Customer Relationship Management Data Research Articles

Predictive Analytics in ADAS Development: Leveraging CRM Data for Customer-Centric Innovations in Car Manufacturing

The rapid evolution of Advanced Driver Assistance Systems (ADAS) has transformed the automotive landscape, necessitating a shift towards more customer-centric development strategies. This paper explores the integration of predictive analytics with Customer Relationship Management (CRM) data to foster innovations in ADAS development. By harnessing insights from customer interactions, preferences, and feedback, manufacturers can anticipate market demands and tailor ADAS features to enhance user experience. Through a comprehensive analysis of case studies and industry practices, we demonstrate how predictive analytics can improve decision-making processes, facilitate the identification of emerging trends, and optimize resource allocation. The findings underscore the potential of leveraging CRM data to drive customer- focused innovations, ultimately resulting in enhanced vehicle safety, satisfaction, and competitive advantage in the automotive sector.

Technological Integration in CRM: A Case Study of Madurai's Hospitality Sector

This study examines the role of technological integration in Customer Relationship Management (CRM) within Madurai's hospitality sector, focusing on how digital tools and software solutions enhance customer engagement and service delivery. As the tourism and hospitality industry in Madurai continues to grow, driven by its cultural significance and heritage, effective CRM practices have become essential for maintaining competitive advantage. This case study investigates how hotels, restaurants, and tour operators in Madurai leverage CRM technologies to streamline operations, personalize guest experiences, and foster long-term relationships. The research analyses the adoption of CRM software, mobile applications, and data analytics in enhancing customer satisfaction, increasing retention rates, and improving operational efficiency. Findings reveal that while the use of technology in CRM has significantly improved service standards and customer interactions, challenges such as system integration, staff training, and cost constraints remain. This study offers insights into the potential of technological CRM solutions to transform the hospitality landscape in Madurai, contributing to both academic literature and practical applications in the tourism and hospitality industries.

Digital Marketing Strategies to Build Customer Loyalty: A Systematic Review of Sustainable Financial Benefits

This study examines the role of integrated digital marketing strategies, including personalized content, Customer Relationship Management (CRM) systems, and social media marketing, in building customer loyalty and driving long-term financial benefits. The research design uses a systematic literature review to examine how these digital tools work together to improve things. This is based on theories like relationship marketing and the resource-based view (RBV). The findings demonstrate that integrating these strategies leads to more muscular customer retention, reduced customer acquisition costs, and enhanced return on investment (ROI). Additionally, it confirms that a multi-channel approach, which aligns CRM data with personalized content and social media, provides a more cohesive customer experience, leading to increased customer lifetime value (LTV). The study highlights practical implications for businesses, emphasizing the need for integrated digital strategies to enhance customer engagement and financial sustainability. However, the study's limitations include its reliance on secondary data and the exclusion of emerging technologies like artificial intelligence. Future research should focus on longitudinal studies and explore industry-specific case studies to provide more tailored insights into the evolving landscape of digital marketing.

The Application of Artificial Intelligence on Customer Relationship Management (CRM) in the Sultanate of Oman Telecommunications Sector

