Determination of Customer Satisfaction using Improved K-means algorithm

A blockchain-based evaluation approach to analyse customer satisfaction using AI techniques

Due to technological advancements and consumer demands, online shopping creates new features and adapts to new standards. A robust customer satisfaction prediction model concerning trust and privacy platforms can encourage an organization to make better decisions about its service and quality. This study presented an approach to predict consumer satisfaction using the blockchain-based framework combining the Multi-Dimensional Naive Bayes-K Nearest Neighbor (MDNB-KNN) and the Multi-Objective Logistic Particle Swarm Optimization Algorithm (MOL-PSOA). A regression model is employed to quantify the impact of various production factors on customer satisfaction. The proposed method yields better levels of measurement for customer satisfaction (98%), accuracy (95%), necessary time (60%), precision (95%), and recall (95%) compared to existing studies. Measuring consumer satisfaction with a trustworthy platform facilitates to development of the conceptual and practical distinctions influencing customers' purchasing decisions.

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.

Customer Satisfaction and Behaviour at Retail Outlets: an Adaptive Fuzzy Regression Model with LINGO Based Analysis

Increasing rivalry for-profit or non-profit is pushing companies to devote more and more attention to pleasing consumers with excellent quality customer services. This study aims to develop a model to analyze customer behavior in a retail store and provide accurate inference for decision making. Another critical objective for this research work is the adaptation of the faceted form of neuro-response, which is substituted by the Adaptive Fuzzy Logistic Regression Model (AFLRM). AFLRM has resulting benefits over Neuro-surface and Mean Demand Heuristic methods. A sample of 100 customers who visited or walked in the retails was used as a sample. Other than neuro-response surfaces (NRSM) and The Mean Demand Heuristic models (MDSM), the present study has accustomed a generalized form known as Adaptive Fuzzy Linear Regression Model (AFLRM) to deliver the benchmark for former models and give the highest level of accuracy for future behavior of a customer. LINGO based Markovian analysis has also been used with the above model to understand the behavior of the system under study. The significance of service and product attributes is implicitly derived via the fuzzy regression model for customer satisfaction measurement. It is observed that the critical gap between the quality of product and services and Customer Satisfaction is Product/Service Satisfaction, Motivation and Buying Experience, and Credibility and Security. The authors’ finding indicates that the effort of listening to the customer's voice should be more critical. Result analysis based on computational results concerning the questionnaire for measuring the customer behavior and the system validates the model under study. Appropriate, useful with reliable action plans for every critical product and service aspect can be developed by applying the adaptive regression methodology to control the quality of service or managing customer satisfaction, thereby providing executives with a competitive gain. Also explored the behavior of the system, i.e., whether the customer will move to the new retail outlets or they will remain in the same state by using the LINGO based software program model. Keywords: heuristic, fuzzy, Markov process, retail customer, customer behavior, LINGO, ISM.