Fuzzy Clustering-Based GMDH Model to Feature Selection in Customer Analysis

Companies are required to be ready to face the competition will be intense with other companies, including micro-finance institutions. Faced more intense competition, has led to many businesses in microfinance institutions find profitable strategy to distinguish from the others. Strategy that can be applied is implementing Customer Relationship Management (CRM) and data mining. Data mining can be used to microfinance institutions that have a large enough data. Determine the potential customers with customer segmentation can help the decision-making marketing strategy that will be implemented . This paper discusses several data mining techniques that can be used for customer segmentation. Proposed method of data mining technique is fuzzy clustering with fuzzy C-Means algorithm and fuzzy RFM. Keyword s : Customer relationship management; Data mining; Fuzzy clustering; Micro-finance institutions; Fuzzy C-Means; Fuzzy RFM

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.

Customer Relationship Management (CRM) plays a crucial role for businesses as technology advances and consumer demands continue to rise. However, CRM implementation faces challenges from both external and internal factors within an organization. Ineffective CRM implementation can lead to customer dissatisfaction, decreased loyalty, reduced profitability, and missed business opportunities. This research aims to improve the MILKY CRM loyalty program by identifying the key root causes and proposing alternative solutions, ultimately determining the best alternative to enhance the program. The methodology includes in-depth interviews and analysis using the Kepner-Tregoe framework, which involves problem analysis, decision analysis, and potential problem analysis. Supported by external and internal analyses such as Porter’s Five Forces, Customer Journey Mapping, and an Integrated CRM Scorecard, the results are then utilized for a SWOT analysis. The problem analysis identified 21 attributes as the root causes affecting MILKY CRM loyalty program performance. These attributes include Consumer Behaviour Change, Aggressiveness of Competitors, CRM Agency Capability, Healthcare Apps Strategic Partnership, Potential New Segments generated by New Entrants, CRM Differentiation, Strong Brand Reputation, Content Management, Ability Services Program, Customer Engagement, Customer Experience Improvement, Win Back Lapsed Customers, Incomplete metrics tracking on CRM scorecard, Financial Budget Constraints, Personalization Services & Rewards, Digital Acquisition Transition, Segmentation & Tiering Review, Balancing Strategy on Customer Acquisition & Retention, IT System Capabilities, and Human Capability on CRM. In the decision analysis section, alternative solutions were generated through TOWS analysis, and the best alternative was determined using the Analytical Hierarchy Process (AHP) tool. The top chosen strategy, Strategy #2: Optimizing CRM & Organizational Strategies for Enhanced Customer Engagement & Operational Efficiency (W/O Strategies), is recommended for future improvement. Focus areas include communication, resources, technology, and KPI metrics to mitigate potential problems. Overall, this research provides valuable insights and recommendations for enhancing the performance of the MILKY CRM loyalty program, ensuring its effectiveness in meeting customer needs and driving business success.

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.

The problem faced by the company is how to determine potential customers and apply CRM (Customer Relationship Management) in order to perform the right marketing strategy, so it can bring benefits to the company. This research aims to perform clustering and profiling customer by using the model of Recency Frequency and Monetary (RFM) to provide customer relationship management (CRM) recommendation to middle industrial company. The method used in this study consists of four steps: data mining from transaction history data of customer sales, data mining modeling using RFM with K-Means algorithm and customer classification with desicion tree (J48), determination of customer loyalty level and recommendation of customer relationship management (CRM) on the medium-sized industry. This research produces RFM data mining model for medium industrial companies. In addition, the results of the study provide recommendations of four customer segments and characteristics of each customer to perform customer relationship strategy.

Customer Relationship Management (CRM) is adapted “product-centric” to “customer-centric” business model. The relationship marketing strategy needs developed a new management philosophy. Customer segmentation and customer value is the one of the core functions of CRM, customer's access, customer retention and customer value-added processes such as customer relationship management to provide comprehensive support to increase customer satisfaction and customer value. This dynamic development from the perspective of Precision Marketing, customer value in dealing with the problem, explore the interaction model based on a combination of both, to provide new applications for the CRM method.

