6 Ways Advanced Analytics Will Change Media & Entertainment

business| media| machine learning | | Dan Harris

Media and entertainment companies are facing increasingly competitive and uncertain markets - consumers have more ways than ever to get their content, driving the need to operate in smarter, more efficient ways than ever before. At the same time, they need to generate ever-more revenue from delivering that content.


But the media and entertainment sectors have always dealt in data - 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. 

The most cutting-edge companies (like, for example, DAZN) are transforming and executing on predictive analytics and machine learning at scale primarily in these areas:

1. Better Ad Targeting

Of course, advertising is the name of the game, and with advanced segmentation and complete customer views (i.e., the ability to understand what a customer does across data sources), hyper-targeted ads are the future.  Harnessing the power of advanced analytics means easy, fast targeting so that exactly the right people are seeing exactly the right ads that they are more likely to click on, which translates to more return on investment (ROI). 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

Advanced analytics is all about taking data not just from one source but from many diverse sources to derive accurate predictions about users' actions. 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. 


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, when allow for a schedule completely optimized to the audience for maximum views.

3. Finding New Revenue Sources

It can be difficult to grasp how advanced analytics can help find completely new revenue sources in media and entertainment, but one great example is The Weather Channel, who realized the value of their data and were able to create a proprietary ad targeting platform (WEATHERfx) that other channels could then leverage for advanced advertising. In today's market, being able to identify additional, innovative sources of revenue aside from traditional advertising and partnerships is a hugely valuable asset.

4. 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 from the beginning.


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

Using data science and machine learning to dictate what to show users even if they don't know it's what they want is a revolutionary way to drive strategy, but - as we've seen in the Netflix case - it works.

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 curners before - check out our step-by-step guidebook for more.

6. Effective content Recommendations

Of course, there's no better way to increase user engagement than providing high-quality recommendations. But a good recommendation engine is easier said than done and also involves using different types of data and applying different strategies depending on the type of content and the makeup of the userbase. Again, we've written lots on this topic before, and you can take a look at our recommendation engine guidebook for more detail.

Learn More

If you're ready for more on how the best media and entertainment companies are embracing machine learning to get ahead of the competition, download our latest white paper for a look at the landscape plus suggested first steps for getting started.


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