Customer Segmentation in Tourism Industry using Machine Learning Models

Abstract

Manual segmentation of customers consumes a lot of time, in some cases
months, even years to break down information and track down patterns in
it. Customer Segmentation done through machine learning models result in
quick identification of the ideal customers. This research paper focuses on
the tourism industry to target the right customers for their business. By
using the tourism dataset of customers, the research paper aims to produce
a better decision making visualization patterns through histogram, pie charts,
and heatmaps. Moreover, the use of Bayesian Inference Model, Descriptive
Basic Analysis and Linear Regression Analysis only on the important attributes
makes the decision making for the tourism business quite easy. Finally, the use
of clustering unsupervised machine learning models on the dataset generates
the primary, secondary, and tertiary group of customers that the company can
target for the sale of their tourism packages. Clustering models will generate clusters as the output where each cluster showcases a group of customers.
The clustering models employed under this research are K-means, DBSCAN,
Affinity Propagation, Mini Batch K-means and Optics Algorithm. The result
showed that the Mini Batch K-means algorithm had a better accuracy score
for the segmentation than other algorithms used.