An Empirical Investigation into the Effects of Customer Relationship Management on Bank Performance in Nigeria

In the ever-increasing competitive and customer-centered global market, the role of Customer Relationship Management (CRM) is essential to the performance, longevity, competitiveness and profitability of enterprises. The study aims to provide an overview of the impact of emerging technologies on traditional CRM and Social CRM. Therefore, through a critical review, this study explores the use of Artificial Intelligence (AI), Blockchain, Big Data Analytics (BDA), Data Mining and Machine Learning (ML) technologies in CRM. Based on the results, the above-mentioned technologies can significantly improve CRM. More specifically, their successful implementation enables enterprises to stay ahead of competition, to get to know their customers, improve their overall performance, Quality of Experience (QoE) and Quality of Service (QoS), increase profits, and reduce time consumption, risks and costs. Finally, by integrating these technologies, enterprises will offer more personalized and customized products and services and greatly improve customers' satisfaction, acquisition, relationships, loyalty, experience and retention.

Dimensionality reduction is an interesting area of research in data mining. An effective way to reduce dimensions is feature selection that removes irrelevant information meanwhile helping to understand the learning model better and improving prediction accuracy. In this paper, we face a challenge of filter methods to determine number of significant features that achieves better performance since filters don’t evaluate performance based on accuracy but use certain criteria to rank features based on some scores. To handle this challenge, we proposed an effective hybrid approach for feature selection that is a filter-based method inspired by concepts of chi-square, Relief-F, and mutual information. It provides a score for each feature then specifies threshold value automatically based on dataset in use to select important subset of features used to build model, which reduces required execution time and amount of memory. Our proposed approach was analyzed empirically and theoretically to demonstrate its efficiency.

Does the level of experience have an effect on CRM programs? Exploratory research findings

Data Mining is the use of algorithms to extract the information and patterns derived by the knowledge discovery in database process. It is often referred to as supervised learning because the classes are determined before examining the data. In many data mining applications that address classification problems, feature and model selection are considered as key tasks. That is, appropriate input features of the classifier must be selected from a given set of possible features and structure parameters of the classifier must be adapted with respect to these features and a given data set. This paper describes feature selection and model selection simultaneously for Multilayer Perceptron (MLP) classifiers. In order to reduce the optimization effort, various techniques are integrated that accelerate and improve the classifier significantly. The feasibility and the benefits of the proposed approach are demonstrated by means of data mining problem: Direct Marketing in Customer Relationship Management. It is shown that, compared to earlier MLP technique, the run time is reduced with respect to learning data and with validation data for the proposed Multilayer Perceptron (MLP) classifiers. Similarly, the error rate is relatively low with respect to learning data and with validation data in direct marketing dataset. The algorithm is independent of specific applications so that many ideas and solutions can be transferred to other classifier paradigms.

<p>Classification problems often have a large number of features, but not all of them are useful for classification. Irrelevant and redundant features may even reduce the classification accuracy. Feature selection is a process of selecting a subset of relevant features, which can decrease the dimensionality, shorten the running time, and/or improve the classification accuracy. There are two types of feature selection approaches, i.e. wrapper and filter approaches. Their main difference is that wrappers use a classification algorithm to evaluate the goodness of the features during the feature selection process while filters are independent of any classification algorithm. Feature selection is a difficult task because of feature interactions and the large search space. Existing feature selection methods suffer from different problems, such as stagnation in local optima and high computational cost. Evolutionary computation (EC) techniques are well-known global search algorithms. Particle swarm optimisation (PSO) is an EC technique that is computationally less expensive and can converge faster than other methods. PSO has been successfully applied to many areas, but its potential for feature selection has not been fully investigated. The overall goal of this thesis is to investigate and improve the capability of PSO for feature selection to select a smaller number of features and achieve similar or better classification performance than using all features. This thesis investigates the use of PSO for both wrapper and filter, and for both single objective and multi-objective feature selection, and also investigates the differences between wrappers and filters. This thesis proposes a new PSO based wrapper, single objective feature selection approach by developing new initialisation and updating mechanisms. The results show that by considering the number of features in the initialisation and updating procedures, the new algorithm can improve the classification performance, reduce the number of features and decrease computational time. This thesis develops the first PSO based wrapper multi-objective feature selection approach, which aims to maximise the classification accuracy and simultaneously minimise the number of features. The results show that the proposed multi-objective algorithm can obtain more and better feature subsets than single objective algorithms, and outperform other well-known EC based multi-objective feature selection algorithms. This thesis develops a filter, single objective feature selection approach based on PSO and information theory. Two measures are proposed to evaluate the relevance of the selected features based on each pair of features and a group of features, respectively. The results show that PSO and information based algorithms can successfully address feature selection tasks. The group based method achieves higher classification accuracies, but the pair based method is faster and selects smaller feature subsets. This thesis proposes the first PSO based multi-objective filter feature selection approach using information based measures. This work is also the first work using other two well-known multi-objective EC algorithms in filter feature selection, which are also used to compare the performance of the PSO based approach. The results show that the PSO based multiobjective filter approach can successfully address feature selection problems, outperform single objective filter algorithms and achieve better classification performance than other multi-objective algorithms. This thesis investigates the difference between wrapper and filter approaches in terms of the classification performance and computational time, and also examines the generality of wrappers. The results show that wrappers generally achieve better or similar classification performance than filters, but do not always need longer computational time than filters. The results also show that wrappers built with simple classification algorithms can be general to other classification algorithms.</p>

