Today's style of viewing and interacting with content is vastly different from a decade ago. The rapid growth and adoption of over-the-top (OTT) and media streaming services, as well as the emergence of high-speed internet and the spread of smartphone culture, have changed watching habits. We can watch videos whenever and wherever we want, and we're seeing an entire generation of "Cord-Nevers" for the first time. A group of people who have never had access to commercial cable television and are only familiar with streaming entertainment.

The moment to capitalize is now, with research estimating that the OTT and VoD (video-on-demand) markets will expand by 13.6 per cent annually (CAGR 2021-2025) and bring in around US$6,122 million this year. But how do you do it?

OTT apps are currently being developed by broadcast media firms to distribute video content to their customers; however, as the market grows, it will become more competitive. Customer attrition, for example, has emerged as one of the most serious difficulties for OTT organizations. It's simple to jump ship and locate another provider if one firm isn't offering the viewer's desired experience. This makes ensuring that their customer base has the maximum customer lifetime value tough.

As more providers enter the market, OTT providers must devise tactics to attract consumers to use the service beyond the initial viewing experience and become lifelong subscribers. Broadcast media organizations need a mix of interesting content, easy user experience, personalization, and data and technology integration to prosper.

In order to prevent audience churn, these services must improve the consumer experience by delivering relevant and compelling content across all of these platforms. As a result, content personalization is critical for gaining greater watching time and increasing market share.

Personalization and Big Data Streaming Analytics

Understanding the client and immediately responding to their needs—whether for content, user experience, or business model—is the key to a good OTT service.

Because the 'viewer' is at the centre of the business, OTT executives must consider big data streaming analytics to enable meaningful learning of consumer behaviour.

OTT users expect personalized, relevant, and contextual content. However, with new streaming services launching virtually every other week, there is more content available today than ever before. To give the proper content to consumers, recommendation engines require additional customization and personalization capabilities.

To get to the Netflix model, OTT content must use big data streaming analytics, which allows the provider to instantly provide material based on individual preferences. OTT providers may fine-tune their recommendation engine and ensure that the right material reaches the right consumer by merging massive user data sets and metadata for analysis.

Deep big data streaming analytics also allows OTT providers to gain a better understanding of their audience. It enables them to identify popular content categories, as well as what the audience wants at different times of the day, and what they skip. OTT providers can make informed decisions about content distribution based on this information.

Viewer Churn and Big Data Streaming Analytics

Without a doubt, the OTT market has grown saturated. Customers have an expanding number of providers to pick from as the number of OTT companies grows, making viewer churn a serious problem to tackle in order to keep OTT profitable.

After its debut, most OTT platforms struggle with retention, and viewer acquisition is growing more expensive and difficult as markets become overcrowded. Big data streaming analytics, on the other hand, can level the playing field by offering extensive statistics on viewer and subscriber attrition rates, allowing you to ask questions such as, "Which clients are most likely to churn next month?"

OTT providers can use big data streaming analytics to aggregate data sources and generate a 360-degree customer view. OTT providers can employ more precise churn prediction models, as well as real-time and historical data, user data and behaviour, and other related data, to identify subscriber clusters at high risk of churn. They also gain precise insights on the major causes of churn, allowing them to take proactive steps to address the issue.

Takeaways at the End

The world of OTT is being transformed by big data streaming analytics, which is improving the user experience by offering more accurate and personalized recommendations. It provides for more tailored advertising depending on user choices. Big data and analytics may also aid with cross-selling and upselling initiatives by providing more precise forecasts on the next best offers.

OTT providers have an advantage because they already have unwitting access to vast volumes of important data. Knowing how to make this data work, how to push and modify it, and how to apply the correct analytics may assist OTT providers in designing the greatest service that will lead to customer pleasure, retention, and profitability.