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In this paper, we propose a flexible machine learning framework to predict customer lifetime value (CLV) in the Business-to-Business (B2B) Software-as-a-Service (SaaS) setting. The substantive and modeling challenges that surface in this context relate to more nuanced customer relationships, highly heterogeneous populations, multiple product offerings, and temporal data constraints. To tackle these issues, we treat the CLV estimation as a lump sum prediction problem across multiple products and develop a hierarchical ensembled CLV model. Lump sum prediction enables the use of a wide range of supervised machine learning techniques, which provide additional flexibility, richer features and exhibit an improvement over more conventional forecasting methods. The hierarchical approach is well suited to constrained temporal data and a customer segment model ensembling strategy is introduced as a hyperparameter model-tuning step. The proposed model framework is implemented on data from a B2B SaaS company and empirical results demonstrate its advantages in tackling a practical CLV prediction problem over simpler heuristics and traditional CLV approaches. Finally, several business applications are described where CLV predictions are employed to optimize marketing spend, ROI, and drive critical managerial insights in this context. [ABSTRACT FROM AUTHOR]

Digitalization has posed severe challenges to traditional businesses. Traditional firms are still not sure of the benefits of digitally engaging their customers. In contrast, the new-age firms have successfully leveraged digital media. This article examines the relationship between digital adoption by customers and customer lifetime value (CLV). This study analysed the mobile recharges by 13 million rural and urban prepaid telecom customers over 60 million transactions from January 2019 to June 2019 in the Indian telecom industry. The computed predictive CLV has been computed and compared across various customer segments (digitally engaged, partially digitally engaged and digitally unengaged customers). The studied data were statistically validated using the Kruskal–Wallis test. The data proved to be non-normally distributed as per the Kolmogorov–Smirnov test. The results supported that digital adoption helps increase customer engagement, loyalty and CLV. The study presents several managerial implications, such as digitally engaged customers being a surrogate for high value and more profitable customers. Also, digitally engaged customers are relatively more loyal, contributing higher CLV than digitally unengaged customers. [ABSTRACT FROM AUTHOR]

With regards to data and model sophistication, the new mantra for financial services and FinTech seems to be 'the more the better', supported by attractive business cases in risk underwriting, fraud detection, customer lifetime value management, conditional investment risk/return optimisation and improving customer journeys, to name a few. However, more data and more sophisticated models are not always a universal panacea and may lead to bad business outcomes if not managed appropriately in the context of the desired business outcome. This paper summarises the evolving business cases for increasing data and models in the risk management domain and their associated risks. Making the associated risks transparent naturally leads to the conclusion that a timeless risk mitigation approach — common sense — is critically necessary to complement the more structured model risk management (MRM) framework that is evolving. [ABSTRACT FROM AUTHOR]

We study how consumers decide which tariff plan to choose and whether to churn when their friends churn in the mobile industry. We develop a theoretical model showing conditions under which users remain with their carrier and conditions under which they churn when their friends do. We then use a large and rich anonymized longitudinal panel of call detailed records to characterize the consumers' path to death with unprecedented level of detail. We explore the structure of the network inferred from these data to derive instruments for friends' churn, which is typically endogenous in network settings. This allows us to econometrically identify the effect of peer influence in our setting. On average, we find that each additional friend that churns increases the monthly churn rate by 0.06 percent. The observed monthly churn rate across our dataset is 2.15 percent. We also find that firms introducing the pre-paid tariff plans that charge the same price to call users inside and outside the carrier help retain consumers that would otherwise churn. In our setting, without this tariff plan the monthly churn rate could have been as high as 8.09 percent. We perform a number of robustness checks, in particular to how we define friends in the social graph, and show that our results remain unchanged. Our paper shows that the traditional definition of customer lifetime value underestimates the value of consumers and, in particular, that of consumers with more friends due to the effect of contagious churn and, therefore, managers should actively take into account the structure of the social network when prioritizing whom to target during retention campaigns. [ABSTRACT FROM AUTHOR]

