Enhance Customer Relationship Research Articles

A Study on the Performance of Employees in the Banking Sector and its Impact on the Organizational Health

Purpose: The enactment of workers in the banking sector is intricately linked to the organizational health of the institution. Nurturing a work environment that prioritizes employee well-being, promotes collaboration, and supports growth can lead to improved employee performance, enhanced customer experiences, and ultimately, sustainable success for the bank. To explore two interconnected aspects that play a pivotal role in the banking sector: employee performance and the impact of organizational health. The performance of employees within a banking institution is not only a reflection of their individual capabilities but also an outcome of the organizational environment in which they operate. Design: This research implemented the case study method by using an imaginative research design. Secondary data has been collected and analyzed to find out the factors affecting the Banking sector. Findings: The findings regarding employee performance in the banking sector and the impact of organizational health underscore the symbiotic relationship between engaged employees and a thriving organization. As the industry continues to evolve, recognizing the significance of fostering a supportive work culture and prioritizing employee well-being emerges as a strategic imperative. By cultivating a positive organizational environment, banks can elevate employee performance, enhance customer satisfaction, and position themselves for sustainable success. Studies indicate a strong connection between organizational health and employee performance in the banking sector. Banks with healthy work environments tend to achieve better financial results due to improved employee satisfaction, enhanced customer relationships, and efficient operations. Originality/value: This study includes a detailed analysis of the performance of employees in the Banking Sector and the impact of organizational health. Paper Type: Conceptual Research.

Service exchange activities in the sharing economy: Professional versus amateur peer providers

Motivated by the need to enhance customer relationship management in the sharing economy, this paper identifies and explains heterogeneity in the service activities of different peer providers. Drawing on social exchange theory and using data gathered from 3027 properties on a Chinese online home-sharing platform, we compare different peer providers' use of economic and social exchange activities and explore the differential impacts of these exchange activities on customers' response behaviors. The results demonstrate that professional hosts place more emphasis on economic exchange activities (i.e., pricing and service response), while amateur hosts focus on social exchange activities (i.e., social interaction and customer-perceived social belonging) more strongly. Both economic exchange and social exchange activities have significant effects on customers' purchasing behaviors, but only social exchange activities have significant effects on customers' cleaning behaviors. Furthermore, collaborative rule depth has significant moderating effects on relationships between these exchange activities and customers' response behaviors. This paper contributes theoretically to discussions of peer-provider heterogeneity, service exchange activities, and governance in the sharing economy. We discuss the implications of our findings for sharing economy platforms and peer providers—for example, specifying when rule customization might be used most effectively.

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

Servitization of business updated: Now, new, next

Coined in 1988, “servitization” has become the go-to concept for enhancing corporate competitiveness through value-added services. Over the years, the notion of value-added services to create a competitive edge, enhanced customer relationships, and market power has changed, facilitated by ever advancing technology. The authors look at this progression, spanning from the 1970s to the 2020s and into the future, with examples in both a B2B and B2C context. This is intended to assist both academics and practitioners in positioning themselves and taking steps to advance their research, thinking, and strategies.

COVID-19 Pandemic Learning: The Uprising of Remote Detailing in Pharmaceutical Sector Using Sales Force Automation and Its Sustainable Impact on Continuing Medical Education

The availability of resources is vital when rapid changes and updated medical information in the provision of care are needed, such as in the fight against COVID-19, which is not a conventional disease. Continuing medical education plays an essential role in preparing for and responding to such emergencies. Workflow has improved based on the virtual meetings, online trainings, and remote detailing conducted by medical representatives in order to deliver educational content instantly through digital tools, such as salesforce automation (SFA), webinars, etc. In terms of its regulatory barriers, the pharmaceutical industry mainly targets healthcare professionals, unlike most businesses that reach end users directly. Medical representatives are equipped with an SFA to enhance customer relationship management (CRM) and closed loop marketing (CLM) capabilities in pharmaceutical companies. This study aimed to fill a gap in the literature by investigating the use of SFA in work patterns, such as health professionals’ loyalty and involvement in their medical knowledge in Turkey, and how it allows for differentiating training from marketing. This study intended to compare the data on internists and medical products gathered from a well-known pharmaceutical company’s SFA. The data covered the first three months of the year 2020, when medical representatives had a normal daily routine, and that of 2021, when Turkey experienced the most powerful surge of the COVID-19 pandemic. The analysis was based on simple correspondence analysis (SCA) and multiple correspondence analysis (MCA) for 11 variables. Monitoring product, physician’s segment, and medical representatives’ behaviors with SFA had a significant influence on the pharma-physician relationship strategy, as expected. The findings supported the view that SFA technologies can be deployed to advance the medical knowledge of physicians, in addition to managing and designing superior CRM and CLM capabilities.

