Effective customer segmentation using the recency, frequency, and monetary framework, combined with density-based clustering

As businesses navigate the digital landscape, the proliferation of electronic transactions has led to an abundance of valuable data that can be harnessed for strategic decision-making. This study explores the application of CRM and RFM analysis for customer profiling and segmentation, utilizing e-invoice data as a rich source of information. By leveraging these advanced statistical techniques, the research aims to uncover hidden patterns within electronic transaction records, allowing for the identification of distinct customer segments based on their purchasing behavior. The methodology involved collecting and pre-processing one year of e-invoice data from Fit IT Company, followed by applying statistical models to uncover underlying structures and relationships. Furthermore, the research examines the implications of customer segmentation on marketing strategies, customer relationship management, and personalized service offerings. CRM and RFM analyses were performed on the annual sales data obtained as a result of e-invoice usage service to customers. When the results of the analysis were analyzed, the number of transactions belonging to the sender, recipient, and parties in the top 10 every month were extracted. It has been demonstrated that customer segmentation can be conducted more comprehensively by using CRM and RFM analyses together. While CRM analysis focuses on transaction volume and customer relationships, RFM analysis provides a more detailed perspective on customer behavior by evaluating purchase frequency, recency, and monetary value. In the study, by analyzing e-invoice data through these two methods, the most valuable customer groups were identified, and how strategic marketing approaches can be developed for these groups was illustrated. The combined use of CRM and RFM analyses allows for more accurate customer segmentation based on both transaction volume and spending habits. This approach concludes that strategies can be developed to increase customer loyalty, optimize marketing strategies, and improve business performance.

Maintaining healthy organization-customers relationship has positive influence on customers’ behavioral tendencies as regards preference to products and services, buying behavior, loyalty, satisfaction, and so on. To achieve this, an in-depth analysis of customers’ characteristics and purchasing behavioral trend is required. This paper proposes a hybrid unsupervised learning framework consisting of k-means algorithm and self-organizing maps (SOMs) for customer segmentation and behavior analysis. K-means algorithm was used to partition the entire input space of customers’ transaction dataset into 3 and 4 disjoint segments based on customers’ frequency (F) and monetary value (MV). SOM provided visualization of the underlying clusters and discovered customers’ relationships in the dataset. Interaction of F and MV clusters resulted in 12 sub-clusters. An in-depth analysis of each sub-cluster was also performed and appropriate customer relationship management (CRM) strategies established for each sub-cluster. Discovered knowledge will guide effective allocation of resources to each customer cluster and other organizational decision support functions much required by CRM systems.

big data is a hot business topic today. In business organizations, customer-relationship management (CRM) is an important pillar to achieve competitive advantages. Big data refers to practices of integrating big data into an organizational CRM process to achieve the objectives of improving and sustaining customer service. The alternative goal of big data is to combine internal CRM data with customer behavior and buying patterns from the environment external to the organization. Several tools exist that can integrate big data with other CRM data to improve customer analysis and understand buying behavior and patterns. This paper reports research evaluating the role of big-data technology in enhancing the effective use of CRM. Research proves that data's predictive model is enhanced by analyzing customer buying patterns. Issues and challenges related to the implementation of big data are discussed and benefits of appropriate implementation of big-data technologies are highlighted. The research further demonstrates some tangible benefits of implementing big-data technologies in CRM.

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.

The ever-increasing competition in retail industry puts pressure on retailers to deal with their customers more efficiently. Currently most companies use Customer Relationship Management (CRM) systems to maximize the customer satisfaction level by trying to understand more about their behaviors. However, one disadvantage of the current approaches is that they focus on the segmentation of customers into homogenous groups and they disregard examining the one-to-one relationship of each individual's behavior toward each product. Therefore, individual behavior cannot be captured in detail. Modeling individual behavior for each product enables several strategies of pricing by keeping the customer satisfaction at the maximum level. One example is offering a personal discount on a particular item to a customer who is price sensitive to that particular product. Therefore, you can still sell other products at the non-discounted level to this customer by keeping him satisfied. In this paper, individual pricing approach is discussed. The aim of this study is to develop a conceptual framework to analyze the feasibility of individual pricing. Customer behaviors can be modeled individually with respect to each product for a grocery store. Several factors can be used to determine these behaviors such as customer's need, brand loyalty and price sensitivity. Each customer can be modeled as an adaptive agent using qualitative descriptions of behaviors (i.e., highly price sensitive). Then, the overall shopping behavior can be simulated using a multi-agent Monte-Carlo simulation. It is expected that with this approach, retailers will be able to determine better strategies to obtain more profits, better sales and better customer satisfaction.

