Customer Lifetime Value Models Research Articles

Business Analytics in Customer Lifetime Value: An Overview Analysis

ABSTRACTIn customer‐oriented systems, customer lifetime value (CLV) has been of significant importance for academia and marketing practitioners, especially within the scope of analytical modeling. CLV is a critical approach to managing and organizing a company's profitability. With the vast availability of consumer data, business analytics (BA) tools and approaches, alongside CLV models, have been applied to gain deeper insights into customer behaviors and decision‐making processes. Despite the recognized importance of CLV, there is a noticeable gap in comprehensive analyses and reviews of BA techniques applied to CLV. This study aims to fill this gap by conducting a thorough survey of the state‐of‐the‐art investigations on CLV models integrated with BA approaches, thereby contributing to a research agenda in this field. The review methodology consists of three main steps: identification of relevant studies, creating a coding plan, and ensuring coding reliability. First, relevant studies were identified using predefined keywords. Next, a coding plan—one of the study's significant contributions—was developed to evaluate these studies comprehensively. Finally, the coding plan's reliability was tested by three experts before being applied to the selected studies. Additionally, specific evaluation criteria in the coding plan were implemented to introduce new insights. This study presents exciting and valuable results from various perspectives, providing a crucial reference for academic researchers and marketing practitioners interested in the intersection of BA and CLV.

AI-Powered Predictive Customer Lifetime Value: Maximizing Long-Term Profits

In an era where data-driven decision-making is critical to business success, understanding and optimizing Customer Lifetime Value (CLV) has become a strategic priority for companies across industries. CLV, which estimates the total revenue a business can expect from a customer throughout their relationship, is crucial for identifying high-value customers and tailoring marketing strategies to maximize profitability. However, traditional methods of calculating CLV often rely on historical data and linear models, limiting their accuracy and adaptability in a rapidly changing market environment. The integration of Artificial Intelligence (AI) into predictive analytics has brought about a paradigm shift in how businesses approach CLV. AI-powered predictive models leverage machine learning algorithms to analyze vast amounts of data, uncover complex patterns, and make highly accurate CLV predictions. These models can dynamically adjust to changes in customer behavior, market conditions, and other external factors, providing businesses with a more precise and actionable understanding of their customer base. This article explores the transformative potential of AI in predictive CLV modeling, examining the various techniques and data sources that drive these advanced models. We will discuss the strategic benefits of AI-driven CLV, including personalized marketing, optimized customer segmentation, and enhanced customer retention strategies. Additionally, we will address the challenges associated with implementing AI-powered CLV models, such as data privacy concerns, integration with existing systems, and the interpretation of AI-generated insights. Through a detailed analysis of industry case studies, this article highlights the practical applications of AI-powered CLV models in maximizing long-term profits. We will also explore future trends in AI technology and their potential impact on CLV predictions, offering insights into how businesses can stay ahead of the curve in an increasingly competitive landscape. By the end of this article, readers will have a comprehensive understanding of how AI can revolutionize CLV predictions and drive sustained business growth.

How Users Drive Value in Two-Sided Markets: Platform Designs That Matter

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.

Proposed Marketing Strategy Based on Customer Value Using CLV Model (Case Study of Natione Tech)

This research focuses on customer engagement strategies for technology companies to become industry leaders. It emphasizes the importance of leveraging profitable customers through Customer Lifetime Value (CLV) analysis, segmentation and targeted marketing. By analyzing real data from Natione Tech, the study identified three key factors: current value, potential value, and customer loyalty. Based on this analysis, 57 customers are categorized into high, medium, and low-profit segments, each requiring tailored marketing approaches. The strategies include personalized communication, cross-selling, exceptional customer service for high-profit customers; flexibility, trust-building, and direct assistance for medium-profit customers; and competitive pricing, loyalty programs, and targeted campaigns for low-profit customers. These strategies empower companies to excel in the customer engagement platform market and strengthen their position as technology leaders.

A novel approach to predicting customer lifetime value in B2B SaaS companies

AbstractIn 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.

