Artificial Intelligence Marketing: An application of a novel Lightly Trained Support Vector Data Description
Abstract Artificial Intelligence Marketingis a form of direct marketing which leverages the techniques of database marketing along with the concepts and models of AI such as machine learning. Direct marketing can increase marketing efficiency by directing the efforts specifically towards the right customer.Even though the popularity of direct marketing campaigns is increasing over time, the selection of contacts is becoming more rigorous, and it has become essential to reduce the number of contacts to do, due to economic factors as well as consumer privacy issues. Direct marketing analytics requires algorithms that are efficient and scalable. In this study we explore the use ofSupport Vector Data Description(SVDD) to facilitate the selection of contacts. Support Vector based classification methods have been proved to be successful in a variety of applications; but they tend to take more computing time when dealing with large scale data. Here we apply a novel low complexity SVDD method for direct marketing. Experiments on a real world dataset related to a direct marketing campaign have demonstrated that SVDD based methods can provide promising accuracies when compared to other approaches. Also, the accuracy achieved by the proposed low complexity method is comparable with theclassical SVDD and it has huge advantage over it in terms of the execution time.