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Extant research has popularized the perspective that strong network effects produce "winner-take-all" outcomes, which leads platforms to invest in user growth and encourages investors to subsidize these platforms. However, user growth does not necessarily imply strong user stickiness. Without user stickiness, strong network effects in the current period may fade in future periods, thus rendering a user growth strategy ineffective. By adding a time dimension to network effects, we developed a model of cross-period and within-period network effects to explain how different types of network effects drive value. We emphasize that the cross-period same-side network effect contributes to user stickiness, while the within-period cross-side network effect persists conditional on user stickiness. We propose that one reason for platforms having heterogeneous cross-period same-side network effects is because of the "product learning" mechanism: it is expected that products with higher uncertainty have a stronger cross-period same-side network effect. Based on different drivers, we extend the customer lifetime value model (CLV2) to two-sided platform markets, allowing us to measure how different interventions drive platform value. Using Groupon data, we verify our insights and discuss platform design choices that enhance user stickiness when the cross-period same-side network effect is weak. [ABSTRACT FROM AUTHOR]

The new challenge confronting leaders is investing in improving customer satisfaction or perceived innovativeness. The challenge also reflects the focus of leadership: to make operations more efficient and effective or invest in tomorrow's solutions? Intuitively, both are linked to the financial impact of the firm. In this article, the author explores the mechanisms "behind the scenes" driving perceived quality, and customer satisfaction on one side, and perceived innovativeness and relative attractiveness on the other side. Both routes are linked to customer loyalty, customer lifetime value, customer equity, and firm value. The findings underscore the importance of customer-centric innovation for market performance, suggesting that innovations must resonate with customer needs and expectations to foster market adoption and growth. The article advocates for the extension of this research into global contexts and digital transformation, highlighting the potential for these insights to inform strategi decision-making in an increasingly digitalized business environment. Bridging academic theory with practical application, the article offers a nuanced understanding of how innovation perceptions influence market outcomes. It suggests future research directions, including exploring strategies to enhance customer perception of innovation and leveraging these perceptions for competitive advantage. [ABSTRACT FROM AUTHOR]

Introduction: In the modern competitive landscape of football, clubs are increasingly leveraging data-driven decision-making to strengthen their commercial positions, particularly against rival clubs. The strategic allocation of resources to attract and retain profitable fans who exhibit long-term loyalty is crucial for advancing a club's marketing efforts. While the Recency, Frequency, and Monetary (RFM) customer segmentation technique has seen widespread application in various industries for predicting customer behavior, its adoption within the football industry remains underexplored. This study aims to address this gap by introducing an adjusted RFM approach, enhanced with the Analytic Hierarchy Process (AHP) and unsupervised machine learning, to effectively segment football fans based on Customer Lifetime Value (CLV). Methods: This research employs a novel weighted RFM method where the significance of each RFM component is quantified using the AHP method. The study utilizes a dataset comprising 500,591 anonymized merchandising transactions from Amsterdamsche Football Club Ajax (AFC Ajax). The derived weights for the RFM variables are 0.409 for Monetary, 0.343 for Frequency, and 0.248 for Recency. These weights are then integrated into a clustering framework using unsupervised machine learning algorithms to segment fans based on their weighted RFM values. The simple weighted sum approach is subsequently applied to estimate the CLV ranking for each fan, enabling the identification of distinct fan segments. Results: The analysis reveals eight distinct fan clusters, each characterized by unique behaviors and value contributions: The Golden Fans (clusters 1 and 2) exhibit the most favourable scores across the recency, frequency, and monetary metrics, making them relatively the most valuable. They are critical to the club's profitability and should be rewarded through loyalty programs and exclusive services. The Promising segment (cluster 3) shows potential to ascend to Golden Fan status with increased spending. Targeted marketing campaigns and incentives can stimulate this transition. The Needs Attention segment (cluster 4) are formerly loyal fans whose engagement has diminished. Re-engagement strategies are vital to prevent further churn. The New Fans segment (clusters 5 and 6) are fans who have recently transacted and show potential for growth with proper engagement and personalized offerings. Lastly, the Churned/Low Value segment (clusters 7 and 8) are fans who relatively contribute the least and may require price incentives to potentially re-engage, though they hold relatively lower priority compared to other segments. Discussion: The findings validate the proposed method's utility through its application to AFC Ajax's Customer Relationship Management (CRM) data and provides a robust framework for fan segmentation in the football industry. The approach offers actionable insights that can significantly enhance marketing strategies by identifying and prioritizing high-value segments based on the club's preferences and requirements. By maintaining the loyalty of Golden Fans and nurturing the Promising segment, football clubs can achieve substantial gains in profitability and fan engagement. Additionally, the study underscores the necessity of re-engaging formerly loyal fans and fostering new fans' growth to enable long-term commercial success. This methodology not only aims to bridge a research gap, but also equips marketing practitioners with data-driven tools for effective and efficient customer segmentation in the football industry. [ABSTRACT FROM AUTHOR]

