6 Ways AI Will Change Media & Entertainment

Use Cases & Projects, Scaling AI Nancy Koleva

As content consumption behaviors are becoming increasingly complex and evolving more rapidly than ever, media and entertainment companies are facing increasingly competitive and uncertain markets, which are driving the need to reduce operating costs and simultaneously generate more revenue from delivering content.

camera filming on focus with blurry neon lights in the background

Companies, in turn, are tailoring their offerings and business models to revolve around personal preferences, leveraging data and usage patterns to pitch their products not at audiences of billions, but at billions of individuals. But media and data have always gone hand in hand — ratings, subscription numbers, etc. So what's new?

Well, the new age of data means making changes to the business constantly based on real-time input from all kinds of data sources. In turn, cutting-edge media and entertainment companies are leveraging AI and machine learning at scale primarily in these areas:

1. Hyper-Targeted Advertising

The possibility of combining data from different sources in one place can allow companies to look at their customers as a whole and deliver unique, hyper-targeted offers. In TV and advertising, this is evoked in the concept of addressability: the ability to interact with consumers based on what their specific choices reveal about their interests and preferences.

Hence, thanks to AI and ML, media and entertainment companies can predict churn rates more accurately, place advertising at the right time and in the right place, and have more appropriate, personalized offers to increase conversion.

Here's a real-life example of how hyper-targeted advertising changed the way one media company, InfoPro Digital, does busienss. 

2. Optimized Media Scheduling

AI and data-driven solutions are all about taking data not just from one source but from many diverse sources and derive accurate predictions about users' actions in real time. For optimized scheduling, even unexpected external data sources can be useful. Like the weather, for example - a business may adjust media scheduling streams based on a more captive audience on a rainy day. 

foggy window by a bedside on a rainy day

Businesses may adjust scheduling streams real-time based on, say, the weather (e.g., a captive audience is more likely on a rainy day).

And it doesn't stop there; detailed predictions of who will be more likely to watch what, and on what device, will allow for a schedule completely optimized to the audience for maximum views.

3. Programmatic Ad Buying

Traditionally, advertisement slot  buys are based on an analysis of audience data (age/gender/geography etc), but they do not account for the high level of fluidity in viewership. What's more, the ad buying process itself is manual and cumbersome. Hence the arrival of programmatic ad buying, which leverages real-time data analysis and automation to purchase ads across a wide variety of media platforms: broadcast TV, cable, satellite, over-the-top services like Hulu and Netflix, and online video services like YouTube. This new method of ad-buying involves systems that can constantly monitor audience dynamics across multiple channels and respond purchase ad space as soon as it becomes available.

4. Predictive Modelling for Targeted Content Generation

When Netflix created the hit series House of Cards, they claim they already knew it would be a hit because data actually inspired their creative direction. Meaning: instead of getting people together in a room, coming up with an idea, creating a pilot, and only then using data to see how it performs, they rely heavily on data and predictive modelling from the beginning of the creative process.

House of Cards Netflix banner with main characters

Machine learning and predictive analytics can help produce low-risk, sure-thing content

Thus, predictive modelling helps media and entertainment companies not just by allowing them to react to consumers in real time, but also to anticipate their behavior, influencing long-term investments, for instance, what kinds of movies in which consumer micro-segments will be popular two years from now. In addition, companies can make predictions about which customers are more likely to view a given type of content, and what device they will be using when viewing it.

5. Churn Prevention

Not specific to the media and entertainment industry, but worth mentioning nonetheless. Knowing which customers will churn and being able to specifically target the ones likely to come back with offers and tailored marketing is critical to success. We've written a lot about using data science and machine learning to predict churners before - check out our step-by-step guidebook for more.

6. Smart Recommendations & Personalized Content Experiences

Recommendation engines have been widely used in the media industry to predict what kind of information or content customers would be interested in. Companies can combine structured and unstructured data and machine learning methods to match people and content, thus improving the relevance of content recommendations and efficiency of content distribution. With leading tech media players such as TikTok and Netflix venturing more and more into AI-based interactive and smart content, we’re likely to see a shift from simpler content recommendation systems to an entire AI-driven personalized content experience.

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