google with YouTube and Netflix with its recommendation engine are spearheading the opportunity. In an academic paper by Gomez-Uribe and Netflix's Chief Product Officer Neil Hunt, they assert that "the combined effect of personalization and recommendations save us more than USD1bn per year."
Pay-TV operators use data in the following ways
Yet leveraging data analytics and big data goes beyond traditional ways of number crunching and dashboarding. Companies need to track real time trends in consumer behavior through various tools and technologies that will allow businesses to identify consumer demands that drive repeat viewing. For the recommendation engine, Netflix uses complex algorithm to identify similarities between shows such as date of creation, similar ratings, time, date, location, device, user behavior (i.e. browsing, playing, searching, scrolling behavior, pause and leave content, volume, etc.), director, production house, etc. Netflix suggestions estimates that 75 percent of viewer activity on its platform is driven by recommendation. Data analytics derives its value from users’ unwillingness to provide explicit feedback. Metadata has complemented this and has been found to be more successful in prediction.
Integrating analytics into the overall corporate strategy is an important part in the future. Analysis of consumer behavior enables service providers to pinpoint the challenges and opportunities and how these should be aligned with corporate strategy, cascaded into KPI’s to be monitored periodically to enhance customer experience giving the business a moat in this highly competitive market.