Improve Customer Relationship Research Articles

Research the Impact of Online Marketing on Customers Perceived Value of Vietnamese Joint Stock Commercial Banks

Research the impact of online marketing on customer perceived value at Joint Stock Commercial Banks (JSCBs) is a significant and highly applicable topic in marketing and management business. The analysis and conclusions from this study can provide valuable information about how online marketing influences customer perceptions and the value they place on their banking experience. This will help businesses better understand how to interact with customers, optimize their marketing strategies, and improve customer relationships to increase business efficiency. The authors have proposed a theoretical model and scale based on a comprehensive study of published documents. Through qualitative research and in-depth interviews with experts, an official research model and scale have been formed, including six variables (five independent variables and one dependent variable) with 33 observed variables. Conducting quantitative research with a sample size of N = 355, the model has been determined to suggest five independent variables with 28 observed variables and one dependent variable with 5 observed variables (through Cronbach's Alpha and EFA coefficients), and the model adjusted model with five independent variables with 27 observations and one dependent variable with 05 observed variables. The study has determined the impact of online marketing on customer perceived value at Vietnamese joint stock commercial banks, arranged in descending order of influence: ((1) Online Communication; (2) Online Customer Service; (3) Online Offer; (4) Online retail Pricing, and (5) Online Distribution.

Impact of Employee Environmental Concerns on Sustainable Practices: Investigating Organizational Commitment and Job Satisfaction

Environmental issues are gradually increasing due to increases in carbon emissions, overproduction, plastic use, the burning of fossil fuels, etc. Therefore, ecological sustainability is a primary concern for society and the upcoming generation. Considering this, the present research objective is to examine the factors influencing employee eco-friendly behavior (EEBH) and firm environmental performance (FEP). Cross-sectional data were accumulated from 403 employees working in casual dining restaurants in China and analyzed through partial least squares structural equation modeling (PLS–SEM) approaches (i.e., PLS algorithm, bootstrapping, blindfolding, and PLS prediction). The findings support the significant effect of environmental concern (ENVC) on employee organizational commitment (EOC), employee job satisfaction (EJoS), and EEBH. In addition, EOC and EJoS directly and indirectly influence EEBH. Furthermore, EOC also mediates the link between ENVC and EJoS. Finally, the findings also show that EEBH stimulates environmental performance. By showing the links between these elements, it has been emphasized that EEBH is a precondition of good organizational productivity and how it contributes to enhancing the EJoS and overall firm’s health. It challenges managers to create a work environment that is conducive to sustainability for improved customer relations and a strong reputation for the organization in society.

Impact of Employee Environmental Concerns on Sustainable Practices: Investigating Organizational Commitment and Job Satisfaction

Environmental issues are gradually increasing due to increases in carbon emissions, overproduction, plastic use, the burning of fossil fuels, etc. Therefore, ecological sustainability is a primary concern for society and the upcoming generation. Considering this, the present research objective is to examine the factors influencing employee eco-friendly behavior (EEBH) and firm environmental performance (FEP). Cross-sectional data were accumulated from 403 employees working in casual dining restaurants in China and analyzed through partial least squares structural equation modeling (PLS–SEM) approaches (i.e., PLS algorithm, bootstrapping, blindfolding, and PLS prediction). The findings support the significant effect of environmental concern (ENVC) on employee organizational commitment (EOC), employee job satisfaction (EJoS), and EEBH. In addition, EOC and EJoS directly and indirectly influence EEBH. Furthermore, EOC also mediates the link between ENVC and EJoS. Finally, the findings also show that EEBH stimulates environmental performance. By showing the links between these elements, it has been emphasized that EEBH is a precondition of good organizational productivity and how it contributes to enhancing the EJoS and overall firm’s health. It challenges managers to create a work environment that is conducive to sustainability for improved customer relations and a strong reputation for the organization in society.

Knowledge retrieval and diagnostics in cloud services with large language models

Efficient customer support is the foundation for any service provider trying to improve customer relationships. An important measure of successful support is the mean time to resolve issues. The complexity and large scale of modern cloud environments make it unrealistic to reduce the resolution time without deploying intelligent solutions. The latest also provides an exceptional opportunity to leverage cross-customer product usage data for proactive solutions when the troubles of some users can be analyzed in advance to prevent similar issues of other users. We build a recommender system that matches customer support requests to other resolved support requests or knowledge base articles that contain valuable information for problem remediation. This system can be used by customers or support teams to quickly find problem-resolution tips or detect trending issues to warn vulnerable users. We utilize large language models, fine-tune for better performance, and discuss capabilities and possible improvements. During our research, we highlighted several evaluation metrics such as mean time to resolve issues and the accuracy of recommendations. However, estimating accuracy is challenging due to insufficient datasets with precise and comprehensive recommendations. Despite this, our support managers provided some estimates regarding the remediation durations. Typically, identifying and resolving an issue takes several days or weeks. With appropriate recommendations, this time can be significantly reduced to several hours and, in some simple cases, even lead to self-service capabilities.