The insurance industry is one of the largest financial areas that due to its service nature, customers can create different values, and the marketing efforts of insurance companies in attracting and retaining different groups of customers have led them to implement various loyalty programs, in which one of the criteria for grouping customers is the customer lifetime value (CLV). In this regard, this research has been conducted with the aim of designing and evaluating the loyalty program of insurance industry customers based on their lifetime value as an applied mixed exploratory, and cross-sectional research. The statistical population of the classification stage was 800 customers registered in the database of Shiraz insurance branches in 2018, and the statistical sample for prioritizing loyalty programs was 40 customers who were randomly selected using the Morgan table and based on access and willingness to participate in the study, the sample was completed. In the first stage, through systematic review, 28 loyalty programs were identified, and 15 valid programs were extracted using the content validity ratio (CVR), and these programs were categorized, and the Kappa index was used to assess categorization reliability. Therefore, in the second stage, the weights of LRFM indexes were determined with the analytic hierarchy process (AHP) and then the customers were classified into four groups of keys, intermittent, prodigal, and uncertain clusters customers using their data in insurance branches databank. Finally, the customers' priority of identified programs in each group was determined using the fuzzy best–worst method (FBWM). The findings show that from the perspective of customers, intangible and long-term programs are more attractive to key customers, while for intermittent customers, long-term tangible loyalty programs, for uncertain customers, immediate loyalty programs, and for prodigal customers, intangible loyalty programs are more attractive. The results of this study can be used in the formulation of loyalty programs in the insurance industry, in such a way that suitable loyalty programs are considered for each group of customers depending on the customers' CLV. Such as in designing loyalty programs, the tangibility and time horizon must be considered. Also, each group of programs must be suitable to customers' CLV. [ABSTRACT FROM AUTHOR]

Developing an informed lapse management strategy (LMS) is critical for life insurers to improve profitability and gain insight into the risk of their global portfolio. Prior research in actuarial science has shown that targeting policyholders by maximising their individual customer lifetime value is more advantageous than targeting all those likely to lapse. However, most existing lapse analyses do not leverage the variability of features and targets over time. We propose a longitudinal LMS framework, utilising tree-based models for longitudinal data, such as left-truncated and right-censored (LTRC) trees and forests, as well as mixed-effect tree-based models. Our methodology provides time-informed insights, leading to increased precision in targeting. Our findings indicate that the use of longitudinally structured data significantly enhances the precision of models in predicting lapse behaviour, estimating customer lifetime value, and evaluating individual retention gains. The implementation of mixed-effect random forests enables the production of time-varying predictions that are highly relevant for decision-making. This paper contributes to the field of lapse analysis for life insurers by demonstrating the importance of exploiting the complete past trajectory of policyholders, which is often available in insurers' information systems but has yet to be fully utilised. [ABSTRACT FROM AUTHOR]

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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.

Purpose: The current approach to valuing customers is based on the notion of discounted profit generated by the customers over the lifetime of the relationship, also known as customer lifetime value (CLV). However, in the financial services industry, the customers who contribute the most to the profitability of a firm are also the riskiest customers. If the riskiness of a customer is not considered, firms will overestimate the true value of that customer. This paper proposes a methodology to adjust CLV for different types of risk factors and creates a comprehensive measure of risk-adjusted lifetime value (RALTV). Design/methodology/approach: Using data from a major credit card company, we develop a measure of risk adjusted lifetime value (RALTV) that accounts for diverse types of customer risks. The model is estimated using Stochastic Frontier Analysis (SFA). Findings: Major findings indicate that rewards cardholders and affinity cardholders tend to score higher within the RALTV framework than non-rewards cardholders and non-affinity cardholders, respectively. Among the four different modes of acquisition, the Internet generates the highest RALTV, followed by direct mail. Originality/value: This paper not only controls for different types of consumer risks in the financial industry and creates a comprehensive risk-adjusted lifetime value (RALTV) model but also shows empirically the value of using RALTV over CLV for predicting future performance of a set of customers. Further, we investigate the impact of a firm's acquisition and retention strategies on RALTV. The measure of risk-adjusted lifetime value is invaluable for managers in financial services. [ABSTRACT FROM AUTHOR]