Integrated customer lifetime value models to support marketing decisions in the complementary and alternative medicine industry

PurposeCustomer 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/approachThis 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.FindingsThe 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/valueThe 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.

Integrated customer lifetime value (CLV) and customer migration model to improve customer segmentation

Integrated customer lifetime value (CLV) and customer migration model to improve customer segmentation

An integrated model for customer equity estimation based on brand equity

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.

An Optimal Control Approach to Customer Lifetime Value

It is very important for a company to determine spending for acquisition and retention of customers which affect the Customer lifetime value of the company. In this paper, we formulate the Customer lifetime value model as an optimal control problem. The obtained problem is to find an acquisition policy which maximizes the present value of all future profits generated from a customer. Here the state variable is the average margin for each customer about how much each customer contributes to the company and the control variable is the cost of acquisition. We use the maximum principle for optimality to solve the problem, estimate some parameters of the problem using statistical data for Mobile service sector of Mongolia and calculate the optimal Customer lifetime values of the sector for some values of parameters.

Predictive Performance of Customer Lifetime Value Models in E-Commerce and the Use of Non-Financial Data

The article contributes to the knowledge of customer lifetime value (CLV) models, where extensive empirical analyses on large datasets from online stores are missing. Based on this knowledge, practitioners can decide about the deployment of a particular model in their business and academics can design or enhance CLV models. The article presents predictive performance of selected CLV models: the extended Pareto/NBD model, the Markov chain model, the vector autoregressive model and the status quo model. Six large datasets of medium and large‑sized online stores in the Czech Republic and Slovakia are used for a comparison of the predictive performance of the models. Online stores have annual revenues in the order of tens of millions of euros and more than one million customers. The comparison of CLV models is based on selected evaluation metrics. The results of some of the models which use additional non‑financial data on customer behaviour - the Markov chain model and the vector autoregressive model - do not justify the effort which is needed to collect such data. The advantages and disadvantages of the selected CLV models are discussed in the context of their deployment.

Segmentation Model of Customer Lifetime Value in Small and Medium Enterprise (SMEs) using K-Means Clustering and LRFM Model

The CLV model is a measure of customer profit for a company that can be used to evaluate the future value of a customer. The CLV model is a measure of customer profit for a company that can be used to evaluate the future value of a customer. This study aims to obtain Customer Lifetime Value (CLV) in each customer segment. Grouping uses the K-Means Clustering method based on the LRFM model (Length, Recency, Frequency, Monetary). The cluster formation process uses the Elbow Method and SSE with the best number of clusters = 2 clusters. CLV values are generated from the multiplication of the results of normalization of LRFM and the LFRM weight values are then summed, and carried out on each cluster that has been formed. The highest ranking among the 2 clusters is at the second cluster with the CLV value being far the highest from the other cluster average of 0.362. Based on LRFM matrix, this cluster has a high loyalty value with the symbol LRFM L ↑ R ↑ F ↑ M ↑ which is a loyal customer (the best segment that has high customer loyalty value). Based on the LRFM symbol, the company can make a strategy to retain customers and acquire customers to become loyal customers with high profitability.

Modeling patient's value using a stochastic approach: An empirical study in the medical industry

Background and objectiveThe rapid growth of computer methods encourages and creates competitive advantages in the medical industry. Nowadays many health centers try to build successful and beneficial relationships with their patients using customer relationship management (CRM) methods, to recognize target patients, attract potential patients, increase patient loyalty and maximize profitability. Customer lifetime value (CLV) is a metric that can help organizations to calculate their customers’ value or group them; therefore in this research we aim to develop a new CLV model for the medical industry that groups patients using computer-based methods. MethodsTo model CLV for the medical industry, we will use two computer-based methods. First, to model patients’ behavior, a data mining approach is required: the K-means algorithm is used to cluster patients and the decision tree technique is used to analyze patient clusters. Next, Markov chain model, a stochastic approach, is utilized to predict future behavior of customers ResultsThis paper proposes a new CLV model for the medical industry that has some benefits over other CLV papers. It is patient behavior based, helping us to predict the future behavior of each patient as well as helping to modify managerial strategies for each type of patient. The derived CLV model includes less than 0.08 error rates. ConclusionsUsing the derived CLV model helps health centers to group their patients by computer-based methods, which makes their decision making more accurate and trustworthy. The present research helps organizations within the health industry to group and rank their patients by a new CLV model and fit their strategies to each patient group, based on his/her behavior type.

