Improve Customer Relationship Research Articles

Revolutionizing Finance: The Comprehensive Impact and Application of Machine Learning

The adoption of ML in finance has great potential to reform financial practices. This paper delves into the pivotal role of machine learning (ML) in revolutionizing the financial domain, including enhancing predictive accuracies, reducing cost, catalyzing financial innovation, and improving customer relationships. By dissecting the advantages, this paper summarizes the application of ML in finance like stock market forecasting, fraud detection, credit risk prediction, portfolio management and asset evaluation. Meanwhile, it also acknowledges existing obstacles such as data quality, transparency of models, and output homogenization. Finally, the paper seeks to provide comprehensive insights into the future landscape shaped by ML technologies in finance.

Intention to use determinants of AI chatbots to improve customer relationship management efficiency

Intention to use determinants of AI chatbots to improve customer relationship management efficiency

The Effect of Product Innovation and Customer Relationship Management on Satisfaction Mediated by Purchasing Decisions in the Coffee Shop Industry

The coffee shop industry faces increasingly fierce competition, where consumers have many choices. To retain and attract customers, coffee shops must continuously develop product innovation and improve customer relationships through Customer Relationship Management (CRM). This study aims to identify the effect of product innovation and CRM on customer satisfaction and understand the role of purchasing decisions as a mediator in this relationship. This research uses a survey method by collecting data from coffee shop customers through questionnaires. The research sample consisted of coffee shop visitors who were active during the research period. The collected data were analyzed using statistical techniques, including regression analysis. The results showed that product innovation has a significant positive impact on customer satisfaction. CRM also has a significant favorable influence on customer satisfaction. Furthermore, purchasing decisions were found to mediate the relationship between product innovation and customer satisfaction, as well as between CRM and customer satisfaction. These findings indicate that coffee shops focusing on developing innovative products and implementing effective CRM strategies can increase customer satisfaction. In addition, purchase decisions are an essential intermediary in linking product innovation, CRM, and customer satisfaction. Therefore, coffee shop management must consider better strategies for integrating product innovation, CRM, and purchase decisions to achieve long-term success in this competitive industry. This research contributes significantly to understanding the factors influencing customer behavior and the strategies coffee shop industry players can use.

ANALYSIS OF THE IMPACT OF DIGITALIZATION ON THE DEVELOPMENT OF OPPORTUNITIES FOR SMALL AND MEDIUM-SIZED ENTERPRISES

The article analyzes how the use of information and communication technologies and digital marketing tools contribute to increasing competitiveness, optimizing business processes, expanding sales markets, and improving customer relations. This article offers a comprehensive examination of the intricate dynamics between innovation, entrepreneurship, and economic growth, drawing from an extensive review of empirical studies, theoretical frameworks, and policy implications. Through a meticulous analysis of the symbiotic relationship between innovation and entrepreneurship, it elucidates their multifaceted contributions to driving sustained economic prosperity. By delving into the nuanced mechanisms and contextual factors that shape this relationship, the article provides insights for policymakers, researchers, and practitioners seeking to foster innovation-led growth in today's complex global economy. In the context of contemporary economic development, innovation and entrepreneurship have emerged as central pillars driving productivity growth, technological progress, and market dynamism. This paper embarks on a journey to unravel the complex interplay between these two phenomena and how their synergistic relationship shapes the trajectory of economic growth. By examining the underlying mechanisms, empirical evidence, and policy implications, we aim to shed light on the transformative potential of innovation and entrepreneurship in fostering sustainable development and competitiveness. Innovation and entrepreneurship are essential engines of economic growth. Innovation is characterized by the creation and adoption of novel ideas, technologies, and business models, and is a catalyst for entrepreneurial activity by providing opportunities for market differentiation and value creation

Application of GWO-attention-ConvLSTM model in customer churn prediction and satisfaction analysis in customer relationship management

Customer Relationship Management (CRM) is vital in modern business, aiding in the management and analysis of customer interactions. However, existing methods struggle to capture the dynamic and complex nature of customer relationships, as traditional approaches fail to leverage time series data effectively. To address this, we propose a novel GWO-attention-ConvLSTM model, which offers more effective prediction of customer churn and analysis of customer satisfaction. This model utilizes an attention mechanism to focus on key information and integrates a ConvLSTM layer to capture spatiotemporal features, effectively modeling complex temporal patterns in customer data. We validate our proposed model on multiple real-world datasets, including the BigML Telco Churn dataset, IBM Telco dataset, Cell2Cell dataset, and Orange Telecom dataset. Experimental results demonstrate significant performance improvements of our model compared to existing baseline models across these datasets. For instance, on the BigML Telco Churn dataset, our model achieves an accuracy of 95.17%, a recall of 93.66%, an F1 score of 92.89%, and an AUC of 95.00%. Similar results are validated on other datasets. In conclusion, our proposed GWO-attention-ConvLSTM model makes significant advancements in the CRM domain, providing powerful tools for predicting customer churn and analyzing customer satisfaction. By addressing the limitations of existing methods and leveraging the capabilities of deep learning, attention mechanisms, and optimization algorithms, our model paves the way for improving customer relationship management practices and driving business success.

