How They Watch What We Watch: Big Data Analytics Is Transforming the Pay TV Space
The pay TV and digital media industry is experiencing a period of disruption. Consumers are demanding multiscreen viewing options, greater flexibility in their content packages, and personalized experiences. The innovation and creativity that spring from digital transformation can result in new business models, enhanced customer experiences, and improved financial performance.
At the end of 2017, pay TV penetration among Central and Eastern Europe (CEE) households was estimated at 74%, according to the Central and Eastern Europe Pay TV 2016–2021 Forecast published by IDC. Total CEE subscribers numbered 76.2 million across cable, satellite, IPTV, and DTT services. Cable, which accounted for 46.6% of this total, remains the primary method of pay TV distribution across much of the region. Nevertheless, IDC expects that IPTV will continue to have the highest subscriber growth rate.
In spite of steady growth, the CEE pay TV market is facing various challenges, including intensifying competition, piracy, and regulatory issues. Content is the primary "glue" that keeps subscribers onboard. Local content strongly influences the adoption of over-the-top services. The use of big data analytics to deliver targeted or personalized advertising is having a big impact on the TV business, as is the use of social networks as distribution channels.
Analytics play important role in meeting service expectations and supporting pricing propositions, while providing a strategic tool for content decision-making. Content personalization is enabling individual end users to exclusively watch what interests them most – at the same time, they are being “watched”.
Initially, analysts were limited to tracking only four customer data points: customer ID, movie ID, rating, and the date that the program or show was watched. The scope of potential data points of interest increased when streaming became the primary delivery method. New customer insights become accessible based on data that tracks, among other things, the time of day that movies are watched, time spent selecting movies, and even how often playback is stopped.
Netflix is one of the most successful disrupters in the realm of broadcast media and provides a great example of intelligent use of analytics. Long ago in 2006, when the company was still primarily a DVD mail-order business, Netflix offered a $1 million “prize” to the person or company that came up with the best algorithm for predicting how its customers would rate a movie based on previous ratings. The rest, as they say, is pay TV history.
Dedicated pay TV operators have been collecting a range of data from their customers for years. Understanding customer segment preferences helps them to build more accurate insights that can support programmatic decision making and strategies for pricing. Pay TV service providers still enjoy privileged access to consumers’ TV sets, and that’s a unique source of data. While it might be as extensive as the data that can be captured on personal devices such as PCs, tablets, and phones, it is still a valuable mine for TV advertisers and, increasingly, Smart City ecosystem partners. Collecting data on consumer viewing habits, streaming on the go, linear TV viewing panels, and even social media gives pay TV operators insight into what customers are watching, how they are watching it, and on what platform. Operators may then use that data to retain customers or even convince them to increase their spending.
Operators are also interested in the bigger picture, detecting trends and identifying consumption patterns by groups of customers. Collected data is anonymised, and may include anything from minutes watched and time of day to types of device used for viewing and geographical locations.
In the current fray for market share, providers are increasingly focusing on delivering connected, interactive, and seamless pay TV experiences across multiple devices. Ultimately, insights gained from valuable data is enabling decision-making process “wins” and leading to the kind of expertise that differentiates successful pay TV players.