Making OTT Analytics Work for You

Making OTT Analytics Work for You

As the range of content available online grows exponentially, it is becoming more important than ever that we understand users’ preferences, and put them to good use. Unfortunately, the industry hasn’t yet fully taken advantage of the potential opportunities. Many aggregators, distributors and content owners may be addicted to consuming the data generated by the services they run, but they aren’t successfully turning that data into knowledge that can enhance their offering and customer experience.

Many readily note with interest the fact that a percentage of their customers watch a certain video on their Android phone for, say, ten minutes. “That’s interesting!” they exclaim; then do nothing with that knowledge. Knowing this information does not lead to them understanding their customers any better, nor does it lead to a refinement of the service. Yet a lot of companies will point to the fact that they know this single statistic as proof that they use analytics to better know their customers. But if you don’t understand what the information shows – if you’re not looking at it through the correct lens - it’s immensely difficult to comprehend what you’re seeing.

It is clear that there is so much data available that it becomes difficult to identify what’s useful and what’s not. Taking individual data points like the one cited above only compounds that problem. Without details of the viewing context, the information merely remains a single data point lost amidst an ocean of other single details.

Without understanding, information never becomes knowledge and no one learns anything. Recommending to a user “You watched series one of Breaking Bad, so we recommend series two” is not putting your knowledge of their viewing habits to best use. The user will naturally move on to series two by themselves if they liked the first– they don’t need your help to do that. What they want, what discovery actually means, is for their service to give them content that they didn’t know existed or didn’t know they would like. However, the kinds of content that are most relevant to them change all the time.

Where value can truly be derived - both from a commercial and a quality of user experience perspective - is in delivering content that is relevant to what the user is doing at the time or place or device they’re using. Looking at user interactions in that fashion begins to reveal how best to deliver an experience that matters to the customer. Once we understand the context of their viewing, the details of what they did in any one viewing session (watched a video for ten minutes on their Android phone) become useful. This is why value-based analytics – that is, creating actionable intelligence about a user, their behavior and preferences - is so very important.

For example, our hypothetical user who watched season one of Breaking Bad generally viewed an episode per day. If we look deeper, and look at where and when they watched their daily episode of Breaking Bad, we would see that they generally watch the first ten minutes of each day’s episode on the bus on their way home from work on their phone. Knowing this means that we could deliver that portion of each episode to them each working day, without them ever needing to search for it. That’s convenient for the user, and gives us (the hypothetical service provider) a much greater insight into their behavior - the next time they search for content on the go, we know the kind of thing they would most likely be in the mood for.

Using that understanding as the springboard, why not delve further? Why not start thinking about the kinds of advertising that might most appeal to our hypothetical user when they’re on the go? If this kind of viewing habit is a trend across a broad range of users, why not try to solicit advertising from specific companies that would appeal to this use case? Why not focus on acquiring content that will best satisfy this use case as well?

By doing this, we’re truly putting our knowledge and understanding to use, evolving and deriving value from our analytics. The service can start acquiring content, pursuing advertising relationships or even adding subscription tiers to meet the demonstrated needs of users in how they commonly engage with the service.

Understanding customers and their needs - what will surprise them, what will delight them, what will prompt them to give more money - isn’t as mysterious as one might think. All it requires is to understand the full range of information the user provides that’s relevant to the occasion, and respond to it appropriately – it truly is as simple as that.