Exploring customer retention dynamics: A comparative investigation of factors affecting customer retention in the banking sector using mediation-moderation approach

The purpose of this research is to explore the impact of service quality, customer trust, and cultural disparities on customer satisfaction within the banking sector. Additionally, the study examined the mediating effect of customer satisfaction and the moderating effect of financial technology. Data for this investigation were gathered from customers utilizing services offered by public banks in Pakistan and China. The analysis was conducted using the Smart-PLS software, with 281 samples from Pakistan and 312 samples from China being included in the study. Results show that service quality, customer trust, and cultural differences positively affect customer satisfaction in the context of Pakistan and China. Results also reveal that service quality and customer trust have a positive effect on customer retention behavior in China, but customer trust has an insignificant impact on customer retention behavior in Pakistan. Analysis specifies that customer satisfaction positively mediated the relationship between service quality, customer trust, cultural differences, and customer retention behavior in Pakistan and China. More importantly, financial technology moderated the relationship between customer satisfaction and customer retention behavior, but it was insignificant in the context of China and Pakistan. The study seeks to provide significant insights for strategic decision-making and improve customer relationship management techniques in the dynamic field of financial services by including mediating and moderating elements.

Does service-oriented manufacturing improve customer stability?

Stabilizing customer relationships plays an important role in improving supply chain resilience. Based on a sample of A-share listed companies in the manufacturing industry from 2009 to 2022, this work investigates the impact of SOM(service-oriented manufacturing) on supply chain resilience from the perspective of customer stability. The results showed that SOM has a positive impact on customer relationship stability, indicating that manufacturing enterprises carrying out SOM can improve customer relationship stability, and this stable relationship is long lasting. Analysis of service-oriented manufacturing structure reveals that service that supports the supplier's product (e.g., after-sale services) is less effective in promoting customer relationship stability than service that supports the customer (e.g., training service). This study theoretically expands the research on the allocation of enterprise resources among supply chains, promotes the deep integration of modern service industry and advanced manufacturing industry, and provides experience for implementation of relevant decision-making and deployment of supply chain security and stability in practice.

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.

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.

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.

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.

Brand image, service quality, and customer retention: Basis for improved customer relationship management framework for international students

Brand image, service quality, and customer retention: Basis for improved customer relationship management framework for international students

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

The Effects of Digitalization on the Sustainability of Small Farms

Digitalization of agriculture is one of the priorities of the EU’s rural development strategy “From Field to Table”, which promotes the creation of more added value and climate change mitigation in agriculture. A growing body of the literature argues that digitalization enables better information management, reduces production costs, and increases the potential for farm income growth, but only a few papers provide empirical studies on how digitalization improves the performance of small farms. To fill this gap in the literature, this paper presents a case study as empirical evidence of the impact of digital innovation on smallholder performance through a sustainable development lens. This paper reports research based on a pilot digitalization project implemented on a small organic farm. It examines the identification of logical links between the digitalization processes introduced and the impact of digitalization on the economic, social, and environmental performance of the small farm. The case study data were collected through semi-structured interviews and based on the results of a pilot project. The findings of this study provide evidence that the introduction of digital technologies has improved the economic performance of the farm, including a reduction in labor costs, improved customer relations, improvements in farmers’ investment planning, and process redesign. Based on this study, recommendations are made to policymakers on how to promote the uptake of digital technologies in smallholder farming.

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.

Strategy Analysis Using the Business Model Canvas Method

This research aims to analyze the Business Model Canvas of Hendys Bakery, which comprises nine elements: customer segments, value propositions, channels, customer relationships, revenue streams, key resources, key activities, key partnerships, and cost structure. The research adopts a qualitative descriptive research method, employing semi-structured interviews for data collection. The analysis reveals that Hendys Bakery's customer segments encompass individuals of all ages, including tourists visiting attractions in Deli Serdang Regency and Medan City. Value propositions consist of quality products with delicious and fresh taste (bread and cakes), attractive packaging, affordable prices, and a variety of more than eight product types. Customer relationships are fostered through effective communication, easy ordering processes, bulk purchase discounts, and secure packaging for online transactions. Channels for distribution include online promotions and sales via social media, direct sales, collaborations with large supermarkets, and participation in events or bazaars. The primary revenue streams for Hendys Bakery originate from direct sales and sales through gift shops, minimarkets, or supermarkets in Tembung. Key resources comprise production facilities, equipment, high-quality cake ingredients, and appealing packaging. Key activities involve diverse product manufacturing processes and direct sales. Key partnerships for Hendys Bakery include raw material suppliers (flour, butter, sugar producers), local government, financial institutions, supermarkets/minimarkets, and gift shops. To enhance its business model, Hendys Bakery needs to focus on improving customer relationships, channels, revenue streams, key resources, key activities, key partnerships, and cost structure. However, customer segment and value proposition elements require maintenance rather than improvement.

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.