Travel demand analytics based customer-service decision model

Abstract

The tourism industry adds high value to the economy in Sri Lanka by attracting people from all around the world. Within the tourism industry, a large amount of data is collected with regards to user demographics and their purchases on a daily basis. Though less used in the tourism sector of developing countries like Sri Lanka, data analytics techniques can be utilized to understand the customer better and thereby add additional value to tourism organizations. This research uses customer records from a tourism operator to develop a model to predict the complexity of a customer’s requirements as well as group customers according to the complexity. While K-means clustering is used to group the customers as easy, moderate and complex customers, an ordinal logistic model is used to build the predictive model. The outputs obtained through the data analytic models proposed in this study will support the study organization to manage their customers efficiently and provide better attention to their needs.