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

作者：Abrahams Alan S^*; Coupey Eloise; Zhong Eva X; Barkhi Reza; Manasantivongs Pete S

来源：Expert Systems with Applications, 2013, 40(8): 2777-2791.

DOI：10.1016/j.eswa.2012.10.068

• Media planning
• Advertising
• Targeting
• Neural networks

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.

• 出版日期2013-6-15
• 单位Virginia Tech; 美国弗吉尼亚理工大学(Virginia Tech)