Prediction and Optimal Scheduling of Advertisements in Linear Television

TL;DR

This paper addresses the challenge of scheduling advertisements in linear television by comparing viewership prediction methods and proposing an optimal scheduling approach to maximize revenue while meeting campaign impressions.

Contribution

It introduces a novel method for using viewership predictions to generate optimal TV advertising schedules, improving efficiency and revenue.

Findings

Viewership prediction methods vary in accuracy

Optimal scheduling can significantly increase advertising revenue

The proposed method outperforms baseline approaches

Abstract

Advertising is a crucial component of marketing and an important way for companies to raise awareness of goods and services in the marketplace. Advertising campaigns are designed to convey a marketing image or message to an audience of potential consumers and television commercials can be an effective way of transmitting these messages to a large audience. In order to meet the requirements for a typical advertising order, television content providers must provide advertisers with a predetermined number of "impressions" in the target demographic. However, because the number of impressions for a given program is not known a priori and because there are a limited number of time slots available for commercials, scheduling advertisements efficiently can be a challenging computational problem. In this case study, we compare a variety of methods for estimating future viewership patterns in a target demographic from past data. We also present a method for using those predictions to generate an optimal advertising schedule that satisfies campaign requirements while maximizing advertising revenue.