Yield optimization

On the sell side, responsible for data analysis to determine which inventory and products are performing well; they make optimizations to improve performance and grow revenue.

Yield optimization for TV advertising is a holistic effort to increase overall portfolio value by delivering the right inventory at the right time and price to the right viewer. It represents a new approach to collaboration within seller sales organizations, and it discourages efforts to improve the yield of any one campaign or ad product that fail to account for the impact on the portfolio as a whole.

To be successful, TV advertising yield optimization requires the dynamic allocation of inventory into buckets by campaign and schedule. Pricing must also be fluid to ensure that every deal is optimized within the larger business portfolio.

The challenge of portfolio-level management becomes greater as premium, targeted or advanced advertising products are introduced into the TV and digital ecosystems. This makes TC advertising yield optimization increasingly difficult to execute with confidence—and nearly impossible with only Excel in your toolkit.

The only way media companies can contend with these challenges effectively and efficiently is with the help of technology. Today, media companies can use data science and AI to better predict—and continually improve—forecasting and optimization accuracy.

When combined with data science techniques for more traditional forecasting models, AI also lets media companies account for the impact of trends, seasonality, adjacency and competitive programming. As a result, it can suppress the influence of anomalies in historical input data on a continual basis, increasing overall yield.

The bottom line is that sellers of TV advertising should be actively managing their individual “assets” (i.e., their ad products) to ensure they’re optimizing the value of their portfolio, similar to investors. This calls for continuously assessing the relative contribution of each ad product category to total revenue and rebalancing available inventory allocation to ensure that clients are well-served and the greatest total revenue is achieved.

This approach is fundamentally different—and much more subtle—than just selling every tranche of inventory for the highest price it will fetch. It’s predicated on the idea of hedging bets and not going all-in on shiny objects by default when demand is high.

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