How to Build a Sustainable Cloud TV Business With Data

Gideon Gilboa
Updated June 20 2021
Sustainable Cloud TV Business With Data - man analyzes data charts on a computer
Gideon Gilboa
Updated June 20 2021

Content and pricing alone are no longer enough to earn viewers’ loyalty. According to Nielsen Total Audience Report, households consider up to 13 video streaming attributes when deciding between subscriptions. This is where smart data-driven strategies for Cloud TV businesses can help to stand out from the competition and create sticky and engaging experiences based on all the data collected from their users.  


One of the most common challenges these players are facing is having this great data lake they are all ready to swim in, but not quite confident on how to get their first toe in the water.   


We’ve gathered a few tips that can help you get started and give you floaters when you first enter the deep data lake waters.   


Getting Started With Your Data-Driven Strategy  

To leverage the power of data and get your share of the OTT market you need a well-structured strategy. It all starts by being able to link the content you offer with your business and marketing goals, combined with the KPIs by which you will measure your success. This boils down to building a holistic infrastructure that will collect the data, normalize it, and correctly process it to produce insights, all ready to start acting on this data.  


Key Steps for Your Data-Driven Strategic Transformation 

 Now that your toes are in the water, it is equally important to nip any challenge in the bud before jumping in at the deep end of your data lake. Here are the most important ones to consider in the first stages of your Cloud TV business data journey:  


  • Fix data fragmentation: Cloud TV service providers usually experience huge data fragmentation driven by the numerous data sources they use. A well-structured data lake can help you centralize your data to create a single source of truth, in a normalized and unified manner - basically, getting rid of anomalies that can later complicate the data analysis (e.g. data deletion, duplication, redundancy, and overwriting, to name a few).   


  • Build unified insights: Having all your data stored in one repository doesn’t necessarily mean that you have unified insights. Selecting the right data points based on your business metrics, understanding the connections between them, and presenting them together under a single dashboard is what unified insights means. When reading it right, this can help you achieve a realistic overview of your service performance you can act upon.  

For instance, if churn rate is one of your relevant metrics, the number of active users and the time they spend on the service are the usual suspects to link towards improvement of your churn KPI. But what if the latest content you uploaded to your service also links to churn and is impacting your business? Using more advanced algorithms can help you link more data points leading towards predictive analytics, which handles much of the data magic we are all fascinated with. 


  • Scale matters for predictive analysis: predictive analytics is not the same data science as big data. Big data describes the data itself, while predictive analytics identifies meaningful patterns of big data to predict future events and assess the appeal of diverse options. Scale is vital for the latter – being able to predict the future behavior of the millions of subscribers using your service (e.g. future churners or trial converters). You need to make sure your technology infrastructure, as a Cloud TV service provider, has the capabilities to manage and process just the relevant and exact data you need at scale without incurring unsustainable processing costs.   


  • The maturity of your business also matters: All data is important, but some data is more meaningful than other, depending on the maturity of your service. If your Cloud TV business is in the launch phase, it is worth focusing on conversion-related data – how to optimize acquisition channels, what trials are most likely to convert to new viewers or subscribers, etc. However, when your service reaches its growth phase, the focus needs to shift to retention - churn rate, engagement, quality of your content catalog, how the different audience segments are being treated, what channels are driving the longest customer lifetime value, etc. At this stage, predictive analytics will be more likely a key focus of your chosen data approach.  


  • Make data available to your entire organization: if you want your employees to use data to reinvent your business model or the service’s UX to make a greater impact on your viewers, you’ll have to democratize access to data. But not all to everyone – the data should be tailored for specific stakeholders to meet exactly the right and relevant data for them. A UX designer, for example, will not use the same data as a product owner to assess areas for improvement on your Cloud TV service. And the last thing you want to do is to overwhelm them with an abundance of data they are unable to read or simply do not need. Lastly, train your people on how to read the data right, and insist on using it as a primary source to initiate discussions. 


This is, in a nutshell, how you start building and implementing data-driven strategies for Cloud TV. As we said before, it is a journey that needs to be developed carefully and step by step. 


To learn more about the growing role of data and analytics in the video services market, I welcome you to check the insightful panel ‘Data and Decision Making in Video Services from Parks Associates. You’ll learn about some of the most burning topics in data-driven decision-making and how to maximize it as a competitive advantage for your Cloud TV business.