Peer influence in social networks (SN) is vastly important to our economies and societies. The authors are offering a method of separating peer influence from homophily, an econometric model of the setting that they exercise on an extensive data panel, and conclusions that expand our theoretical and pragmatic knowledge. They show how peer influence exerts itself in a stepwise reaction of friends and that the customer lifetime value understates the value of customers as social beings, with friends whom they influence. [Extracted from the article]

Customer segmentation is a challenging task in marketing that aims to build homogeneous segments of customers based on their similar characteristics and activities. This problem is considered multi-objective since it requires the evaluation of several variables including descriptive and predictive characteristics of customers. However, given that most exiting segmentation methods are based on the optimisation of a single-objective function, the identification of homogeneous customer segments in terms of both predictive and descriptive variables becomes a major issue. Descriptive and predictive characteristics are usually considered as two different and independent objectives, which cannot be optimised together. To deal with this problem, we propose a multi-objective segmentation approach based on three conceptual axes: descriptive, predictive, and quality-validation. In addition to the specificity of design of the multi-objective model, our proposed approach has the specificity of directly optimising the multi-objective problem using a customised genetic algorithm that directly approximates a set of Pareto-optimal solutions. We have applied and evaluated the proposed approach in an empirical study which aims to segment bank credit card customers using their descriptive characteristics and their predictive behaviour. Obtained results have shown the ability of the proposed approach to look for effective homogeneous segments and help decision-makers propose more tailored marketing strategies. [ABSTRACT FROM AUTHOR]

The insurance industry faces significant challenges, including fraudulent claims and customer churn, impacting profitability and sustainability. This research presents a comprehensive data-driven approach to increase profitability in the vehicle insurance sector. This research employs advanced methodologies for Cross sell prediction, Vehicle Insurance Claim Prediction Models, customer survival analysis, churn prediction, and fraud detection using Machine Learning and Deep Learning. By leveraging diverse data sets encompassing policy details, demographics, sentiment analysis, imagery, and historical claims, the study achieves predictive accuracy between 85% to 95%, and fraud detection rates of approximately 80% to 85%. The project introduces the transformative app, VEGO, benefiting both insurance companies and policyholders. Additionally, a rigorous survival analysis addresses critical questions on customer churn dynamics, demonstrating remarkable retention rates. Survival analysis techniques, including KaplanMeier Survival Curves, Log-Rank Test, and Coxproportional hazard models, were employed to analyze customer retention rates over a 72-month period. These models provided valuable insights into risk factors and their cumulative impact on survival time in the insurance context. The research yields insights into risk factors and their collective impact on survival time, while introducing a conservative approach to estimating customer lifetime value. This endeavor enhances analytical foundations in vehicle insurance, ushering in a customer-centric industry landscape. [ABSTRACT FROM AUTHOR]

This research examines the role of commercial banks in influencing customers' perceived financial well-being under the basic psychological need theory and the impact of customers' perception of financial health on their bank loyalty. We firstly conducted in-depth interviews to explore bank-related factors that could help satisfy the needs for autonomy, relatedness, and competence that a bank customer may have in their financial life. A structural equation model linking personalization of service offerings, interpersonal adaptive behaviour, communication, service quality, customer satisfaction, perceived financial well-being and customer loyalty is tested using data collected from 391 Vietnamese individual bank customers. The results indicate that the personalization of service offerings, interpersonal adaptive behaviour, and service quality could help enhance financial well-being among bank customers. This could be explained by the impacts of these factors in either satisfying customers' psychological needs or enhancing overall customer satisfaction with banking services. Further, we also find that financial well-being not only positively affects customer loyalty but also moderates the relationship between customer satisfaction and bank loyalty. This research provides crucial strategies for commercial banks to improve financial well-being among their customers and hence, further increase customer retention and lifetime value. [ABSTRACT FROM AUTHOR]

