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

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.

► We propose the use of neural networks for automated media planning (ad targeting).
► We model the semantic and sentiment features of over 5000 historic B2B print ads.
► New advertisements are automatically matched to appropriate industries.
► Classification accuracy is high: area under ROC curve ⩾ 0.74 for all industries.
► Prominent sentiments, semantic categories, and words are found for each industry.