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For Networks, Knowledge Can Be Power

machine learning
(Image credit: Getty Images)

The 2020 U.S. presidential election was likely one of the most-watched televised events in recent history. Since multichannel video programming distributors (MVPDs) won’t report subscription numbers until closer to Inauguration Day, networks won’t know who subscribed to their services for election coverage for as long as three months. Until then, they remain in the dark on the distribution revenue generated by the historic vote — or any content, for that matter.

Karen Bleiler of Symphony MediaAI

Karen Bleiler (Image credit: Symphony MediaAI)

As networks perpetually play catch-up, other sectors like finance, retail and manufacturing are leveraging technology not only to understand revenue in real time, but to predict future customer behavior. Fortunately, networks can harness tech in the same way.

If networks had systems in place that could pull data from past noteworthy events — say, presidential and midterm elections from 2004 through 2020 — they could then forecast how subscriptions likely rose ahead of voting, more accurately predict the revenue they’re likely to see and use that information to make more informed financial decisions and also in negotiations with their distributors.

That capability would be welcome at any time. But it’s especially game-changing at a time like right now, as networks negotiate new licensing agreements with MVPDs before their current deals expire, many of which will be at the end of this year.

A Negotiating Edge

At present, networks won’t have November’s subscriber data in time to leverage it to negotiate license fees. But if they had AI to learn from and make predictions based on past years’ data, networks could enter these negotiations armed with predictive data based on prior subscription numbers. Further, those predictive analytics could save networks from the significant revenue adjustments that will take place once the MVPDs ultimately do remit subscription numbers. In sum: content creators would make better financial decisions, and potentially more revenue.

MVPDs are not the bad guys here. They have not reported subscriber data from the November election because they are taking their time to close out their books. (They need to manage their customers and cash flow, too.) And most, but not all, are doing it all with desktop tools that were invented as far back as 1985 (e.g., Excel), when distribution revenue models were far less complex and the appetite for data and analytics far less than is the case today.  

The industry has outgrown these delayed and manual workflows, though, and the financial implications are severe. Given the lack of timely information available, projecting revenue is extremely difficult and often painful for networks to do. Understanding how distributors are performing — and using that information to negotiate licensing agreements, make business decisions, and create compelling content to retain customers — consumes significant overhead. Additionally, when MVPD data finally becomes available to creators, it’s in multiple formats stored in multiple locations and typically with very limited insightful data points. 

Organizations that can find a solution to these dilemmas have an advantage. Although media companies may not be in a position to displace outdated self-reporting models that depend entirely on decentralized data, they do have the opportunity to leverage centralized AI tools that not only predict performance but also automate data storage, revenue workflows and financial analysis, reducing operating costs and yielding higher returns. That shift entails a wealth of productivity measures, too, like moving workflows from desktops and on-premise tools to cloud platforms, enabling tighter user access controls, remote collaboration, streamlined reporting, reduced accounts receivable cycle times and better decision-making.

ABOUT THE AUTHOR

Karin Bleiler is senior VP of revenue management at Symphony MediaAI

Layering the power of AI into these processes creates opportunities for networks to analyze distributor performance, consumer trends and countless other data points that can be leveraged to strengthen their financial position. As it accumulates data, AI can additionally predict future scenarios and help networks adapt to fast-moving trends among viewers, subscribers and other stakeholders in the media landscape.

Making Data More Valuable

AI can’t eliminate MVPD reporting delays, but it can dramatically increase the value of the data that is already available and that which will become available. The financial service teams responsible for revenue management have long been underserved by technology. We’re overdue for solutions that can analyze, predict and achieve the revenue outcomes that networks need to thrive in a competitive environment. That’s intelligence that can be the difference between growth and the alternative.