A holistic approach to pay TV data analytics

June 22, 2018
I recently had the opportunity to take part in a panel discussion at the Connected TV World Summit 2018 in London, where we looked at the issue of how the industry can maximize the value of data. The adoption of ...

I recently had the opportunity to take part in a panel discussion at the Connected TV World Summit 2018 in London, where we looked at the issue of how the industry can maximize the value of data.

The adoption of data into broadcast TV hasn't taken place at the same rate as other industries. And while data capture at the set-top box level has been a technical reality for more than 15 years in some markets, few operators have made smart use of it and the resulting insights.

And while it's true that the pay TV industry has invested in data analytics systems and tools, focus has primarily been on addressing specific vertical challenges by department and function, such as 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 artificial intelligence (AI)-based insight brings.

Today, this is becoming a major issue in a world where the likes of Netflix (NASDAQ:NFLX) have built their success on the ability to capture, analyze 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 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 goal for pay TV operators specifically 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 behavior on the set-top and big screen remains a challenge, given that multiple users are often interacting with the system at once. That said, automated behavior 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's simply a question of the right analytics platform.

Conventional methods like audience measurement are only useful to drive pay TV business forward if they deliver 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 behavior in a more effective, holistic way.

But let's 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, but 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 optimization 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 technology to take a more horizontal approach.

What's more, if the channel-platform 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 that succeed will be the ones that effectively address the limitations of traditional vertical data analytics systems, overcoming cross-department issues and focusing on business impact and subscriber satisfaction.

Simon Trudelle is senior director of product marketing at NAGRA.

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