Organizing and sustaining customer relationships is vital to a business in achieving its aims in terms of profit and surviving in a competitive environment. Today's global market and the rapid progression of technology, particularly the internet and computer, have placed companies in a position where they must use technology to sustain their competitive advantage. Objective of the study is to scrutinize the application of artificial intelligence in customer relationship management in Oman telecommunication sector. Since the implementation of AI in telecommunication sector around the world has an impressive record and success. Therefore, the main objective is to see and prove that AI has brought success in telecommunication sector. This way, telecommunication sector in Oman can also follow the same path and reach the same success. AI has been known to be able to cut cost and increase efficiency. This also will create a competitive level between telecommunication companies in Oman. This will actually further extends to the growth of telecommunication infrastructure in Oman. Based on our research conducted on the topic "The Application of Artificial Intelligence in Customer Relationship Management in the Sultanate of Oman Telecommunication Sector," our findings show that among the two telecommunication giants in Oman, Omantel is far ahead of Ooredoo in CRM and at the same time far behind the global standards of CRM. Both the companies do not use AI in their CRM. They both know the importance of AI in CRM. Global best practice analysis is a technique in which a company measures the performance of its business processes and activities against world-class companies' performance, to learn how those companies achieved high performance levels and to adopt the techniques they used. This is precisely the one thing Omantel needs to do as explained above. AI can be used to automate the CRM data warehouse analysis which provides a great starting point for CRM analytics. Data warehouse is a relational database used for query and analysis. This can help both the companies to compare themselves with global standards and make the necessary changes to improve their customer relationships. At the same time, Ooredoo is at a starting point of CRM; the global best practice analysis can help them to turn the odds in their favor. Although Ooredoo will have to wait for AI CRM analysis applications in the data warehouse, till they reach the sufficient level of data in the data warehouse, but they can still make use of AI in other areas. To ensure the successful implementation of AI in CRM, this research suggests that Omani telecommunications companies focus on the following seven recommendations: the first recommendation is to start with small manageable pilot projects, the second recommendation is to align AI initiatives with clear business objectives, The third recommendation is to build a consortium to oversee AI governance, the fourth recommendation is to redesign work processes around AI. AI is a powerful tool for automating and augmenting business processes, the fifth recommendation is to re-skill the workforce, the sixth recommendation is to experiment with different talent models, Unlocking the full potential of AI will likely require Omani telecommunications companies to acquire new talent, the final recommendation is to ensure a robust data strategy.

Concurrent Data Processing in Microsoft Dynamics CRM Using Python

The realm of Customer Relationship Management (CRM) has seen significant improvements with the integration of automation and data analytics. Python, known for its robust data manipulation libraries, offers a seamless experience for handling data in Microsoft Dynamics CRM. This paper aims to serve as a comprehensive guide on how Python can be employed to perform CRUD operations— Read, Update, Insert—on data in Microsoft Dynamics CRM. We delve into the intricacies of using Python's 'requests' library for API calls and 'concurrent futures' for parallel processing, thereby optimizing data manipulation tasks. The paper also presents a performance evaluation showcasing the efficiency gains achieved through these methods. Furthermore, the paper highlights the challenges associated with large-scale data management in CRM systems and proposes Python-based solutions as a scalable and effective approach. The paper concludes with a discussion on the scope of this approach in the broader context of CRM data analytics and automation. The methods and findings presented herein are expected to be of particular interest to data engineers, software developers, and CRM administrators.

BlockChain (BC) Upending Customer Experience: Promoting a New Customer Relationship Management (CRM) Structure Using Blockchain Technology (BCT)

This study aims to explore the role of BC and its impact on CRM by suggesting an extended CRM on the basis of BC capabilities thru developing an analytic hierarchy planning-based framework to establish criteria weights developing a new self-assessment model to determine the most critical factors impacting the BC investment in CRM to enhance customer experience and to enable parties to work together in a trusted technology environment.
 An analytical hierarchical process (AHP) approach was utilized to prioritize and weigh the factors affecting the BC investment in modern CRM in the service industry based on the extant literature and its interpretation. This approach resulted in a ranking of 19 sub-factors based on experienced customer service professionals and technologists’ evaluations. Findings revealed a significant insight into proposing a new generation of CRM based on BCT, focusing on using the powerful BC platform considering all factors influencing the BC investment in modern CRM from a business perspective. Understanding the new combination of BC and CRM can solve the challenges and dilemmas linked to the untrusted environment of handling CRM data in the information systems field. This study provides valuable information and critical analysis of BC regarding CRM integration. Directions for future research are also included.

