Customer Satisfaction Analysis Research Articles

Optimizing Online Shopping: Leveraging Llama for Product Review Summarization

This article highlights a unique "Customer Satisfaction Analysis" module and presents an innovative online commodity search system. The system's goal is to rapidly retrieve product data such as search results, product details, and reviews—through e-commerce API endpoints and display it in an intuitive user interface. The "Customer Satisfaction Analysis" section is particularly noteworthy as it utilizes sophisticated algorithms to examine customer feedback and produce detailed summaries that accentuate the salient features of user opinion. This methodology endows prospective purchasers with a more profound and perceptive comprehension of product attributes and client contentment. Sentiment analysis is integrated into the search process, allowing consumers to make well-informed buying selections. Our approach improves consumers' capacity to make decisions by synthesizing product features and user feedback. This allows consumers to make better educated choices and ultimately have a more fulfilling online shopping experience. Key Words: API, Large Language Model , Llama, Search System

Optimizing Business-to-Business Customer Satisfaction Analysis through Advanced Two-Stage Clustering: Insights from Industrial Parks

Traditional research on customer satisfaction (CS) estimation has focused on the business-to-customer (BTC) business mode. Customers in the BTC mode have been assumed to be familiar with the full range of services or products and to be able to make estimations of their CS. However, in the business-to-business (BTB) mode, diverse services have often been required and provided. It may be difficult to find members who have experience with all kinds of services or to generate common CS estimation results supported by different members. In this study, the difference between BTC and BTB was verified using structural equation modeling (SEM), and a model of CS estimation was developed with respect to BTB. The empirical results show that perceived service quality has no direct impact on enterprise satisfaction, indicating that traditional models are limited. A two-stage clustering algorithm was adopted to optimize the traditional CS evaluation model based on SEM, i.e., (1) K-nearest neighbor (KNN) classification and (2) density-based spatial clustering of applications with noise (DBSCAN). In order to verify the feasibility of the proposed model, CS with respect to six industrial parks was estimated empirically. The results show that the proposed model can improve the results of CS estimation compared with the results obtained using traditional methods. During the clustering process, each park generated and eliminated a certain number of noise points to optimize the satisfaction evaluation results. Specifically, park A generated and eliminated seven noise points, while park C generated and eliminated five noise points. The results of the satisfaction evaluation of each park obtained using the proposed model are more realistic, i.e., park A > park B > park C > park E > park D > park F. The proposed model extends the existing research on CS estimation in theory and can support applications in the BTB business mode.

Effect of the Management Accounting on the Financial Performance of Small and Medium Enterprises in Rwanda: A Case of Small and Medium Entreprises in Gasabo District

Management accounting provides an excellent opportunity for companies to compete in the market by offering superior products and services at reasonable prices to customers. The general objective of this study was to study the effects of management accounting practices on the Financial Performance of Small and Medium Enterprises in Gasabo district, Rwanda. This study adopted a correlation study design. The target population for this study was the 171 small and medium enterprises in Gasabo district Stratified random sampling method was applied to come up with the sample size, since the population in different Small and Medium Enterprises was considered heterogeneous, implying that a simple random sample is unrepresentative. The study therefore involved 171 Small and Medium Enterprises in Gasabo district. The study collected primary data from the respondents. The data collected was both quantitative and qualitative. Qualitative data is descriptive and non-numerical, while quantitative data is numerical. The study found that Budgeting was the most widely used management accounting practices, including budgeting for planning, activity-based budgeting, flexible analysis, zero-based budgeting, and budgeting for controlling costs. costing systems were the second most widely used management accounting practice, including separation of variable, incremental, and fixed costs; plant-wide overhead rate; activity-based costing; target costing; and cost of quality. Information for decision making was also used by respondents, including stock control models, customer satisfaction analysis reports, product profitability analysis reports, risk analysis reports, and investment analysis reports. Strategic analysis was also highly used by respondents. The study further concludes that the most important elements of management accounting practices amongst the Small and Medium Enterprises in Gasabo district are; the management accounting function identifies key factors that influence Financial Performance and management accounting practices have increased return on equity (ROE), which is a measure of how profitable a company is for its shareholders. This is because management accounting practices help companies to identify and improve risky areas, which can lead to increased profits. This study recommends the creation and enhancement of awareness among firms of the importance of Information for decision making practices as this is the most highly used management accounting practice amongst the Small and Medium Enterprises in Gasabo district. Keywords: Management Accounting Practices, Costing Systems, Budgeting, Information for Decision Making, Strategic Analysis, Financial Performance, Small And Medium Enterprises

