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Research

Using Viewing Probabilities to Explore Advanced Audiences

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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 the availability of advanced audiences from a variety of data sources, more precision in reaching an audience is possible. By understanding how advanced audiences differ from traditional demographics, we stand to gain in advertising efficiency. 

As an example, a campaign for a new dog treat wants to target dog owners. The target could be created using MRI fusion data or a first-party match of a database of dog owners onto Nielsen respondents. Before creating a proposal for the campaign, clypd users could benefit from exploring how the Dog Owner segment has historically viewed – in particular, which networks and dayparts have higher audience indices for this target and are likely to be effective for ad placement. Read More

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

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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 it is his sporting career that gives us important insights into how performances vary and how even hall of famers struggle to maintain the same level of results year after year. And this struggle to maintain performance applies to many things, including TV shows.

Earvin ‘Magic’ Johnson burst onto the scene in 1979, becoming the first and only rookie to win the NBA Finals MVP award. He also holds the NBA record for assists per game (APG), averaging 11.2 across his career in regular season play. When we look at his APG stats by year, we see he improved through the first few years of his career, then hit a high point, culminating in a league-leading 13.1 APG in 1983-4. Heading into 1984-5 it would have been natural to think that he might continue improving, with an APG of 14 or even 15 as the logical next step. But it didn’t happen like that: Magic performed well in the following years, but he never got above 13 again. Read More

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

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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 maximizing advanced target reach (or at least, to have more control over it). Therefore, it is important to understand what reach optimization really is, why it is a difficult computational problem, and what approaches can be taken to approximate a solution in a timely fashion. In this post, I focus on the problem itself. Later on, I will focus on solution approaches.

The way we approach problems in life is informed by our past experiences. In my case, my take on the reach optimization problem starts with what happened at the beginning of the 2013-2014 academic year at the University of Delaware. I was a postdoctoral researcher there at the time, and all the community including faculty, staff, and students were very excited. Our excitement was not due to the return to classes, or because we would see friends again. We were excited because a Starbucks coffee shop had opened on campus! Read More

Using Algorithms to Improve TV Audience Forecasting

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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.

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