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Unlocking the Value of Alternative Linear TV Currencies with Universal Forecasting
Spencer Lambert – Director, Product & Partnership Success, datafuelX
Matt Weinman – Senior Director of Product Management, Advanced Advertising Product, TelevisaUnivision
Matt Weinman (TelevisaUnivision) and Spencer Lambert (datafuelX) shared the methodology and results from testing TelevisaUnivision’s initiative that, with datafuelX’s technology, enabled their advertising partners to choose their preferred currency in forecasting both long- and short-term audiences for their programming. Implementation involved adjusting the business flow for multi-measurement sources but with each source ingested, validated and normalized to the tech standard separately. Forecasting incorporated a programming schedule imputation process which was then fed into a mixed model estimation (MME), and then optimized with linear granular data. Their model revealed gaps that they addressed with a variety of tactics including a ratings adjustment approach that updated network viewership trends, a proportional weight method for advanced audiences, recency weighting to avoid stale rate cards and relying less on forecasting viewers rather than scheduled content. The MME drove strong predictive forecasts and increased the use of long-tail inventory.
Key Takeaways
- Forecasting should always be done based on content.
- In reviewing the accuracy of predicting exact programming, the forecast to actuals had a 71% program match. In predicting programming type, there was 94% accuracy.
- Results for the long-term audience forecasts had 42% MAPE (mean absolute percentage error) improvements overall using big data sources.
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