A genetic-based data mining model for customer relationship management in B2B ecommerce

Customer Relationship Management (CRM) improves the responsiveness and understanding of business strategies among employees and helps to achieve better customer service. However, in Business-to-Business (B2B) eCommerce, CRM data are rarely analyzed across market segments or customer categories, and customer-to-business relationship also poses issues. Thus, making appropriate decisions in the CRM model is difficult. This study presents a modified B2B CRM using the Genetic algorithm and Data Mining Techniques to improve decision making. The model classifies consumers into consumers of Repeat and Shop-and-Go. Modified data mining C5.0 and the Genetic algorithm was employed to optimize rules generated by the decision tree algorithm. The findings showed that the proposed model allocates resources effectively to the most profitable customers’ decisions. The output metrics are machine time, calibration graph, and ROC curve. In comparison with the conventional C5.0, k-NN, and Support Vector Machine, the proposed model has greater accuracy of 89.3 percent.
 Keywords: Customer Relationship Management, B2B eCommerce, Genetic Algorithm, Data Mining

Using the FUSEDIT Framework to combine lead and sales data for a customer relationship management integration project

This paper introduces the FUSEDIT framework — a new framework for the integration of customer relationship management (CRM) sales and lead data that marketers can use to better understand the value of marketing leads. The name ‘FUSEDIT’ is an acronym for the various stages of the process: function and purpose of the integration; unique identifier on form completion; storing of data in the CRM database and other platforms; extracting CRM data; destination and integration of data; insights and visualisation of data; and tactics for channel optimisation and using data for decision making. To illustrate the benefits of the framework, the article presents case studies for two companies that have adopted it, namely, Domino Printing and Switch My Business. These case studies demonstrate how, in the context of a lead-generation business, CRM integration strategies can play an important role in marketing decisions.

Causes of Failure in the Implementation and Functioning of Information Systems in Organizations

When implementing or starting up an information system, there are usually a number of causes that can lead to its failure. Today, there are few companies that do not rely on technology to carry out their business processes. Wanting to have a competitive advantage over its competitors and the changing global business, puts pressure on the implementation of information systems implementation projects, be it an ERP (Enterprise Resource Planning), a CRM (Customer Relationship Management) or Big Data projects to manage a central repository of all internal and external data that a company can manage. Although it is an illusion for the company to start a project to implement an information system, its failure can lead to its key business processes not being carried out correctly. This article has the purpose of exposing the most common causes when implementing an information system, but also during the operation of the system, which can lead to organizational chaos and to take measures that no company wishes to take. A real case of failure is exposed during the implementation of an information system in an important Mexican company. The research team was allowed to interview general and systems area managers as well as employees. In addition, a survey was carried out among 30 people between managers and heads of department who followed closely on the implementation process of the global operations and technology system within of the company. The most influential factors were a deficient administration, a bad definition of the project and inappropriate consultancy.

KERANGKA KERJA PERENCANAAN PENGEMBANGAN PRODUK SEBAGAI PENINGKATAN DAYA SAING INDUSTRI KECIL MENENGAH

This study developed a new product development planning framework for small and medium enterprise (SME) in creative industry sector to increase the business competitiveness. The development process referred to the stages of design science research. The development stage adapted the first two stages of the modular function deployment (MFD), those are the description stage of consumer need through the house of quality, and functional decomposition. The study explained that the application of MFD adaptation is effective and adequate to the creative SME condition. The technical concepts were able to be screened and scored by using pugh selection matrix. However, every technical concepts could not be described into detailed domain functions and means. Therefore, the addition of methods applied in parallel was carried out, that is the concept decomposition with the function means tree, and the technical solution synthesis with the axiomatic design so that the design process is easier to handle. It’s recommended to eliminate the zigzagging process in axiomatic design, since it has been done on the function means tree and the use of customer relationship management data base to support voice of customer.