COMPARATIVE ANALYSIS OF SELECTED PROBABILISTIC CUSTOMER LIFETIME VALUE MODELS IN ONLINE SHOPPING

The selection of a suitable customer lifetime value (CLV) model is a key issue for companies that are introducing a CLV managerial approach in their online B2C relationship stores. The online retail environment places CLV models on several specific assumptions, e.g. non-contractual relationship, continuous purchase anytime, variable-spending environment. The article focuses on empirical statistical analysis and predictive abilities of selected probabilistic CLV models that show very good results in an online retail environment compared to different model families. For comparison, eleven CLV models were selected. The comparison has been made to the online stores’ datasets from Central and Eastern Europe with annual revenues of hundreds of millions of euros and with almost 2.3 million customers. Probabilistic models have achieved overall good and consistent results on the majority of the studied transactional datasets, with BG/NBD and Pareto/NBD models that can be considered stable with significant lifts from the baseline Status quo model. Abe's variant of Pareto/NBD have underperformed multiple criterions and would not be fully useful for the studied datasets without further improvements. In the end, the authors discuss the deployment implications of selected CLV models and propose further issues for future research to address.

Impact of Workforce Flexibility on Customer Satisfaction: Empirical Framework & Evidence from a Cleaning Services Platform

Problem definition: Contrary to classic applications of matching theory, in most contemporary on-demand service platforms, matches can not be enforced because workers are flexible – they choose their tasks. Such flexibility makes it difficult to manage workers while keeping customers satisfied. We build a framework to compare platform matching policies with less flexible and more flexible workers, and empirically quantify by how much worker flexibility hurts customer satisfaction and customer equity. Academic/Practical relevance: In academic literature, there is no established framework that allows for the comparison of matching policies in on-demand platforms. Further, the link between worker flexibility and customer satisfaction is understudied. Methodology: We propose a tripartite framework for empirical evaluation and comparison of the operational policies with different degrees of worker flexibility. Step 1: Predictive modeling of customer satisfaction based on estimation of individual unobservable characteristics: customer difficulty and worker ability (item-response theory model). Step 2: Evaluation of the effect of matching policy (under a given level of flexibility) on customer satisfaction (bipartite matching). Step 3: Quantification of the associated monetary impact (customer lifetime value model). Results: We apply our framework to the dataset of one of the world's largest on-demand platforms for residential cleanings. We find that customer difficulty and cleaner ability are good predictors of customer satisfaction. Granting full flexibility to workers reduces customer satisfaction by 3% and customer lifetime revenue by 0.2%. We propose a family of matching policies that provide sufficient flexibility to workers, while alleviating 75% of the detrimental effect of worker flexibility on customer satisfaction. Managerial implications: Our results suggest that, in platforms with flexible workforce, the presence of worker and customer heterogeneity reduces customer satisfaction through matching inefficiency. Our empirical framework helps practitioners to decide on the right level of worker flexibility and the means for achieving it.

Modeling and Application of Customer Lifetime Value in Online Retail

This article provides an empirical statistical analysis and discussion of the predictive abilities of selected customer lifetime value (CLV) models that could be used in online shopping within e-commerce business settings. The comparison of CLV predictive abilities, using selected evaluation metrics, is made on selected CLV models: Extended Pareto/NBD model (EP/NBD), Markov chain model and Status Quo model. The article uses six online store datasets with annual revenues in the order of tens of millions of euros for the comparison. The EP/NBD model has outperformed other selected models in a majority of evaluation metrics and can be considered good and stable for non-contractual relations in online shopping. The implications for the deployment of selected CLV models in practice, as well as suggestions for future research, are also discussed.

