A Hybrid Method for Customer Segmentation in Saudi Arabia Restaurants Using Clustering, Neural Networks and Optimization Learning Techniques

Dining out is one of the biggest expenditures for travelers worldwide and is essential for tourism-dependent destinations. Market segmentation gives industries the potential to classify similar customers and categorize their preferred target markets to ensure marketing expenses' operative management. It has been a practical approach for business improvement in tourism and hospitality. Big data are fundamentally changing the management of the hospitality sector and the relationship between the customer and business by simplifying the decision-making process based on large amounts of data. The data provided in social media have played an important role in customer segmentation. In fact, the data provided by the customers in social media have been a valuable source for decision-makers to precisely discover the customers' satisfaction dimensions on their services. Therefore, there is a need for the development of data-driven approaches for social data analysis for customers segmentation. This research aims to develop a new data-driven approach to reveal customers' satisfaction in restaurants. Specifically, k-means and Artificial Neural Network (ANN) with the aid of the Particle Swarm Optimization (PSO) technique are, respectively, used in data clustering and prediction tasks. In this research, the data of customers on the service quality of restaurants are collected from the TripAdvisor platform. The results of the data analysis are provided. We evaluate the prediction model through a set of evaluation metrics, Mean Squared Error (MSE) and coefficient of determination (R(2)), compared with the other prediction approaches. The results showed that k-means-PSO-ANN (MSE = 0.09847; R(2) = 0.98764) has outperformed other methods. The current study demonstrates that the use of online review data for customer segmentation can be an effective way in the restaurant industry in relation to the traditional data analysis approaches.