Purpose: The current research was conducted to identify the key Propelling of customer lifetime value in sports clubs of Iran.Methodology: This research is practical in terms of purpose and mixed in nature and method. The research was conducted in two parts, qualitative and quantitative. The sample in the qualitative section consisted of 16 sports experts and 413 sports managers of sports clubs in the quantitative section. The sample selection in the qualitative part was targeted with a snowball approach, and the data was collected with semi-structured interviews. In the quantitative section, the sample size was selected from the stratified random cluster sampling method, and a questionnaire output collected the data from the qualitative part. Collecting the data was done by the Delphi method, and structural equation Modelling was used to confirm the model. Statistical analyses were performed with SPSS 25 and Amos 26.Findings: The results show that the key Propelling of customer lifetime value includes hardware, software, financial, strategic, social, motivational, behavioral, process, and executive factors.Originality: Few studies have been conducted on customer lifetime value in sports. This paper is the internal research that identifies critical Propelling that affects customer lifetime value in sports clubs. Also, the results of this research show that the attention of sports managers to the nine identified factors effectively increases the lifetime value of sports customers. In line with the results, it is suggested that managers, by considering the components of motivational, executive, and behavioral factors in customer satisfaction and loyalty, improve their trust and confidence, and by considering strategic and financial factors, the ability to predict. They got the future status of the customers.

Human activities tend to burst at specific times, followed by long dormant periods. This study analyzes detailed trading records, as well as the demographic profiles of 486,049 customers from a major securities company and shows that an entropy measure of non-Poisson trading patterns has advantages over the canonical recency, frequency, and monetary value framework in the financial services sector. The LASSO logistic regression, the information gain metric in gradient boosting decision trees, and the relative importance method in neural networks all lend support to the conclusion that the clumpiness measure of trade clustering plays a significant role in explaining customers' future churning. Furthermore, it appears that recently developed statistical learning techniques reduce churn prediction errors to a greater extent. A metric-based parsimonious RFMC approach coupled with machine learning techniques can be effectively used to better gauge customer lifetime value. [ABSTRACT FROM AUTHOR]

This customer lifetime value (CLV) study developed and refined an instrument to measure CLV from a retailer's perspective using both financial and nonfinancial constructs. The authors created scale items to measure the financial (monetary value and marketing costs) and nonfinancial constructs (trust, loyalty, purchase frequency, recency, and churn rate). They assessed composite reliability as well as discriminant and convergent validity. A varimax rotation indicated strong items for trust and recency under the nonfinancial factors as well as monetary value and marketing costs under the financial factors. Additionally, the measurement model indicated a strong model fit. The findings reinforce the notion of using financial factors to determine CLV. However, nonfinancial factors are also relevant for explaining CLV. These findings fundamentally shift the argument about the determinants of CLV as well as open the door for further research about the nonfinancial factors of CLV. This is the first study to create scale items for measuring the financial and nonfinancial constructs of CLV. The research provides useful theoretical and managerial insights regarding the consideration of nonfinancial factors for refocusing marketing and retailing efforts toward consumers. The study findings reinforce the notion that all customers are not equally valuable. [ABSTRACT FROM AUTHOR]

Cluster analysis and RFM model are widely used to gain a deeper understanding of customers' characteristics and needs due to its simplicity and applicability in analyzing customer purchasing behavior. However, the lack of considering the future value of customers or whether current customers exhibit a pattern of likely attrition or switching to a competitor into the original segmentation models is a big concern in segmenting customers for further strategic and personalized campaigns. How can organizations integrate their customers' lifetime value and customer migration, which refers to the probability that their customers will likely return in the future, as parts of the RFM and cluster analysis to improve marketing decisions? Our modified segmentation models are then validated in the context of complementary and alternative medicine in the healthcare industry to demonstrate the practical validity of our proposed methods. [ABSTRACT FROM AUTHOR]