The development of business strategies, particularly in the marketing of SMEs, requires the utilization of business intelligence as the foundation for objective decision-making. This research aims to develop a business intelligence scheme for SMEs and design targeted assistance strategies for SME support institutions. The implementation of business intelligence involves leveraging transactional data from SMEs to ascertain customer segmentation and correlating it with Customer Relationship Management (CRM) strategies. Transactional data is processed into a Recency, Frequency, Monetary (RFM) data model. Customer segmentation is achieved through a clustering process using the K-Means algorithm, and the results yield distinct profiles for SME customers. Evaluation processes are conducted to determine the optimal solution for the number of customer segments. Evaluation methods, including the Elbow Method, Silhouette Scores, and Davies–Bouldin Index, are employed to determine the optimum cluster. The evaluation results indicate that the optimum cluster is 3, with the best Silhouette Score being 0.548 and Davies–Bouldin Index at 0.76. The first customer segment exhibits the highest shopping frequency and monetary value, categorizing them as active and profitable customers. Special loyalty services are recommended for this segment. The second segment, despite having the largest number of customers, exhibits a shopping frequency of only 1-2 times, with an average recency of approximately the last 2 months. These customers require effective after-sales service. The third segment consists of customers who last shopped more than 6 months ago, making them a low-priority segment. Re-engagement strategies, such as email marketing, are suggested for this segment. Support institutions can focus on CRM assistance targeting these three identified segments.

Subject area Marketing-managing customer relationship, market positioning, target marketing, product strategy, distribution, retailing and supply chain management, integrated marketing communications. Study level/applicability Undergraduate and graduate students in international marketing; business administration; strategic decision making and general management courses. Case overview The case study focuses on the current scenario within the Indian automotive lubricants industry, in order to provide an understanding of the marketing challenges, especially in retailing and distribution, faced by organisations within this highly competitive sector. The case examines the implementation of marketing strategies into practice and provides an insight into the importance of branding, market segmentation, market positioning, product and pricing strategies and customer relationship management (CRM). Expected learning outcomes The case study enables the students to understand and analyse: the current business environment and dynamics of emergence in the Indian automotive lubricants market; the critical success factors for doing business in the Indian automotive lubricants market and the associated opportunities and challenges; the importance of distribution and retailing strategies in the Indian context; Izo's growth and expansion strategy in India; and Izo's sales management and CRM systems and there importance to the success of the business. Supplementary materials Teaching note.

Abstract: The study examines the role of Customer Relationship Management (CRM) in enhancing customer retention, especially in competitive markets where retaining customers is more cost-effective than acquiring new ones. Using a mixed-methods approach—combining literature review, surveys, and interviews—it explores CRM’s evolution from relationship marketing and its key components: operational, analytical, and collaborative CRM. Findings show that strategic CRM implementation positively impacts customer retention by enabling personalized interactions, data-driven decisions, and proactive engagement. Successful use of tools like customer segmentation and predictive analytics leads to higher satisfaction and lower churn rates. However, challenges such as data silos, system integration issues, and insufficient training hinder CRM effectiveness. The study concludes that CRM, when part of a customer-focused strategy, significantly boosts retention and business performance. It recommends optimizing CRM practices and calls for further research into the use of AI and machine learning in CRM systems. Keywords: Customer Relationship Management, CRM, customer retention, customer satisfaction, loyalty, relationship marketing

The company producing textile fabrics in Java face the problem of the level of the segment of business customers for the products of local production of textile fabrics which tend to be relatively low. The situation is suspected because of the performance attributes of the product and Customer Relationship Management developed by the company is still not optimal. The research objective is to obtain results of a study on the performance of the product attributes and the implementation of Customer Relationship Management and its influence on the level of customer share of textile fabric products in the area of Java. The method used is descriftive and explanatory method. The unit of analysis is the company’s textile fabric industry in Java. Time horizon is a cross-sectional, where research is done in one time simultaneously. Data collected from a questionnaire to retailers of textiles and interviews with the managers and interviews mendalamdengan several national textile practitioners. Data were analyzed descriptively and quantitatively. Testing the hypothesis through the PLS model (Partial Least Square). The findings indicate that the performance attributes of the product and Customer Relationship Management developed by the company textile fabric in Java has not been good, resulting in the customer segment level is still low. The conclusion of the study is that the contribution of the performance attributes of the product is more dominant than the Customer Relationship Management on the level of customer segment. Keywords: Performance Product Attributes, Customer Relationship Management, customer segment

