Lifetime value in business process

Measuring and managing customer relationship risk in business markets

In today’s business world, identifying the customers and analysis of their behavior is important for banking industry. Customer Relationship Management (CRM) is the process of maintaining profitable customer relationships by delivering customer value and loyalty. Moreover, CRM helps to improve the business relationships with customers. The goal of CRM is to maximize the lifetime value of a customer to an organization. Customer Lifetime Value (CLV) can rank and classify customers based on their lifetime value to identify valuable customers and retain them. There are several models for CLV estimation using the past data of customers. This subject helps organizations in their attempts to retain valuable customers. The banks must use appropriate data mining techniques to extract pattern and information from the existing data to gain competitive advantage. Therefore, data mining techniques have an important role to extract the hidden knowledge and information. The goal of this study is to review data mining techniques used for analyzing bank customers in order to help the banks to better identify their customers and design more efficient marketing strategies. The literature covered in this paper is related to the past seventeen years (2001–2017) and these approaches are compared in terms of data sets, prediction accuracy, and so on. We also provide a list of data sets available for the scientific community to conduct research in this field. Finally, open issues and future works in each of these items are presented.

Accurately predicting Customer Lifetime Value (CLV) is paramount in optimizing customer relationship management. This study introduces a novel deep learning approach, employing a neural network model to forecast CLV for a leading international e-commerce retailer. This investigation delves into the interplay of key performance indicators—Net Promoter Score (NPS), Average Transaction Value (ATV), and Customer Effort Score (CES)—and their collective impact on CLV. The research utilized a dataset of 15,000 customer profiles and crafted a sequential neural network with dense layers optimized through hyperparameter tuning and regularization to thwart overfitting. The model's efficacy was assessed on a 10 % test set, revealing its adeptness at capturing intricate, nonlinear predictor-customer lifetime value (CLV) relationships. Consistency in training and test performance metrics underscored the model's generalizability, while high R-squared and explained variance scores confirmed the predictive strength of the chosen factors. This research seeks to provide a solution for building an artificial intelligence model with an artificial neural network algorithm to predict customer lifetime value. By incorporating this principle, the study aims to leverage the power of neural networks to forecast CLV, enabling retailers to make informed decisions and optimize customer relationships accurately. This study finds that Net Promoter Score (NPS) and Customer Effort Score (CES) have a powerful impact on the neural network model's ability to predict Customer Lifetime Value (CLV) accurately. On the other hand, while Average Transaction Value (ATV) exhibits the least impact, it still significantly contributes to the accuracy of Customer Lifetime Value (CLV) predictions. These results underscore the importance of incorporating customer feedback metrics like NPS and CES when building predictive models for CLV estimation. Integrating critical customer metrics into neural networks gives retailers enhanced insights, enabling precise customer segmentation, resource allocation, and strategic growth. The study paves the way for future research to refine further CLV prediction, including dataset expansion, customer profile enrichment, and prescriptive marketing optimization.

This paper presents a direction of development of 'the customer value' concept relying on the results of previous research of the models for the calculation of customer lifetime value (CLV), presented in scientific journals. Evolutionary path of this concept has been observed in the context of relationship marketing and customer relationship management. The aim of this paper is to present the achievements in the development of this concept since 1980 to date and to encourage our country's scientific and professional community to further research the models of calculation of customer lifetime value, as well as management of companies to apply these models to calculate CLV and CE (Customer Equity) of their customers.

This paper will address a topic common among customer relationship management (CRM) practitioners — the use of lifetime value (LTV) models, especially for customer marketing segmentation. It will draw comparisons between LTV and other common marketing/segmentation metrics and how these are used. It will also discuss some of the challenges facing the analyst who wishes to use LTV and how these might be overcome. Finally, it will illustrate how LTV-based segmentation can be incorporated into a comprehensive model that maximises yield from a finite marketing budget.

As there are a lot of booming online retailers in e-commerce industry in the Internet age, the need of maintaining competitive advantages has become to pay attention to customer relationship management (CRM). To build a successful CRM strategy, it is needed to know individual customer class which can be calculated from Customer Lifetime Value (CLV): the monetary value of customers purchased from the business during their lifetime. CLV modelling allows us to identify customer's predicted business value. It provides the retailers for effectively allocating the resource in their business. This predictive model has been taken on the global Super Store Retail dataset with almost ten thousand transactions. Our model will predict the customers' class of the next year based on their CLV that will help the online retailer to decide which customer should be invested to get long term CRM. Random Forest (RF) algorithm is utilized to train our model and Random Search tuning is conducted to get the best predictive accuracy. The experimental analysis is performed to compare with AdaBoost algorithm on the same dataset.