Despite the demonstrated importance of customer sentiment in social media for outcomes such as purchase behavior and of firms' increasing use of customer engagement initiatives, surprisingly few studies have investigated firms' ability to influence the sentiment of customers' digital engagement. Many firms track buyers' offline interactions, design online content to coincide with customers' experiences, and face varied performance during events, enabling the modification of marketer-generated content to correspond to the event outcomes. This study examines the role of firms' social media engagement initiatives surrounding customers' experiential interaction events in influencing the sentiment of customers' digital engagement. Results indicate that marketers can influence the sentiment of customers' digital engagement beyond their performance during customers' interactions, and for unfavorable event outcomes, informational marketer-generated content, more so than emotional content, can enhance customer sentiment. This study also highlights sentiment's role as a leading indicator for customer lifetime value. [ABSTRACT FROM AUTHOR]

We study how consumers decide which tariff plan to choose and whether to churn when their friends churn in the mobile industry. We develop a theoretical model showing conditions under which users remain with their carrier and conditions under which they churn when their friends do. We then use a large and rich anonymized longitudinal panel of call detailed records to characterize the consumers' path to death with unprecedented level of detail. We explore the structure of the network inferred from these data to derive instruments for friends' churn, which is typically endogenous in network settings. This allows us to econometrically identify the effect of peer influence in our setting. On average, we find that each additional friend that churns increases the monthly churn rate by 0.06 percent. The observed monthly churn rate across our dataset is 2.15 percent. We also find that firms introducing the pre-paid tariff plans that charge the same price to call users inside and outside the carrier help retain consumers that would otherwise churn. In our setting, without this tariff plan the monthly churn rate could have been as high as 8.09 percent. We perform a number of robustness checks, in particular to how we define friends in the social graph, and show that our results remain unchanged. Our paper shows that the traditional definition of customer lifetime value underestimates the value of consumers and, in particular, that of consumers with more friends due to the effect of contagious churn and, therefore, managers should actively take into account the structure of the social network when prioritizing whom to target during retention campaigns. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses the articles in the issue on topics including method of separating peer influence from homophily; effects of monetary incentives; and the backer motivations of reward-based crowdfunding.

Authors have described selected problems related to valuation and value creation of Internet related companies. In case of intangible assets related to the new economy and Internet companies, valuation is usually much more complicated due to rapid volatility, limited historical information and constantly emerging new technologies that change the image of the market. The same situation is in case of young companies, which current, tangible assets are of little value, while the value of the company is concentrated in intangible assets and development options The paper presents general valuation indicators for Internet companies, which are a root point for a client division allowing a further model improvement. Based on the Internet companies evaluation indicators review, authors decided that for this proposal, the division between one-time (non-loyal) and repetitive (loyal) customers is of the greatest use. Following, the method of valuing companies based on valuation of Customer Lifetime Value was described. Considering different kinds of the Internet clients, a valuation model for one time and continuous relation was proposed. Authors suggest modifying traditional model by dividing the customers into two segments. The consumer's loyalty measured by behavioral outcome - the frequency of purchase is the criteria of segmentation. Such segmentation allows better extrapolation of company's value. [ABSTRACT FROM AUTHOR]

Cross-selling is one of the most important sales strategies retail organizations adopted to drive business revenue and increase customer lifetime value. While considerable efforts have been devoted to developing data-based cross-selling models, little is known about how and when store managers can drive frontline service employees (FSEs) to cross-sell. Drawing on work meaningfulness literature, we propose that a high-quality resource exchange relationship with the store manager (i.e., leader–member exchange, LMX) endows FSEs' work with meaningfulness of serving others, which in turn promotes their engagement in cross-selling. We further contend that when store managers possess high person-organization fit, the impact of their LMX relationships on FSEs' work meaningfulness of serving others and subsequent cross-selling would be stronger. A three-wave survey data from 166 FSEs and their store managers in a retail chain in China (i.e., Study 1) and an experiment among 120 U.S.-based working employees (i.e., Study 2) support our predictions. The present research offers important theoretical and practical implications for retailing management area. Graphical Abstract [ABSTRACT FROM AUTHOR]

