 | Issue: | Asia-Pacific III 2015 |
| Article no.: | 11 | |
| Topic: | The impact of Big Data on the Pay-TV Industry: How to increase personalization and user engagement | |
| Author: | David Leporini | |
| Title: | EVP, Marketing, Products and Security | |
| Organisation: | Viaccess-Orca | |
| PDF size: | 205KB |
About author
David Leporini is the Executive Vice President of Marketing, Products and Security, leading Viaccess-Orca’s overall marketing and product strategy.
David was previously serving as CTO as part of Viaccess Executive Board, in charge of innovation, security, and product design as well as architecture for the company.
Prior to joining Viaccess-Orca, David was Operations Manager for Trusted Logic responsible for security consultancy activities, and held several positions in CANAL+ and CANAL+ Technologies, notably Head of Digital Rights Management and Copy Protection.
David graduated from Institut National des Télécommunications and received the Doctorat degree with highest honours in statistical signal processing from the Université Paris-Sud.
Article abstract
This article examines the effect that Big Data has on the pay-TV market, taking a look at the challenges and various applications, with an emphasis on how it can be used to improve the user experience and boost engagement.
Full Article
Big Data is starting to make a significant impact on the pay-TV world, as content service providers look for ways to improve service planning, monetize multiscreen, and increase personalization and user engagement. We no longer live in a world where television and advertising content is pushed to consumers in a completely random way. A lot of what consumers watch on the TV set, smartphone, or tablet nowadays is targeted based on an individual’s personal habits and viewing history.
CBS, a major television network in the United States, recently announced a new program that merges data from a number of different third parties into a single set of information, enabling advertisers to optimize the impact of their campaigns. Aside from enhancing advertising, big data can be utilized to provide personalized content recommendations to viewers, increasing their satisfaction with the service.
We’re seeing big data being used increasingly in the pay-TV industry thanks to the dramatic shift in consumer viewing habits. Today’s viewers expect content that is geared toward their own interests and preferences. According to recent research from Accenture, 43 percent of consumers prefer finding new video content by using personalized recommendation engines that track what they’ve watched and suggest similar content. They also want the freedom to move between OTT offerings such as Netflix and traditional pay-TV services.
Big Data challenges
While the use of Big Data can certainly have a positive effect on pay-TV services, it’s important for content service providers to understand that sometimes, too much data can be detrimental. There can be a trade-off between the quantity of stored data, performance, and the agility of ad-hoc data discovery and processing.
Storing a monumental amount of data can be beneficial in some respects, as it gives operators insight into trends that may not have been predicted otherwise. Leveraging in-depth data, operators can increase the level of personalization that users experience. However, too much data can create an unnecessary load on the system, create performance issues for analytics to be performed in real-time, and significantly increase costs from a storage standpoint. To avoid this scenario, operators need to improve the processing infrastructure of the data that is used frequently/in real-time, while also creating an infrastructure for less common use cases. Both can be used to gain insight into viewer preferences.
Doing A/B testing on the service is also important. A/B testing measures the effect of a manipulation on a specific use case. With regards to personalization, A/B testing can be used to evaluate the effectiveness of different personalization algorithms, or of different recommendation user interfaces, based on various usage data that has been collected.
Another challenge is privacy and security. If operators are using personal information about users, it’s imperative to make them feel comfortable about sharing information. For example, users need to know exactly what data is being collected, understand how it is being used (e.g., for personalization purposes or targeted advertising), and the benefits they may expect. Furthermore, users may feel more inclined to share information if they have access to their personal data and can request not to share certain facts.
Different Applications for Big Data and Analytics
Across various industries, a number of tangible applications are emerging for Big Data, with interesting implications. Some examples include fine-tuned health therapy, computer-created recipes, and Web mining for meaningful content presentation and exploration with projects like Google’s Knowledge Graph. We can expect more services relying on data enrichment and adding value via rich information set around almost any topic in the world.
Within the pay-TV industry, in particular, there are several ways that content service providers can utilize Big Data to enhance their overall business operations and service offering:
• Service planning and optimization: Content service providers can use Big Data to determine what kind of content they want to license and not license. In addition, analytics can give operators insight into how to best adapt their service packages to address certain types of viewers, for instance, targeting millennials.
For example, Netflix recently announced plans to broaden its reach across Asia by introducing streaming services in Hong Kong, Singapore, South Korea, and Taiwan. Big data was likely a determining factor in Netflix’s decision to expand into the APAC region, as recent research from Sandvine found that BitTorrent traffic in Asia-Pacific is increasing more than 50 percent year over year.
• Customer relationships: Leveraging Big Data content service providers can better understand user trends and get a clear-cut picture of what the competition is offering. By gaining insights on characteristics of video content that is in high demand, service providers can reduce or prevent churn.
• Monetization and advertising: Content service providers need to strike a good balance between ad dollars versus the experience of the users. Through Big Data, providers can determine whether it is best to deliver pre-roll or mid-roll ad content based on certain parameters such as the device type and the user persona.
• Recommendations and personalization: A common complaint made by consumers is that the content recommendations they are getting are not relevant. This is an area where big data can certainly lend a hand. The key to enhancing content recommendations for every screen is to choose an advanced content discovery and recommendation platform such as Viaccess-Orca’s COMPASS. Utilizing a content discovery and recommendation platform that relies on advanced algorithms and search technology, content service providers can recommend relevant content and personalize each user’s experience in an engaging way. Recently, COMPASS was deployed by Telekom Romania, offering advanced search and explore capabilities and making it easy to find content on any screen. Personalization also goes beyond just recommendations. Consumers want access to their own content consumption environment, including their personal bookmarks and recordings, favorite channels, etc., on every device, from the smartphone to the tablet, game console, and TV screen. With big data, content service providers can make this become a reality.
• User engagement: Maximizing user engagement cannot be done effectively without understanding the journey of the consumer. Operators should look at the ways that consumers interact with their content services such as the VOD catalog. When a user is browsing the video catalog and selects a movie, content service providers need to know the path to conversion. It may be something that was completely unrelated to the service offering, such as a discussion on social media networks. By gaining insight into the consumer journey, prior to the point that they engage with the service, content service providers can successfully increase user engagement. The solution may be as simple as providing social recommendations from friends, creating a community around certain content such as a specific show or sport, and through second-screen social media apps like Facebook and Twitter. By gaining additional insight into user behaviors, operators can provide users with a targeted and personalized experience that not only increases user satisfaction and loyalty, but also boosts content consumption and monetization.
Conclusion
In the near future, the success of service providers will be defined by their use of Big Data and analytics. Yet, Big Data doesn’t just mean the customer data; it also means the data about the content being provided through the service. By optimizing enriched content metadata and collecting relevant information about user behaviors, content service providers can provide the ultimate content experience on every screen, reducing the time it takes users to discover content they care about and increasing user engagement.
