❝ In such a rapidly changing pay- TV landscape , operators ’ priorities when it comes to data should be on using new technologies to take a more horizontal approach .❞
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12 THURSDAY
@ ConnecTechAsia2018
28 June 2018
Actionable analytics & AI : A game changer for the pay-TV industry
BY SIMON TRUDELLE
Many industries are jumping onto the bandwagon of digital disruption by harnessing the capabilities of artifical intelligence ( AI ) to innovate and improve their business model .
Today at ConnecTechAsia2018 , I will have the opportunity to take part in the panel discussion , Successful Implementation of AI in Broadcast Industry , where we will look at the issue of how the industry can maximise the value of data .
As we all know , the adoption of data into broadcast TV has not taken place at the same rate as other industries . And while data capture at the set-top box ( STB ) level has been a technical reality for more than 15 years in some markets , few operators have made smart use of it , nor the resulting insights .
And while it is true that the pay-TV industry has invested in data analytics systems and tools , the focus has primarily been on addressing specific vertical challenges by department and function , like churn reduction from a marketing angle or network failure from a technical perspective . As a result , this silo-based approach has prevented service providers from adapting to the fast-changing environment and offering additional value to customers that
Many industries are jumping onto the bandwagon of digital disruption by harnessing the capabilities of artifical intelligence ( AI ) to innovate and improve their business model .
AI-based insight brings .
Today , this is becoming a major issue in a world where the likes of Netflix have built their success on the ability to capture , analyse and act on usage and network data to deliver a top-notch overall experience .
Of course , one of the challenges in emulating tech giants ’ use of data is that they have ( for the most part ) cultivated a halo effect around their brands , where consumers feel comfortable offering up their data because they know it will be
❝ In such a rapidly changing pay- TV landscape , operators ’ priorities when it comes to data should be on using new technologies to take a more horizontal approach .❞
— Simon Trudelle , Senior Director ,
Product Marketing ,
NAGRA
PHOTO CREDIT : ISTOCK BY GETTY IMAGES
beneficial to their user experience .
This is the bar for capturing and leveraging data for all service providers , and now the onus is on them to use data insights to improve their products , their consumer relationships and their brand image .
Essentially , the specific goal for pay-TV operators is to become a legitimate data collector for consumers that also offers something relevant in return . Say , a more reliable service , more relevant content or superior customer service .
When it comes to data collection , capturing behaviour on the STB and big screen remains a challenge , given that multiple users are often interacting with the system at once . That said , automated behaviour analysis can help to reduce that uncertainty .
To achieve a new level of efficiency and added value , pay-TV players must use non-traditional sources for their data — from pirate viewership data to the social media comments viewers make on programming or customer service . This is achievable — it is simply a question of the right analytics platform .
Conventional methods such as audience measurement are only useful to drive pay-TV business forward if it delivers actionable insights — that is , data that can be used to improve the quality of experience ( QoE ), user experience and recommendations , advertising , content acquisition or churn reduction . Otherwise , you may as well stick to Nielsen-style consumer surveys that only sample audiences .
And , in turn , connecting conventional viewing and marketing data to other data sets will also enable operators to understand consumer behaviour in a more effective and holistic way .
But let us be clear — starting with massive data collection without having a consumer-focused business objective in mind is never going to be the right approach . Operators must shift their mindsets from working to collect all the data they can for solving a vertical issue , to collecting the data they need to make a smart business decision — that is , smart data .
Capturing data about every video consumption point may be something that the Internet giants are getting close to achieving . However , the reality is that emerging AI-based analytics systems work best with a relevant subset of data . When service providers start with the business questions that are most important to their unique needs , they will be able to determine the best data needed to address them , allowing for the rapid optimisation of the machine learning algorithms . They can then decide on the best course of action to drive real business results .
Turning relevant raw data into real actionable intelligence is key for the industry . And in such a rapidly changing pay-TV landscape , operators ’ priorities when it comes to data should be on using new technologies to take a more horizontal approach .
Furthermore , if the channelplatform relationship can evolve to collectively understand data as a new currency , the entire ecosystem can grow stronger and continue to be relevant to consumers .
For the next generation of content delivery , the pay-TV operators who succeed will be the ones who effectively address the limitations of traditional vertical data analytics systems , overcoming cross-department issues and focusing on business impact and subscriber satisfaction .
With the right platform such as NAGRA Insight , pay-TV operators can leverage AI-driven analytics in many ways to maximise business impact , and generate intelligent actions to improve value , content management , operations and advertising revenues .
NAGRA is located at Communic- Asia2018 booth 1J2-01 .