Short-term and seasonal time series models for online marketing campaigns

Abstract

Marketing companies use the market response to price products, determine advertising expenditures, forecast sales or prepare and test the effectiveness of various marketing plans and campaigns. Predictions of future traffic for online marketing campaigns can be based on data analysis and market response models. Mathematical models have become the main tools for marketing decision-making. The main goal of this paper is to describe and show how to use behavioral modelling of potential customers in online marketing campaigns. In addition to the basic ARMA model for short-term website traffic forecasting, we evaluate the TBATS and Prophet models. Both models comprehensively capture seasonal and holiday fluctuations. More specifically we show how time series modelling can be incorporated into the evaluation of online marketing campaign traffic forecasts for marketing agency clients.