A Comparative Quantitative Analysis of Contemporary Big Data Clustering Algorithms for Market Segmentation in Hospitality Industry
This work addresses the need for effective data analysis in the hospitality industry to improve customer experience and revenue, but it is incremental as it reviews and compares existing methods without introducing new algorithms.
The paper conducted a comparative analysis of existing big data clustering algorithms for market segmentation in the hospitality industry, implementing and quantitatively comparing their performance across different scenarios to provide recommendations for hoteliers.
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.