This case is designed to illustrate how to utilize a business intelligence framework and a geographic information system to make better decisions on franchise opening requests. The case started when Jong, the senior certified analytics professional, and his team were drafting a proposal presentation on a new franchise approval process to present to Tony, the director of Coffee Refresh. Currently, Coffee Refresh was experiencing a significant delay in approving franchise opening requests. In addition, the current approval process was relied on the appraisal teams' evaluations, which involved self-judgment as to the accessibility and visibility of the store location in each franchise opening request and a manual count of the potential targeted customers at each location, which impacted how the appraisal teams estimated the expected revenue for each location. During the first meeting with Jong and his team, Tony was frustrated by the current evaluation process, resulting in a significant backlog of requests pending approval, which was far behind the target for its franchise branch expansion strategy. It had also been reported that the branches that opened during the past few years had a relatively low success ratio (losses between 4.8% and 16.5%) and that the rate of branch closures had increased from 2% to 7.5%. Currently, eight franchise opening requests were pending, and Jong had to provide a recommendation for which requests should be approved. Coffee Refresh could approve all of them instantly, approve some of them, or even decline all of them. This was a great opportunity for Jong and his team to revisit the current franchise approval process and to demonstrate how business intelligence systems could improve the request approval process as well as address the issues of the current franchises' underperformance and the closure rates. [ABSTRACT FROM AUTHOR]

Copyright of Dokuz Eylul University Muhendislik Faculty of Engineering Journal of Science & Engineering / Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi is the property of Dokuz Eylul Universitesi Muhendislik Fakultesi Fen ve Muhendislik Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The aim of this study is to analyse the research gap regarding the relationship between environmental, social and governance dimensions (ESG) of corporate sustainability initiatives and customer lifetime value (CLV). We divide an entire data sample (547 U.S. listed firms from the Refinitiv Thomson Reuters Eikon database) of both industrial and technological industries into three segments, using prediction-oriented modelling segmentation to test the hypotheses and evaluate the predictive validity of a partial least squares (PLS) model. As a result, we show that environmental, social and governance dimensions (ESG) encompass ten sustainability initiatives that, in turn, are the precursors of future financial firm performance, represented by CLV. Moreover, we found different poor-to-medium effects of each ESG dimension on CLV in segment 1. However, a stronger effect of the social dimension on CLV in segment 3 is completed with a poor effect, both positive by governance and negative by environmental dimensions, on CLV, while only the environmental dimension had greater effects on CLV in segment 2. The contribution of this research to the body of literature is twofold. First, it deepens the impact of each ESG dimension instead of considering sustainability initiatives as a whole. Second, it evaluates sustainability initiatives with a customer-based corporate firm valuation approach. [ABSTRACT FROM AUTHOR]

Today, the role of customer relationship management as a strategic tool in the development of production and service organizations as well as attracting and retaining customers in competitive industries is undeniable. The main core of customer relationship management is identifying, valuing, segmenting customers and optimally allocating resources to customers according to the value they bring to the organization. Customer lifetime value is a tool for calculating the value that a customer brings to businesses during the lifetime of their relationship. The purpose of this article is to present the framework of variables affecting CLV in the service sector by systematic review method. 83 variables were extracted from the 31 final selected articles in the field of CLV in various service industries. Based on the results, five categories of variables have an effect on CLV in the service sector, which include customer variables, financial variables, product variables, selling variables, and demographic variables. [ABSTRACT FROM AUTHOR]

