A Knowledge Mining Approach for Effective Customer Relationship Management

The volume of information available on the Internet and corporate intranets continues to increase along with the corresponding increase in the data (structured and unstructured) stored by many organizations. In customer relationship management, information is the raw material for decision making. For this to be effective, there is need to discover knowledge from the seamless integration of structured and unstructured data for completeness and comprehensiveness which is the main focus of this paper. In the integration process, the structured component is selected based on the resulting keywords from the unstructured text preprocessing process, and association rules is generated based on the modified GARW (Generating Association Rules Based on Weighting Scheme) Algorithm. The main contribution of this technique is that the unstructured component of the integration is based on Information retrieval technique which is based on content similarity of XML (Extensible Markup Language) document. This similarity is based on the combination of syntactic and semantic relevance. Experiments carried out revealed that the extracted association rules contain important features which form a worthy platform for making effective decisions as regards customer relationship management. The performance of the integration approach is also compared with a similar approach which uses just syntactic relevance in its information extraction process to reveal a significant reduction in the large itemsets and execution time. This leads to reduction in rules generated to more interesting ones due to the semantic clustering of XML documents introduced into the improved integrated mining technique.

Customer Relationship Management (CRM) systems are essential for businesses to manage customer interactions, enhance engagement, and drive customer satisfaction. Traditional CRM solutions face challenges such as data security risks, inefficiencies in processing customer data, and limitations in providing personalized experiences. Emerging technologies like blockchain and machine learning offer promising solutions to address these challenges. Blockchain enhances CRM by providing decentralized, secure, and tamper-proof data storage, ensuring transparency and reducing the risk of fraud. Machine learning improves CRM by enabling predictive analytics, customer segmentation, and automation of customer interactions. This paper provides a comprehensive survey of existing techniques that integrate blockchain and machine learning in CRM, analyzing their impact on security, efficiency, and customer engagement. The study compares various approaches, highlighting their advantages and limitations while assessing their practical applications in real-world CRM systems. Additionally, this paper examines how these technologies enhance decision-making, optimize customer management strategies, and ensure compliance with data protection regulations. The findings suggest that while blockchain strengthens data security and trust, machine learning enhances automation and personalization, making CRM systems more intelligent and efficient. This survey provides insights into the evolving landscape of CRM technologies and offers a comparative analysis of how blockchain and machine learning are shaping the future of customer relationship management. By reviewing current methodologies and their effectiveness, this paper aims to guide businesses and researchers in understanding the potential and challenges of these technologies in CRM applications.

Multi-dimensional semantic clustering of large databases for association rule mining

This scholarly article explores the transformative evolution of customer relationship management (CRM) systems by integrating predictive analysis, artificial intelligence (AI), and machine learning. Traditional CRM systems exhibit weaknesses in areas such as customer privacy exploitation and differential treatment, necessitating a reevaluation of their foundational principles. Integrating advanced analytics and machine learning algorithms emerges as a strategic avenue for modernizing CRM, allowing organizations to anticipate customer needs, address issues proactively, and foster meaningful relationships. The paper details the re-architecting of CRM systems, emphasizing considerations for organizations transitioning from traditional to modern approaches. Scalability, flexibility, and adaptability are essential principles for ensuring CRM systems evolve with dynamic customer needs. The article also addresses the challenges and ethical considerations of integrating predictive analysis and AI into CRM, emphasizing responsible data practices and transparent decision-making processes. Furthermore, the paper projects future trends in CRM evolution, outlining the significance of automation, personalization, reliance on AI, and social media integration. The convergence of these trends is poised to redefine customer experiences, creating a landscape where hyper personalization and seamless omnichannel interactions are the norm. In conclusion, the transformative evolution of CRM systems extends beyond technological upgrades to encompass a strategic shift in organizational culture and customer-centricity. By embracing the potential of predictive analysis, AI, and machine learning, organizations can navigate challenges, ensuring CRM remains committed to genuine customer satisfaction, trust, and enduring partnerships.

