The Use of Big Data in Tourism Sales Forecasting

Abstract

Background. The explosion of big data (BD), automation, and machine learning have allowed contemporary businesses to better understand and predict human behavior. In scientific research big data have been widely used to study consum­er journey and opinions. One of the tools enabling forecasting of sales volume is the Bass diffusion model, which universal nature has been proven in many appli­cations in forecasting the sale of products belonging to various market segments. This article considers the use of BD as exogenous variables in the Bass model to predict the sales of tourist packages. Research aims. The purpose of the research is to assess the impact of using big data on improving the accuracy of forecasts for the sale of tourist packages. The Generalized Bass Model (GBM) has been thus expanded to include big data, which means that exogenous variables include: (1) marketer-generated content (MGC) and (2) user-generated content (UGC), including volume of web search and blog posts. Methodology. This article analyzes online news, blog posts and web search traf­fic volume related to tourist packages, and then integrates the information into the Bass model, treating it as part of the exogenous variables representing the mar­keting efforts of tour operators. It has been assumed that the volume of tour opera­tors’ web news is a proxy for content generated by marketers (MGC), while the vol­ume of blog posts and web search traffic constitute user-generated content (UGC). Key findings. The empirical analysis found that by incorporating big data into the Bass model provides more accurate prediction of tourist packages’ sales vol­ume. In addition, UGC (as an exogenous variable) is better at predicting sales volume than MGC. UGC is a fairly good tool explaining the level of interest and involvement of potential tourists. However, it has been shown that forecasting efficiency is different for blog posts and web search traffic volumes.