Prediction of customer response in online advertising

Abstract

Typically, businesses might considerably benefit from user behavior when developing their advertising tactics. Click-through Rate (CTR) is one of the most efficient metrics that provide insights into advertising effectiveness. Moreover, CTR analysis is also used to develop advertising tactics for online marketing. Since a person's lifestyle has changed from offline to online during the COVID-19 pandemic, online-to-offline (O2O) commerce has emerged. O2O commerce is an efficient business model that links offline business activities with online platforms, e.g., Facebook ads. In online situations, CTR analysis can predict the state or fact of something's being likely, the probability that something on an online review and website advertisements will be clicked. Firstly, this paper considers a problem of customer response in online advertising based on CTR prediction. Afterward, a research framework for CTR prediction based on customer response in online advertising using regression models, i.e. Linear regression, support vector regression, multi-layer perceptron regression, and random forest regression, is proposed. Such methods only use certain parameters for learning and ignore temporal variance and changes in user behavior. The experiments evaluate the regression model’s accuracy using R-squared. The experimental results are visualized on scatter plots to describe the relationship between the number of predicted likes and actual likes. The R- squared of the random forest regression model is higher than the others, so the random forest regression model outperforms the other models in analyzing customer response in a tech company's Facebook ads.