Online retailers are increasingly focused on maintaining a long-term relationship with customers, encouraging repeat visits rather than single-time purchases to increase customer lifetime value. To help retailers maximize the probabilities of customers' revisiting and purchasing, we develop a two-stage model to better characterize and predict these two fundamental customer activities. In the first stage, we characterize the propensity of a customer revisiting the retailer's website. In the second stage, we develop a stochastic model that predicts revisits while also incorporating individual customer heterogeneity in exerted search effort during repeated visits. This heterogeneity is based on individual customer preferences in the choice of consideration sets, product information, pricing, and the search environment. Using customer level clickstream data, we show that our approach is not only better at predicting repeat customer visits, compared to existing methods, but also explainable and managerially interpretable. Most importantly, using computationally efficient simulation-based prescriptive analytics, we leverage our modeling approach to propose practical intervention strategies that maximize the joint likelihoods of customers revisiting and purchasing at the individual customer level. [ABSTRACT FROM AUTHOR]

Developing an informed lapse management strategy (LMS) is critical for life insurers to improve profitability and gain insight into the risk of their global portfolio. Prior research in actuarial science has shown that targeting policyholders by maximising their individual customer lifetime value is more advantageous than targeting all those likely to lapse. However, most existing lapse analyses do not leverage the variability of features and targets over time. We propose a longitudinal LMS framework, utilising tree-based models for longitudinal data, such as left-truncated and right-censored (LTRC) trees and forests, as well as mixed-effect tree-based models. Our methodology provides time-informed insights, leading to increased precision in targeting. Our findings indicate that the use of longitudinally structured data significantly enhances the precision of models in predicting lapse behaviour, estimating customer lifetime value, and evaluating individual retention gains. The implementation of mixed-effect random forests enables the production of time-varying predictions that are highly relevant for decision-making. This paper contributes to the field of lapse analysis for life insurers by demonstrating the importance of exploiting the complete past trajectory of policyholders, which is often available in insurers’ information systems but has yet to be fully utilised.

Purpose: Customer lifetime value (CLV) is one of the key indicators to measure the success or health of an organization. How can an organization assess the organization's customers' lifetime value (LTV) and offer relevant strategies to retain prospective and profitable customers? This study offers an integrated view of different methods for calculating CLVs for both loyalty members and non-membership customers. Design/methodology/approach: This study outlines eleven methods for calculating CLV considering (1) the deterministic aspect of NPV (Net present value) models in both finite and infinite timespans, (2) the geometric pattern and (3) the probabilistic aspect of parameter estimates through simulation modeling along with (4) the migration models for including "the probability that customers will return in the future" as a key input for CLV calculation. Findings: The CLV models are validated in the context of complementary and alternative medicine (CAM)in the healthcare industry. The results show that understanding CLV can help the organization develop strategies to retain valuable customers while maintaining profit margins. Originality/value: The integrated CLV models provide an overview of the mathematical estimation of LTVs depending on the nature of the customers and the business circumstances and can be applied to other business settings. [ABSTRACT FROM AUTHOR]

This study aims to provide a comprehensive understanding of the needs, preferences, and behaviors of its target market in the Indonesian full-service restaurant (FSR) sector. Firstly, it seeks to map the customer journey of FSRs in the food supplies purchasing process, identifying key touchpoints, brand awareness levels, information sources, and factors influencing brand choice. Secondly, it evaluates the effectiveness of various digital marketing channels, such as social media advertising, search engine marketing, and email marketing, by analyzing cost-per-lead and conversion rates to identify the most efficient channels for acquiring new FSR customers for PT MBI. Finally, the research intends to develop a data-driven digital marketing strategy that optimizes customer interactions, enhance brand visibility, and improve lead generation at each decision-making stage to drive sales conversions and increase customer lifetime value. The results of this study show that the current focus of PTXYZin the FSR segment lies between two segments: Relationship-Driven Traditionalists and Hybrid Decision-Makers. [ABSTRACT FROM AUTHOR]