Human Touch Experience: A Strategy for Reducing Consumer Switching Behavior in The Financial Service Industry

This research provides an in-depth exploration of the Human Touch Experience (HTE) as a strategy for reducing consumer switching behavior in the financial service industry (Non-Bank). Amidst increasing competition and digitalization, maintaining customer loyalty has become a significant challenge for the industry. The study employed a mixed-methods approach, combining quantitative survey data from 1,207 respondents with qualitative data from 30 in-depth interviews. The findings underscore that high levels of HTE are associated with increased customer satisfaction and loyalty and a decreased intention to switch service providers. Furthermore, financial institutions that incorporate HTE practices into their operations report lower rates of customer churn and higher customer satisfaction and loyalty. The results suggest that, despite the digital revolution, a balance between efficient digital services and personalized human interaction can enhance customer relationships.

Enhancing Customer Relationship Management through Sentiment Analysis and Social Media Data Mining

Customer Relationship Management (CRM) is a crucial aspect of modern business that enables companies to maintain healthy relationships with their customers. In today's digital age, customers interact with companies through multiple channels, including social media, email, and phone. Therefore, analyzing customer feedback and sentiment has become increasingly important in understanding their needs and improving the overall customer experience. To this end, this work proposes a new system that applies deep learning for sentiment analysis in a way that improves the performance of CRM by analyzing customer feedback from various sources, companies can gain valuable insights into customer needs and preferences and identify areas for improvement in their products and services. Then, we present a case study of a company that implemented the proposed system in its CRM strategy. The results showed that our system could improve customer satisfaction and retention rates and enable the company to identify and address customer concerns more efficiently.Our approach can be applied as a powerful tool to enable companies to gain valuable insights into customer needs and preferences, identify areas for improvement in their products and services, and develop targeted marketing campaigns and personalized communication strategies.

Text and Social Analytics with Fusion Techniques Enhance Hospital Health Management

the impact of social analytics on hospital health management: a multilevel fusion approach for data-driven decision-making and brand improvement. The hospital health management center should use feature extraction techniques to learn more about customers' feelings towards their services and optimize their business strategies and promotions accordingly. The proposed multi-levelhybrid level fusion system architectures can effectively integrate dataimages from multiple sources, including social networks, to collect and process essential data for score level and rank level decision-making. This approach leverages intelligent techniques, such as deep learning models, fuzzy logic, and optimization algorithms, to improve fusion scores and achieve optimal fusion performance. The proposed framework can also be extended to various applications, including multimedia data fusion, e-systems data fusion, and spatial data fusion, to enable intelligent systems for information fusion and decision-making in diverse domains. Therefore, this paper proposes Improved Customer Relation and Business Operations (ICR-BO) to enhance customer relationships in business development using text and social analytics. A case study is carried out to explore the online debate of computer brands operated in hospital environments and Twitter suppliers. The authors used text-mining strategies and social analytics to analyze business operations. Social Media uses data sets to view important observations and trends to identify consumer awareness after collecting critical tweets using Twitter search. The experimental results show that ICR-BO achieves the highest customer relation compared to other existing methods.

Enhancing Customer Relationship Management Using Fuzzy Association Rules and the Evolutionary Genetic Algorithm

The importance of Customer Relationship Management (CRM) has never been higher. Thus, companies are forced to adopt new strategies to focus on customers, given the competitive climate in which they operate. Also, companies have been able to maintain customer data within large databases that contain all information related to customers, thanks to the tremendous technological development seen recently. Multilevel quantitative association mining is a significant field for achieving motivational associations between data components with multiple abstraction levels. This paper develops a methodology to support CRM to improve the relationship between retail companies and their customers in the retail sector to retain existing customers and attract more new customers, by applying data mining techniques using the genetic algorithm through which an integrated search is performed. The proposed model can be implemented because the proposed model does not need the minimum levels of support and trust required by the user, and it has been confirmed that the algorithm proposed in this research can powerfully create non-redundant fuzzy multi-level association rules, according to the results of these experiments.

Application of robotic process automation (RPA) technology in enhanced customer relationship management performance

Robotic process automation (RPA) is a modern technology that enables the automation of routine and repetitive tasks in business processes, helping businesses save time, reduce costs, and improve efficiency and operating performance. Since its inception, RPA has been widely applied in many industrial fields, evolving according to the business needs of each enterprise. In the field of Customer Relationship Management (CRM), RPA was developed to promptly respond to customers' needs and increase customer satisfaction with stable quality and productivity by automating some important processes. This study delves into the application of RPA in CRM, focusing on building CRM automation processes and research simulations using the Uipath tool and on the Bitrix24 CRM platform. UiPath is the primary implementation tool because of its user-friendliness, robustness, ease of installation and use, cost savings in deployment, and ease of portability across enterprises. Specific processes are presented, including automatically updating customer information on the CRM system and automatically answering customer inquiries on the system. Testing with data sets of 50, 100, 300, 500 shows that the bot works stably and accurately, and implication has been suggested.

