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Cristoforo Kanoy
Right. So owning the customer from a Netflix perspective is about the discovery experience, the actual interaction. We saw this, to go back to Apple, you never joined the Apple TV experience because that was the aspect that was important to you.
Tell me about the ad story. You mentioned it before, that was actually a case maybe of desperation. How did Netflix finally start selling ads? I mean, my perception is you were a big advocate and maybe some past leadership was more opposed. Is that a good read of things?
Yeah, which I thought was remarkable in and of itself, but also very encouraging with regards to Netflix. I mean Netflix is a pretty old company at this point, but to be able to pivot and build something like that so quickly to me was a great sign.
Right, that sounded a little like your animation bet to an extent. And yet to your point, you put gaming as a contrast to that and you mentioned today on the earnings call, most of your success and your focus has been on licensing content that already exists, Grand Theft Auto, for example, has been a big success in that regard.
The other one that really I take away with me is probably the under-recognized benefits of being extremely disciplined about having top talent in all your positions and this goes a little bit back to that strategic mindset versus what it looks like in practical operations, and having great people everywhere just means you can get tons of stuff done.
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The strategic group map positions cable, satellite, and telecommunications companies based on their subscriber base, price, and services. From top to bottom, the companies are arranged from higher to lower prices. From left to right, they increase in the number and innovativeness of services offered. Comcast is at the top right as the largest subscriber base and most innovative services provider. Time Warner Cable and Dish Network are in the middle as their attributes are average among peers. DirecTV is close to Dish Network and higher than others due to its large international subscriber base.Read less
There are situations that are more likely than not to be actual violations, such as consistent log-ins to the same account during prime time at several repeating geographic locations within the same city, or daily simultaneous use in different countries or continents. There may exist isolated cases of legitimate use even within these occurrences, but they are hard to imagine and would certainly be in the minority.
Unsupervised learning allows for users with similar viewing habits to be classified into clusters without having to identify those habits in advance. In the example below, 15,100 internet users are grouped into five clusters using 51 usage metrics. As can be seen in Figure 1, not all clusters contain the same number of users. In fact, Clusters 2, 3 and 5 contain only 25 users between them. If this were my data set, I would take a closer look at these clusters to try to understand how they differ from the remaining 15,075 people.
That information may be found in the parallel cord plot below in Figure 2, where each line represents a cluster. Looking at the leftmost extreme of the plot, it seems the three users in Cluster 5, on average, had a larger tot_HO (total hours online) than the users in any of the other clusters, on average. This may be one of the metrics that distinguishes this cluster from the others. There are others as well, as indicated in the portions of the plot where the lines deviate.
The next step would be to consider these differences and think logically about which clusters are composed of users who are most likely violating their service agreements. The nice thing about JMP is that initially, if nothing concrete emerges, the platforms are designed to let analysts keep exploring the data until something meaningful pops out at them. Once that happens, they'll have a good idea of the accounts to target the warnings.
From there, a streaming platform like Netflix could monitor these accounts to find out whether there are differences in their usage habits. Doing so might indicate that some of the accounts had multiple households sharing passwords, and the warnings were heeded. A clear outcome would be to do this as a test in a localized region and see if there is a spike in subscriptions in the days or weeks that followed.
Perhaps streaming companies have ways of monetizing viewership apart from the subscription fees, such as product placement, and would rather err on the side of more viewers rather than fewer. Of course, how many viewers gained versus subscription fees lost would be the type of information needed before making those decisions. JMP can be used at every step of this process. Our software enables domain experts to wade through large and complex data sets until insights emerge that only they, being experts, can understand. It further allows those experts to distill the insights and present them in a way that is easily digested by stakeholders, thus ensuring any resulting actions align with the strategic needs of the business.
It'll be interesting to see what actions the various streaming services take to address this issue moving forward. One thing is certain: They, like many other organizations, will have leveraged the power of analytics to inform and augment the decisions driving them.
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