Knowledge retrieval and diagnostics in cloud services with large language models

Efficient customer support is the foundation for any service provider trying to improve customer relationships. An important measure of successful support is the mean time to resolve issues. The complexity and large scale of modern cloud environments make it unrealistic to reduce the resolution time without deploying intelligent solutions. The latest also provides an exceptional opportunity to leverage cross-customer product usage data for proactive solutions when the troubles of some users can be analyzed in advance to prevent similar issues of other users. We build a recommender system that matches customer support requests to other resolved support requests or knowledge base articles that contain valuable information for problem remediation. This system can be used by customers or support teams to quickly find problem-resolution tips or detect trending issues to warn vulnerable users. We utilize large language models, fine-tune for better performance, and discuss capabilities and possible improvements. During our research, we highlighted several evaluation metrics such as mean time to resolve issues and the accuracy of recommendations. However, estimating accuracy is challenging due to insufficient datasets with precise and comprehensive recommendations. Despite this, our support managers provided some estimates regarding the remediation durations. Typically, identifying and resolving an issue takes several days or weeks. With appropriate recommendations, this time can be significantly reduced to several hours and, in some simple cases, even lead to self-service capabilities.

The use of artificial intelligence and machine learning in e-commerce marketing

The object of this research is the use of artificial intelligence (AI) and machine learning (ML) in e-commerce marketing strategies. Traditional e-commerce marketing approaches often lack a personalized customer experience and find it difficult to adapt to changing consumer behavior. The integration of artificial intelligence and machine learning offers a solution to these problems, enabling real-time marketing initiatives and data analysis. Studies have shown that the use of artificial intelligence and machine learning in e-commerce marketing has led to improved customer relationship management, increased operational efficiency, and more customer-centric advertising strategies. In addition, technologies such as visual search, virtual personal shoppers, and real-time product targeting have changed the e-commerce landscape by providing interactive and personalized shopping experiences. Artificial intelligence and machine learning algorithms analyze vast amounts of customer data to identify patterns, preferences and trends, enabling e-commerce businesses to conduct targeted marketing campaigns and optimize product offerings. Using advanced technologies, companies can streamline operations, increase customer satisfaction and stay ahead of the competition in the digital marketplace. This data suggests that integrating artificial intelligence and machine learning into e-commerce marketing strategies can benefit businesses by improving customer engagement, increasing sales, and gaining a competitive advantage. However, a successful implementation requires access to quality data, a robust AI infrastructure, and ongoing monitoring and optimization to ensure effectiveness and relevance in a dynamic marketplace.

EXAMINING THE IMPACT OF PRODUCT QUALITY ON CUSTOMER SATISFACTION, LOYALTY, AND REPEAT PURCHASE BEHAVIOR

In today's highly competitive business environment, providing high-quality products is no longer a luxury but a necessity for organizations seeking to build strong customer relationships and to promote sustainable growth. This paper investigates the central role of product quality on customer satisfaction, loyalty, and repeat purchase behavior. By examining the impact of different aspects of product quality on these key indicators, this study aims to provide valuable insights for companies looking to improve customer relationships and achieve long-term success. The results of this study are expected to highlight the significant influence of product quality on customer satisfaction, loyalty, and repeat purchase behavior, factors such as performance, Reliability, durability and perceived quality appear to be key factors in these results. Key Words: Product Quality, Customer Satisfaction, Loyalty, Repeat Purchase

A STUDY ON THE EFFECTIVENESS OF BATCH TRAINING TOWARDS THE PERFORMANCE OF CUSTOMER RELATIONSHIP OFFICERS WITH REFERENCE TO BANK BAZAAR