Marketing firms routinely interact with their panelists via email. While sending an invitation to respond to a survey may seem virtually costless, over-solicitation could lead to panelists unsubscribing or ignoring future emails. Since online panels are a crucial resource for a marketing research firm, such attrition is a major issue. We account for the unobserved cost of solicitations in a joint model of response and attrition propensities. Using a data set of more than 150,000 email solicitations sent over three years, we demonstrate that additional solicitations not only temporarily decrease the likelihood of future participation but also increase the attrition rate, likely due to wearout. The model where solicitations "kill" panelists outperforms out of sample a benchmark model that assumes dropout is caused by the passage of time instead. Since the impact of solicitations is both transient (on the response model) and permanent (on the dropout process), managers should wait for the temporary impact to dissipate before risking to "kill" their panelists with another solicitation. We illustrate the economic importance of this finding using a differential evolution method that optimizes the firm's solicitation strategy under different scenarios and show a 30.7% improvement. In the long term, a greedy strategy (targeting the best-responding panelists) performs worse than a random policy. [ABSTRACT FROM AUTHOR]

Customer lifetime value (CLV) modeling underpins modern marketing analytics, enabling the development of tailored customer relationship management strategies based on the predicted future value of their customers. As part of Amperity's enterprise customer data platform (CDP), we deploy and maintain a CLV prediction system that caters to a rapidly growing list of brands across various industries, purchase behaviors, and scales. Given the impracticality of developing bespoke models for each brand, our solution must be adaptive, generalizable, and high-performing "out of the box". Furthermore, our platform demands daily prediction updates to facilitate prompt marketing decisions. This paper introduces a turnkey CLV prediction system that achieves state-of-the-art performance across a diverse set of brands. This system has several contributions: 1) the use of encodings and embeddings to incorporate signals from high-cardinality data; 2) a multi-stage churn-CLV modeling framework that augments additional flexibility in adjusting churn probabilities, subsequently reducing CLV prediction errors while maintaining a synergistic learning process; 3) a feature-weighted ensemble of both generative and discriminative models to accommodate diverse underlying purchase patterns. Empirical results show that our enhanced model consistently surpasses benchmark performances for twelve retail brands across six evaluation intervals from June 2020 to September 2022. [ABSTRACT FROM AUTHOR]

Customer lifetime value is a core measure that allows companies to predict the potential net profit from future relationships with their customers. It is a metric that is computed by recording customer behavior over a long term and helps to build customized business strategies. However, existing research focuses either on a conceptual model of customer CLV s or assumes that all variables required for the computation of CLV are readily available. In this research, we employ a real customer dataset of insurance policies to construct a holistic framework that covers all aspects of CLV computation. In addition, we develop an extensive validation process, aiming to verify our results and obtain an understanding as to which CLV models perform best in the insurance context. In this research, we deliver a framework which comprises all aspects of CLV estimation using a real insurance policy dataset provided by a large business partner. The framework addresses the creation of a unified customer record, classification of customers into ranked groups, interpolation of missing parameters, through to the calculation and validation of individual CLV values. Our method also includes a robust validation with both subjective and objective evaluations of our findings. [ABSTRACT FROM AUTHOR]

The aim of this study is to analyse the research gap of the relationship between customer citizenship behaviour (CCB) and customer lifetime value (CLV) in the customer engagement framework (CE). We discuss how marketing analytics gains information from the digital environment related to data, metrics, and online aspects to predict business performance through motivational drivers and engagement. We divide an entire data sample (306 observations) of telecom service customers using prediction-oriented segmentation to test the hypothesis and evaluate the predictive quality of our second-order partial least squares (PLS) model. Results show that brand attitude–attachment, social value, and benevolence are precursors of these voluntary, discretionary, and extra-role customer behaviours called CCBs, and that intentional loyalty plays an essential mediating role in achieving future financial firm performance (CLV). This research analyses from a theoretical and empirical perspective the impact of the customer engagement formation from customer citizenship behaviour on customer lifetime value. [ABSTRACT FROM AUTHOR]

