 "We no longer want to be bound by the rules of time schedules: J Graeme Noseworthy")
We want to be able to turn a system on and tell it what content we would like, J Graeme Noseworthy tells Masoom Gupte
A bulk of the conversation around big data has been restricted to the retail space. When you put the concept of big data in the context of media and entertainment companies, how does this conversation change?
Media and entertainment companies are dealing with a variety of data types. They are dealing with data that is coming from both within and outside the enterprise. They are also dealing with linear and non-linear data, social data, information coming off set-top boxes and, of course, data coming from second screens. What we are noticing is that content providers are transitioning from simply being deliverers or producers of content to being enablers of lifestyle. And now that consumers want their content delivered across a number of devices and channels, whether bundled or unbundled, the amount of data being produced is only increasing in quantum.
Media and entertainment companies are now understanding the opportunities that live within that data. The idea is to discover those insights and drive relevance. On our part, it is a win-win situation we are trying to create. The internal win is that we are trying to improve our processes, our content, the delivery engines and getting to know our customers as individuals. This creates opportunities for teams internally to collaborate. So you start seeing chief marketing officers interacting with chief information officers, chief technology officers or content officers. You may see marketing play an important role in customer service and in the technologies and strategies that are being deployed not only to deliver content but also to improve processes across the enterprise.
You've touched on the issue of bundling and unbundling of channels by media and entertainment companies. In India, direct-to-home players are yet to unbundle the channel packages down to individual ones. It works mostly in clusters based on, say, language. But in the United States, for instance, there is an active debate on whether unbundling really works. Does it end up taxing the consumer?
Let me answer this with an example. Netflix reinvigorated a very popular show in the United States called Arrested Development recently. The only way you can get the show is if you are a member of Netflix and you must watch that on demand. The advantage to Netflix is that it is increasing its subscriptions and producing original, award-winning content. The advantage to the consumer is that they can watch the content at their leisure. But because they are 'opting' to watch the show they are creating a lot of exhaustive data, giving data-driven companies like Netflix an opportunity to learn more about audiences.
Take my family as an example. We watch a lot of Dr Who. Based on our choice of viewing, there is a lot of data driven video recommendation that is happening. That is augmenting the value of unbundling. Another good example would be Pandora (internet radio). I listen to my music, I create my own playlists, I post what I like or don't like. As these channels are perfected with time, Pandora is learning more and more about me. I have noticed that they are not only improving the content they are delivering to me but understanding me better. They are targeting me with relevant and accurate ads.
When I first started using it, they were relevant with their advertising but not as accurate. Now they are both. The combination of relevant and accurate advertising creates unprecedented opportunities for marketers to improve the returns on their campaigns by giving consumers what they want.
Properties like Netflix and Pandora work with the on-demand model or require a more proactive role of the user. But regular channels are mostly about passive viewing. While the former can throw up a lot of data on the user, is it just as true for the latter? Is targeted advertising a possibility for regular television channels?
Absolutely. We are moving the conversation to traditional broadcast networks here. They understand the power of social data and third party data. They are also getting a lot of set top box data. They are getting the content's ratings which reveal when and how viewers are consuming content.
What we are seeing is that these traditional broadcast companies are beginning to expand. For example, the Public Broadcasting Service in the United States has an app. I can watch its popular shows like Frontline or Nova and augment that experience by looking at extended interviews on the second screen. I can even share the content with my social networks. Even though the programming here runs on a network and by that one means on schedule, it is unbundled in part. It is optimising the data coming from the set-top box and the second screen and is using it to optimise my experience.
It is established that the copious amounts of data being generated by organisations can help them understand consumers better and serve them better as well . Are companies looking for ways to monetise big data?
The conversation on this front has indeed started gaining momentum. Organisations have begun to put together data from various sources internally as opposed to data that previously existed in silos. That is enabling them to build systems of engagement that allow them to improve value at every touch.
Now companies are able to take data and put it in systems where they can build profiles and combine it with predictive analytics where they can forecast consumer demand. This could be in terms of the kind of offers they will want, their propensity to churn, their fan engagement levels, among other things. And as we start to predict them, we can start talking to them and actually start to monetise that data. So it is not always that they are turning around and selling that data.
Earlier, customer service used to hold on to their data. Marketing teams held on to theirs. They are now speaking about unifying and combining that data and utilising it. And this is where the revenue opportunities truly lie.
Before IBM, Graeme worked across sectors, though always as a marketer. He has to his credit stints at an advertising agency, Neal Advertising, heading operations and client engagement and at staffing portal, Monster Worldwide.