This study delves into the effectiveness of batch training programs designed for customer relationship officers (CROs) with the overarching goal of refining their performance and elevating customer satisfaction levels. Employing a methodical research framework encompassing a thorough literature review, meticulously crafted research design, systematic implementation, and rigorous data analysis, we scrutinize the repercussions of batch training on the proficiency, productivity, and interpersonal engagements of CROs. To ascertain the efficacy of batch training, a control group is included, facilitating a comparative analysis between batch-trained CROs and their non-trained counterparts. By intertwining quantitative assessment of performance metrics with qualitative exploration of participant feedback, this study paints a comprehensive picture of the impact of batch training on CROs. The insights gleaned from this study furnish organizations with invaluable guidance in refining their training paradigms, thereby fostering heightened CRO performance to align with the evolving demands of the customer landscape. As businesses increasingly prioritize customer satisfaction and loyalty, effective training of employees in customer relationship management (CRM) becomes paramount. The research investigates whether batch training, characterized by grouping employees for training sessions, enhances their ability to manage and improve customer relationships compared to other training methods. Through a mixed-methods approach, data was collected from employees undergoing batch training and assessed against key performance metrics including customer satisfaction, loyalty, and retention. Results indicate that batch training positively influences customer relationship performance, suggesting its potential as an effective training method in CRM initiatives. The study contributes to the understanding of optimal training approaches for fostering strong customer relationships and offers practical insights for businesses seeking to enhance their customer-centric strategies.

Deep learning techniques for predicting the customer lifetime value to improve customer relationship management

Deep learning techniques for predicting the customer lifetime value to improve customer relationship management

Applying advanced sentiment analysis for strategic marketing insights: A case study of BBVA using machine learning techniques

In the digital era, understanding public sentiment toward brands on social media is essential for crafting effective marketing strategies. This study applies sentiment analysis on Banco Bilbao Vizcaya Argentaria (BBVA) tweets using advanced machine learning techniques, particularly the eXtreme Gradient Boosting (XGBoost) algorithm, which showed remarkable precision (91.2%) in sentiment classification. This process involved a systematic approach to data collection, cleaning, and preprocessing. The precision of XGBoost highlights its effectiveness in analyzing social media conversations about banking. Additionally, this paper achieved improvements in neutral tweet classification, with accuracy rates at 87-88% and a reduced misclassification rate, enhancing the analysis reliability. The findings not only uncover general sentiments toward BBVA but also provide insight into how these sentiments shift in response to marketing activities and global events. This gives marketers a valuable tool for real-time assessment of campaign effectiveness and brand perception. Ultimately, employing the XGBoost algorithm for sentiment analysis offers BBVA a strategic advantage in understanding and engaging its online audience, demonstrating the significant benefits of using sophisticated machine learning in banking. The study emphasizes the crucial role of data-driven sentiment analysis in developing informed business strategies and improving customer relationships in the banking industry’s competitive landscape.

Determination of Customer Loyalty in the International Cargo Company

The purpose of this research is to understand the Factors Influencing Customer Loyalty: This study probably aims to investigate the factors that most influence customer loyalty in the freight forwarding sector. This can include aspects such as the relationship between the company and customers, service quality, customer satisfaction, price, and other relevant factors and measure the level of customer loyalty to freight forwarding companies in East Jakarta. This can be done through surveys, interviews, or analysis of historical data. The study used quantitative methods, which used random sampling for respondents to find data, the population is a user of freight forwarders in the East Jakarta Area by disseminating online and directly. Data analysis was processed using SEM PLS series 4 to find a definite answer from the results of the questionnaire distribution that lasted for two months. In this study, only the relationship between relationship and customer loyalty showed statistical significance, but the direction of the relationship did not match initial predictions. The relationship between Customer Satisfaction and Customer Loyalty has not been shown to be statistically significant. Recommendations in the freight forwarding sector, increasing customer loyalty can be achieved through improved customer relationships, consistent service quality, and ensuring customer satisfaction. By focusing on these aspects, companies can strengthen long-term relationships with customers and improve their performance in the freight forwarding industry

Determining the impact of the CRM system on the financial indicators of the enterprise using econometric methods

CRM systems help to improve customer relationship management, provide an opportunity for more accurate sales forecasting, optimization of marketing campaigns and increase in overall customer satisfaction. The purpose of the paper is to determine the impact of the CRM system on the financial indicatots of the enterprise. The study is based on the methods of econometric modeling, system analysis, financial analysis, works of domestic and foreign scientists, materials from periodicals, and data from the financial statements of the enterprise. A correlation analysis was conducted between the selected independent indicators to determine the degree of their relationship. Based on this, it was determined which factors have the highest correlation with each other, which made it possible to focus on the most important aspects of financial management. A regression multivariate model of the relationship between such factors as inventory, accounts receivable, sales costs, other operating expenses, and the resultant indicator - net working capital (NWC) - is built. The significance of the model as a whole and the significance of the regression coefficients are determined. It is predicted that the net working capital of the enterprise in 2023 will be less than in 2022, which is due to factors such as a high level of accounts receivable, sales costs and other operating expenses. After the integration of the CRM system, the company implemented a number of measures aimed at improving the management of these factors. In particular, the company introduced automation of receivables management processes, optimized sales and other operating expenses. As a result of these measures, the company's NWC increased at the end of 2023. This is confirmed by both the econometric model and the actual results.