The general perspective of the chapter is focused on discovering marketing knowledge based on Customer Relationship Management (CRM) systems. The question is: “How to do automate processes in the implemented CRM system to discover the knowledge that is useful for marketing?” It is a natural question because the stored data creates a large volume and it is difficult to set up a marketing with hands. This chapter focuses on finding the necessary product specifications to automate the marketing needs this CRM system must offer to be optimal in today's modern global society. The existing controversy is between IT for everyday use, real IT capabilities, human skills, and knowledge to support more complex implemented processes. Emphasis is placed on automation and intelligence. The analysis shows that CRM systems are interested in managing customer relationships in the form of a single agent or process to perform the necessary actions using implemented algorithms that utilize various intelligence, statistical methods, multi-criteria decision-making, and automated learning predictions.

The customer relationship management (CRM) system has been adopted by many organizations to get better understanding of their customers’ needs and efficient utilization. The aim is to help organizations draw their strategies and enhance the quality of their products and services. Both public sector and government entities can use CRM systems to understand citizens needs and enhance services provided accordingly. This paper presents a case study measuring the efficiency and effectiveness of CRM systems in public sectors. The case study is conducted through an anonymous survey that is applied to one of the Dubai’s government entities. This survey focuses on eight questions which was answered by users who are using a CRM system in a governmental entity in Dubai. The results showed that 21% of respondents are extremely satisfied with the outcomes of the CRM. Moreover, 67% of the users showed moderate satisfactions, and the remaining respondents tends to be slightly unsatisfied. The in-depth gap analysis suggested that enhancements are essential to improve user’s satisfactions with CRM usage and expected outcomes.KeywordsCustomer relationship managementKnowledge managementCustomers knowledgePublic sectorDubai governmentCitizen’s benefits

This research paper explores the critical role of Customer Relationship Management (CRM). systems in enhancing patient engagement and improving overall healthcare delivery. With the increasing emphasis on patient-centered care, healthcare organizations are seeking innovative solutions to foster stronger relationships with patients, streamline interactions, and improve service quality. Drawing on insights gained from implementing CRM systems at eMedic Technologies Africa, the study analyzes how these platforms can significantly enhance communication, facilitate personalized care, and drive operational efficiency within healthcare settings. CRM systems serve as a powerful tool for managing patient interactions, enabling healthcare providers to gather and analyze data on patient preferences, behaviors, and needs. This data-driven approach allows organizations to tailor services and communications, leading to increased patient satisfaction and loyalty. By streamlining administrative processes, CRM systems reduce the burden on healthcare staff, allowing them to focus on delivering high-quality care. The paper proposes a comprehensive framework for integrating CRM tools into healthcare operations. This framework includes key components such as data management, communication strategies, and patient feedback mechanisms. By effectively leveraging CRM capabilities, healthcare organizations can improve appointment scheduling, enhance follow-up care, and provide timely reminders for preventive services. Furthermore, the study emphasizes the importance of fostering a culture of patient engagement, where healthcare providers actively seek feedback and involve patients in their care decisions.Ultimately, this research highlights the transformative potential of CRM systems in healthcare delivery. By prioritizing patient engagement through effective CRM implementation, healthcare organizations can not only improve patient outcomes but also achieve greater operational efficiency. The findings underscore the need for a strategic approach to adopting CRM technologies, ensuring that healthcare providers are well-equipped to meet the evolving demands of patients and navigate the complexities of modern healthcare. This paper serves as a valuable resource for healthcare leaders looking to enhance patient engagement and optimize service delivery through innovative CRM solutions. Keywords: CRM Systems, Patient Engagement, Healthcare Delivery, Operational Efficiency, Digital Healthcare Solutions

Nowadays, companies have as key element for their marketing techniques the customer relationship management (CRM) systems. The purpose of this study is to examine the factors that affect the implementation of CRM in a business. A survey was conducted with the completion of structured questionnaires by collecting data from business in the area of Eastern Macedonia and Thrace (EMT). The findings showed that companies of EMT implement satisfactorily strategic methods relating to CRM, putting their customers at the heart of their business. Moreover, they invest heavily in the management of their customers, wanting to gain a long-term relationship with them and simultaneously to integrate customer-oriented approach to the culture and the structure of the company. The external and internal factors that act strongly in maintaining and developing the customer relationship with the business, were identified. The number of employees and the year of foundation are critical factors in the implementation of CRM.

