A Theory of Customer Valuation: Concepts, Metrics, Strategy, and Implementation

2018 ◽  
Vol 82 (1) ◽  
pp. 1-19 ◽  
Author(s):  
V. Kumar
Keyword(s):  
Social Media ◽  
Portfolio Management ◽  
Customer Value ◽  
Economic Value ◽  
Valuation Theory ◽  
Economic Theories ◽  
Customer Valuation ◽  
Customer Lifetime ◽  
Lifetime Value ◽  
Multiple Constituencies

Customer value refers to the economic value of the customer's relationship with the firm. This study approaches the topic of customer value for measuring, managing, and maximizing customer contributions by proposing a customer valuation theory (CVT) based on economic principles that conceptualizes the generation of value from customers to firms. The author reviews the established economic theories for valuing investor assets (e.g., stocks) and draws a comparison to valuing customer contributions. Furthermore, the author recognizes the differences in the guiding principles between valuing stocks and valuing customers in proposing CVT. Using CVT, the author discusses the concept of customer lifetime value (CLV) as the metric that can provide a reliable, forward-looking estimate of direct customer value. In addition, economic models to estimate CLV, ways to manage CLV using portfolio management principles, and strategies to maximize CLV are discussed in detail. The author extends the customer value concept by discussing ways that a customer can add value to the firm indirectly through incentivized referrals, social media influence, and feedback. Finally, the benefits of CVT to multiple constituencies are offered.

Customer Lifetime Value Measurement using Machine Learning Techniques

2014 ◽  
pp. 565-573
Author(s):  
Tarun Rathi ◽  
Vadlamani Ravi
Keyword(s):  
Machine Learning ◽  
Customer Value ◽  
Customer Lifetime Value ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Customer Lifetime ◽  
Learning Techniques ◽  
Lifetime Value ◽  
Additive Regression ◽  
Or Organization

Customer Lifetime Value (CLV) is an important metric in relationship marketing approaches. There have always been traditional techniques like Recency, Frequency and Monetary Value (RFM), Past Customer Value (PCV) and Share-of-Wallet (SOW) for segregation of customers into good or bad, but these are not adequate, as they only segment customers based on their past contribution. CLV on the other hand calculates the future value of a customer over his or her entire lifetime, which means it takes into account the prospect of a bad customer being good in future and hence profitable for a company or organization. In this paper, we review the various models and different techniques used in the measurement of CLV. Towards the end we make a comparison of various machine learning techniques like Classification and Regression Trees (CART), Support Vector Machines (SVM), SVM using SMO, Additive Regression, K-Star Method and Multilayer Perception (MLP) for the calculation of CLV.

scholarly journals A marketing view of the customer value: Customer lifetime value and customer equity

2013 ◽  
Vol 44 (4) ◽  
pp. 47-64 ◽  
Author(s):  
A. M. Estrella-Ramón ◽  
M. Sánchez-Pérez ◽  
G. Swinnen ◽  
K. VanHoof
Keyword(s):  
Customer Value ◽  
Customer Lifetime Value ◽  
Customer Equity ◽  
Future Research ◽  
Customer Profitability ◽  
Profitability Analysis ◽  
Customer Lifetime ◽  
Lifetime Value ◽  
Equity Measures

Throughout this research the customer valuation trend in marketing is going to be reviewed, emphasizing Customer Lifetime Value and Customer Equity measures. The main theoretical contributions in the development and evolution of the Customer Lifetime Value concept are analysed. Customer Lifetime Value is also differentiated from Customer Equity and Customer Profitability analysis to estimate customer value in terms of firm profitability. Customer Lifetime Value and Customer Equity concepts are formally defined. Additionally, a classification of a set of published researches into Customer Lifetime Value and/or Customer Equity is developed. This classification has been posited according to several criteria that serves as a guide to key requirements for developing these types of models. Finally,several conclusions, suggestions and future research streams are highlighted.

scholarly journals Review and Analysis of Selected Customer Value Measurement Methods

2020 ◽  
Vol 1/2020 (32) ◽  
pp. 34-46
Author(s):  
Maria Kubacka ◽  
Keyword(s):  
Literature Review ◽  
Customer Value ◽  
Measurement Methods ◽  
Value Assessment ◽  
Important Goal ◽  
Financial Measures ◽  
Customer Lifetime ◽  
Lifetime Value ◽  
Value Measurement ◽  
Constant Interest

The methods of measuring customer value are of constant interest. This area is widely described in the literature, but nevertheless somewhat generally. It is important to analyse the different methods comprehensively in the context of the customer and the company. The aim of this paper is to analyse the methods of measuring customer value most often presented in the literature, as well as to assess the possibility of their reliable application and to indicate the limitations resulting from the construction of models of their calculations. One very important goal is to constructively propose solutions to the problematic issues in the methods analysed. In order to achieve the objectives of the paper, methods of measuring customer value are presented. On this basis, the author has identified problematic elements that constitute guidelines for further research. A literature review and analysis of studies carried out with the use of the methods discussed is applied. The conclusions of this research indicate ambiguity in the methods of measuring customer value, their diversity and, most importantly, the inability to reliably determine certain components of the methodology, especially for customer lifetime value. The analysis presented constitutes a proposal for a comprehensive look at the issue of customer value assessment and an element of further scientific and empirical discussion. The originality of the research consists in presenting a proposal for quantification of non-financial measures, which are a component of the methodology of measuring customer value.