The integration of big data analytics into Customer Relationship Management (CRM) systems has emerged as a transformative approach for enhancing customer segmentation and predicting Customer Lifetime Value (CLV). This study presents a comprehensive conceptual model that leverages advanced big data analytics techniques to optimize CRM strategies. The model incorporates machine learning algorithms, clustering methods, and predictive analytics to facilitate more accurate customer segmentation and CLV estimation. By synthesizing existing literature, this research identifies critical gaps in traditional CRM approaches and proposes a data-driven framework that addresses scalability, real-time processing, and personalization challenges. The study highlights the theoretical underpinnings of big data analytics in CRM, emphasizing its role in improving marketing efficiency, customer retention, and profitability. Furthermore, the research discusses practical implications for businesses adopting these advanced analytics techniques, along with potential limitations and future research directions. The findings underscore the importance of integrating artificial intelligence and real-time analytics into CRM systems to achieve dynamic customer behavior modeling and enhanced decision-making capabilities.

Artificial Intelligence (AI) is reshaping Customer Relationship Management (CRM), with generative models driving a new wave of innovation. This study investigates the potential of generative AI models in optimizing customer experience and streamlining CRM processes. By enabling more tailored customer interactions, predictive analytics, and automating intricate tasks, generative AI technologies are creating opportunities to enhance CRM efficiency. The research explores how generative AI can transform CRM practices, focusing on customer segmentation, sentiment analysis, and dynamic response generation. Through case studies and data exploration, the paper illustrates how organizations can leverage AI-driven CRM tools to improve customer satisfaction, engagement, and retention. It also examines challenges such as data security, model accuracy, and the complexities of integrating AI systems with traditional CRM platforms. By offering an in-depth analysis of AI’s role in CRM transformation, this research outlines strategies for future customer-centric approaches and underscores the growing impact of generative AI on improving customer management.

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.

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.

One of the major developments within today's business practice is the increasing interest in Customer Relationship Management (CRM). The strategy known as CRM optimises values as profitability, revenue and customer satisfaction (what and why) by organising around customer segments, fostering customer-satisfying behaviours and implementing customer-centric business models (how). CRM technologies should enable greater customer insight, increased customer access and more effective customer interactions (outcomes). In the last years, however, scepticism has replaced the initial enthusiasm about CRM. Many of the CRM initiatives fail for a number of reasons; three of the most significant ones are the following: (a) the board has little customer/CRM understanding or involvement, (b) lack of specifically designed strategy and (c) no measures or monitoring of benefits derived from CRM. To date, the body of research on CRM has ignored these strategic implementation issues. This paper exhibits a methodology for the development, mapping and performance measurement of CRM strategies. In the context of this methodology, two frameworks are introduced.

Customer Relationship Management (CRM) has become a key business strategy for retaining customers. As data continues to grow in variety and volume, more advanced solutions are needed. The integration of Artificial Intelligence (AI), CRM, and Big Data offers promising support in addressing modern business challenges in the era of digitalization. This study explores the application of Artificial Intelligence in Customer Relationship Management (AI-CRM) through a literature review. We adopted the Kitchenham and Charters method for conducting the review and initially identified 356 studies. Data were collected from 33 studies published in the Scopus, ScienceDirect, and IEEE databases between 2020 and 2025. The results show that supervised learning remains the most widely used AI technique, while deep learning has grown significantly in recent years, indicating a shift toward more sophisticated CRM solutions. Most applications were found in Analytical CRM, particularly for churn prediction, customer segmentation, and personalization. However, challenges related to data quality, bias, privacy, and transparency remain prevalent. Additionally, areas such as B2B and Strategic CRM remain underexplored. This review emphasizes the need for organizational readiness before adopting AI-CRM and highlights AI’s transformative potential to enhance CRM strategies and gain a competitive advantage. The findings deliver useful insights into the application of AI in data-driven CRM.