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Playing OTT ‘Moneyball’: Data-Driven Content Acquisition

[Updated and republished]

MONEYBALL, by Michael Lewis, is a book about his experience assembling a competitive baseball team despite a particularly small budget. To accomplish this task, he introduced a groundbreaking new strategy of valuing potential acquisitions based solely on analytical, evidence-based metrics.

Budget-minded OTT networks can leverage our vast library of viewership-generated data to help them make the decisions that will drive the highest number of subscribers with the least amount of churn, while keeping the cost of content acquisition as low as possible. Essentially, we have applied the 'Moneyball' strategy to the process of planning and acquiring content for any OTT network.

To do this, we first defined that the two primary attributes of a classic ‘subscription driver’ are :

1) Its ability to pull in new subscribers to the network over a distinct time period (momentum’),

2) Its ability to maintain a sense of scalable viewer loyalty over a long period of time (strength of viewership’).

Clearly those highly sought after ‘subscription drivers’ will score well in both categories, but they are also amongst the most expensive shows to license.

But what if we looked at ‘momentum’ and ‘strength of viewership’ scores independently? What if we could identify shows that scored particularly well in one, but not necessarily in both of categories? Wouldn’t we then be able to find those less sought after 'hidden gems' that could be picked up at a bargain, but that would individually still provide real value any OTT Network?

This question led us to find a way to customize our vast library of OTT streaming viewership data for the Network in a way that would specifically identify shows that performed well in one of the two categories.

Niche Attractors

The first new category we have created in our OTT Moneyball paradigm is one that illustrates a show’s ‘momentum’ by looking for those titles whose viewership numbers are growing at a faster rate than average, but whose total viewership might not yet be considered notable.

Churn Reducers

On the other end of the spectrum, we look for shows where 'strength of viewership' is strong but where numbers of views are not necessarily growing rapidly. These

are less flashy, OTT 'comfort shows' that stand the test of time but that may well be flying under the radar, as well.

Once armed with this new data-supported and cost-effective way of grading shows, a network can begin to build a content library with a healthy balance of Niche Attractors and Churn Reducers. And, while they will also spend some dollars on the higher priced 'Subscription Drivers,' they will do so in a far more educated way.


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