Conceptual study on e-banking systems and customer satisfaction using deep learning and blockchain

Conceptual study on e-banking systems and customer satisfaction using deep learning and blockchain

Studying the influencing factors on customer's trust and satisfaction in E-Commerce and adopting appropriate strategies about these factors are among the effective methods in achieving success in E-Commerce area. This article focuses on the study of effective factors on customer's trust and satisfaction in group discount websites. In this research, firstly the factor influencing on customer's satisfaction in E-commerce has been extracted through the conceptual study and the review of literature and the structural equation modeling has been presented. Then, E-questionnaire was given to the customers of group discount websites in Iran in order to evaluate the model and the relationships among the variables of the model. The analysis of the obtained results conducted through Partial least Square method proved the hypotheses of this model. The results of this research have provided with useful insight for those people who work in E-commerce; hence, they can design successful group discount websites based on group purchase income model.

Banks confronted tough competition in acquiring and maintaining consumers with their e-banking platforms due to the rising rate of use of e-banking systems. Essentially, improving e-banking service quality is seen as the best strategic strategy for increasing client loyalty to the e-banking system. The purpose of this paper was to empirically investigate a comprehensive mechanism for enhancing customer loyalty toward e-banking platforms via e-banking service. Reliability, website design, privacy and security, and customer service and support were the dimensions used. The objectives of this research included exploring relationships between factors such as customer satisfaction regarding online banking, reliability of service, quality and performance, service privacy and security in transactions, website design and its use, and the service and assistance in the usage, and customer loyalty. The respondents were among part 2 to part 5 students from Human Resources Studies in a higher learning institution. The researcher distributed the questionnaire to the respondents through the online method using social media accounts like WhatsApp, Instagram, and Telegram. The findings of the research included that, value and relevance were insights strategies to develop loyalty among student users. This included development and promotions with products like educational loan assistance, scholarship tracking tools, budget budget-friendly debit cards with student-specific features. Rewards, points, and other incentives should be used to make sure that online banking is being used frequently. Plugging the site along with its social features through a community forum made sure the students provided peer-to-peer support in sharing financial tips and experiences.

The development of digital technology has changed the interaction patterns between companies and customers, particularly through the increasingly widespread implementation of digital marketing in digital marketplace platforms. This study aims to analyze the impact of digital marketing on customer satisfaction and loyalty by highlighting how digital marketing strategies play a role in building long-term relationships between customers and brands. This research uses a qualitative approach based on conceptual studies, conducted through a systematic review of various scientific literature and relevant previous research results. The analysis process is carried out by examining the relationships between variables and the strategic implications of digital marketing on customer behavior in the digital marketplace environment. The results of the study indicate that the implementation of effective digital marketing strategies can increase customer satisfaction through easier, faster, and more personalized interaction experiences. High customer satisfaction subsequently strengthens loyalty to the digital platform used. This study provides a conceptual contribution to the development of digital marketing theory and offers practical insights for business actors to optimize digital marketing strategies to maintain customer loyalty in an era of increasingly dynamic digital competition.

This paper aims to analyse the application of QR codes in enhancing customer satisfaction within the restaurant industry. Specifically, the study identifies the impact of QR code usage on customer satisfaction by reviewing existing literature. Data is gathered by reviewing journals, articles, and research studies using argumentative techniques. In today's world, characterized by a shift towards contactless interactions, the restaurant industry has embraced QR codes for seamless, contactless payments and operations. This study addresses the need for a deeper understanding of QR code applications by determining three critical elements: usefulness, acceptability, and feasibility. The findings are expected to guide industry professionals and researchers in identifying the most effective QR code implementations, thereby enhancing customer satisfaction. Through this exploration, the study contributes to bridging the knowledge gap and fostering improved practices in the restaurant industry, ultimately promoting the wide-spread adoption of QR codes as a valuable tool for customer engagement and satisfaction. However, this study profound a limitation with the suggested model leading from limited participant perspectives and experiences. This restricts the ability to form a comprehensive and solid view, potentially affecting the study's findings. Future research should involve a more diverse group of participants with varied experiences to enhance the understanding and application of the model.

