Today, almost half of the population in the US streams Over-the-Top (OTT) content directly to their television for an average of 1 hour 40 minutes daily. Users are getting inclined toward having an increasing number of live experiences for sports and news content. OTT has inevitably unfolded into a multi-million-dollar industry and it is set to grow at 17.2% CAGR to 2020. Most of the OTT platforms provide video metrics out of the box. However, the broadcasters, content providers, and advertisers face inimitable challenges when they try to holistically comprehend the performance of OTT content, target the right segments and formulate an effective business strategy. Also, compiling data from numerous data sources to make meaningful insights can get quite exhaustive and time-consuming. The existing platforms fall woefully short to help uncover intelligence and insights to drive effective business outcomes.
The key stakeholders for this OTT analytics comprise of 1) Advertisers – who want to get associated with the right content and target the right audience segment 2) Content Providers – who want to understand and invest in ideas comprehending audience behavior and interests 3) Marketeers– who want to be enabled with the right audience to succeed in their marketing initiatives.
There are various challenges faced by these stakeholders while analyzing the OTT content. How to retain and increase subscriber growth rate? Who is watching and how are they getting disengaged? How do we measure the performance of content across audiences? Which region drives the most engagement for the content? How can the audience be segmented to offer personalized programs or ads? Which platforms provide the best ROI? How do we effectively market the content?
To overcome the above challenges, we need to start with access to quality data. Lack of quality data is the biggest challenge in data analytics. Initially, broadcasters’ view was limited to Nielsen ratings from sample audience data. However, data collection has expanded progressively with the launch of streaming services like YouTube, Netflix etc. and now spans to thousands of parameters of metadata collected. The clickstream data has transformed into big data. Data that gets generated directly from the video platform is the key data for content analytics. This is also called first party data. In addition to this, the second and third-party data collected from data management platforms help to provide correlations and enrich the analysis. Once we have access to the right data, we can perform predictive analysis engaging techniques like data mining, statistical modeling, and machine learning to take the data to the next logical level. We can create a dashboard that can help in formulating the content strategy, promotions, personalization of content, etc.
The success of any OTT solution lies in being able to decipher customer behavior and optimize omnichannel marketing efforts to explore better business direction, garner personalized insights through segmentation and predictive modeling to boost operational efficiency and extract value from copious data for smart decisions. It should also be able to automate data aggregation and empower us with better decisions. The objectives of the OTT solution should be clear. The approach, cost, and complexity will vary based on the objective. We can get profound insights by applying techniques like Machine Learning (ML) and Artificial Intelligence (AI).
We need to remember that, data may not contain the answer, but if you torture it long enough- it can tell you anything. Garnering data is not the end objective; neither the reporting or building of dashboards. Rather, it all starts with asking the right questions. What are you looking for? What is it that you want the data to answer? Only if you have the right question, you can derive answers from the available data.
We’re listening.
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