We introduce a novel stochastic control model for the problem of a service firm interacting over time with one of its customers who probabilistically churns depending on the customer's satisfaction. The firm has two service modes available, and they determine the drift and volatility of the Brownian reward process. The firm's objective is to maximize the rewards generated over the customer's lifetime. Meanwhile, the customer might churn probabilistically if the customer's satisfaction, modeled as an Orstein–Uhlenbeck process controlled by the firm's service mode, is below a certain threshold. We build upon Markov processes with spatial delay to solve this problem, and we explicitly characterize the firm's optimal policy, which is either myopic or a sandwich policy. A sandwich policy is one in which the firm deploys the service mode with inferior reward rate when the customer satisfaction level is in a specific interval near the satisfaction threshold and uses the myopically optimal service mode for all other satisfaction levels. Specifically, we find that the firm should use the safe service mode when the customer is marginally satisfied and the risky service mode when the customer is marginally unsatisfied. We find numerically that the customer lifetime value under the optimal policy is large relative to that under the myopic policy. Our results are robust to a variety of alternative model specifications. Funding: J. Lu gratefully acknowledges financial support from Natural Science Foundation of China (NSFC) [Project 72192805]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/moor.2021.0179. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to capture how the association between a multichannel relational communication strategy (MRCS) and customer performance is contingent upon such customer performance (low- versus high-performance customers) and to reconcile past contradictory results in this marketing-related topic. To this end, the authors propose and validate the method of quantile regression as an unconventional, yet effective, means to proceed to that reconciliation. Design/methodology/approach: This study collected data from 4,934 customers of a private pension fund firm and accounted for both firm- and customer-initiated relational communication channels (RCCs) and for customer lifetime value (CLV). This study estimated a generalized linear model and then a quantile regression model was used to account for customer performance heterogeneity. Findings: This study finds that specific RCCs present different levels of association with performance for low- versus high-performance customers, where outcome customer performance is the dependent variable. For example, the relation between firm-initiated communication (FIC) and performance is stronger for low-CLV customers, whereas the relation between customer-initiated communication (CIC) and performance is increasingly stronger for high-CLV customers but not for low-CLV ones. This study also finds that combining different forms of FIC can result in a negative association with customer performance, especially for low-CLV customers. Research limitations/implications: The authors tested the conceptual model in one single firm in the specific context of financial services and with cross-sectional data, so there should be caution when extrapolating this study's findings. Practical implications: This study offers nuanced and precise managerial insights on recommended resource allocation along with relational communication efforts, showing how managers can benefit from adopting a differentiated-customer performance approach when designing their MRCS. Originality/value: This study provides an overview of the state of the art of MRCS, proposes a contingency analysis of the relationship between MRCS and performance based on customer performance heterogeneity and suggests the quantile method to perform such analysis and help reconcile past contradictory findings. This study shows how the association between RCCs and CLV varies across the conditional quantiles of the distribution of customer performance. This study also addresses a recent call for a more holistic perspective on the relationships between independent and dependent variables. [ABSTRACT FROM AUTHOR]

A retention strategy based on an enlightened lapse model is a powerful profitability lever for a life insurer. Some machine learning models are excellent at predicting lapse, but from the insurer's perspective, predicting which policyholder is likely to lapse is not enough to design a retention strategy. In our paper, we define a lapse management framework with an appropriate validation metric based on Customer Lifetime Value and profitability. We include the risk of death in the study through competing risks considerations in parametric and tree-based models and show that further individualization of the existing approaches leads to increased performance. We show that survival tree-based models outperform parametric approaches and that the actuarial literature can significantly benefit from them. Then, we compare, on real data, how this framework leads to increased predicted gains for a life insurer and discuss the benefits of our model in terms of commercial and strategic decision-making. [ABSTRACT FROM AUTHOR]

The growing use of online social networks has intensified the focus on customer lifetime value (CLV), particularly how social conformity and influence shape purchasing decisions. Traditional CLV models have primarily captured positive interactions within networks but have largely ignored negative influences and the degree of customer susceptibility to conformity. These oversights limit the effectiveness of existing models when applied to real-world datasets. This paper introduces a novel model that enhances the estimation of CLV by incorporating both positive and negative interactions through sentiment analysis. The model leverages the CASINO algorithm to integrate social influence and conformity extracted from interpersonal interactions, building upon the foundational customer lifetime network value model. We validate our model using both semi-simulated and real-world data, comparing its performance with traditional approaches and illustrating its improved capacity to reflect complex social dynamics in CLV calculations.