A Note on Willingness to Spend and Customer Lifetime Value for Firms with Limited Capacity

The rapid growing of information technology (IT) motivates and makes competitive advantages in health care industry. Nowadays, many hospitals try to build a successful customer relationship management (CRM) to recognize target and potential patients, increase patient loyalty and satisfaction and finally maximize their profitability. Many hospitals have large data warehouses containing customer demographic and transactions information. Data mining techniques can be used to analyze this data and discover hidden knowledge of customers. This research develops an extended RFM model, namely RFML (added parameter: Length) based on health care services for a public sector hospital in Iran with the idea that there is contrast between patient and customer loyalty, to estimate customer life time value (CLV) for each patient. We used Two-step and K-means algorithms as clustering methods and Decision tree (CHAID) as classification technique to segment the patients to find out target, potential and loyal customers in order to implement strengthen CRM. Two approaches are used for classification: first, the result of clustering is considered as Decision attribute in classification process and second, the result of segmentation based on CLV value of patients (estimated by RFML) is considered as Decision attribute. Finally the results of CHAID algorithm show the significant hidden rules and identify existing patterns of hospital consumers.

This study aims to analyze the role of marketing management in building long-term customer value through a systematic approach to literature study. The results show that modern technologies, such as artificial intelligence (AI), metaverse marketing, and digital strategies, such as social media and omnichannel marketing, significantly increase customer engagement, loyalty, and trust. Customer Relationship Management (CRM) effectively increases customer lifetime value (CLV) by up to 30%, while digital personalization increases customer engagement by 42%. In addition, personalized value propositions play an important role in strengthening customer relationships and creating immersive experiences. This study affirms that utilizing technology, personalization, and relationship-oriented strategies is essential in contemporary marketing management. These findings provide an important contribution to understanding how strategic approaches can support the development of long-term customer value, increase retention, and strengthen customer loyalty in the digital era

Customers can create value for firms in a variety of ways. Four of them are parts of customer relationship management (CRM), and mentioned as customer engagement value (CEV) components. The first component is customer lifetime value (CLV); the second is customer referral value (CRV); the third component is customer influencer value (CIV); the fourth component is customer knowledge value (CKV). There are a lot of works concerning the particular CEV element estimates, such as CLV, and recently also CIV or CRV. However, it is important to notice that there is no work on how to estimate CKV. Based on the observations above, finding a model to estimate CKV would be relevant and interesting.

Accurate customer LifeTime Value (LTV) predictions are crucial for customer relationship management, especially in Supply Chain Platforms (SCP), which involve effectively managing the service resources in business decision-making. Previous LTV prediction methods usually rely on ample historical customer data, which is not available in the early stages of a customer's lifecycle. It makes the modeling of the historical customer data a difficult task due to the data sparsity. Besides, the long-tail distribution of customer LTV also brings new challenges to the prediction of LTV. To tackle the above issues, we propose CDLtvS, a novel Cross Domain method for customer Lifetime value prediction in SCP. It leverages rich cross-domain information from upstream platforms to enhance LTV predictions in downstream platforms. Firstly, CDLtvS pre-trains the customer representations by an LTV modeling framework named LtvS in source and target domains separately. Specifically, LtvS incorporates the Expert Mask Network (ExMN), which not only effectively models the long-tail distribution of LTV in single-domain but also resolves cross-domain learning model bias resulting from this distribution. Then, the various-level alignment mechanism is introduced to keep the consistency of knowledge transferring from source to target domains on both sparse and non-sparse data. Comprehensive experiments on real-world data from JD, one of the world's largest supply chain platforms, demonstrate that CDLtvS achieves a normalized mean average error of 0.3378 in LTV prediction, outperforming 16.3% to the baseline. Additionally, the improvements of ≥2.3% across various data sparsity levels (0% -- 80%) provide valuable insights into cross-domain LTV modeling.

