Linear TV remains the primary way of reaching mass audiences effectively. According to Nielsen, in Q1 2017, Americans spent over 11 hours per day interacting with electronic media (including TV, radio and digital). The single largest piece of that was linear TV, accounting for nearly five hours.
So, if you’re a beer brand, why advertise to Men 21-34 when you can advertise to all beer drinkers 21+? Making TV advertising better using data is to everyone’s advantage: advertisers get a better return, media owners use their inventory more efficiently, and viewers get more relevant ads.
This is the first of a series of blogs around building time-series forecasting models. At clypd, we use forecasting models to help media owners and buyers forecast future TV audiences. A successful forecasting model depends on many factors. In this blog, we focus on algorithms, and how we tap into both modern Machine Learning (ML) models and classical statistics models to take advantage of what both offer.
The advancement of Machine Learning and Artificial Intelligence has been creating amazing stories everyday, from the AI assistant and self-driving vehicles to computer programs beating professional Go players. At clypd, we also have lots of success stories of using ML models. With the benefits of better accuracy and better automation, these ML models are an integral part of our forecasting models. At the same time, we also continue to find great value in “conventional” statistics models. So, instead of pitching Data Scientist vs. Statistician, let us look at ML models vs. statistical models, and how we can leverage both types of approaches in building a TV audience forecasting model.