A Comparative Quantitative Analysis of Contemporary Big Data Clustering Algorithms for Market Segmentation in Hospitality Industry

TL;DR

This paper reviews and compares various big data clustering algorithms for market segmentation in the hospitality industry, providing insights to help hoteliers select suitable methods to enhance customer experience and revenue.

Contribution

It offers a comprehensive literature review, implements multiple algorithms, and provides a quantitative comparison and recommendations for their use in hospitality market segmentation.

Findings

Different algorithms perform variably across scenarios

Some algorithms are more suitable for high-velocity data

Recommendations improve clustering effectiveness in hospitality

Abstract

The hospitality industry is one of the data-rich industries that receives huge Volumes of data streaming at high Velocity with considerably Variety, Veracity, and Variability. These properties make the data analysis in the hospitality industry a big data problem. Meeting the customers' expectations is a key factor in the hospitality industry to grasp the customers' loyalty. To achieve this goal, marketing professionals in this industry actively look for ways to utilize their data in the best possible manner and advance their data analytic solutions, such as identifying a unique market segmentation clustering and developing a recommendation system. In this paper, we present a comprehensive literature review of existing big data clustering algorithms and their advantages and disadvantages for various use cases. We implement the existing big data clustering algorithms and provide a quantitative comparison of the performance of different clustering algorithms for different scenarios. We also present our insights and recommendations regarding the suitability of different big data clustering algorithms for different use cases. These recommendations will be helpful for hoteliers in selecting the appropriate market segmentation clustering algorithm for different clustering datasets to improve the customer experience and maximize the hotel revenue.