An RFM and CLV analysis for customer retention and customer relationship management of a logistics firm

To retain a customer in a logistics industry is difficult as it highly depends on the customer relationship management (CRM) and the quality of the service delivered. The existing customers are to be retained for longer period of time for maximum profit contribution. Hence, it is important to perform a data analysis on the CRM data to identify the right segment of customers who are to be retained to maximise profit. This study segments the customers depending on the parameters recency, frequency, and the monetary value, known as RFM analysis. Customer lifetime value estimation is done on segments obtained using the RFM Analysis to identify the profit contribution of each of the segments. The analysis carried out aids the management to adopt decisions on marketing strategies based on the RFM scores and CLV of each segment. Company can also take decisions on budget spending in retaining the right customers.

Tuning of Customer Relationship Management (CRM) via Customer Experience Management (CEM) using Sentiment Analysis on Aspects Level

This study proposes a framework that combines a supervised machine learning and a semantic orientation approach to tune Customer Relationship Management (CRM) via Customer Experience Management (CEM). The framework extracts data from social media first and then integrates CRM and CEM by tuning and optimising CRM to reflect the needs and expectations of users on social media. In other words, in order to reduce the gap between the users’ predicted opinions in CRM and their opinions on social media, the existing data from CEM will be applied to determine the similar behavioural patterns of customers towards similar outcomes within CRM. CRM data and extracted data from social media will be consolidated by the unsupervised data mining method (association). The framework will lead to a quantitative approach to uncover relationships between the extracted data from social media and the CRM data. The results show that changing some aspects of the e-learning criteria that were required by students in their social media posts can help to enhance the classification accuracy in the learning management system (LMS) data and to understand more students’ studying statuses. Furthermore, the results show matching between students’ opinions in CRM and CEM, especially in the negative and neutral classes.

Enhancing revenue management systems performance with CRM systems data

Revenue management systems (RMS) and customer relationship management (CRM) systems are widely used in today's data enriched environments to improve a firm's profitability. Though these systems are used to improve business profitability, their approach and evolution are different, as a result of which these systems are being used separately. In case of RMS, the focus is on short term tactical revenue optimisation, whereas in case of CRM the focus is on developing long term customer relationships. This paper discusses some ways of enhancing performance of revenue management systems by using CRM data. Synergies and conflicts that may arise with the simultaneous use of these systems are discussed, and numerous applications that help in enhancing RMS performance are suggested.

Enhancing revenue management systems performance with CRM systems data

Revenue management systems (RMS) and customer relationship management (CRM) systems are widely used in today's data enriched environments to improve a firm's profitability. Though these systems are used to improve business profitability, their approach and evolution are different, as a result of which these systems are being used separately. In case of RMS, the focus is on short term tactical revenue optimisation, whereas in case of CRM the focus is on developing long term customer relationships. This paper discusses some ways of enhancing performance of revenue management systems by using CRM data. Synergies and conflicts that may arise with the simultaneous use of these systems are discussed, and numerous applications that help in enhancing RMS performance are suggested.

The Role of Buyer Trust in Outsourced CRM: Its Influence on Organizational Learning and Performance