Customer relationship management (CRM) is no longer a consulting buzzword but reality for many companies. Cable and telecommunications companies are prime candidates for a CRM transformation, as competition in this sector increases and excellent service is a critical success factor. This paper discusses needs for CRM and how it can address many operational challenges. It also focuses on enhancing the e-business aspects of interactions to provide a consistent customer experience. With E-CRM in place, customer segmentation is enhanced and high value customers are treated properly. Finally, in addition to technology, all aspects of a CRM transformation must be considered which impacts processes, people and supplier/outsourcer relationships.

A good deal has been written about customer relationship management (CRM), one-to-one marketing and event-driven marketing, and the dramatic potential of these strategies is reasonably clear to all. However, the deployment of the necessary information systems to realise the marketing nirvana of genuine customer-centred enterprises is still a good way off. There is one single overwhelming reason why progress is slow: data is not being harnessed with anything like the degree of sophistication that technology allows. It may even be, from a casual observation of the enterprises that trumpet a CRM strategy, that many fundamentally misunderstand the concept. For a customer-centred strategy to be successful we need to comprehensively understand customer behaviour and responsiveness. This, in turn, will allow us to understand customer life cycle, customer loyalty, customer risk, customer profitability and customer segmentation. From here we may proceed to a corporate strategy based on this intelligence; a strategy that will undoubtedly precipitate a radical overhaul of business processes and culture. The systems associated with this customer discovery process are termed ‘analytical CRM’, in contrast to the operational CRM systems that are deployed in a call centre to handle the interaction with the customer. Many enterprises have invested heavily in the infrastructure for operational CRM (call centre technology, funnel management systems, campaign management technology, loyalty cards) without having any coherent strategy for analytical CRM. The result is that they are well positioned to deal with individual customer interactions but hopelessly blind to the history and pattern of customer transactions over time. The outcome is, generally, disappointing and predictable. The source of failure can be located in the contradiction of attempting to deal with customers as distinct individuals without retaining a corporate memory of those individuals.

In the era of digital transformation, organizations generate vast volumes of customer data through online transactions, customer relationship management systems, and social media interactions. Analyzing this data is essential for understanding customer behavior and improving business decision-making. Customer segmentation is a widely used analytical technique that divides customers into meaningful groups based on shared characteristics. Machine learning techniques, particularly clustering algorithms, have significantly enhanced the effectiveness of segmentation methods. Among these techniques, the K-Means clustering algorithm is one of the most popular due to its simplicity, efficiency, and scalability. This study presents a comprehensive analysis of customer segmentation using K-Means clustering. The proposed methodology involves data collection, preprocessing, feature selection, clustering, and cluster evaluation. The study also discusses the workflow of customer segmentation, its applications across various industries, and the limitations associated with clustering algorithms. The results demonstrate that K-Means clustering provides valuable insights into customer behaviour, enabling organizations to design targeted marketing strategies and improve customer relationship management. The research highlights the practical significance of clustering techniques in modern business analytics and decision-support systems.