Purpose: The current research was conducted to identify the key Propelling of customer lifetime value in sports clubs of Iran. Methodology: This research is practical in terms of purpose and mixed in nature and method. The research was conducted in two parts, qualitative and quantitative. The sample in the qualitative section consisted of 16 sports experts and 413 sports managers of sports clubs in the quantitative section. The sample selection in the qualitative part was targeted with a snowball approach, and the data was collected with semi-structured interviews. In the quantitative section, the sample size was selected from the stratified random cluster sampling method, and a questionnaire output collected the data from the qualitative part. Collecting the data was done by the Delphi method, and structural equation Modelling was used to confirm the model. Statistical analyses were performed with SPSS 25 and Amos 26. Findings: The results show that the key Propelling of customer lifetime value includes hardware, software, financial, strategic, social, motivational, behavioral, process, and executive factors. Originality: Few studies have been conducted on customer lifetime value in sports. This paper is the internal research that identifies critical Propelling that affects customer lifetime value in sports clubs. Also, the results of this research show that the attention of sports managers to the nine identified factors effectively increases the lifetime value of sports customers. In line with the results, it is suggested that managers, by considering the components of motivational, executive, and behavioral factors in customer satisfaction and loyalty, improve their trust and confidence, and by considering strategic and financial factors, the ability to predict. They got the future status of the customers. [ABSTRACT FROM AUTHOR]

Organizations have used Customer Lifetime Value (CLV) as an appropriate pattern to classify their customers. Data mining techniques have enabled organizations to analyze their customers’ behaviors more quantitatively. This research has been carried out to cluster customers based on factors of CLV model including length, recency, frequency, and monetary (LRFM) through data mining. Based on LRFM, transaction data of 1865 customers in a software company has been analyzed through Crisp-DM method and the research roadmap. Four CLV factors have been developed based on feature selection algorithm. They also have been prepared for clustering using quintile method. To determine the optimum number of clusters, silhouette and SSE indexes have been evaluated. Additionally, k-means algorithm has been applied to cluster the customers. Then, CLV amounts have been evaluated and the clusters have been ranked. The results show that customers have been clustered in 4 groups namely high value loyal customers, uncertain lost customers, uncertain new customers, and high consumption cost customers. The first cluster customers with the highest number and the highest CLV are the most valuable customers and the fourth, third, and second cluster customers are in the second, third, and fourth positions respectively. The attributes of customers in each cluster have been analyzed and the marketing strategies have been proposed for each group.

Online games make up the largest segment of the booming global game market in terms of revenue as well as players. Unlike games that sell games at one time for profit, online games make money from in-game purchases by a large number of engaged players. Therefore, Customer Lifetime Value (CLTV) is particularly vital for game companies to improve marketing decisions and increase game revenues. Nowadays, as virtual game worlds are becoming increasingly innovative, complex, and diverse, the CLTV of massive players is highly personalized. That is, different players may have very different patterns of CLTV, especially on churn and payment. However, current solutions are inadequate in terms of personalization and thus limit predictive performance. First, most methods just attempt to address either task of CLTV, i.e., churn or payment, and only consider the personalization from one of them. Second, the correlation between churn and payment has not received enough attention and its personalization has not been fully explored yet. Last, most solutions around this line are conducted based on historical data where the evaluation is not convincing enough without real-world tests. To tackle these problems, we propose a general system to predict personalized customer lifetime value in online games, named perCLTV. To be specific, we revisit the personalized CLTV prediction problem from the two sub-tasks of churn prediction and payment prediction in a sequential gated multi-task learning fashion. On this basis, we develop a generalized framework to model CLTV across games in distinct genres by heterogeneous player behavior data, including individual behavior sequential data and social behavior graph data. Comprehensive experiments on three real-world datasets validate the effectiveness and rationality of perCLTV, which significantly outperforms other baseline methods. Our work has been implemented and deployed in many online games released from NetEase Games. Online A/B testing in production shows that perCLTV achieves a prominent improvement in two precision marketing applications of popup recommendation and churn intervention. [ABSTRACT FROM AUTHOR]