How organizations leverage digital technology to develop customization and enhance customer relationship performance: An empirical investigation

Digital technology (DT) is critical for realizing customization to create business value. However, research on how DT affects customization and customer relationship performance is limited and lacks clarity. Drawing upon organizational learning theory, we examine the impact of firms' DT use on customization and subsequent customer relationship performance. Using survey data from 214 Chinese firms, we find that both DT use for customer exploitation and DT use for customer exploration have positive effects on customization. The effect of DT use for customer exploration on customization is moderated by absorptive capacity. Customization has a positive impact on customer relationship performance. This study contributes to the current literature and managerial practice by promoting a systemic conceptualization of customization and empirically examining the mechanism of DT-driven customization and its benefits.

Impact of Live Chat Service Quality on Behavioral Intentions and Relationship Quality: A Meta-Analysis

The purpose of this research is to synthesize by means of a meta-analysis knowledge acquired to date on the impact of live chat service quality on customer behavioral intentions and relationship quality. An analysis of twenty-nine topical studies demonstrates that live chat capability unquestionably contributes to the formation of customer behavioral intentions, while enhancing customer relationship quality. The study also identifies those variables which exert a moderating effect on behavioral intentions and relationship quality (e.g., age, culture, product typology), and therefore sheds light on the conditions in which these services generate the most benefits. Based on the findings of the meta-analysis, the authors point to gaps in the literature, propose a varied choice of topics for future research by academics, and suggest an extensive lineup of tools and advice for managers.

Translating responsible leadership into team customer relationship performance in the tourism context: the role of collective job crafting

PurposeThis study aims to unravel the role of teams’ job crafting in translating responsible leadership into their customer relationship performance.Design/methodology/approachThe data were gathered from 87 managers and 608 employees from tour companies. The data analysis was performed via multilevel structural equation modeling.FindingsThe results demonstrated that crafting of team tasks played a mediating role for the link of responsible leadership to the team’s customer relationship performance. Task interdependence, outcome interdependence and their interaction attenuated the influence of responsible leadership on collective job crafting.Practical implicationsThe results suggest that tourism managers can enhance customer relationship performance of their team through training and development of responsible leadership, encouraging team members’ crafting of team tasks, as well as enhancing outcome and task interdependence.Originality/valueThis research expands the literature by identifying how and when responsible leadership promotes team customer relationship performance in tourism companies.

Usability factors that drive continued intention to use and loyalty of mobile travel application

The expeditious development of mobile applications has transformed how travel e-commerce firms enhance customer relationships to implement their business strategies successfully. However, the literature has identified that a major determinant affecting customers' considerations to reject a mobile application is a deficiency of usability. Therefore, unlike the typical spotlight on general usability variables found in the prior literature, this study concentrated on investigating more specific usability factors that drive the continued intention to use and mobile application loyalty of a mobile travel e-commerce application. To achieve this, this study utilized the mobile application usability, which we adapted to form an Indonesian version. The dataset was obtained through a survey of 248 users of an Indonesian mobile travel e-commerce application, Traveloka. The collected data were analyzed using partial least square structural equation modeling (PLS-SEM) to test the hypotheses. The results indicated that application utility and the user interface structure emerge as significant drivers for both outcomes. This study could help travel e-commerce firms to develop their mobile strategies effectively, especially in terms of their usability, which may ensure that their customers continue to use the mobile application.

Brand Love: Role of Online Customer Experience, Value Co-creation, and Relationship Quality.

Customer experience is a source of retailers' long-term competitive advantage. This study has examined the relationship between online customer experience and brand love through the mechanism of relationship quality in the context of online shopping in Pakistan. The moderating effect of value co-creation on the relationship of online customer experience with relationship quality and brand love has also been examined. Data were collected from a purposive sampling of 189 online customers in an online survey. Results showed that online customer experience significantly impacts customer relationship quality, which leads to brand love. The relationship between online customer experience and relationship quality is found more robust at high levels of value co-creation. However, we observed a significant negative moderating effect of value co-creation on the direct relationship between online customer experience and brand love. It suggests that including customers in the value co-creation process and affording them a pleasurable online shopping experience may be an excellent way to enhance customer relationship quality and brand love. Theoretical and practical implications of these findings are discussed.

Environmental innovation ambidexterity and customer relationship performance: evidence from the manufacturing industry in China.