Analysis Of Customer Satisfaction With Packaged Drinking Water Products Using Service Quality (SERVQUAL) And Importance Performance Analysis (IPA) Methods

PT. XYZ is a bottled mineral water manufacturer in Gresik, striving to deliver customer satisfaction and stay competitive. This study aims to quantify consumer satisfaction with PT. XYZ's product quality. We collected data through a questionnaire and analyzed it using the Servqual method and Importance Performance Analysis (IPA). The questionnaire included nine quality attributes, categorized into five dimensions. IPA was utilized to prioritize service quality attributes. The Servqual method revealed a negative gap, indicating suboptimal service. The Cartesian IPA diagram highlighted two critical areas for improvement: enhancing staff interaction during service and improving personnel demeanor, specifically their politeness, and friendliness. These improvements are vital as they align with consumer expectations and are essential for enhancing overall service quality at PT. XYZ. Keywords: Service Quality, Importance Performance Analysis (IPA), SERVQUAL

An improved IPA approach driven by big data and its application to customer satisfaction research of energy-saving appliance

Against the backdrop of increasing global demand for reducing greenhouse gas emissions, promoting the use of energy-saving and environmentally friendly products has become a crucial aspect of low-carbon economic development. Customer satisfaction plays a vital role in the promotion of these products. To address the challenges of dealing with big data in the conventional customer satisfaction analysis tool, Importance Performance Analysis (IPA), a machine learning-based method is proposed to improve IPA. Firstly, the Latent Dirichlet Allocation (LDA) model is used to capture users’ opinions on different product topics. Then, the Support Vector Machine (SVM) and Random Forest (RF) algorithms are employed respectively to assess the satisfaction and importance of product attributes, enabling an objective measurement of customer satisfaction and adapting to the current trend of big data. The proposed method is applied to the analysis of water heater satisfaction on the JD platform, obtaining satisfaction levels for 10 topics. The research findings demonstrate that the improved IPA method based on SVM-RF effectively explores customer satisfaction and can provide some improvement strategies for platform managers and manufacturers.

Differentiation Strategy to Improve the Level of Customer Satisfaction. Case: Industrial Process Company, Peru.

Today the experience we provide to customers is very important for the company, a good shopping experience allows for a happy customer, generate benefits for the company such as increased sales. Customer satisfaction is the way in which we have been treated as consumers when making a purchase, this depends both on the treatment and on the product that the consumer receives. Maintain that "Multiple studies indicate a significant relationship between the perception of the quality of services and customer satisfaction." On the other hand, when a customer is in a state of "customer satisfaction" they tend to repeat their purchasing behavior, generating a state of "customer loyalty". Therefore, customer satisfaction and loyalty maintain a strong link. Getting customers to feel happy with the company's products and for them to return to make new purchases requires time and dedication, since the sale does not end when the product is delivered, quite the contrary, the customer must be followed up. after the sale, this will allow the customer to be satisfied and expand that list of customers in the company. As indicated above, services or products are intangible entities, for which it is important to know customer satisfaction in order to analyze them. The general objective of the research was to propose a Differentiation Strategy to improve the level of Customer Satisfaction in the Industrial Processes Company. The research was descriptive and the non-experimental design, descriptive cross-sectional, it was used as a technique to the survey and as an instrument the questionnaire that measured Customer Satisfaction through the Likert Scale. Crombach's alpha test was used to determine the validity and reliability of the instrument. Likewise, an interview guide was applied to the business administrator to carry out the qualitative analysis of customer satisfaction. The theoretical precedents that support the present investigation are based on the theories that, according to Lovelock and Patterson, maintain that service is any act, performance or experience that one party can offer to another and that it is essentially intangible and does not result in ownership. you're welcome, but nevertheless create value for the recipient. Its production may or may not be linked to a physical product. essful in any business. The results allowed to know the level of customer satisfaction, which is at a high level which is equivalent to 37.71%, which means that the company has acceptable strengths regarding the level of customer satisfaction such as variety of products, prices according to the market, among others, it was also possible to design the differentiation strategy, validated through the expert judgment technique.

Realtime Facial Emotion Detection

Abstract: A crucial component of communication is facial emotion through facial expression. This makes the problem of evaluating human emotions via computer systems an intriguing one that has attracted increasing amounts of attention in the past few decades. The main connection is that facial expression recognition could be used in a variety of industries, including virtual reality, video games, HCI, and customer satisfaction analysis. Face detection, facial feature extraction, and expression categorization are the final three basic steps in the emotions determination (recognition process). The Russell circular approach, which has up to 24 emotional expressions, and Plutchik's Wheel of Emotions are two other categorization schemes that are frequently encountered. Ekman's classification, which has six emotional expressions (or seven, if it is a neutral expression), is the most common.