Companies should focus on customer lifetime value, its management, and on profitable customers for company growth. The objective of the study is to analyze customer lifetime value and to manage it effectively. The methodology adopted is a conceptual analysis of various aspects of customer lifetime value and its management. Companies should appreciate the importance of customer lifetime value and the connections among customer lifetime value, brand equity, and customer equity. They should develop and build customer lifetime value through various measures (viz., improvement in customer services, customer engagement, enhancing the growth potential of customers, management of unprofitable customers, rewarding profitable customers, and developing customer touch points). They should develop customer loyalty through trust and measure customer lifetime value. Academicians may suggest models that are effective in measuring customer lifetime value and identifying profitable customers. Managers may suggest effective strategies and initiatives for better management of customer lifetime value. [ABSTRACT FROM AUTHOR]

There is growing interest in "customer-based corporate valuation"—that is, explicitly tying the value of a firm's customer base to its overall financial valuation using publicly disclosed data. While much progress has been made in building a well-validated customer-based valuation model for contractual (or subscription-based) firms, there has been little progress for noncontractual firms. Noncontractual businesses have more complex transactional patterns because customer churn is not observed, and customer purchase timing and spend amounts are more irregular. Furthermore, publicly disclosed data are aggregated over time and across customers, are often censored, and may vary from firm to firm, making it harder to estimate models for customer acquisition, ordering, and spend per order. The authors develop a general customer-based valuation methodology for noncontractual firms that accounts for these issues. They apply this methodology to publicly disclosed data from e-commerce retailers Overstock.com and Wayfair, provide valuation point estimates and valuation intervals for the firms, and compare the unit economics of newly acquired customers. [ABSTRACT FROM AUTHOR]

We proposed an approach of retailer segmentation using a hybrid swarm intelligence algorithm and recency frequency monetary (RFM)-location model to develop a tailored marketing strategy for a pharmacy industry distribution company. We used sales data and plug it into MATLAB to implement ant clustering algorithm and K-means, then the results were analyzed using RFM-location model to calculate each clusters' customer lifetime value (CLV). The algorithm generated 13 clusters of retailers based on provided data with a total of 1,138 retailers. Then, using RFM-location, some clusters were combined due to identical characteristics, the final clusters amounted to 8 clusters with unique characteristics. The findings can inform the decision-making process of the company, especially in prioritizing retailer segments and developing a tailored marketing strategy. We used a hybrid algorithm by leveraging the advantage of swarm intelligence and the power of K-means to cluster the retailers, then we further added value to the generated clusters by analyzing it using RFM-location model and CLV. However, location as a variable may not be relevant in smaller countries or developed countries, because the shipping cost may not be a problem. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the importance of Customer Lifetime Value (CLV) as part of the modern marketing strategy. There is no doubt that CLV is one of the most important metrics to be considered in Customer Relationship Management and Relationship Marketing. It represents an instrument meant to enhance the client's loyalty and to bring some clearance for the marketing strategies regarding the customer segmentation. At the same time, it is one of the most difficult to calculate. As part of this study, we explore how the concept is approached in the academic literature, the notoriety of the concept, the areas, and the topics with which it is associated. In this investigation, we are using WordStat and QDA Miner software applications for content analysis and text mining aiming to extract themes and trends regarding the approached topic from a significant number of academic articles.

Customer lifetime value is one of the most important tasks for enterprises to maintain customer relationships. However, due to the limitations of using a single data mining method, the measurement of customer lifetime value under the condition of noncontractual relationship has always been a research difficulty. This paper focuses on customer value measurement and customer segmentation based on customer lifecycle value theory, and carries out customer value measurement and customer segmentation research from the perspective of customer value, and constructs customer segmentation model. This paper first conducts feature engineering, such as data selection, data preprocessing, data transformation, and knowledge discovery, and then conducts customer value segmentation based on machine learning algorithms and customer relationship management analysis models and builds a customer value segmentation identification model under the condition of noncontractual relationship. Finally, empirical analysis is carried out with the real customer transaction data of the actual online shopping platform, which verifies the validity and applicability of the customer segmentation method and value calculation method proposed in this paper.