scholarly journals A Dual Perspective of Value in a Bundle of Product and Service

2017 ◽  
Vol 8 (4) ◽  
pp. 27-41
Author(s):  
Niusha Safarpour ◽  
Ilkka Sillanpää
Keyword(s):  
Customer Value ◽  
Business Models ◽  
Real Life ◽  
Value Proposition ◽  
A Value ◽  
Modern Age ◽  
The Arts ◽  
Customer Lifetime ◽  
Lifetime Value ◽  
Dual Perspective

AbstractFocusing on value creation in marketing has always been the key to success for companies. As a result, the definition, analysis and communication of value has gained importance. Companies are making an attempt to make a value proposition that is not only lucrative for the customer, but also has great returns for the company itself. Although this might sound simple on paper, since it is the basis for business logic, it is much more complicated in real life situations. With the service elements in the offering and the emergence of technologies such as smart and connected phenomenon, the business models become more innovative and more complexity is added to the analysis of value.The objective of this paper is to introduce a method for the dual perspective of value in a bundle of product and service in a smart and connected context. This method draws from the customer value and customer lifetime value concepts to offer an all-inclusive study on value. This assists companies in crafting an appealing value proposition in a cost-saving offering for a client that offers value to the company over its lifetime. This study specifically deals with the state of the arts smart and connected phenomenon and provides a view on how value works in that context.The framework created through this study serves to help the company choose a client that is of most value to the firm over the time of their cooperation. It then leads the company towards a better fabrication of the offering that is not only an attractive proposition to the client but also for the company. It gives a close insight onto where the benefit comes from and how a smart and connected bundle of products, services and relationships must be put together for maximum results in the modern age.

scholarly journals New GRFM Approach and Fuzzy LPMetric weighting score

2020 ◽  
Vol 16 ◽  
pp. 43-59
Author(s):  
Elham Eshrati ◽  
Afshin Safaee
Keyword(s):  
Customer Value ◽  
Business Processes ◽  
Market Competition ◽  
Numerical Code ◽  
Essential Point ◽  
Customer Lifetime ◽  
The Family ◽  
Lifetime Value ◽  
The Status ◽  
Lp Metric

Nowadays a thorough understanding of the business processes and the organization`s customers is the essential point to survive in the market competition. In this study, a new idea was applied to import customer purchased basket data into data mining computations. First, the products were divided into families, and we assigned a numerical code for each product in the family. The sum of these assigned numbers indicates the status of the basket. After this step, the transactions were clustered based on their basket values. Customers are then clustered in each cluster using the RFM method. Using a new fuzzy LP-metric approach and pairwise comparisons, RFM indices were weighted and we obtain customer value per cluster. Then we will proceed by averaging the customer value according to the presence of each customer in different clusters. Then we clustered customers based on customer lifetime value.

The Impact of Forward-Looking Metrics on Employee Decision-Making: The Case of Customer Lifetime Value

2016 ◽  
Vol 92 (3) ◽  
pp. 31-56 ◽  
Author(s):  
Pablo Casas-Arce ◽  
F. Asis Martínez-Jerez ◽  
V. G. Narayanan
Keyword(s):  
Decision Making ◽  
Default Risk ◽  
Customer Value ◽  
Customer Lifetime Value ◽  
Customer Relationship ◽  
Significant Shift ◽  
Customer Lifetime ◽  
Lifetime Value ◽  
The Impact ◽  
Forward Looking

ABSTRACT This paper analyzes the effects of forward-looking metrics on employee decision-making. We use data from a bank that started providing branch managers with the customer lifetime value (CLV)—an estimate of the future value of the customer relationship—of mortgage applicants. The data allow us to gauge the effects of enriching the employees' information set in an environment where explicit incentives and decision rights remained unchanged. On average, customer value increased 5 percent after the metric's introduction. The metric's availability resulted in a significant shift in attention toward more profitable client segments and some improvement in cross-selling. However, the use of CLV did not negatively impact pricing or default risk, as the literature predicts. Finally, branch managers with shorter tenure displayed a stronger response, consistent with information substituting for experience.

Customer Equity Evaluation

2014 ◽  
Vol 2014 (6) ◽  
pp. 80-96
Author(s):  
Yuriy Sozonov
Keyword(s):  
Customer Value ◽  
Evaluation Methods ◽  
Customer Lifetime Value ◽  
Customer Equity ◽  
Data Sources ◽  
Customer Lifetime ◽  
Lifetime Value ◽  
Research On Application ◽  
Equity Evaluation ◽  
Practical Recommendations

The article outlines the results of the research on application of customer equity evaluation methods. In the study customer equity of Russian B2B-company was evaluated on the basis of the following metrics: customer lifetime value (CLV), past customer value (PCV), integrated index of customer equity (RFM). The evaluation is complemented with practical recommendations on metrics application for customer equity maximization. On the basis of the conducted research, toolkit of customer equity evaluation is developed. It involves a set of metrics, indicators for evaluation and data sources. This toolkit could be potentially adapted and employed by Russian B2B-companies.

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