Customer service plays a crucial role for a company. As an important aspect of e-commerce companies, they would be required to directly interact and try to solve customers’ problems that might occur anywhere and anytime. However, the limitation of human man hours became a barrier to overcome customers’ problems. On one hand, the rapid development of technology was predicted to replace the traditional human customer service with an Artificial Intelligence agent. On the other hand, this replacement affects the customer satisfaction. This paper performed a study literature review to discover the effect of chatbots and its impact towards customer satisfaction. In an e-commerce customer service use case, chatbots could be implemented in a number of methods. The methods implemented by chatbots are avatar-based, verbal-based, text-based, and menu-based. Research showed that text-based chatbot is the most commonly used methodology and has advanced the most, where some are implementing higher level machine learning methods, such as deep learning. The usage of such chatbot in e-commerce customer service systems will lower the cost but might also lower customer satisfaction, due to reasons such as unsatisfying answers and inhuman behavior. Research showed that even a more sophisticated chatbot doesn’t always mean higher customer satisfaction, even with high accuracy ratings. To look into customer satisfaction, this paper has identified 4 aspects of a chatbot that are relevant to customer satisfaction, which are privacy, reliability, personalization, and responsiveness. Chatbots currently excel in some of these quality measures, but require further research to effectively replace human customer service agents.

Customer satisfaction (CSAT) is vital in service and marketing, indicating how well products or services meet customer expectations. Traditional CSAT methods like the American Customer Satisfaction Index (ACSI) and Net Promoter Score (NPS) face challenges such as survey fatigue and low response rates. This study introduces a novel framework using advanced machine learning (ML) and deep learning (DL) techniques, specifically Bidirectional Encoder Representations from Transformers (BERT), to classify customer feedback into distinct CSAT drivers. Integrating term frequency-inverse document frequency (TF-IDF) methods with BERT-based embeddings, the framework significantly improves prediction accuracy. Using a proprietary dataset of 5,943 customer feedback responses from 39 companies across 13 industries, the fine-tuned BERT model achieved an F1 score of 0.84, surpassing traditional methods like TF-IDF and support vector machine (SVM) with an F1 score of 0.47, and TF-IDF with multi-layer perceptron (MLP) networks at 0.50. A hybrid approach combining BERT and TF-IDF embeddings with MLP networks yielded an F1 score of 0.71. The results show the transformative potential of DL techniques, particularly fine-tuned BERT models, in enhancing CSAT prediction accuracy. This research bridges the gap between traditional and advanced text mining methods, setting a new standard for CSAT modeling and offering a robust framework for extracting actionable insights from customer feedback. It highlights the importance of adopting advanced ML and DL models for strategic decision-making and improving customer satisfaction measurement.

The swift growth of ride-hailing services in urban transportation has transformed the mobility landscape, necessitating that providers grasp customer sentiment to enhance their offerings. This research conducts a comparative sentiment analysis of user feedback for Ola, Uber, Rapido, and Namma Yatri, with the objective of deriving significant insights regarding customer satisfaction and service quality. A comprehensive dataset comprising 40,000 reviews from the Google Play Store for each application was gathered and classified into positive, neutral, and negative sentiments. To analyze sentiment trends, both advanced deep learning models (including LSTM, GRU, and Hybrid LSTM-CNN) and traditional machine learning models (such as Random Forest and Decision Tree) were utilized. The results show that even though the Random Forest model had the highest accuracy among the traditional methods, the Hybrid LSTM-CNN model was the best among all, reflecting the power of deep learning architectures in identifying complex sentiment patterns. The findings obtained from this study gave significant recommendations for ride-hailing companies to boost their customers’ satisfaction levels and streamline business processes. Keywords — Sentiment Analysis, Ola, Uber, Rapido, Namma Yatri, Machine learning, Deep learning, LSTM, GRU, Hybrid LSTM-CNN

<span lang="EN-US">Customer satisfaction is the key for every business successful. Therefore, keeping the current customer portfolio and expanding it over time is the main goal for any business. Hence, we need first to satisfy these clients. The customer satisfaction helps to retain consumers of its products, increase the life value of the customer, also make known its brand through positive word of mouth to get a better reputation and thus increase turnover. For this reason, several studies have been conducted on this subject to explore all tools and technologies that will help retain customers and reduce their churn rate. Based on various customer satisfaction studies for different types of businesses, this paper shows the review of promising research areas and artificial intelligence (AI) application models in predicting customer satisfaction. The results of this study allowed the identification of the best algorithms with the highest score of performance metrics that can be applied as part of the customer satisfaction prediction, through a detailed benchmark performed. The result shows that random forest (RF) and gradient boost (GB) algorithms in machine learning (ML) and </span><span lang="EN-US">convolutional neural network - long short-term memory (CNN-LSTM) in deep learning (DL) are giving the best performance. The most used metrics are accuracy and<br />F1-score. In addition, DL models outperform ML models in most cases.</span>