Customer churn is considered a critical issue for all businesses, as customer loss leads to a decrease in future profits. Although acquiring new customers can address such losses, this process tends to cost more than retaining existing customers. Therefore, identifying potential churners and then retaining them is important. While churn prediction has been studied widely, current research must analyze more complex business models in response to their increase, such as platform businesses. However, modeling churn prediction for these businesses is challenging because consumer behavior over platforms is more complicated. Past approaches to churn prediction can be improved upon using recent advancements in deep learning that capture the nonlinear relationships behind the data in a data-driven manner, especially for complex business models. This study proposes a method of extracting latent features from purchase histories as explanatory variables for churn prediction using a variational autoencoder with the actual customer distribution as a prior. The proposed method is validated using real purchase data from a platform business and shows a 1.5% improvement in F-measure against the baseline, and a 20% improvement for customers with recent transactions. Subsequently, the variables are examined using several methods for data analysis to interpret the meanings of the extracted features and underlying customers' purchasing behavior in the group of potential churners. With these analyses, the model provides practical implications for understanding what kind of purchasing behavior may lead to churn and planning effective retention strategies. [ABSTRACT FROM AUTHOR]

Background: Understanding customers' electricity consumption patterns is essential for developing predictive analytics, which is needed for effective supply and demand management. Objective: This study aims to understand customers' segmentation and consumption behaviour using a hybrid approach combining the K-Means clustering, customer lifetime value concept, and analytic hierarchy process. Methods: This study uses more than 16 million records of customers' electricity consumption data from January 2019 to December 2020. The K-Means clustering identifies the initial market segments. The results were then evaluated and validated using the customer lifetime value concept and analytical hierarchy process. Results: Three customer segments were identified. Segment 1 has 282 business customers with a total capacity of 938,837 kWh, peak load usage of 27,827 kWh, and non-peak load usage of 115,194 kWh. Segment 2 has 508,615 business customers with a total capacity of 4,260 kWh, a peak load of 35 kWh, and a non-peak load of 544 kWh. Segment 3 has 37 business customers with a total capacity of 2,226,351 kWh, a peak load of 123.297 kWh, and a non-peak load of 390,803. Conclusion: A business strategy that could be taken is to base customer relationship management (CRM) on the three-customer segmentation. For the least profitable segment, aside from retail account marketing, a continuous partnership program is needed to increase electricity consumption during the non-peak period. For the highly and moderately profitable segments, a premium business-to-business approach can be applied to accommodate their increasing energy consumption without excessive electricity use in the peak period. Special account executives need to be deployed to handle these customers. [ABSTRACT FROM AUTHOR]

Nowadays, the customer network effects are becoming a central issue for the companies, while the previous studies have been only limited to customer lifetime value and its related models. Therefore, this paper aims to presents a model for calculating customer lifetime value, and simultaneously the network effects are considered. For this purpose, an oligopoly market is considered in which companies compete with each other. The companies individually have a number of buyers and sellers. Interestingly, their policy is based on offering services to their buyers free, and receiving the membership fees from the sellers instead. Customers influence each other, and their word-of-mouth marketing leads to a change in the number of companies' customers. This interaction is also observed among the sellers. In the absence of buyers, the presence of sellers is meaningless. In other words, there is a remarkable proportion between the number of buyers and sellers, which directly affects the companies' profitability. Each company seeks to determine the optimal marketing and pricing policies, considering the effects of the network. By applying differential game theory, the companies are able to receive the market share, advertising, and pricing strategy. The Genetic Algorithm is employed to solve the model. Finally, a numerical example and model validation are provided to demonstrate the proposed model capabilities. [ABSTRACT FROM AUTHOR]