ANALISIS KEPUASAN PENGGUNA DENGAN SISTEM CHATBOT PEMESANAN MAKANAN DAN MINUMAN VIA TELEGRAM DI RESTORAN

In the era of technological advancement, the utilization of Chatbots for food and beverage menu ordering through the Telegram Bot platform emerges as an intriguing alternative to enhance customer experiences in the Food and Beverage industry. This research aims to analyze user satisfaction with the Chatbot implementation using the Heuristic method. In this implementation, the Chatbot is supported by Natural Language Processing (NLP) with a focus on customer satisfaction analysis. The Heuristic method serves as the primary evaluation tool to measure aspects such as service speed, user-friendliness, and information clarity. Analysis results reveal positive user responses to the Chatbot, indicating a high level of satisfaction in system usage. These findings support the notion that employing Chatbots with the Heuristic method can deliver a satisfying experience for customers in food and beverage menu ordering. Practical implications of this research can assist restaurants in enhancing service and efficiency through Chatbot implementation that considers user satisfaction aspects measured by the Heuristic method.

Analysis of BRI UNAS Unit Customer Satisfaction in Using Brimo to Improve Service Quality (National University Student Study)

This research adopts a qualitative approach to analyze the satisfaction of Bank Rakyat Indonesia (BRI) National University Unit (UNAS) customers in using BRImo digital banking services, with a focus on National University students. The research method involves observing students as the main data collection instrument. This research aims to gain in-depth insight into customer perceptions and experiences regarding the use of BRImo and to identify the factors that influence their satisfaction. The qualitative data collected was then analyzed using a descriptive approach, paying attention to general patterns and interesting findings that emerged from student responses. The research results show that factors such as ease of use, transaction speed, security, and availability of additional features have a significant impact on customer satisfaction. Apart from that, qualitative aspects such as interaction experiences with the application and perceptions of service quality also shape positive or negative views of BRImo

Analysis of customer satisfaction of Kebun Refugia Magetan agrotourism in Magetan

Analysis of customer satisfaction of Kebun Refugia Magetan agrotourism in Magetan

Analysis of Customer Satisfaction with Seller of Aglaonema Flower Plants

Ornamental plants are a type of plant that provides additional aesthetic value. One of the ornamental plants that is currently trending and popular with consumers is the aglaonema flower. Humans always need the aglaonema flower ornamental plant to fulfill consumer satisfaction. The research aims to determine the level of consumer satisfaction with the seller of aglaonema ornamental plants in Tulus Rejo Village, Pekalongan District, East Lampung Regency. The research location is in Rejo Ikhlas Village, Pekalongan District, East Lampung Regency with the consideration that this area is a production center for sales of aglaonema flower ornamental plants. The research respondents were 30 consumers who bought Aglaonema flowers from within and outside the Pekalongan area, determined by the census method. The sampling technique was accidental sampling technique. The analysis used is Customer Satisfaction Index (CSI) and Importance Performance Analysis (IPA). The results of the research on the sale of decorative aglaonema flower plants in Tulus Rejo Village, Pekalongan District, East Lampung Regency are in the "satisfied" category, this can be seen from the calculation CSI of 71.20 percent, which means consumers are satisfied with the seller of aglaonema ornamental plants on the attributes of aglaonema ornamental plants having good quality and quality so that they succeed in satisfying consumers of aglaonema ornamental plants.

A hybrid neural network model based on transfer learning for Arabic sentiment analysis of customer satisfaction

AbstractSentiment analysis, a method used to classify textual content into positive, negative, or neutral sentiments, is commonly applied to data from social media platforms. Arabic, an official language of the United Nations, presents unique challenges for sentiment analysis due to its complex morphology and dialectal diversity. Compared to English, research on Arabic sentiment analysis is relatively scarce. Transfer learning, which applies the knowledge learned from one domain to another, can address the limitations of training time and computational resources. However, the development of transfer learning for Arabic sentiment analysis is still underdeveloped. In this study, we develop a new hybrid model, RNN‐BiLSTM, which merges recurrent neural networks (RNN) and bidirectional long short‐term memory (BiLSTM) networks. We used Arabic bidirectional encoder representations from transformers (AraBERT), a state‐of‐the‐art Arabic language pre‐trained transformer‐based model, to generate word‐embedding vectors. The RNN‐BiLSTM model integrates the strengths of RNN and BiLSTM, including the ability to learn sequential dependencies and bidirectional context. We trained the RNN‐BiLSTM model on the source domain, specifically the Arabic reviews dataset (ARD). The RNN‐BiLSTM model outperforms the RNN and BiLSTM models with default parameters, achieving an accuracy of 95.75%. We further applied transfer learning to the RNN‐BiLSTM model by fine‐tuning its parameters using random search. We compared the performance of the fine‐tuned RNN‐BiLSTM model with the RNN and BiLSTM models on two target domain datasets: ASTD and Aracust. The results showed that the fine‐tuned RNN‐BiLSTM model is more effective for transfer learning, achieving an accuracy of 95.44% and 96.19% on the ASTD and Aracust datasets, respectively.

