Maximizing Customer Lifetime Value Research Articles

Strategies for Maximizing Customer Lifetime Value through Effective Onboarding and Renewal Management

In today's competitive market, maximizing Customer Lifetime Value (CLV) is essential for sustaining growth and profitability. Effective onboarding and renewal management are critical strategies in achieving this goal. This paper explores the interplay between customer onboarding experiences and renewal processes, emphasizing their role in fostering long-term relationships and enhancing customer satisfaction. A comprehensive onboarding program not only educates customers about product functionalities but also helps to set expectations and establish trust, significantly influencing their initial engagement. By utilizing personalized onboarding experiences, organizations can ensure that customers quickly recognize the value of their products or services. Furthermore, the renewal management process is pivotal in retaining customers and securing ongoing revenue streams. By implementing proactive communication strategies, such as timely reminders and tailored renewal offers, businesses can enhance customer retention rates. Additionally, leveraging data analytics enables organizations to anticipate customer needs and identify potential churn risks, allowing for targeted interventions. This study also highlights the significance of continuous feedback mechanisms to refine onboarding and renewal strategies, ensuring they align with evolving customer expectations. Ultimately, a synergistic approach that combines effective onboarding with strategic renewal management can significantly boost CLV, resulting in a more loyal customer base and improved financial performance. By investing in these critical areas, companies can create a sustainable competitive advantage and drive long-term success.

Research on Marketing Strategy Management based on Customer Retention

This paper aims to explore marketing strategy management based on customer retention to enhance a company's competitiveness in a highly competitive market. In today's business landscape, where competition is fierce and customer acquisition costs are high, customer retention has emerged as a crucial strategy for businesses to maintain profitability and achieve sustained success. Through a comprehensive literature review and empirical research, we delve into the concept of customer retention and its significance in marketing strategy management. Customer retention refers to the ability of a company to retain existing customers and foster their loyalty over time. It goes beyond simply acquiring new customers and recognizes the value of nurturing and maintaining relationships with existing ones. By examining the interrelationship between customer retention and marketing strategy management, we shed light on the various strategies and tactics that companies can employ to enhance customer retention. This includes not only focusing on the initial sale but also on building long-term customer relationships. We delve into the importance of understanding customer needs, preferences, and behaviors to develop targeted marketing approaches that resonate with customers on a personalized level. Based on the literature review and empirical research, we propose a theoretical framework for analyzing the key factors and influencers of customer retention. This framework takes into account factors such as customer satisfaction, loyalty programs, and customer service, which have been identified as critical drivers of customer retention. By understanding and addressing these factors, companies can implement effective strategies to improve customer retention rates. Ultimately, the adoption of effective customer retention strategies can lead to sustainable competitive advantages for businesses. By reducing customer churn, increasing customer loyalty, and maximizing customer lifetime value, companies can differentiate themselves in the market, increase market share, and achieve long-term profitability.

The Effect of Affiliate Programs and Consumer Behavior on Profit Margins: The Mediating Role of Customer Acquisition Cost (CAC) and Customer Loyalty

The purpose of this study is to investigate the influence of affiliate programs on profit margins in online businesses, with a focus on understanding the mediating role of Customer Acquisition Cost (CAC) and Customer Loyalty. The study aims to bridge the knowledge gap and provide insights for business practitioners and researchers to design more effective affiliate program strategies. This research adopts a comprehensive approach, reviewing current literature on affiliate programs and online businesses. The study focuses on the mediating role of Customer Acquisition Cost (CAC) and Customer Loyalty in the relationship between affiliate programs and profit margins. The research methodology includes data analysis and interpretation to unravel the impact of affiliate programs on CAC, customer loyalty, and profit margins with 200 respondents as participant and use SMART-PLS 3.0 as statistical tools. The findings of this research reveal that implementing affiliate programs can result in higher acquisition costs due to commissions paid to affiliate partners. However, effective affiliate programs can create customer attachment, leading to increased customer retention and maximizing customer lifetime value. Affiliate programs contribute to overall business profitability by increasing sales volume, broadening the customer base, and generating additional revenue. Moreover, the study highlights the significant positive effect of consumer behavior on customer acquisition cost. However, the influence of customer acquisition cost on profit margins is not significant, contrary to traditional business theory. This research makes an important contribution to the understanding of the influence of affiliate programs in online businesses and the mediating role of CAC and Customer Loyalty in driving profit margins. The study offers valuable insights for business practitioners and researchers to design more effective affiliate program strategies and maximize profit margins in the competitive e-commerce environment. Further research is needed to explore the relationship between customer acquisition cost and profit margins in more depth and identify other related factors.

Sooner or Later? Promising Delivery Speed in Online Retail

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 .

Determinants of Customer Switching towards Mobile Number Portability

Customer churn is haunting Indian mobile telecom services in a big way. Mobile number portability has further aggravated this problem for mobile telecommunication companies, although it has rightly empowered the subscribers of these services. Telecom sector is a very capital-intensive sector, so a customer’s stay with a cellular services operator effects its earning. The efforts are being made to predict and model the customer churn towards telecom services, so as to maximize customer lifetime value for the company. Before making predictions of customer switching behaviour, it is imperative to understand those variables which determine customer’s loyalties towards a mobile service’s vendor. So this study provides an exploratory analysis of the factors which become reasons for discontinuing services of a cellular operator. The results have identified factors like: network, tariffs, technology, advertising, rewards programme and external factors which lead to switching of a mobile services operator.