The integration of Artificial Intelligence (AI) into Customer Relationship Management (CRM) has revolutionized the financial services industry by enhancing customer engagement, fraud detection, predictive analytics, regulatory compliance, and marketing strategies. This study systematically reviews 83 scholarly studies, including peer-reviewed journal articles, industry reports, and financial institution case studies, to assess AI’s impact on financial CRM. The findings indicate that AI-powered chatbots, virtual assistants, and sentiment analysis tools have significantly improved customer interactions, reducing response times by 57% and operational costs by 38%, while increasing customer retention rates by 28%. AI-driven fraud detection systems have enhanced transaction monitoring, reducing false positives by 52% and improving fraud detection efficiency by 74%, leading to a 43% decrease in financial losses related to fraud. Predictive analytics has transformed credit risk assessment, improving loan approval accuracy by 67%, expediting loan processing by 29%, and reducing default rates by 23%. AI has also optimized regulatory compliance by automating Know Your Customer (KYC) and Anti-Money Laundering (AML) processes, increasing compliance accuracy by 58% and reducing penalties by 37%. Additionally, AI-driven marketing strategies have strengthened customer targeting, increasing engagement by 53% and boosting product adoption rates by 31%, while Customer Lifetime Value (CLV) models have contributed to a 27% increase in long-term customer retention and a 22% improvement in per-customer profitability. This study provides a comprehensive analysis of AI-driven CRM’s measurable benefits in financial services, demonstrating its role in enhancing decision-making, streamlining operations, improving financial security, and fostering long-term customer loyalty. The findings contribute to the expanding literature on AI in financial CRM and offer strategic insights for financial institutions, policymakers, and technology developers aiming to optimize AI adoption for sustainable growth and competitive advantage.

This study investigates the relationship between electronic customer relationship management (CRM) and electronic customer lifetime value and their analysis in the electronic business environment in the form of a conceptual framework. CRM is a tool that different industries, especially in competitive conditions, use to maintain customers and increase their satisfaction. An important concept that arises as customer lifetime value that specifies the expected amount of value that the client in a given period of time creates is undoubtedly related to the benefits that accrue to the organisation. The global nature of business as well as the development of information and communication technology is forcing organisations to take advantage of emerging technologies to maintain their competitiveness. The use of e-business is a prominent example. The results of this study could help both researchers and executives of organisations and businesses with the subject of research, and provide good insights for them. Keywords: Electronic business (e-Business), electronic customer relationship management (e-CRM), electronic customer lifetime value (e-CLV), relationship marketing;

In today's competitive business landscape, customer relationship management (CRM) systems have evolved into critical platforms for data-driven marketing intelligence. Recent advances in CRM-driven marketing intelligence have enabled organizations to shift from reactive to predictive strategies, thereby enhancing conversion rates and optimizing customer lifetime value (CLV) models. This transformation is fueled by the integration of artificial intelligence (AI), machine learning (ML), and advanced analytics, which allow for the continuous aggregation, segmentation, and interpretation of complex customer data. With these technologies, marketers can develop hyper-personalized campaigns, conduct real-time behavioral tracking, and deploy dynamic content tailored to individual customer journeys. Moreover, CRM platforms are increasingly being integrated with omnichannel communication tools, enabling consistent and context-aware engagement across touchpoints. These capabilities not only improve customer acquisition efficiency but also foster long-term loyalty and retention. Furthermore, CLV modeling has become more robust with predictive insights that factor in churn probability, upsell potential, and engagement metrics. As organizations gain deeper insights into the drivers of customer behavior and profitability, they can allocate resources more effectively and refine their value propositions. However, successful implementation requires overcoming challenges related to data silos, privacy concerns, and the alignment of marketing and IT teams. Overall, the synergy between CRM systems and marketing intelligence represents a significant advancement in customer-centric marketing strategies. By leveraging these innovations, firms can build sustainable competitive advantages through improved decision-making, targeted messaging, and enhanced customer experiences. The ongoing evolution of CRM-driven intelligence is poised to redefine how businesses perceive and cultivate value throughout the customer lifecycle. This review explains the strategic and technological underpinnings of CRM-enhanced marketing intelligence and its growing impact on performance metrics such as conversion rates and lifetime value in the digital economy.

The customer lifetime value (CLV) is an important concept increasingly considered in the field of general marketing and in the management of firms, of organizations to increase the captured profitability. It represents the total value that a customer produces during his or her lifetime, or better represents the measure of the potential profit generating by a customer. The companies use the customer lifetime value to segment customers, analyze probability of churn, allocate resources or formulate strategies and, therefore, they increasingly derive revenue from the creation and from sustenance of long-term relationships with their customers. For this reason, the customer lifetime value is increasingly considered a touchstone for the management of customer relationships. In this article, the authors deepen the concept and use of customer lifetime value and present some mathematical models for its determination. There is many models for this purpose but most of them are theoretic, complex and not applicable. Though not exhaustive, the major contribution of this paper is that it provides a general mathematical formulation to estimate the CLV and that it has a context less specific compared to papers, present in literature, on the customer lifetime.