How important is AI to the future of the video industry? -NCS

The video industry has moved from all cost thinking to what is defined by retention, engagement and profitability. It is clear that AI is the ability to provide more automation and efficiency, and a higher level of user satisfaction will be at the heart of what comes next. Whether you're surfaced content that your audience doesn't know they didn't want, improved ad targeting, or quietly tweaking the user interface to suit someone's behavior, AI is set to be the viewing engine for your viewing engine.

However, although the appetite for using AI-enhanced analysis is strong, its application remains relatively basic. According to a new research study conducted by Accedo in collaboration with Professor Serguei Netessine of Wharton School, the video service understands the possibilities of AI-powered data analytics in driving personalized, predicting churn, improving user engagement, and extracting maximum values ​​from data. Video providers struggle to turn raw data into meaningful and actionable insights, and it helps many people bet on AI and do just that.

Current state of play

With the right data, AI theoretically optimizes the viewing experience, coordinates every part of the UX and UI, predicts potential churn risks, makes content more discoverable, and enables data-driven decisions. In reality, few video providers have yet to reach this level of refinement.

Video services typically collect a variety of audience data points, including streaming performance, content consumption, service quality, app behavior, and onboarding data. Of these, streaming services ranked content consumption analytics as the most important factor, and surprisingly, service quality analytics as the most important factor. This is particularly interesting considering that user retention and satisfaction are just as dependent on quality as content relevance. The research also found that the majority of streaming services collect and report data in real time, but not all providers have yet to reach this level of maturity. Furthermore, only 27% of streaming services used predictive analytics to predict user behavior and trends.

The analytical tools ecosystem remains fragmented. As identified in this study, streaming services use a combination of third-party analytics tools, with Google Analytics leading the pack, followed by Adobe, Conviva, NewRelic, Jump and others. Many also rely on unique tools provided by our in-house systems and vendors. Using different tools is very problematic when combined with the fact that data can sometimes be inconsistent and held in silos. Without unified, consistent and reliable data, trustworthy data will be compromised and data-driven decisions are difficult, if not impossible. This is a real problem for video services, especially when executives can be influenced by external market expectations rather than internal insights.

The data is based

AI is as good as the data supplied to it. Quality insights that help video services optimize viewing experiences and improve engagement are only possible if the underlying data is solid in the first place. Therefore, data should be considered a strategic asset, not a by-product of user interaction. If the dataset is inconsistent, Gappy, or outdated, then the algorithm will not produce the correct results no matter how sophisticated it is. Instead, it may have the opposite effect of what was intended. You'll be chasing your audience by providing poor recommendations, unrelated ads, ineffective personalization, or touch UX/UI. This is why data cleansing and validation processes are important for effective AI applications for data analysis.

With data foundations introduced, AI-driven analytics retains the great potential of video services. Predictive analytics can be used to predict and prevent churn, identify inefficient monetization strategies, and identify revenue leaks such as fraud and fraudulent account sharing. Additionally, AI-driven recommendation systems can be used to dig into individual users' preferences, view content into surface content tailored to your audience, and delve into contextual data. Additionally, for ad-supported services, AI-driven ad targeting, placement optimization, and audience segmentation are all considered areas that are likely to improve the ROI that will again provide the underlying data reliable.

The onboarding process is another area where AI-driven data analytics is considered to be enhanced and refined. As identified in this study, video services have already used data analytics to streamline sign-up flows or identify friction points during onboarding. If this process can be further improved by implementing better onboarding and transformation AI-driven analytics, then video services exist to benefit from the measurable benefits of transformation. Even small tweaks can have meaningful effects when data insights are applied thoughtfully.

A more predictable, aggressive and personalized future

It is clear that video services are not satisfied with traditional analytics. They are not only analyzing existing data, but are actively investigating AI technology. They want a system that can predict user behavior, drive engagement before dropping engagement, and adapt the experience in real time. Research shows that personalization is a top priority for analytics usage, and there is a demand for AI-powered tools that can enhance recommendations through AI-driven metadata enrichment.

There is also a true appetite in intelligent, AI-driven analytics, which can flag the risk of churn and suggest meaningful ways to inspire audiences. The service also seeks to better understand how audiences engage with content. This creates the demand for AI-driven tools that can improve content engagement analytics at a detailed level for deeper insights.

Additionally, there is growing interest in building a more complete picture of audience emotions in integrating insights from multiple sources and channels, including social media and service interactions and feedback. At the forefront of advertising, AI is applying dynamically optimizing AD placement based on real-time audience behavior. Content acquisition decisions are also data-driven, and there is a demand for AI-driven tools that can provide real-time performance analytics to shape license and investment decisions.

In a landscape defined by rapid change, services with AI capabilities and tools to turn raw data into relevance set the criteria for how streaming will look.