Strategy for Maximizing Customer Lifetime Value: A Study on Fairness Cream Users of Bangladesh

Customer lifetime value is one of the most important concepts in marketing on which lots of scholarly writings have been done to analyze this concept. In this study, we have used this concept to study fairness cream market of Bangladesh. Through this study we have tried to find out the factors that increase customer lifetime value in fairness cream market. To conduct the study, we have used simple random sampling technique where 300 respondents have been interviewed. We have utilized multiple regression technique to analyze the data. The findings that have been generated from the analysis is that fairness cream companies can increase customer usage months of fairness cream product or customer lifetime value through proper application of three factors which are creating fear that experimentation may be dangerous for skin, strong positioning that user’s brand is the best brand in the market and marketer’s brand has international recognition. DOI: http://dx.doi.org/10.3329/jbt.v8i1-2.18286 Journal of Business and Technology (Dhaka) Vol.8(1-2) 2013; 63-78

When is the best time to reactivate your inactive customers?

In a noncontractual setting, it is impossible to tell whether a specific customer is still active or he/she has already defected. A popular approach to identify active customers is to calculate the probability of being active based on the Pareto/negative binomial distribution (NBD) model. Building on the Pareto/NBD, we assume that a firm can take direct marketing actions to reactivate an “inactive” customer with a certain cost. So, the firm has to determine the optimal cutoff threshold of the probability of being active to identify inactive customers to trigger reactivations. We propose a continuous time dynamic model, which aims at maximizing customer lifetime value and finding the optimal time to reactivate inactive customers. We develop a Markov chain Monte Carlo algorithm to obtain the model parameters at individual level. The empirical study shows that selecting optimal threshold for reactivation can be a profitable strategy to influence the lifetime value of customers.

Developing A Model For Measuring Customers Loyalty And Value With Rfm Technique And Clustering Algorithms

In today’s competitive world, moving toward customer-oriented markets with increased access to customer’s transaction data, identifying loyal customers and estimating their lifetime value makes crucial. Since knowledge of customer value provides targeted data for personalized markets, implementing customer relationship management strategy helps organizations to identify and segment customers and create long-term relationships with them, and as a result, they can maximize customer lifetime value. Data mining techniques are known as a powerful tool for this purpose. The purpose of this paper is customer segmentation using RFM technique and clustering algorithms based on customer’s value, to specify loyal and profitable customers. We also used classification algorithms to obtain useful rules for implementing effective customer relationship management. This paper used a combination of behavioral and demographical characteristics of individuals to estimate loyalty. Finally, the proposed model has been implemented on a grocery store’s data, during 1997 to 1998 in Singapore, to measure customer’s loyalty during these two years.

Moving from customer lifetime value to customer equity

We study the consequence of moving from Customer Lifetime Value maximization to Customer Equity maximization. Customer equity has traditionally been seen as the discounted sum of the lifetime earnings from all current and future customers and thus it has been largely assumed that maximizing customer lifetime value would lead to maximum customer equity. We show that the transition from CLV to CE is not that straightforward. Although the CLV model is appropriate for managing a single non-replaceable customer, the application of a CLV model to the acquisition and valuation of customers as an ongoing concern for the firm leads to sub-optimal customer relationship management and acquisition strategies. This leads the firm following a CLV maximization approach to have a smaller and less profitable customer base than one that follows a CE maximization strategy.

Social Relationship and Transactional Marketing Policies—Maximizing Customer Lifetime Value

We present an analytic approach to address the problem of how sellers can establish and maintain a long-lasting relationship with a buyer and, at the same time, maximize customer lifetime value (CLV). To model the evolution of a relational exchange between a seller and a buyer, we extend a well-known mathematical model of “love dynamics.” The growth of each partner’s commitment to the relationship is a sum of negative and positive terms. The negative term describes each partner’s propensities for opportunism, while the positive terms describe each partner’s trust in the commitment of the other, and the reaction to marketing efforts. The seller controls the evolution of the relationship through social relationships and transactional marketing efforts. The main findings are as follows: (1) Loyal (committed) customers and long-term relationships do not always generate better cash flows, especially when buyers either look for superior current value in each purchase opportunity or are short-term oriented. (2) Without mutual trust between partners, the seller should treat old customers over time as new ones, making the reduction of retention costs impossible. (3) It is only cheaper to retain current customers rather than acquiring new ones if mutual trust between partners overcomes propensities for opportunism and the seller slightly discounts future cash flows.

Netflix, Inc.: DVD Wars

Netflix pioneered the online video rental industry and developed a successful business model that effectively managed customer acquisition, retention, and lifetime value. Blockbuster Video intends to slow Netflix's growth by launching an online rental program that appears to be seamlessly integrated with its huge network of offline video stores. Will Blockbuster's multichannel program hurt Netflix's customer acquisition and rentention rates? Simultaneously, competition from video retailers such as Wal-Mart challenges the core business model that Netflix built to maximize customer lifetime value.