Feature analysis and evaluation of hotel customer satisfaction based on decision tree

The global hotel industry is in the process of rebounding from the impacts of the COVID-19 pandemic. Despite a surge in demand, traveler and customer satisfaction are experiencing a decline. To ensure the prosperity of hotels during this recovery phase, it becomes imperative to pinpoint the pivotal factors that dictate customer satisfaction. This research employs a decision tree algorithm for in-depth data analysis, mathematical modeling, and feature selection, all aimed at unveiling the determinants of passenger contentment. The model is subsequently refined and streamlined through the careful selection of the most impactful features. This study successfully identifies the paramount components that exert influence on customer satisfaction, with notable emphasis on factors such as hotel location, pricing, and booking procedures. Possessing insight into these determinants empowers hotels to concentrate their efforts on specific areas of enhancement, thus elevating the overall level of customer satisfaction. The implications of these findings hold practical significance for hotels, offering invaluable insights for the enhancement of service quality and customer contentment. Addressing the highlighted factors allows hotels to optimize operational efficiency, fortify their competitive edge, and secure enduring success in the post-pandemic recovery era. Moreover, by grasping customer preferences and augmenting satisfaction, hotels can safeguard their brand reputation and foster unwavering customer loyalty.

Analysis of Customer Satisfaction Using the End User Computing Satisfaction Approach Toward Users of Digital Post-Payment Application Gopay Later

The purpose of this study is to identify and quantify the factors that affect consumers' satisfaction when they use pay later. This study uses a quantitative methodology. Researchers used questionnaires to get information. To pick 100 respondents from the research population, which was made up of pay later users who were actively using the service, the researchers used purposive selection approaches. Partial least squares structural equation modeling is the data analysis technique used in this study (PLS-SEM). The research findings indicated that there was a high degree of goodness and excellence in the data quality and the data outcomes acquired through the outer and inner models. All hypotheses tested in this study were accepted, indicating that the variables measured, such as content, accuracy, format, and ease of use, have a significant effect on the level of end-user satisfaction. Testing of the model structure also confirmed the acceptance of all hypotheses, with accuracy proven to have the greatest influence on ease of use. This research shows that the accuracy aspect of the use of a system or product has the most significant impact on user satisfaction. However, it is important to note that all variables, including content, format, and ease of use, also positively contribute and cannot be ignored. Therefore, even though accuracy has the greatest influence, the presence of all these factors remains an important aspect of analyzing customer satisfaction in this research.

Membangun Customer Satisfaction Melalui Service Quality Dan Customer Trust Di Ruang Rawat Inap Amarilis 2 RSUD Tugurejo Semarang

This study aims to measure the model of achieving customer satisfaction through service quality and influenced by customer trust. Data collected from 100 inpatients of Amarilis 2 room of RSUD Tugurejo Semarang. processed using Structural Equation Model-Partial Least Square (SEM-PLS) modeling technique. The results showed that service quality positively and significantly affects customer trust of inpatients of Amarilis 2 room of RSUD Tugurejo Semarang. Customer trust and service quality variables have a significant positive effect on customer satisfaction of inpatients of Amarilis 2 room at Tugurejo Semarang Hospital. This research contributes to the management of Amarilis 2 room of Tugurejo Hospital Semarang by providing appropriate strategies to understand customer satisfaction of inpatients of Amarilis 2 room of Tugurejo Hospital Semarang. This strategy is then expected to boost the increase of customer satisfaction of inpatients of Amarilis 2 room of RSUD Tugurejo Semarang. The novelty of this study lies in the fact that it is the first to empirically use the service quality model through excellent service by integrating customer trust to analyze customer satisfaction of inpatients of Amarilis 2 room of RSUD Tugurejo Semarang.