ABSTRACTPurpose: Company outsourcing of customer relationship management (CRM) functions is increasing (Kalaignanam and Varadarajan 2012). Although outsourcing CRM may provide financial benefits, the tasks of developing and utilizing the complex, cross-functional processes needed to gain enhanced customer knowledge from CRM may be more difficult when some or all CRM activities are outsourced. Trust in the information provided by the outsourced CRM supplier is vital. In this study, the authors examine the influence of buyer trust in its outsourced CRM supplier on cross-functional learning processes and firm performance within the buyer firm.Methodology: Data were collected from a survey of marketing managers in 221 firms. LISREL 9.2 was used to assess convergent, discriminant, and nomologic validity using the two-step approach (Anderson and Gerbing 1988). Convergent and discriminant validity were evaluated in the measurement model phase, whereas the structural model provided an appraisal of nomologic validity.Findings: The results provide evidence of buyer firm trust in the outsourced CRM supplier playing a critical role in the buyer firm’s success with information sharing, and both trust and information sharing strongly influencing information interpretation and information access in the buyer firm. All three organizational learning processes positively influence buyer firm customer satisfaction/retention and market performance.Research implications: An important area for future research is the possibility of varying levels of trust needed for success with outsourced CRM depending on the buyer firm’s goals for its CRM system. It is possible that the simpler CRM functions could be outsourced effectively through efficiency strategies that do not require significant levels of trust, whereas the more complex CRM activities that affect organizational learning require more stringent coordination and inter-organizational development. Varadarajan’s (2009) cost versus quality classifications of outsourcing could be a useful starting point for this type of analysis. Considering the finding in this study that information sharing is critical for information interpretation and information access in the buyer firm, another area for future research is possible differences in the extent of information sharing required by firms that are outsourcing CRM versus those that conduct the CRM function in-house. One starting point could be possible differences in relevance among Maltz and Kohli’s (1996) factors affecting information dissemination.Practical implications: For effective use of CRM data, it is important for buyer firms to develop trust in their outsourced CRM supplier. Managers can assist in this by communicating qualifications of the outsourced CRM supplier, such as any trade-specific certifications, awards, information about the supplier’s number of years in business, and examples of other companies the supplier has assisted. Managers can also help employees develop confidence in the supplier’s integrity by sharing the supplier’s code of ethics and serving as a champion for the supplier. In addition, firms engaged in outsourced CRM are encouraged to develop reward systems that motivate employees to build relationships with their counterparts in the supplier firm, and it would be useful for the buyer firm to help its employees understand the importance of the CRM outsourcing relationship to the buyer firm’s success. Finally, it is important for management to provide opportunities for interaction between the outsourcing partner and key buyer firm employees who will use the CRM data, to encourage effective processes in information sharing, information interpretation, and information access.Contribution of the article: This article addresses the significance of outsourcing the CRM function and provides evidence that buyer trust in its CRM supplier is a critical factor in its utilization of CRM data for organizational learning and firm performance. It also demonstrates that effective sharing of information, cross-functional integration of customer data, and CRM information accessibility are critical for firm success.

Improving customer oriented decision making through the customer interaction approach

An organisation that has a robust customer interaction approach can develop a more holistic understanding of its customers. This insight is crucial for reducing related uncertainty in management decision-making. Understanding of the customer is a basic tenet for supporting decision makers in taking the right decisions. The objective of this literature review is to learn about the evolution of the customer interaction approach. The customer interaction approach is stressed as a relationship channel between the customer and organisation. The emergence of the customer interaction approach is discussed in terms of four existing approaches to customer related quality improvement:Total Quality Management (TQM); Service Quality Frameworks (SERVQUAL), Customer Relationship Management (CRM), and Customer Experience Management (CEM). This analysis indicates a number of key observations about CRM and CEM as the most recent customer interaction approaches. In the existing literature, CRM and CEM are well-defined. Distinctly, the relationship between CRM and CEM is complementary in nature. This means CRM promotes the management of customer data in a systematic way within a firm, while CEM reaches beyond the boundary of the organisation to support a holistic customer interaction approach underpinned by CRM data.

A hybrid multi-criteria decision model for supporting customer-focused profitability analysis