The platform economy evolved rapidly due to technological breakthroughs, hence new challenges emerged in terms of pricing strategies affected from different user behaviors and market demand dynamics. Traditional pricing methods are not up to this type of challenge, and it requires a model able to be more agile — in real time. The study presents “DeepPrice,” a dynamic pricing model using deep learning namely Convolutional Neural Networks (CNN) and Deep Reinforcement Learning (DRL) to achieve the optimal platform pricing strategies in response to user behavior and market signals. The research has an experimental design for the development and testing of the DeepPrice model. The model is pre-trained by the transaction data of an important e-commerce platform for this task. CNNs learn user profiles and product properties via the encoding layer, while DRL models implement the strategy of adjusting price according to behavior actions in tensor form. Metrics such as platform revenue, user conversion rates, and customer satisfaction are used to validate the model back to the model performance. Our solution helped DeepPrice to generate incremental 20% platform revenue on average and better adjust to the market challenges. It performed better than standard pricing solutions, especially in times of high demand, and effectively personalized price-pointing for top-value customers to drive higher conversions and improve customer satisfaction. This study points to the promise of using deep learning to improve dynamic pricing in platform economies. Flexible & Scalable Solution for any IndustryDeepPrice Nevertheless, challenges regarding the computational cost of implementing personalized pricing strategies and ethical debates surrounding such strategies remain to be studied in more detail. In platform economy, the reinforcement learner has a significant potential to provide a reliable real-time pricing solution with CNN in DeepPrice to improve both profit and customer satidfaction.

Predicting E-commerce customer satisfaction: Traditional machine learning vs. deep learning approaches

In the airline industry, customer satisfaction occurs when passengers’ expectations are met through the airline experience. Considering that airline service quality is the main factor in obtaining new and retaining existing customers, airline companies are applying various approaches to improve the quality of the physical and social servicescapes. It is common to use data analysis techniques for analyzing customer propensity in marketing. However, their application to the airline industry has traditionally focused solely on surveys; hence, there is a lack of attention paid to deep learning techniques based on survey results. This study has two purposes. The first purpose is to find the relationship between various factors influencing customer churn risk and satisfaction by analyzing the airline customer data. For this, we applied deep learning techniques to the survey data collected from the users who have used mostly Korean airplanes. To the best of our knowledge, this is the one of the few attempts at applying deep learning to analyze airline customer propensities. The second purpose is to analyze the influence of the social servicescape, including the viewpoints of the cabin crew and passengers using aircraft, on airline customer propensities. The experimental results demonstrated that the proposed method of considering human services increased the accuracy of predictive models by up to 10% and 9% in predicting customer churn risk and satisfaction, respectively.

In the realm of digital marketing for the banking industry, the integration of deep learning methodologies, particularly Convolutional Neural Networks (CNNs) such as VGG16, Resnet50, and InceptionV3, has revolutionized strategic decision-making and customer satisfaction. This study explores how deep learning models leverage neural networks with multiple layers to analyze vast and complex datasets, uncovering intricate patterns in customer behavior and preferences. By enhancing customer segmentation, optimizing campaign performance, and refining personalized experiences, CNNs empower banks to make precise, data-driven decisions that elevate customer satisfaction and loyalty. Comparative analyses demonstrate CNNs' superior performance over traditional models like Random Forest and Logistic Regression, achieving accuracies up to 89% and F1 scores of 88%, thereby highlighting their transformative potential in reshaping digital marketing strategies within the banking sector. This research underscores the critical implications of adopting advanced deep learning techniques to meet the evolving demands of customers in today's dynamic digital landscape.

Predicting and explaining customer satisfaction: A deep learning and sentiment analysis of emotional impacts.

In the context of hotel revenue management, dynamic pricing plays a crucial role in maximizing revenue while maintaining a delicate balance with customer satisfaction. Traditional pricing strategies often depend on static rules or overly simplistic models that lack the ability to adapt to real-time changes in market demand, evolving customer behavior, and competitive trends. These outdated approaches can lead to missed revenue opportunities and suboptimal guest experiences. This research addresses these challenges by proposing a dynamic pricing strategy driven by deep reinforcement learning, which integrates real-time data streams with predictive analytics to create a highly responsive and intelligent pricing system. At the core of the proposed methodology is a novel framework that combines advanced deep neural network architectures with adaptive optimization algorithms. This framework is designed to optimize both pricing and inventory decisions across multiple booking channels simultaneously. The Innovative Pricing Transformer (IPT) model underpins this framework by leveraging attention mechanisms and temporal sequence modeling to accurately forecast future demand and recommend context-aware pricing decisions. In addition, the Adaptive Yield Optimization Strategy (AYOS) refines this process by incorporating real-world operational constraints such as overbooking policies, price parity requirements, and channel-specific pricing rules, ensuring practicality and compliance. Empirical analysis conducted on real-world datasets reveals that our approach consistently outperforms traditional pricing models, not only in revenue enhancement but also in improving overall customer satisfaction. The proposed strategy represents a scalable, efficient, and intelligent solution for modern hotel revenue management, enabling hotels to remain agile and competitive in dynamic and uncertain market conditions.