Topic Sentiment Using Logistic Regression and Latent Dirichlet Allocation as a Customer Satisfaction Analysis Model

Buying and selling goods now is more interesting through e-commerce or marketplaces because of the ease of carrying out online transactions. Each transaction usually generates a response from the customer. The transaction response on the Shopee platform is still in paragraph form and needs to be more specific. Therefore, this research aims to build a model analysis of customer satisfaction using the best algorithm between support vector machine (SVM), random forest, and logistic regression. This research method uses sentiment classification with logistic regression because the logistic regression algorithm has the best accuracy, with an accuracy of 90.5. Meanwhile, the SVM algorithm achieved an accuracy of 90.4, and random forest reached 90.2. The three algorithms were tested three times, splitting data train:test at 80:20, 70:30, and 60:40. The best results were obtained by splitting data at 60:40. The best model is used to predict data without labels. The prediction produces 12,844 positive sentiment comment data, 112 negative sentiment comment data, and 70 neutral sentiment comment data. The results of this research continued to topic modeling using latent dirichlet allocation (LDA) to generate a trending topic of customer satisfaction on sales products. Implications of discussing each trend topic can be used as a reference for improving products and services, especially in communicating with customers.

LLMs in e-commerce: A comparative analysis of GPT and LLaMA models in product review evaluation

E-commerce has witnessed remarkable growth, especially following the easing of COVID-19 restrictions. Many people, who were initially hesitant about online shopping, have now embraced it, while existing online shoppers increasingly prefer the convenience of e-commerce. This surge in e-commerce has prompted the implementation of automated customer service processes, incorporating innovations such as chatbots and AI-driven sales. Despite this growth, customer satisfaction remains vital for E-commerce sustainability. Data scientists have made progress in utilizing machine learning to assess satisfaction levels but struggled to understand emotions within product reviews’ context. The recent AI revolution, marked by the release of powerful Large Language Models (LLMs) to the public, has brought us closer than ever before to understanding customer sentiment. This study aims to illustrate the effectiveness of LLMs by conducting a comparative analysis of two cutting-edge LLMs, GPT-3.5 and LLaMA-2, along with two additional Natural Language Process (NLP) models, BERT and RoBERTa. We evaluate the performance of these models before and after fine-tuning them specifically for product review sentiment analysis. The primary objective of this research is to determine if these specific LLMs, could contribute to understanding customer satisfaction within the context of an e-commerce environment. By comparing the effectiveness of these models, we aim to uncover insights into the potential impact of LLMs on customer satisfaction analysis and enhance our understanding of their capabilities in this particular context.

Customer satisfaction analysis with Saudi Arabia mobile banking apps: a hybrid approach using text mining and predictive learning techniques

Customer satisfaction analysis with Saudi Arabia mobile banking apps: a hybrid approach using text mining and predictive learning techniques

Analisis Kepuasan Menggunakan Aplikasi DANA dengan Menerapkan Metode TAM

Currently, technology has been present in helping to facilitate human work so that human work is easier and more effective. The role of technology in various aspects of activities can be understood. Technology has penetrated into the financial sector by using intelligent systems. One of the smart system technologies is the DANA application. The DANA application is an abbreviation of Dompet Digital Indonesia which is based on a mobile application. The various conveniences offered, from buying food, topping up credit, and paying bills have made many people use them, especially the younger generation. Sometimes, we want to measure the level of user satisfaction of the DANA application so that we can find out how easy and friendly the DANA application is. For this reason, a research is needed to determine customer satisfaction or the user of the DANA application. The method that can be used to determine the level of customer or user satisfaction is to apply the technology acceptance model (TAM) method. This model is a model of acceptance of information technology systems that will be used by users. This research is expected to provide the optimal solution in analyzing customer satisfaction or DANA application users

The Influence of Social Media Marketing and Corporate Image on Customer Loyalty is Mediated by Customer Satisfaction (Study of 5 Star Hotel Customers in Malang City)

This research aims to test and analyze the influence of social media marketing on customer loyalty, test and analyze the influence of corporate image on customer loyalty, test and analyze the influence of social media marketing on customer satisfaction, test and analyze the influence of corporate image on customer satisfaction, test and analyze the influence customer satisfaction on customer loyalty, testing and analyzing customer satisfaction mediates the influence of social media marketing on customer loyalty, and testing and analyzing customer satisfaction mediates the influence of corporate image on customer loyalty. The sample in this study was 150 respondents. The data analysis technique uses SEM-PLS. The analysis results show that social media marketing has an effect on customer loyalty. Corporate image influences customer loyalty. Social media marketing influences customer satisfaction. Corporate image influences customer satisfaction. Customer satisfaction influences customer loyalty. Customer satisfaction mediates the influence of social media marketing on customer satisfaction. Customer satisfaction mediates the influence of corporate image on customer satisfaction.