October 9, 2019

MRC, Clients, and clypd’s Cross-Platform Solution

On September 4, 2019 the Media Rating Council (MRC) released its Cross-Media Audience Measurement Standards. In the words of the MRC, this is an “important milestone in MRC’s […]
November 27, 2018

Using Viewing Probabilities to Explore Advanced Audiences

Because age and gender demographics have traditionally been used by the advertising industry, viewing patterns have been well established and are mostly consistent over time. With […]
September 17, 2018

Regression to the Mean: What is it and Why Does it Matter for TV Advertising?

What can Magic Johnson teach us about advanced TV advertising? He has appeared in many commercials over the years (including AT&T, Coca Cola and MasterCard), but […]
August 29, 2018

Maximizing Reach in Linear TV, or How to Choose the Location of a New Coffee Shop

As audience-based linear TV campaigns become more common, we are seeing that the default goal of campaign optimization is shifting from maximizing advanced target impressions to […]
January 10, 2018

Using Algorithms to Improve TV Audience Forecasting

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.