The way we view and interact with content is far different today than it was a decade ago. The accelerated growth and acceptance of OTT and media streaming platforms, the rise of high-speed internet, and the proliferation of the smartphone culture have transformed viewing behavior. We can view content whenever and wherever we choose, and for the first time, we see an entire generation of “Cord-Nevers.” A group of people who have never used commercial cable for television service and only know the world of streaming content.
With research predicting that the OTT market and VoD (video-on-demand) market will grow annually by 13.6% (CAGR 2021-2025) and bring in roughly US$6,122M this year, the time to capitalize is now. But how?
Today, broadcast media companies develop OTT apps to deliver video content to their subscribers; however, as the market matures, it gets more competitive. For example, Customer churn has become one of the most significant challenges for OTT businesses. If one company isn’t providing the viewer’s desired experience, it’s easy enough to jump ship and find another service. This makes ensuring the highest customer lifetime value out of their customer base is difficult.
As more providers enter the market, OTT providers must develop strategies to entice viewers to continue using the service after the initial viewing experience and become lifelong customers. To succeed, broadcast media companies need a combination of engaging content, intuitive user experience, personalization, and integration of data and technology
With the increasing overlap of content across all these platforms, these services must improve the consumer experience by delivering relevant and engaging content to prevent audience churn. Content personalization is, therefore, vital to acquire more viewing time and strengthen market share.
The key to an excellent OTT service begins with understanding the customer and promptly responding to their needs-whether for content, the user experience, or the business model.
Since the ‘viewer’ lies in the heart of the business, OTT managers have to look at big data streaming analytics to enable actionable learning of customer behaviors.
Personalized, relevant, and contextual content is what OTT viewers demand. However, with new streaming services that come online almost every other week, there is more content today than ever has been produced in history. The recommendation engines need more customization and personalization powers to deliver the right content to the users.
OTT content needs to leverage big data streaming analytics to get to that Netflix model, where the provider can quickly serve content based on individual preference. By combining large user data sets and metadata for analysis, OTT providers can fine-tune their recommendation engine and ensure that the right content reaches the correct user.
Deep big data streaming analytics also gives OTT providers deeper audience insights. It helps them understand genres of content in high demand, what content the audience demands at what time of the day when they pause, or what they skip. Based on this data, OTT providers can make informed decisions on content dissemination.
The OTT market has, without a doubt, become oversaturated. The sheer number of OTT players means that customers have an increasing number of providers to choose from, making viewer churn a real problem to solve to maintain profitability in the OTT universe.
Most OTT platforms struggle with retention once they launch, and viewer acquisition is becoming more expensive and challenging as markets become overpopulated. However, big data streaming analytics can level the playing field by providing detailed analytics regarding viewer and subscriber churn rate to answer questions like ‘which customers are most likely to churn next month’?
Big data streaming analytics gives OTT providers the capacity to aggregate data sets and develop a 360-degree customer view. OTT providers can use more accurate churn prediction models and use real-time and historical data, user data and user behavior, and other associated data to identify subscriber clusters with a high churn risk. They also get detailed insights into the leading causes of churn and can proactively solve this problem.
Understanding location-based nuances of user behavior and gaining insights into device demographics and platform infrastructure becomes essential as OTT providers attract international audiences. Additionally, gaining specific real-time data across live and on-demand services becomes necessary to improve customer experience and stay on top of the OTT game.
Big data streaming analytics play a significant role in providing deep insights into all the customer experience influencers by looking at the data intelligence. Analytics help offers a thorough understanding of the viewer experience. It gives providers detailed information that is needed to benchmark things that matter most, identify disruptions that impact engagement, and make intelligent business decisions without ambiguity influencing it.
Using behavior-based audience insights and fan analytics enables OTT providers to profile the viewers accurately. This helps them make more informed business decisions on programming choices, marketing effectiveness, predictable cross-selling, and upselling opportunities, making it more relevant and contextual to the viewer.
Big data streaming analytics are transforming the world of OTT by enhancing the user experience by providing more accurate and personalized recommendations. It allows for advertising to become more targeted based on user preferences. Big data and Analytics also give insights into making more accurate predictions on the subsequent best offers and help fuel cross-selling and upselling initiatives.
OTT providers have an advantage because they already have access to enormous amounts of valuable data without even knowing it. Knowing how to make this data work, push and manipulate this data, and use the right analytics can help OTT providers design the best service that will lead to customer satisfaction, retention, and profitability.