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Why Media Companies Are Turning to Digital Data Intelligence

Telos Alliance multi-device
(Image credit: Telos Alliance)

COVID-19 has caused consumers to dramatically and suddenly adopt new media consumption patterns. And now they are consolidating their choices. Companies in media & entertainment, in turn, are rushing to see where consolidation will occur, and in some cases are attempting to move faster than they would like to beat the competition. This rush to meet the consumer where they’re headed next is already disrupting the industry.

How do consumers choose the right media companies to serve their needs? And how can media organizations understand faster how to be that right choice? In the next decade, we’ll look back at 2020 and the start of 2021 and see how data operations and the advent of enterprise artificial intelligence were the hallmarks of the companies that discovered where their consumers were headed and meet them there.

Here’s why.

A TURNING POINT

Digital media organizations with direct-to-consumer distribution models can collect and monitor viewership data in real time. That’s a vital advantage if we consider the big picture of what the media industry is confronting today. 

Amidst COVID-19 lockdowns and social distancing measures that have severely limited traditional family activities, consumers increasingly look to replace outings to restaurants, concerts and movie theaters, and to keep the kids out of their hair while working from home. 

Netflix, Amazon Prime, Hulu and other streaming services now complement traditional cable and satellite television. Kids who had been told to stop spending so much time on the screen are now being told to get on the iPad to attend online school and connect with friends remotely to chat, play games and watch content.

As we move into an uncertain future, media consumers won't be looking back; their changed viewing habits are firming up. Across the media spectrum, the Global Web Index found more than 80% of U.S. and U.K. consumers taking in more content since the onset of COVID-19.

People of all ages are looking for news updates in a chaotic period that looks to extend into 2021. But Generation Z is more likely to be listening to music. Younger people are more likely to be engaging with each other through digital games. Millennials are searching for cooking recipes and healthy eating. Each has favored channels to get what they need.

SURVIVAL OF THE FRICTIONLESS

Consumers who rushed to sign up for every streaming service available at the onset of the coronavirus pandemic, worried there could not possibly be enough options to keep themselves and their families occupied, now have skyrocketing monthly digital entertainment bills they can no longer justify. 

As PricewaterhouseCoopers recently noted, viewers have started deciding which services have proved more useful than others, and which to jettison. For content providers, it's imperative to get a grip on this fluid situation by analyzing viewership numbers and demographics. 

Effectively tracking how many subscribers have been gained over the past eight months, how many have been lost (and why) and producing accurate predictive models for 2021 and beyond are vital to forming strategies to increase profits, cut costs and stay relevant. 

To do this, media companies need precise tools that can centralize disparate data and derive actionable insight—continuously, and in real time. Data analytics tools must provide a 360-degree view of distribution, and they need to take data from siloed systems of record, add additional data outside of those systems of record and get that into decisionmakers’ hands. Today, this is often accomplished manually or with multiple point solutions, but streamlined intelligence is the better and scalable route.

Without this centralized intelligence, companies can end up overestimating or low-balling viewership, taking too long to recognize the mistake and falling behind in the market rather than driving it. Those slip-ups leave revenue on the table.

EASIER AND FASTER DIGITAL

If deployed correctly, digital solutions provide a logical vehicle for media companies to harvest reliable data at scale and choose wisely which path to follow when presented with the many forks in the road they’ll be sure to encounter in the market. 

These digital solutions include cloud-based business software like revenue auditing software, software that tracks subscriber activity and connects it to revenue and cash flow as well as getting on-premise software that can’t connect with other software and data well.

Essentially, executives want to tag data automatically so they can see how the value of content changes at every step of the process, and how it connects to everything else. Technologists call it a 360-degree view.

For example, if an artist sells rights to distribute their show or music, how do you know it was shown or played when you’re told it was? When you can get that data automatically from the distributor’s system, and check it automatically, that helps. When you can also see how many subscribers signed on when the show was shared on streaming, versus linear television, or what other content was available next to it, you can start to see more value than you did before. These software solutions connect with other software and systems to see how your product’s value changes in the hands of other people.

These data and software solutions can improve efficiency and effectiveness while simultaneously reducing risk exposure. Enterprise AI should be accessible to technical and non-technical business users to arrive at the most accurate and valuable insight. 

With these solutions at hand, media companies will move past retrospective questions about how their business coped with the disruption and dramatic changes caused by COVID-19. They will utilize superior insight to monetize that data in the form of predictive insight and prescriptive recommendations. The smartest will emerge from the current tumult aware of the powerful capabilities to understand and predict consumer behavior.

Let’s take churn. Media companies accept churn as a reality. But what can we understand from churn? Why does a customer leave or come back? If we can comprehend that, the knowledge can be used to reduce customer acquisition cost and win back lost subscribers. Let’s call it “churn learnings.” 

The first step is to normalize data; the second step is to analyze it to capture insights. The third is to use those insights to improve the business, capturing the full value of that data.

Through this, we get closer to answers about which customers are subject to churn and why. Predictive analytics can empower companies to race out ahead of the competition, improving engagement for subscribers according to the content they need and desire. 

The companies that use digital technologies and cloud software that sets the stage for enterprise AI and other advances in the next year will find and keep the customers they earn.

Mark Moeder is CEO of Symphony MediaAI.