Customer relationship management (CRM) has been increasingly adopted by companies as the core of IT – driven business strategy and firms have started to invest in CRM. However, the rate of successful CRM implementations is about 30%. Many enterprises pursue expensive CRM initiatives without first understanding of the challenges and costs involved. This approach often results in CRM projects that fail to meet measurable benefit objectives. Evaluating costs and benefits associated with CRM systems acquiring and implementation and applying financial measurement methodologies can help a company make the right CRM investments solution and build a compelling justification for its CRM project. Return on investment (ROI) financial measurement methodology, its theoretical and practical aspects are analyzed in this study. Authors of this paper provide a detailed list of CRM cost categories, which can assist an organization to easily calculate its CRM system acquisition and ongoing spending. Also the authors of this paper present survey results of CRM system costs and investment efficiency evaluation in Lithuanian companies.

The biggest key aspect to the success of a business is a satisfied customer. For this reason, it is possible to state that the growing trend of focusing on the customer and his/her needs has prevailed in recent years. The aim of this article is to analyze the use of CRM (Customer Relationship Management) systems in small- and medium-sized enterprises (SMEs) in the Czech Republic and to find the determinants for CRM system implementation. The best practice for CRM implementation suitable for SMEs is clarified using a specific case of a global enterprise. A fully functional CRM system can be considered a competitive advantage, and this is not only the case for global companies, but also for small and medium enterprises. Using a functional CRM interconnected with an ERP system, enterprises are able to manage business and direct marketing activities, as well as the company’s overall profits. These functional systems lead to an integrated system called funnel management, which improves customer relationship management and leads to a sustainable business.

One of the most important data mining problems is mining association rules. In this paper we consider discovering association rules from large transaction databases. The problem of discovering association rules can be decomposed into two sub-problems: find large itemsets and generate association rules from large itemsets. The second sub-problem is easier one and the complexity of discovering association rules is determined by complexity of discovering large itemsets. In this paper, we suggest Apriori-based algorithm for discovering large itemsets. Actually, we suggest a new procedure for large itemsets generation which is more efficient than the appropriate procedure of the original Apriori algorithm. For its implementation, we suggest a modified sort-merge-join algorithm, which is more efficient than nested-loop-join algorithm, which is suggested in the original Apriori algorithm. Besides, we propose a way in which Apriori Multiple finishes in just two iterations.

Background: Decline in epidemiology cohort response rates has been documented over decades, with precipitous declines in recent years. Incentives and repeated contact attempts have been shown to increase retention to varying degrees, but the effects of the different types of incentives and methods of contact—details necessary for reliable replication—remain poorly understood. Numerous factors related to study implementation and participant characteristics likely affect response rates, but few cohort studies have published the detailed empirical data that would be necessary to assess their effect. The small number of cohort studies that have published analyses of their recruitment protocols raises the question of how many studies are systematically and rigorously examining their methods. A 2011 review suggested that cohort studies may be less likely to engage in these activities due to concerns of risk to participant loyalty and the cost of assessment. Epidemiology cohorts often involve many concurrent activities and overlapping projects, which may also contribute to the complexity of the task. Considering the long duration of cohort studies, some may perceive that study staff can accurately intuit best practices. However, these challenges are not unique to epidemiology cohorts. The private sector is also concerned with attracting and retaining customers, and as such, companies have developed tools that enable rapid, low-cost reporting on their methods. In the field of marketing, customer relationship management (CRM) systems facilitate both the desired outcome (a customer buying a product) and the collection of data to identify factors that influenced the outcome. Aspects of these targeted marketing approaches and their related software tools provide both a model and a means by which epidemiology cohorts can better understand the constellation of factors that affect attrition among their own populations. Methods: The California Teachers Study (CTS) is a large prospective cancer epidemiology cohort that began in 1995. Since 2013, the CTS has used a commercially available CRM platform, modified with a variety of native applications (apps) and integrations, to manage participant recruitment and data collection for multiple projects. To manage a large-scale biobanking project and multiple tissue collection projects, we began with the platform’s standard contact data management and activity tracking features. We added an app for geolocation fieldwork management and a custom mobile app for surveys and biospecimen chain-of-custody tracking. To manage two follow-up questionnaires, we used available integrations for survey development, email marketing automation, a cloud-based call center with automated activity logging, and a web form for participants to submit information securely from our study website. Central features of all projects have included native reports and dashboards, workflow automations that reduce data entry, and data security measures. Results: The CRM platform has scaled to meet the needs of multiple concurrent projects with highly varied study designs and staffing requirements. By conducting our recruitment activities within the CRM, each interaction with the system (an incoming call from a participant, a blood collection, an automated survey reminder) passively generates useful metadata. These metadata, in concert with structured and unstructured data from participants and staff, enable timely monitoring and retrospective analysis using native reporting tools within the system. The ability to view and analyze clean data from the outset of each project revealed key—and sometimes unexpected—factors that were influencing response. Analysis of data across projects has revealed that characteristics of participants, staff, protocol decisions, technology, and equipment all influence outcomes in distinct ways. Conclusion: Existing commercially available tools, such as a CRM system, can be readily configured to meet the specific research, process, and security requirements of population sciences research. In the CTS, using a CRM system helped reveal that different factors affected participation differently, even within the same project. This presentation will discuss these factors, the approaches studies can use to collect data about these factors, and lessons learned about ways to further improve response rates. Broader adoption of CRM tools could enable the type of data collection and analysis necessary to more precisely define the factors that affect participation and develop more targeted and successful recruitment methods. Citation Format: Jennifer Benbow. Data-driven recruitment: Implementation of digital marketing strategies for improved participant response rates and retention [abstract]. In: Proceedings of the AACR Special Conference on Modernizing Population Sciences in the Digital Age; 2019 Feb 19-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(9 Suppl):Abstract nr IA04.