This research sheds light on consumer motivations for participating in the sharing economy and examines downstream consequences of the uncovered motivations. We use text-mining techniques to extract Airbnb hosts' motivations from their responses to the question "why did you start hosting." We find that hosts are driven not only by the monetary motivation "to earn cash" but also by intrinsic motivations such as "to share beauty" and "to meet people." Using extensive transaction-level data, we find that hosts with intrinsic motivations post more property photos and write longer property descriptions, demonstrating greater engagement with the platform. Consequently, these hosts receive higher guest satisfaction ratings. Compared to hosts who want to earn cash, hosts motivated to meet people are more likely to keep hosting and to stay active on the platform, and hosts motivated to share beauty charge higher prices. As a result, these intrinsically motivated hosts have a higher customer lifetime value compared to those with a monetary motivation. We employ a multimethod approach including text mining, Bayesian latent attrition models, and lab experiments to derive these insights. Our research provides an easy-to-implement approach to uncovering consumer motivations in practice and highlights the consequential role of these motivations for firms. [ABSTRACT FROM AUTHOR]

The expansion of Palembang's tourism industry increasingly has positive results. The numerous awards given to the city of Palembang and the rise in tourist numbers are evidence of this. This is influenced by CRM, whose application seeks to increase Customer Lifetime Value (Usage Quantity, Loyalty, Word of Mouth, and Purchase Intention). However, only some studies have examined the relationship between CRM, Relationship Quality and CLV (Usage Quantity, Loyalty, Word-of-Mouth, and Purchase Intention) in the hotel industry. Consequently, a model analysis is required to assist the hotel industry in identifying the relationship between Relationship Quality and CLV (Usage Quantity, Loyalty, Word of Mouth, and Purchase Intention) because of utilizing CRM. This research took the point of view as a customer to investigate the influence of CRM on RQ. Moreover, this research also test whether the RQ will influence CLV factors. To determine the impact of CRM on RQ and RQ on CLV in the hotel industry, this project use Structural Equation Modeling (SEM) approach. The result of this research is expected to help hotel management in Palembang improve their performance by knowing the relationship between all factors. [ABSTRACT FROM AUTHOR]

Abstract. Problem definition: Online retailers have to provide customers with an estimate of how fast an order can be delivered before they decide to make the purchase. Retailers can strategically adjust this delivery speed promise online without changing offline infrastructure, and doing so may fundamentally impact business outcomes. It can influence consumers' purchasing decisions and postpurchase experiences, often in the opposite direction. On one hand, an aggressive (i.e., faster) delivery estimate could ensure that more customers meet their deadlines and thus, may increase their purchases ex ante. On the other hand, an aggressive estimate tends to overpromise, potentially leading to a longer than expected wait time, which can lower customer satisfaction and increase product returns ex post. In this research, we estimate the causal effect of retailers' delivery speed promise on customer behaviors and business performance. Methodology/results: Collaborating with Collage.com, an online retailer that sells customized photo products across the United States, we exogenously varied the disclosed delivery speed estimates online while keeping the physical delivery speed unchanged. Using the difference-in-differences identification strategy, we find that a faster promise increases sales and profits, but it also increases product returns and reduces customer retention. In addition, we propose a data-driven model that uses the estimated parameters to optimize delivery promises to maximize customer lifetime value. Managerial implications: Our findings provide managerial insights and a data-driven policy that retailers can leverage to optimize and customize their delivery promises. Funding: T. Sun acknowledges research support from CKGSB Research Institute. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0174. [ABSTRACT FROM AUTHOR]

In the Telecommunication and entertainment Industry, customer churn detection is one of the most important research topics that the company must deal with retaining on-hand customers. Churn means the loss of customers due to existing offers from the competitors or maybe due to network issues. In these types of situations, the customer may tend to cancel the subscription to a service. The Churn rate has a substantial impact on the lifetime value of the customer because it affects the future revenue of the company and the length of service. Due to a direct effect on the income of the industry, the companies are looking for a model that can predict customer churn. The model developed in this work uses machine learning techniques. By using machine learning algorithms, we can predict the customers who are likely to cancel the subscription. Using this, we can offer them better services and reduce the churn rate. These models help telecom services to make them profitable. In this model, we used Logistic Regression, Random Forest, and SVM for churn rate prediction. [ABSTRACT FROM AUTHOR]

