Predicting Performance of Text Assets Across Responsive Search Ads

Abstract

Dynamic ads that respond to search inputs and automatically combine text assets to maximize performance are now commonplace. In addition to extant needs in traditional ad creation, automated generation of text assets can also greatly benefit from having some foreknowledge of how outputs might perform. This paper describes the development of such a performance prediction model, including an application of Kolmogorov-Arnold Networks; the best model overall achieved Spearman's rank correlation coefficient of 0.41 on the validation dataset using asset texts alone.