Using Big Data To Enrich Video Streaming Experiences

Big Data And Video Streaming Solutions

In the digital age of prolific data and online video streaming, entertainment is now delivered in a multitude of ways than it was offered ever before. The acceptance and accelerated growth of OTT in cahoots with media streaming platforms such as Amazon, Netflix, and Hulu have fuelled the surge in online viewership, user engagement, and content monetization. Moreover, the proliferation of smartphone culture and the rise of high-speed internet have also transformed viewing behavior on a massive scale. With research indicating that the VoD (Video on Demand) and OTT market expanding annually at a CAGR of 13.6% by 2026, the time to capitalize on digital media streaming is now. But the question is, how?

As of now, broadcast media companies build OTT applications that deliver engaging video content to their viewers and subscribers, however as the market space matures, it gets even more competitive. For instance, customer churn has become one of the most prominent challenges for the video streaming industry. If a media company is not offering the viewer’s desired experience, it is easy enough for users to jump and seek another service. This ultimately makes the highest customer lifetime value out of the total audience base incessantly difficult and hard to scale. In addition to this, as more and more streaming media service providers enter the market, OTT companies must develop robust strategies to entice viewers and encourage them to continue using the services and become lifelong subscribers.

One significant solution to these challenges is big data. Successful analysis and implementation of big data can highlight hidden value and insights within a customer base. By studying anomalies and patterns within large data sets, OTT businesses can make informed decisions about how to interact and target their prospective customers. 

In this blog, we will elucidate the benefits of using big data analytics for VoD, OTT, and live video streaming services. Let’s commence with the implications.

Solving Viewer Churn With Big Data Streaming Analytics

The OTT landscape has, without a doubt, become a colossal and competitive market. The sheer number of OTT players in the industry means that the customers have the options to choose from a wide array of media service providers to choose from. This makes viewer churn a real problem to solve whilst maintaining engagement and profitability in the OTT universe.

As the market is steadily becoming overpopulated, most OTT platforms often struggle with viewer acquisition and retention post-launch as it is becoming increasingly expensive and challenging. However, big data streaming analytics can significantly level up the playing field by furnishing detailed analytics in accordance with viewer and subscriber churn rates. The data helps to solve problems and answer questions such as “Which customers are likely to churn next month?”

Moreover, big data streaming analytics enables OTT providers to aggregate data sets and construct 360-degrees audience views. Digital media streaming companies can leverage more accurate churn prediction models by using user behavior data, real-time and historical data, and other relative datasets to recognize subscriber clusters with a risk of high churn. They can also get detailed insights into the most significant causes of churn so that the underlying problems can be solved proactively.

Enhancing Video Streaming Service Quality With Big Data

If there is one thing that viewers hate, it is when they are halfway through a piece of streamed content and the “loading” circle comes into view. Whether it is a live sports tournament or an episode of web series, this disruption detracts from the overall video streaming experience.

However, big data solutions can help put a final end to the buffering issue by constantly measuring the performance of a video, adjusting its bitrate settings, or making other technical calibrations to mitigate playback pauses. 

Improving Suggestions and Recommendations With Big Data

Despite the best efforts, a lot of video content available for streaming does not even reach the eyes of viewers who might actually enjoy watching it. Today mega streaming players such as Hulu and Netflix use their own proprietary algorithms to generate target-based content suggestions but they usually base pick on shows that users have already watched. For other businesses, mostly, the full picture of the audience's interest is not captured.

Big data, on the other hand, has the potential to preeminently improve recommendation algorithms by capturing a full-swing snapshot of each viewer. It helps in showcasing not only TV viewing history but also moviegoing preferences, spending habits, and more. This detailed portrait allows streaming businesses to pinpoint which video or series a viewer is most likely to gravitate towards with much greater precision. 

Also Read: Latest Tech Advancements in Video Streaming and AI

Offering Personalization With Big Data Streaming Analytics

The key to a remarkable OTT service begins with an efficacious understanding of the customers and promptly responding to their demands, expectations, and requirements – whether it is for the user experience, content diversity, or the business model. 

Since viewers are the core of the video streaming business, OTT businesses should consider opting for big data streaming analytics to enable actionable learning of user behaviors. 

Relevant, contextual, and personalized content is what VoD and OTT audiences demand. However, with updated and new streaming solutions that emerge almost periodically, there is much more content present today than it has ever been produced in the past. The recommendation engines require robust personalization and customization powers to furnish the right content to the audiences. Without a doubt, businesses should integrate OTT media content with big data streaming analytics to reach that Netflix model, where the service providers can swiftly serve video content on the basis of individual preferences. By fusing large user data sets as well as metadata for analysis, businesses can fine-tune their streaming recommendation engines and make sure that the relative and right content reaches the correct end-user.

Big data in tandem with the capabilities of deep analytics also provide OTT service providers with crisp and deeper audience insights. It enables them to decode various genres of content that are high in demand, what type of content users intend to see at what time of day, or whenever they pause or skip. On the basis of these aforementioned segregations, digital media streaming companies can make smart decisions on content dissemination.

Final Takeaways

As big data collects more and more information about us from our ever-increasing connected lives, it extends a valid host of opportunities for businesses intending to thrive in the competitive streaming landscape. Thanks to big data integrations, in the not-too-distant future, users would not have to scroll through large amounts of pages of content with mounting frustration. 

Nevertheless, based on user preferences, it also allows for advertising to become more target-based. Big data analytics is useful for making accurate predictions on subsequent subscription offers and help power upselling and cross-selling initiatives.

If you are looking for building feature-rich OTT applications with big data analytics integrations and support, feel free to connect with our experts. We will get back to you within 24 hours.