A Methodology for Calculating Customer Credit Score Based on Customer Lifetime Value Model

Proper customer relationship management is among the facets that contribute to productivity at institutions. It is a requirement for customer relationship managers, especially at financial and credit institutions and at banks, to calculate and determine the customer’s creditworthiness and credit score. The aim of this study is to present a solution for calculating the customers’ value and their credit score without incurring the costs for collecting extra information. The primary source of data for this study is operation system database. Due to differences among operation systems, a comprehensive schema of the database is defined first. Only conventional indices and variables have been used in this schema, so that the presented solution can be generalized and will be applicable to most economic institutions. The calculation of the customer’s creditworthiness is performed with regard to the three variables of the “recency” of contact, the “frequency” of transactions, and the “monetary” amount. The collected data is divided into the two populations of “good” and “bad” customers. Variables from those two populations that possess significant differences are identified using statistical methods. Those variables are used in determining the customer’s credit score. Next, a solution is presented for comparing the efficiency of the models for the identification of the customer’s credit score. We will test and compare two statistical methods, the Logistic Regression model and the Fisher Discriminant Analysis, and two soft computing methods, the Multilayer Perception Network and the Vector Machine for determining the customer’s credit score. Additionally, a solution is offered for setting the number of layers and the number of neurons in the Multilayer Perception Network.

An Empirical Assessment of Customer Lifetime Value Models within Data Mining

An Empirical Assessment of Customer Lifetime Value Models within Data Mining

Classification of customer lifetime value models using Markov chain

A firm’s potential reward in future time from a customer can be determined by customer lifetime value (CLV). There are some mathematic methods to calculate it. One method is using Markov chain stochastic model. Here, a customer is assumed through some states. Transition inter the states follow Markovian properties. If we are given some states for a customer and the relationships inter states, then we can make some Markov models to describe the properties of the customer. As Markov models, CLV is defined as a vector contains CLV for a customer in the first state. In this paper we make a classification of Markov Models to calculate CLV. Start from two states of customer model, we make develop in many states models. The development a model is based on weaknesses in previous model. Some last models can be expected to describe how real characters of customers in a firm.

A model to improve management of banking customers

PurposeThe purpose of this paper is to provide a customer lifetime value (CLV) model to carefully assess and classify banking customers using individual measures and covering customers’ relationships with a portfolio of products of the company.Design/methodology/approachThe proposed model comprises two sub-models: (sub-model 1) modelling and prediction of CLV in a multiproduct context using Hierarchical Bayesian models as input to (sub-model 2) a value-based segmentation specially designed to manage customers and products using the latent class regression. The model is tested using real transaction data of 1,357 customers of a bank.FindingsThis research demonstrates which drivers of customer value better predict the contribution margin and product usage for each of the products considered in order to get the CLV measure. Using this measure, the model implements a value-based segmentation, which helps banks to facilitate the process of customer management.Originality/valuePrevious CLV models are mostly conceptual, generalisation is one of their main concerns, are usually focussed on single product categories using aggregated customer data, and they are not design with a special emphasis on their application as support for managerial decisions. In response to these drawbacks, the proposed model will enable decision makers to improve the understanding of the value of each customer and their behaviour towards different financial products.

V(CLV): Examining Variance in Models of Customer Lifetime Value

While accurate point estimation of customer lifetime value (CLV) has been the target of a large body of academic research, few have focused on the variance of CLV (V(CLV)), which represents the degree of uncertainty associated with a customer's expected CLV. This is ironic because academics have long known that V(CLV) is one of the most important characteristics that defines and differentiates customers from one another, affecting firms on many fundamental levels. No closed-form, forward-looking statistical procedures have been derived to estimate individual-level V(CLV). For the first time, the authors derive, predict, and validate V(CLV) using a powerful combination of stochastic models for the flow of transactions over time and the company's profit on each transaction. They provide these estimates for 561,100 customers of an omnichannel retailer tracked over a 2.25-year period, making this one of the largest-scale CLV analyses to date. They highlight the importance of V(CLV), analyze its relationship to observable summary statistics such as recency, frequency, and monetary value, and uncover many substantive variance-related insights regarding customer segmentation, scoring, targeting, and more.