Audience targeting by B-to-B advertisement classification: A neural network approach

Authors:
Alan S. Abrahams;Eloise Coupey;Eva X. Zhong;Reza Barkhi;Pete S. Manasantivongs
Affiliations:
Business Information Technology Dept., 1007 Pamplin Hall, Virginia Tech, Blacksburg, VA 24061, USA;Department of Marketing, Pamplin College of Business, Virginia Tech, 2016 Pamplin Hall, Blacksburg, VA 24061, USA;Business Information Technology Dept., 1007 Pamplin Hall, Virginia Tech, Blacksburg, VA 24061, USA;Department of Accounting and Information Systems, Pamplin College of Business, Virginia Tech, 3007 Pamplin Hall, Blacksburg, VA 24061, USA;Melbourne Business School, 200 Leicester Street, Carlton VIC 3053, Australia
Venue:
Expert Systems with Applications: An International Journal
Year:
2013

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Abstract

As marketing communications proliferate, the ability to target the right audience for a message is of ever-increasing importance. Audience targeting practices for mass media, both in research and in industry, have tended to emphasize demographics, behavior, and other characteristics of customer groups as the bases for matching communications to audiences. These approaches overlook the opportunity to leverage the nature of advertising content, by automatically matching advertisement content to appropriate media channels and target audience. We model the semantic and sentiment content of advertisements with 103 variables. Based on these variables, a neural network classifier is used to assign advertisements to groups that represent different media channels. In its ability to classify unseen advertisements, the model outperforms the classification result generated by a random model, by 100-300%. This method also enables us to identify and describe divergent advertisement characteristics, by industry.