Thursday, January 26, 2017

How MTV And Nickelodeon Use Real-Time Big Data Analytics To Improve The Customer Experience

Viacom, owner of household brands such as Nickelodeon, MTV and Comedy Central, is one of the largest media companies in the world, delivering more than 170 cable, broadcast and online networks in around 160 countries.



Monitoring of the digital networks which are used to serve their content into millions of homes gives them access to a huge amount of data, on how both their systems and their audiences behave.

Like many large organizations, Viacom is currently manoeuvring to position itself as a fully data-driven company – with as few of the important decisions left to guess-work and gut-feeling as possible.


In fact, Viacom is so heavily involved in Big Data, that as well as luring talent from tech companies such as Microsoft in recent years, it has even produced a super rap video starring The Fat Jewish and his partner in knowledge Todrick Hall about it.



As part of this drive, it has developed a number of use cases for the network data it is collecting. Dan Morris, senior director of product analytics, took the time to talk to Forbes about two of them – explaining how they combined this network data with cutting edge real time analytics, to improve viewer experience and increase customer retention.

Improving video experience

Streaming petabytes of video data across the world puts a strain on the delivery systems – we’re all familiar with online videos either failing to load or constantly stuttering as they rebuffer.

At the same time there are areas where excess bandwidth is going to waste – when everyone is asleep or out at work. Viacom has built a real-time analytics platform based around Apache Spark and Databricks, which constantly monitors the quality of video feeds and reallocates resources in real-time when it thinks it will be needed.

Morris told Forbes “Delivery of video is at the core of everything we do, and our goal is to be exceptional at that. But there’re a lot of variables at play – we have internal systems talking to external systems, we have content delivery, we have ad severs, and on the user side there’s a whole bunch of environmental factors like wi-fi connectivity which we really have no control over.”

While they are out of Viacom’s control, monitoring these factors can potentially lead to insights which could be used to predict when problems will emerge. In other words while they may not be able to choose whether a viewer will watch on TV, or on a tablet, their choice can be anticipated and planned for.

“So, with what we’ve built on the data side, we’re able to take all this raw data and do two things – surface trends, and isolate areas of opportunity to present a superior viewing experience.”

Two metrics are particularly closely monitored for this process – “time to first frame” (how long it takes a video to start) and rebuffering rate – how often the video stutters. These have been shown by the analytics to be primary indicators of whether an audience will settle in to watch a video, or search for something else instead. Thanks to improvements driven by their analytics, Viacom has reduced the time-to-first-frame their viewers experience to around one third of what it was, across their network.

“Particularly with younger audiences, we know this is super-important. That split second could be the difference between retaining the user and losing them. So, that was definitely a huge area of focus and the data was an instrumental part.”

Growing the audience

For the second use case, Morris’s team again found their “north star” metric – the insight that could guide them to their goal. While Facebook acknowledges its North Star is to get new users to seven friends within ten days, at which point they are hooked, Viacom deduced, through analytics, that it needs to get viewers hooked on at least two individual shows.

Morris tells me “What we found is that if we can get you to watch two shows you’re 350% more likely to stay with us. If we can get you to watch four shows, that goes up to 700% - seven times more likely.”

This meant that the focus could be switched to getting customers who already regularly enjoy one show to trying out one or two more, to vastly increase their probability of becoming loyal Viacom viewers.

It may sound like common sense, but findings like this are backed by data. In truth there are probably countless “common sense” decisions that could be taken to grow the audience – changes to scheduling, casting, or other factors which will attract viewers. The power of Big Data is that it allows organizations to highlight precisely which of these “common sense” decisions they should spend time and money implementing.

Morris says that although he can’t share precise figures, the initiative has been “highly successful.”

Targeted advertising

TV advertising sales have been consistently falling in recent years as advertisers increase their spending on new media. In reaction to this, Viacom earlier this year announced a partnership with comScore which will allow it to use the ratings organization’s data for its targeted advertising systems.

Accurately assessing viewing figures has become a very complicated job in the online age, as audiences spread their viewing across multiple devices, some of which are watched in a group, and some watched in solitary. No “one size fits all” solution has yet been developed, but by collecting data from more sources, more accurate models of viewership can be built, to attract advertisers to Viacom’s channels.

Looking to the future

Big Data is certainly set to play a big part in Viacom’s immediate future. Now its usefulness has been proven, with concrete results, the next step is to introduce it across the business, wherever a use can be found.

“Now it’s about – we’ve had a lot of success, how can we share this with other teams?” Morris says.

“The value we’ve seen within our team is that it lets us focus on business problems and makes certain processes very simple. Now it’s a question of how do we bring these benefits to others in the organization who might not be aware of what they can do with this type of platform.

“The truth is, there’re endless opportunities – we’re looking at cognitive computing and a whole bunch of different use cases – we really want to embed data into every single process throughout our company.”
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