By Jonathan Weitz and Sanjay Subramanian
More people are consuming more media on more devices, and the millions of events being generated by this consumption are resulting in volumes of valuable data. Hiding in this “big data” is important information service providers and programmers could be using to increase return on investment, to improve engagement, to provide a better customer experience and to attract more viewers. But where to focus analysis and investment next?
The data generated by this activity typically lives independently within various organizations of a service provider’s or a programmer’s business. This makes gleaning quick insights from the data difficult. Efforts to learn from the microscopic details available often are bogged down by quality and data-governance issues, by not focusing on the best correlations or simply by not knowing what analysis to prioritize.
Conquering Data Volumes
Effectively sifting through massive real-time data can seem a daunting process. With the right strategies in place, data analytics activities can deliver value early and frequently, without requiring significant upfront expense. These strategies include defining and properly framing business questions, creation of an internal value chain for data, implementation of A/B testing, and utilizing event collection platforms and analytical capabilities.
Every business decision should be driven by the process of defining business questions that can be answered using existing data. This often reveals relationships between user behavior and business-success metrics. Actionable insights then can be gleaned, analyzed and measured; and questions can refined, with the process repeated until answers point to effective strategies that positively impact the business. This feedback loop presents the opportunity to achieve incremental value throughout each step of the process.
Creation of an internal value chain for data also is a must to help ensure that important information doesn’t live in silos throughout an organization. Usage events alone may serve many useful purposes but, when combined with network events, subscriber events and content metadata, they can provide actionable insights.
Once you’ve determined an action to take based on testing and a holistic analysis of the data, A/B testing can be used to understand how to increase engagement, retention and sales. Whether it’s a particular user-interface feature or enhanced marketing tactics, actual usage data can help you home in on effective strategies that will help to make complex decisions with greater confidence. It’s also important to understand the results of these tests on specific user segments in order to take action based on the viewership of specific segments. This arms you with the knowledge you need to provide more value and to increase profitability of these segments.
Finally, investing in an event-collection platform and analytical capabilities to support enterprise- wide initiatives is important for streamlined and cost-efficient data analytics that will result in effective strategies on an ongoing basis.
Strategies Into Practice
A MSO currently is putting these strategies into practice, collecting video on demand (VOD) usage data from set-top boxes. When combined with service affecting such events as outages in the hybrid fiber coax (HFC) plant and device alarms, the analytical tools can pinpoint problems occurring in the network and can identify root causes in real time. At the same time, the data can measure service reliability at a micro level. Measuring and maintaining high reliability rates for VOD are a precursor to other narrowcast services, including network DVR and cloud-based user interfaces.
As programmers seek to understand engagement across all platforms, they can implement data-analytics strategies that help them determine who is watching on specific platforms and how to more effectively engage viewers. For instance, a programmer might ask, “What factors of the experience or viewer attributes are most correlated with likelihood to have high engagement and loyalty on mobile properties?” This could include A/B promotional testing that trials traditional efforts alongside social media to see which has a greater impact on audience engagement. This insight helps to identify where promotional investments should be prioritized to drive and to measure viewership. Having insight into which attributes lead to higher engagement and loyalty also can yield valuable insights that can drive more profitable ad strategies.
With so much experimentation and investment taking place in new content-delivery and engagement strategies, it is imperative that service providers and programmers learn everything they can from the data available. Getting actionable insights out of the mountain of complex viewership data can seem an overwhelming undertaking but, by implementing the aforementioned proved strategies, you could be well on your way to developing services and offerings that drive revenue and engage viewers.