BACKGROUND AND OBJECTIVES: The use of new marketing techniques in today’s businesses is highly needed by all organizations. One of the important issues in the field of customer retention is the customer’s lifetime value. Customer lifetime value has a great impact on optimizing the performance of companies, including insurance companies. The purpose of this research is to identify the influencing factors on increasing the customer lifetime value of insurance companies. METHODS: This research is a type of mixed research with a qualitative and quantitative approach, which is a survey study in terms of its purpose, application, and in terms of data collection. The research population includes managers, experts, and university professors from insurance companies and organizations. who were selected using the snowball sampling method. In the qualitative part, the data collection tool was an interview, and in the quantitative part, a questionnaire was used to identify the categories, and a semi-structured interview was used, and a questionnaire was used to validate the model. In the qualitative part of the data analysis method, the data theory approach was based on the Strauss and Corbin method, which was compiled using MAXQDA software and using the coding method, and in the quantitative part, the analysis method was based on Kendall’s correlation test. In order to examine the validity of the research in the qualitative part, the Cressol model was used along with content validity and intra-coder and inter-coder reliability, and in the quantitative part, in order to test the validity of the research, content validity and retest validity were used. FINDINGS: The findings of the study identify 9 causal factors, 3 strategic factors, 4 intervening factors, and 3 contextual factors that contribute to increasing customer lifetime value in the insurance industry. CONCLUSION: The conclusion of the research is the presentation of a model that includes causal, contextual, and intervening conditions, along with strategies to increase customer lifetime value and their consequences in the insurance industry. [ABSTRACT FROM AUTHOR]

In the context of the Poisson censored δ-shock model, this paper derives the system reliability by directly calculating probabilities, which is different from the existing literature. When the time is equal to δ, an explicit result that the reliability has a jump is verified. Based on the system reliability, we obtain the expectation of the lifetime of the system. Finally, one application of the model is shown in relationship marketing. The conclusions imply that managers should make a tradeoff between the expected return and variance of return before making decisions. [ABSTRACT FROM AUTHOR]

In the context of the Poisson censored δ-shock model, this paper derives the system reliability by directly calculating probabilities, which is different from the existing literature. When the time is equal to δ, an explicit result that the reliability has a jump is verified. Based on the system reliability, we obtain the expectation of the lifetime of the system. Finally, one application of the model is shown in relationship marketing. The conclusions imply that managers should make a tradeoff between the expected return and variance of return before making decisions. [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]

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]

Companies across the globe are keen on targeting potential high-value customers in an attempt to expand revenue, and this could be achieved only by understanding the customers more. Customer lifetime value (CLV) is the total monetary value of transactions or purchases made by a customer with the business over an intended period of time and is used as a means to estimate future customer interactions. CLV finds application in a number of distinct business domains, such as banking, insurance, online entertainment, gaming, and e-commerce. The existing distribution-based and basic (recency, frequency, and monetary)-based models face limitations in terms of handling a wide variety of input features. Moreover, the more advanced deep learning approaches could be superfluous and add an undesirable element of complexity in certain application areas. We, therefore, propose a system that is able to qualify as both effective and comprehensive, yet simple and interpretable. With that in mind, we develop a meta-learning-based stacked regression model that combines the predictions from bagging and boosting models that are found to perform well individually. Empirical tests have been carried out on an openly available online retail dataset to evaluate various models and show the efficacy of the proposed approach.

Brand equity (BE) and customer equity (CE) are the two crucial and closely linked concepts in marketing research. This research outlines a new conceptual framework to explore the relationship between the critical elements of BE and CE. Furthermore, using marketing activities, the study quantifies the effect of these activities on CE. The value of CE is computed based on a customer lifetime value (CLV) model in which linear, logistic, and beta regression are used to predict BE, customer acquisition, and customer share of wallet, respectively. We conducted an empirical analysis through questionnaires in an elevator company. The results reveal that brand knowledge and brand differentiation positively relate to customer acquisition. Also, for both existing customers and prospects, brand differentiation plays an important role in the share of wallet. The findings also show that marketing activities have a positive and significant impact on brand knowledge and brand differentiation, and consequently, through the mediating role of BE between marketing activities and CE, on customer acquisition and share of wallet. [ABSTRACT FROM AUTHOR]

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.

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]