Unlocking Operational Efficiency: The Influence of Advanced B2B Software Solutions on Supply Chain Processes

In this dynamic and fast-paced modern world, operational efficiency has become necessary for businesses that desire to attain and retain their competitive edge. A comprehensive B2B supply chain solution provides real-time visibility into the supply chain, including your inventory and production schedules, enabling prompt responses, informed decisions, and improved customer relationships. This article discusses the influence that advanced B2B Software Solutions have on Supply Chain Processes. It substantiates the absolute need for businesses to employ modern technology for achieving operational excellence in the highly dynamic modern world where beating the competition is the only way forward and application of sophisticated technology is increasingly becoming a necessity and not a choice.

ЕФЕКТИВНІСТЬ ВИКОРИСТАННЯ ІНТЕРАКТИВНИХ ІНСТРУМЕНТІВ КОМУНІКАЦІЇ ТА CRM-СИСТЕМ В ЦИФРОВОМУ МАРКЕТИНГОВОМУ СЕРЕДОВИЩІ

The article explores the topic of the effectiveness of using interactive communication tools and CRM systems in the modern digital marketing environment. The analysis is based on examining current trends in digital marketing and defining the role of various interactive tools in engaging with the audience. The article investigates the impact of different interactive tools such as social media, webinars, online surveys, messengers, on creating effective communication and engaging the target audience. Special attention is given to the use of CRM systems for collecting and processing customer data, enabling the personalization of marketing strategies and increasing customer satisfaction. In the modern digital marketing landscape, the relevance of using interactive communication tools and CRM systems becomes particularly significant. With increasing competition and rapidly changing consumer preferences, businesses must seek effective ways to attract and retain the attention of their target audience. Interactive tools such as social media, webinars, and messengers provide a unique opportunity to engage with users, making communication more personal and compelling. The growing importance of using CRM systems arises from the need for effective management of customer data in the digital environment. Collecting and analyzing information about consumers enables businesses to personalize their marketing strategies and also maintain and improve customer relationships. All these aspects become key factors in enterprises' pursuit of achieving effectiveness in digital marketing and ensuring a high level of customer satisfaction. Overall, considering the rapid advancement of technology and changes in consumer behavior, the use of interactive communication tools and CRM systems in the digital marketing environment becomes a necessity for businesses aiming to remain competitive and successfully adapt to changes. The positive aspects and challenges associated with the use of these tools in the context of digital marketing are thoroughly analyzed. The article also provides specific practical recommendations and strategies to optimize the use of interactive tools and CRM systems for achieving maximum efficiency. With a scientific approach and practical orientation, the research presented in this article can be valuable for marketers, business development managers, and professionals responsible for brand strategies, seeking to enhance their digital marketing approaches and strengthen customer relationships through interactive tools and CRM systems.

A Machine-Learning-Based Business Analytical System for Insurance Customer Relationship Management and Cross-Selling

Effective cross-selling practices are integral to maintaining strong customer relationships and optimizing business processes within the insurance industry. This study presents a novel three-stage Machine Learning-Based System (MLBS) designed to enhance the identification of potential insurance customers and improve customer relationship management. This study proposes combining under sampling strategies and an ensemble approach to improve prediction performance. The proposed MLBS method involves selecting the best training sample using artificial neural networks and employing the stacking ensemble approach. It yields superior prediction results, exhibiting the highest recall, precision, and Area Under the Curve (AUC). These advancements substantially bolster the efficiency of cross-selling strategies. This research pioneers the application of stacking ensemble learning within the cross-selling domain, representing a novel contribution to the business field. The outcomes underscore the superiority of the MLBS system over baseline models across multiple performance metrics, thereby significantly enhancing support for cross-selling campaigns in various businesses.

