Computational television advertising

Suhrid Balakrishnan, Sumit Chopra, David Applegate, Simon Urbanek

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Ever wonder why that Kia Ad ran during Iron Chef? Traditional advertising methodology on television is a fascinating mix of marketing, branding, measurement, and predictive modeling. While still a robust business, it is at risk with the recent growth of online and time-shifted (recorded) television. A particular issue is that traditional methods for television advertising are far less efficient than their counterparts in the online world which employ highly sophisticated computational techniques. This paper formalizes an approach to eliminate some of these inefficiencies by recasting the process of television advertising media campaign generation in a computational framework. We describe efficient mathematical approaches to solve for the task of finding optimal campaigns for specific target audiences. In two case studies, our campaigns report gains in key operational metrics of up to 56% compared to campaigns generated by traditional methods.

Original languageEnglish (US)
Title of host publicationProceedings - 12th IEEE International Conference on Data Mining, ICDM 2012
Pages71-80
Number of pages10
DOIs
StatePublished - 2012
Event12th IEEE International Conference on Data Mining, ICDM 2012 - Brussels, Belgium
Duration: Dec 10 2012Dec 13 2012

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Conference

Conference12th IEEE International Conference on Data Mining, ICDM 2012
Country/TerritoryBelgium
CityBrussels
Period12/10/1212/13/12

Keywords

  • Computational advertising
  • Media campaign generation
  • Optimization
  • Television advertising

ASJC Scopus subject areas

  • General Engineering

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