Purpose – Strategic analysis of customer profitability for assessing market segmentation and reconfiguring customer relationship management (CRM) activities remains the key factor for achieving high return on CRM investment. The purpose of this paper is to map the profit-based ranking of corporate customers into the current market segments, with a view of determining the relative profitability of each market segment. Design/methodology/approach – This study develops a novel model that combines activity-based costing (ABC), CRM, fuzzy analytic hierarchy process (AHP), and technique for order preference by similarity to ideal solution (TOPSIS) methods to evaluate strategically customer profitability and prioritizing corporate accounts. This case study airline company has invested heavily in CRM over the past seven years on integrating multi-functional departments that touch customers. The airline operations management and marketing functions provide key inputs. Results of the hybrid model validate feasibility of the proposed model. Findings – The airline management makes use of the ranking results to optimize customer profitability by reconfiguring marketing programs, integrated schedule design, fleet assignment, maintenance routing, crew scheduling, and real-time optimization of schedule recovery in the aftermath of disruptions or irregularities. The proposed model also directs the marketing function to customize service offerings and introduce appropriate service levels to engage customers of different segments for the purpose of maximizing corporate profitability. Research limitations/implications – Significant amount of investment is necessary to design and implement the extensive CRM database and systems to assure customer data quality and availability so as to bear fruits in the proposed hybrid model. These data requirements can especially be a critical barrier for small to medium-sized companies. Practical implications – This hybrid model is able to capitalize on the benefits of the ABC, CRM, fuzzy AHP, and TOPSIS methods and offset their deficiencies. Most importantly, it can be applied to various industries without complex modification. Originality/value – This study represents the first move to adopt the fuzzy AHP and TOPSIS methods to analyze the ABC and CRM data inputs of an airline company. In mapping the profit-based ranking of corporate customers into the current market segments, the relative profitability of each market segment can be determined.

Analysis of automobile repeat-purchase behaviour on CRM

Purpose – The purpose of this paper is to suggest a method of selecting efficient customer service programmes and of providing relevant customer information to dealers, based on the analysis of repeat-purchase behaviour data in the automobile industry. Design/methodology/approach – A recurrent event model is proposed and employed to determine which variables affect repurchasing behaviour in consumers' repurchase cycles. Unlike the conventional recurrent event model, the proposed model uses common variables for all strata, as well as stratum-specific variables. Findings – Empirical results show that age, price difference, change in vehicle type, and marketing promotion affect the likelihood of repeat purchase. VIP service centres and repair services are effective marketing tools, and dealers should pay more attention to existing customers having certain characteristics, depending on prior purchase behaviour. Practical implications – Though many customer service programmes are devised and implemented at great cost, Customer Relationship Management (CRM) data reveal that classic car-care services are the most essential. CRM can provide dealers with essential customer information that enables real purchases. Originality/value – Collecting primary data on automobile purchase behaviour and customer service usage is difficult, and therefore, customer behaviour strategy is often formulated using basic principles alone. The paper proposes a method to construct a service strategy and formulate deal guidelines based on CRM data and statistical modelling.

The development and diffusion of customer relationship management (CRM) intelligence in business-to-business environments

CRM data is among the most important and comprehensive information available to management in many organizations. This is particularly the case in business-to-business marketing, where the firm's extended working relationship with its customers is frequently crucial for the maintenance of a healthy business. However, in many instances management has treated CRM data as highly specific to its client relationships and has therefore neglected to analyze this information across market segments, customer categories, and customer–firm relationship forms in order to draw meaningful conclusions for driving business decisions.The authors present a method for effectively classifying CRM information in ways that may help guide management decisions. This can lead to an improved understanding of the in-forming process in the firm's dealings with its clients, the directionality of customer–firm decision-making, the key decision drivers across deals, and the historical record of the firm's relationship with its customers. The end result will be an improved use of valuable historical information for establishing competitive strategies and the related programs and policies for retaining and growing the firm's customer base and other elements of the firm's value delivery network.

An enhanced Customer Relationship Management classification framework with Partial Focus Feature Reduction

Effective data mining solutions have for long been anticipated in Customer Relationship Management (CRM) to accurately predict customer behavior, but from various industry research and case studies we have observed sub-optimal CRM classification models due to inferior data quality inherent to CRM data set. In this paper, one type of CRM data with a distinctive distribution pattern of Reduced Dimensionality is discussed. A new classification framework termed Partial Focus Feature Reduction is proposed to resolve CRM data set with Reduced Dimensionality using a collection of efficient data mining techniques characterizing a specially tailored modality grouping method to significantly improve data quality and feature relevancy after preprocessing, eventually achieving excellent classification performance with the right combination of classification algorithms.