A bulk of the conversation around big data has been restricted to the retail space. When you put the concept of big data in the context of media and entertainment companies, how does this conversation change?
Media and entertainment companies are dealing with a variety of data types. They are dealing with data that is coming from both within and outside the enterprise. They are also dealing with linear and non-linear data, social data, information coming off set-top boxes and, of course, data coming from second screens. What we are noticing is that content providers are transitioning from simply being deliverers or producers of content to being enablers of lifestyle. And now that consumers want their content delivered across a number of devices and channels, whether bundled or unbundled, the amount of data being produced is only increasing in quantum.
Media and entertainment companies are now understanding the opportunities that live within that data. The idea is to discover those insights and drive relevance. On our part, it is a win-win situation we are trying to create. The internal win is that we are trying to improve our processes, our content, the delivery engines and getting to know our customers as individuals. This creates opportunities for teams internally to collaborate. So you start seeing chief marketing officers interacting with chief information officers, chief technology officers or content officers. You may see marketing play an important role in customer service and in the technologies and strategies that are being deployed not only to deliver content but also to improve processes across the enterprise.
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The win on the other side is for consumers because they want information instantly with quick access. We no longer want to be bound by the rules of time schedules. We want to be able to turn a system on and tell it what content we would like. We also want the offers that are personalised and tailored for us. And to do all this, one requires tremendous amount of technology, tools and talent across the enterprise to use these data sources and deliver on those insights.
You've touched on the issue of bundling and unbundling of channels by media and entertainment companies. In India, direct-to-home players are yet to unbundle the channel packages down to individual ones. It works mostly in clusters based on, say, language. But in the United States, for instance, there is an active debate on whether unbundling really works. Does it end up taxing the consumer?
Let me answer this with an example. Netflix reinvigorated a very popular show in the United States called Arrested Development recently. The only way you can get the show is if you are a member of Netflix and you must watch that on demand. The advantage to Netflix is that it is increasing its subscriptions and producing original, award-winning content. The advantage to the consumer is that they can watch the content at their leisure. But because they are 'opting' to watch the show they are creating a lot of exhaustive data, giving data-driven companies like Netflix an opportunity to learn more about audiences.
Take my family as an example. We watch a lot of Dr Who. Based on our choice of viewing, there is a lot of data driven video recommendation that is happening. That is augmenting the value of unbundling. Another good example would be Pandora (internet radio). I listen to my music, I create my own playlists, I post what I like or don't like. As these channels are perfected with time, Pandora is learning more and more about me. I have noticed that they are not only improving the content they are delivering to me but understanding me better. They are targeting me with relevant and accurate ads.
When I first started using it, they were relevant with their advertising but not as accurate. Now they are both. The combination of relevant and accurate advertising creates unprecedented opportunities for marketers to improve the returns on their campaigns by giving consumers what they want.
Properties like Netflix and Pandora work with the on-demand model or require a more proactive role of the user. But regular channels are mostly about passive viewing. While the former can throw up a lot of data on the user, is it just as true for the latter? Is targeted advertising a possibility for regular television channels?
Absolutely. We are moving the conversation to traditional broadcast networks here. They understand the power of social data and third party data. They are also getting a lot of set top box data. They are getting the content's ratings which reveal when and how viewers are consuming content.
What we are seeing is that these traditional broadcast companies are beginning to expand. For example, the Public Broadcasting Service in the United States has an app. I can watch its popular shows like Frontline or Nova and augment that experience by looking at extended interviews on the second screen. I can even share the content with my social networks. Even though the programming here runs on a network and by that one means on schedule, it is unbundled in part. It is optimising the data coming from the set-top box and the second screen and is using it to optimise my experience.
It is established that the copious amounts of data being generated by organisations can help them understand consumers better and serve them better as well . Are companies looking for ways to monetise big data?
The conversation on this front has indeed started gaining momentum. Organisations have begun to put together data from various sources internally as opposed to data that previously existed in silos. That is enabling them to build systems of engagement that allow them to improve value at every touch.
Now companies are able to take data and put it in systems where they can build profiles and combine it with predictive analytics where they can forecast consumer demand. This could be in terms of the kind of offers they will want, their propensity to churn, their fan engagement levels, among other things. And as we start to predict them, we can start talking to them and actually start to monetise that data. So it is not always that they are turning around and selling that data.
Earlier, customer service used to hold on to their data. Marketing teams held on to theirs. They are now speaking about unifying and combining that data and utilising it. And this is where the revenue opportunities truly lie.
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