The role of financial accounting technology in improving customer relationship management in Jordanian banks

The role of financial accounting technology in improving customer relationship management in Jordanian banks

Influence of Cloud Computing Technology on Performance at International Business Machines Africa

Studies have been carried out regarding emerging technologies and performance, but few have been conducted in the IT industry, hence the need to fill the gap within IBM Africa which is an IT firm. Specifically, cloud computing has not been studied within an IT firm. The purpose of the study was to determine the influence of cloud computing technology on performance at IBM Africa. The study adopted a descriptive research design. The study targeted 184 employees from IBM Africa, and a stratified random sampling technique was used. Analysis was done quantitatively by employing inferential and descriptive statistics. Inferential statistics were in the form of linear regression, correlation, ANOVA, and T-test. The study found a weak positive correlation of 0.343 between cloud computing technology and performance. The results of the model summary indicated 10.9% of the variance in performance. The regression coefficient results indicated that cloud computing technology significantly predicted performance indicated by ???? of 0.348. The ANOVA results indicated that cloud computing technology significantly affected performance indicated by an F-value of 13.827. Perceived benefits that come with cloud computing led to the adoption of cloud computing indicated by a mean of 4.24. Cloud computing enhanced the efficiency of operations indicated by a mean of 4.22 and improved customer relationships indicated by a mean of 4.2. The study concluded that, a positive correlation between cloud computing technology and performance. The top management supported the adoption of cloud computing. There exists a positive correlation between data analytics technology and performance. Big data analytics led to better customer-driven business. There exists a positive correlation between AI technology and performance. The study recommends that the management should continually use cloud computing technology, train all its staff on the use, and come up with measures that may hinder effective use of the technology.

Trends Kart

"Trends Kart" focuses on the development of a Customer Relationship Management ,which serves as a digital tool to manage and analyze customer interactions and data throughout the customer lifecycle. Trends Kart system offers businesses a centralized platform to store and track customer information, including contact details, purchase history, and communication history. By leveraging Trends Kart systems, businesses can improve customer relationships, optimize communication, identify sales opportunities, and efficiently handle customer support inquiries. The proposed system emphasizes the importance of considering specific business needs and goals when selecting a Trends Kart system, highlighting key features such as contact and customer data management, sales and marketing automation, customer support and service management, and reporting and analytics. The effective implementation of Trends Kart can result in enhanced customer relationships, increased operational efficiency, and revenue growth. The project further discusses the working modules of the Trends Kart system, including the Admin Module, CR Manager Module, Customer Module, and Business Module, detailing their functionalities and contributions to managing customer relationships and business operations. The website utilizes various technologies and frameworks such as React, NodeJS, Express, MongoDB

Addressing Inconsistent Sales and Determining Continent-Specific Discounts: A Case Study of CeilBrakes Furniture Retail Company

This paper highlights the primary business issues faced by CeilBrakes Furniture Retail Company, namely inconsistent sales across different continents. While Europe demonstrates strong sales, North America, Australia, Asia, and Africa exhibit varying levels of mediocre and low sales in terms of the quantity of products ordered. Additionally, determining appropriate discount percentages for each continent poses a challenge. To address the issue of inconsistent sales, the company aims to enhance advertising efforts, improve customer relationships, and analyze trends in continents with low sales (excluding Europe). For determining continent-specific discounts, a solution involves identifying continents with low sales and offering higher discounts accordingly. In the case of Australia, which experiences low sales, CeilBrakes should provide more significant discounts. Despite Asia and Africa already having high discounts, their sales remain low. This situation calls for considering factors beyond retail price reductions, such as cultural influences. To facilitate easier viewing and analysis, a dashboard has been developed to visualize the aforementioned issues. The dashboard enables the identification of trends and patterns, empowering the company to make informed decisions in addressing the observed problems.
 

Effectiveness of Information Systems on Automation of Business Processes for Nonprofit Organizations in Arusha City

The study examined the effectiveness of information systems on automation of business processes for nonprofit organizations in Arusha City. The study had two specific objectives namely; identify the existing information systems for automation of business processes for nonprofit organizations in Arusha City and to determine the extent to which information systems have improved automation of business processes for nonprofit organizations in Arusha City. The study used descriptive survey research design and quantitative research approach. A sample of 63 respondents was drawn from the population of 75. The study used simple random sampling technique where data were collected through structured questionnaires. Descriptive statistics and Pearson correlation was used to analyze the quantitative data. Findings indicated that there are many information systems but not all of them exist in non-profit organizations in the study area. It was further found that to high extent information systems have improved automation of business processes for nonprofit organizations due to improved supply chain, improved Human Resources management, improved planning and projections, improved sharing of organization data, improved customer service, improved customer relations and improved time management. The study recommends that to achieve their objectives, non-profit organizations should invest in information systems. This can be done through having adequate experts, adopt the sophisticated information systems and train the users of the adopted sophisticated information systems. Moreover, the study recommends that to ensure effectiveness of information systems on automation of business processes for nonprofit organizations; proper ICT infrastructure should be initiated to facilitate the implementation of information systems.