  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.