Managing Customers for Value

This article provides an introduction to and overview of this special issue on “Managing Customers for Value.” The field of customer management has grown rapidly in recent years, with significant research examining approaches with which firms can manage customers as key assets. Starting with the notion that firms seek to maximize customer lifetime value and customer equity, the authors identify eight key challenges that firms and researchers face in understanding, managing, and implementing successful customer management strategies. The authors then present an organizing framework and use it to offer an overview of the rest of the special issue, outlining how each of the remaining articles in the special issue (each addressing one of the key challenges) fits into the framework. Based on a synthesis of the overall insights from the articles, the authors conclude with a research agenda that highlights potentially fruitful avenues for further investigation.

Modeling Customer Lifetime Value

As modern economies become predominantly service-based, companies increasingly derive revenue from the creation and sustenance of long-term relationships with their customers. In such an environment, marketing serves the purpose of maximizing customer lifetime value (CLV) and customer equity, which is the sum of the lifetime values of the company’s customers. This article reviews a number of implementable CLV models that are useful for market segmentation and the allocation of marketing resources for acquisition, retention, and cross-selling. The authors review several empirical insights that were obtained from these models and conclude with an agenda of areas that are in need of further research.

A Dynamic Model of the Duration of the Customer's Relationship with a Continuous Service Provider: The Role of Satisfaction

Many service organizations have embraced relationship marketing with its focus on maximizing customer lifetime value. Recently, there has been considerable controversy about whether there is a link between customer satisfaction and retention. This research question is important to researchers who are attempting to understand how customers' assessments of services influence their subsequent behavior. However, it is equally vital to managers who require a better understanding of the relationship between satisfaction and the duration of the provider-customer relationship to identify specific actions that can increase retention and profitability in the long run. Since there is very little empirical evidence regarding this research question, this study develops and estimates a dynamic model of the duration of provider-customer relationship that focuses on the role of customer satisfaction. This article models the duration of the customer's relationship with an organization that delivers a continuously provided service, such as utilities, financial services, and telecommunications. In the model, the duration of the provider-customer relationship is postulated to depend on the customer's subjective expected value of the relationship, which he/she updates according to an anchoring and adjustment process. It is hypothesized that cumulative satisfaction serves as an anchor that is updated with new information obtained during service experiences. The model is estimated as a left-truncated, proportional hazards regression with cross-sectional and time series data describing cellular customers perceptions and behavior over a 22-month period. The results indicate that customer satisfaction ratings elicited prior to any decision to cancel or stay loyal to the provider are positively related to the duration of the relationship. The strength of the relationship between duration times and satisfaction levels depends on the length of customers' prior experience with the organization. Customers who have many months' experience with the organization weigh prior cumulative satisfaction more heavily and new information (relatively) less heavily. The duration of the service provider-customer relationship also depends on whether customers experienced service transactions or failures. The effects of perceived losses arising from transactions or service failures on duration times are directly weighed by prior satisfaction, creating contrast and assimilation effects. How can service organizations develop longer relationships with customers? Since customers weigh prior cumulative satisfaction heavily, organizations should focus on customers in the early stages of the relationship—if customers' experiences are not satisfactory, the relationship is likely to be very short. There is considerable heterogeneity across customers because some customers have a higher utility for the service than others. However, certain types of service encounters are potential relationship “landmines” because customers are highly sensitive to the costs/losses arising from interactions with service organizations and insensitive to the benefits/gains. Thus, incidence and quality of service encounters can be early indicators of whether an organization's relationship with a customer is flourishing or in jeopardy. Unfortunately, organizations with good prior service levels will suffer more when customers perceive that they have suffered a loss arising from a service encounter—due to the existence of contrast effects. However, experienced customers are less sensitive to such losses because they tend to weigh prior satisfaction levels heavily. By modeling the duration of the provider-customer relationship, it is possible to predict the revenue impact of service improvements in the same manner as other resource allocation decisions. The calculations in this article show that changes in customer satisfaction can have important financial implications for the organization because lifetime revenues from an individual customer depend on the duration of his/her relationship, as well as the dollar amount of his/her purchases across billing cycles. Satisfaction levels explain a substantial portion of explained variance in the durations of service provider-customer relationships across customers, comparable to the effect of price. Consequently, it is a popular misconception that organizations that focus on customer satisfaction are failing to manage customer retention. Rather, this article suggests that service organizations should be proactive and learn from customers before they defect by understanding their current satisfaction levels. Managers and researchers may have underestimated the importance of the link between customer satisfaction and retention because the relationship between satisfaction and duration times is very complex and difficult to detect without advanced statistical techniques.

A Dynamic Model of the Duration of the Customer'S Relationship with a Continuous Service Provider

Many service organizations have embraced relationship marketing with its focus on maximizing customer lifetime value. Recently, there has been considerable controversy about whether there is a link...