For the past decade, customer relationship management (CRM) has been one of the priorities in marketing research and practice. Hence, many companies have invested heavily in CRM systems that, unfortunately, did not meet their expectations. Because such shortcomings may have resulted from unrealistic expectations as well as inappropriate data input, this study provides insights into what companies may expect from CRM and what data they should use. Across the phases of the CRM process, the authors show which CRM objectives have been considered and which customer data have proven to be applicable in the empirical CRM literature. The results indicate that despite differences with respect to influence, a variety of customer data can be used to analyze CRM objectives throughout the entire customer life cycle. Overall, the study provides researchers with a comprehensive review of the empirical research on CRM and offers practitioners insights on the scope of CRM analyses and the applicability of customer data for CRM.

The evolution of the digital economy has significantly influenced Customer Relationship Management (CRM), reshaping business strategies across industries. This study explores the development and challenges of CRM implementation during 2010–2024, with a focus on its adoption by small and medium-sized enterprises (SMEs). Employing a Systematic Literature Review (SLR) guided by the PRISMA framework, 36 scholarly articles were analyzed to identify trends, obstacles, and outcomes associated with CRM in the digital economy. Findings reveal a steady increase in CRM adoption, particularly among SMEs, driven by advancements in electronic CRM (E-CRM) systems and the COVID-19 pandemic’s acceleration of digital transformation. Empirical data highlights that over 60% of Indonesian SMEs adopting CRM experienced a 20% sales increase within a year. Moreover, CRM implementation has enhanced customer loyalty by 25% and reduced marketing costs by 10% in European markets. However, significant challenges persist, including high implementation costs, inadequate technological readiness, and rising cybersecurity threats. For instance, cyberattacks targeting CRM systems have decreased customer trust by up to 15%. The study concludes that while CRM significantly boosts operational efficiency, customer satisfaction, and business innovation, addressing implementation barriers is crucial for its broader adoption. It recommends enhanced government and institutional support to provide SMEs with access to CRM technologies and training. The findings underscore CRM's critical role in fostering competitive advantages and call for future research to examine its long-term impacts in developing economies.

The versatility of customer relationship management (CRM) systems has kept these technologies popular over the years. These solutions have been integrated into organizations of all sizes, from large corporations to small- and medium-sized enterprises. Similarly, CRM systems have also found applications in all types of industries and business sectors. All this has been the driving force behind the proliferation of CRM solutions around the world. In this chapter, the author not only reflects on the impact and democratization of CRM systems on business management and marketing strategies but also explores how these technologies can determine the company's income. In particular, the author presents an experiment that analyzes the extent to which the volume of annual investment in CRM solutions can be used to predict annual net income in a sample of companies. Using time series analysis and applying the autoregressive integrated moving average modeling technique, the researcher examines a sample of 10 companies from different industries, and countries, over a 20-year period. The results show the efficiency of the predictive models developed in nine of the 10 companies analyzed. The findings of this study allow us to conclude that there seems to be an association between the investments made in CRM solutions and the income of the companies that invest in these technologies.