From guesstimate to science: What you need to know about predictive analytics
Opinion
Conventional forecasting has become outdated amid a fast-moving, complex landscape. The growth of AI and machine learning means predictive analytics can anticipate audience and advertiser demand more effectively.
When it comes to forecasting, every media company would love to have a magic crystal ball.
Understanding how many people will watch a film, download and binge-watch a new series or tune in to watch their favourite local sport can mean the difference between successful ad sales or costly undersold inventory.
Short of magic, predictive analytics is proving to be a dramatic improvement in forecasting that has the potential to give companies the foresight they’ve been wanting. Understanding how to implement better forecasting is a critical step for those looking to take their business further.
Traditional forecasting: Fraught with inaccuracy
Nearly every media company has some forecasting in place and very smart analysts working to peer into the future as accurately as possible. No matter how cerebral analysts are, if they are working with traditional methods, they are dealing with issues that create problems.
Forecasts fall into several categories — namely, audience and advertiser forecasts. Media companies want to know how many people will watch a programme as well as what kind of people. They also want to know what demand they can expect for certain content and audiences from their advertiser clients.
Conventional approaches often involve manual analyses, siloed data sources and a heavy reliance on spreadsheets. While serviceable in slower-moving environments, these methods have become increasingly outdated as the media landscape grows more complex and competitive.
When forecasting depends on fragmented information and human calculation, the probability of error rises sharply. Today, new data comes in real time and forecasts that are even a few weeks old could be completely inaccurate. What’s more, it leaves some of the brightest people in the organisation spending their time manually aggregating information.
The risks are real: inaccurate forecasts can lead to unsold inventory, missed revenue opportunities or under-delivery on advertiser commitments — all of which can erode client trust and profitability while costing the company time.
Moving at the pace of business
Media companies need to adopt advanced forecasting tools to remain competitive and move at the pace of business today.
With the growth of AI and machine learning, predictive analytics offers media companies the ability to anticipate audience and advertiser demand more effectively. It easily processes vast amounts of historical and real-time data, uncovering patterns that would be impossible for analysts to detect on their own.
Predictive analytics moves forecasting from a slow and outdated guesstimate to an evidence-based science that saves time, offers more accuracy and can ingest much more data, creating a broader set of outputs.
Another major benefit is that it can quickly adjust based on new data. Unlike manual models, predictive analytics continuously learns and adapts. AI algorithms can also identify correlations between an array of variables, such as seasonal viewing trends, market shifts, economic indicators and even weather patterns.
As new data flows into the system, models update automatically, providing near-real-time insights that enable more confident decision-making.
Trust and credibility
Many media companies are already using these tools to optimise their business, including:
• Pricing strategies — ensuring they maximise yield without leaving money on the table
• Inventory allocation — matching the right spots to the right advertisers based on projected audience engagement
• Dynamic pricing — allowing media companies to adjust rates based on supply and demand in real time, much like airlines and hotels have done for years
Predictive analytics also strengthens stewardship and client relationships. By forecasting potential under-delivery risks before they materialise, companies can proactively manage campaigns, offering makegoods or alternative placements early rather than scrambling to fix issues after the fact.
This not only protects revenue but also reinforces trust and credibility with advertisers that increasingly expect a more accountable and transparent buying experience.
The move towards predictive analytics is not just an operational improvement — it is a strategic necessity for future growth. The advertising ecosystem is rapidly evolving, driven by automation, programmatic transactions and heightened expectations for performance measurement.
In this environment, success belong to organisations that can anticipate change, react faster than competitors and align their inventory and sales strategies with real-time market dynamics.
Dave Dembowski is senior vice-president of global sales and marketing at Operative