Cleaner production and pollution prevention are not only essential for enterprises to improve resource utilization efficiency and competitiveness, but also realistic requirements for enterprises to comply with green manufacturing policies and regulations. Many previous studies have investigated the impacts of environmental innovation on products and environmental performance, but few of them have explored the impact of environmental innovation on customer relationships. The main purpose of this study is to investigate the basic strategies to enhance customer relationship in the context of green development. Building upon the resource-based view, the dynamic capability perspective, and the upper echelons theory, this study develops a conceptual model focusing on the effects of environmental innovation ambidexterity (EIA) on customer relationship performance (CRP), the mediating effects of green product innovation (GPI), and the moderating role of top management's environmental awareness. The results of a survey of 285 high-tech manufacturing enterprises in China show that EIA has a positive effect on CRP. The results of the study also indicate that GPI mediates the relationship between EIA and CRP, and top management's environmental awareness moderates the impact of EIA on CRP. The results offer novel insights for suppliers to improve CRP, and provide theoretical guidance for high-tech manufacturing enterprises to effectively implement pollution prevention and GPI.

A novel hybrid intelligent technique to enhance customer relationship management in online food delivery system

A novel hybrid intelligent technique to enhance customer relationship management in online food delivery system

Deep Learning Based Customer Preferences Analysis in Industry 4.0 Environment

Customer preferences analysis and modelling using deep learning in edge computing environment are critical to enhance customer relationship management that focus on a dynamically changing market place. Existing forecasting methods work well with often seen and linear demand patterns but become less accurate with intermittent demands in the catering industry. In this paper, we introduce a throughput deep learning model for both short-term and long-term demands forecasting aimed at allowing catering businesses to be highly efficient and avoid wastage. Moreover, detailed data collected from a business online booking system in the past three years have been used to train and verify the proposed model. Meanwhile, we carefully analyzed the seasonal conditions as well as past local or national events (event analysis) that could have had critical impact on the sales. The results are compared with the best performing forecast methods Xgboost and autoregressive moving average model (ARMA), and they suggest that the proposed method significantly improves demand forecasting accuracy (up to 80%) for dishes demand along with reduction in associated costs and labor allocation.

Artificial Intelligence Marketing (AIM) for Enhancing Customer Relationships

Based on the literature, we present an artificial intelligence marketing (AIM) framework that enables autonomous machines to receive big data and information, use artificial intelligence (AI) to create knowledge, and then disseminate and apply the knowledge to enhance customer relationships in a knowledge-based environment. To develop the AIM framework, we bring together and curate a wide range of relevant literatures including real-life examples and cases, and then understand how these literatures contribute to the framework in this research topic. We explain the AIM framework from the interdisciplinary perspective, which is an important role of both the artificial intelligence and marketing academia. The AIM framework includes three main components, including the pre-processor, the main processor, and the memory storage. The main processor, which is the key component, uses AI to process structured data processed by pre-processor in order to make real-time decisions and reasonings. The AI approach is characterized by its hypothetical abilities, learning paradigms, and operation modes with human. The strategic use of the developed AIM framework based on the literature to enhance customer relationships, including customer trust, satisfaction, commitment, engagement, and loyalty, is presented. Finally, future potential investigations are presented to drive forward this interdisciplinary research topic.

The effects of customer consumption goals on artificial intelligence driven recommendation agents: evidence from Stitch Fix

The purpose of this study is to investigate the influence of consumer goals on artificial intelligence (AI) driven interactive recommendation agents (IRAs) and examine the effects of consumers’ consumption goals on their attitude toward IRAs and their perception of IRA value in the context of Stitch Fix. In this study, we also investigate the attitude toward IRAs and IRA value that influence the intention to use IRAs. AI powered recommendation agents have achieved noticeable outcomes in the contactless era by boosting revenues and customer value. On the Stitch Fix platform, an online personal styling service platform, AI-driven IRAs provide personalized recommendations to customers encompassing fashion styling and product design. In this study that considers the Stitch Fix context, a successful case of AI-driven IRAs, a survey was used to investigate key questions about the relationships among goals, IRA value, attitude, and intention to use AI-driven IRAs on the Stitch Fix platform. The participants were 321 AI-driven IRA users from South Korea recruited by a professional survey company. In conclusion, the study clarifies the relationship between personal hedonic goals and attitude and the value of IRAs, and the effects of these goals that ultimately influence the intention to use IRAs. The findings offer practical implications for IRAs that can be used to help consumers search and choose products. IRAs can be particularly helpful for engaging consumers with hedonic goals, stimulation, and comfort, especially consumers who seek comfort, as they are more likely to show a positive attitude and value IRAs, which could encourage behavior adherence. Advertising practitioners should target consumers who seek comfort to enhance personalized recommendation strategies via AI-driven IRAs. As IRAs proliferate in the advertising field, the findings offer implications that can enable advertisers and advertising academics and practitioners to better targeted audiences and enhance customer relationships.