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Synthesis of the customer lifetime value and the shareholder value (SHV) approach in order to develop an integrated, marketing‐based method for corporate valuation.

Discusses the limitations and assumptions of existing methods to estimate customer value components and examines the limitations of the SHV concept. By linking the customer equity (CE) and the SHV approach, a formal model to calculate corporate value is developed. The discounted cash flow method is used for modelling the profit streams.

Provides formulas for the estimation of both the individual lifetime value of a customer and CE. Provides a comprehensive model to estimate corporate value based on customer‐related cash flows and traditional financial metrics. Introduces typical cases, in which the use of a customer‐based valuation seems beneficial. Illustrates how our approach can be applied by using a simple case study on M&A in the telecommunication industry. Gives suggestions on how to obtain the necessary data, partially even from publicly available sources.

Advancement of the quantitative techniques for modelling the customer value components would allow for relaxing some restrictive assumptions. The explicit modelling of the future growth of the customer base (the acquisition rate) would increase the applicability of the model. Additionally, taking into account heterogeneity within the customer cohorts is a task for future research. Finally, our model needs to be applied more extensively using real data for the input variables.

A CE‐based valuation approach can guide marketing investments and helps to avoid misallocation of resources. Based on an example in the field of M&A, we demonstrate the usefulness of the approach for obtaining a realistic indicator of firm value. It helps to assess whether an acquisition is economically sensible. We provide evidence for the superiority of a customer‐based approach over traditional financial methods.

While the traditional SHV method considers cash flows at a highly aggregated level, our approach employs disaggregated cash flows on the level of individual customers. Thereby we do incorporate the lifetime values of future customers by considering different cohorts. We do capture customer defection by incorporating retention rates. Our model enables a more detailed and valid estimation of corporate value by accounting for the single customer activities that drive marketing actions. This enables a better forecasting of the free cash flow. Incorporating customer‐related drivers into financial valuation models makes easier to assess the return on marketing investments.

The purpose of this paper is to apply customer lifetime value models to assess the overall value of the service encounter and to establish implications that such an assessment has for managing customer relationships under a fixed‐size salesforce.

Using a specific relationship between customer servicing activities and the buying rhythms of customers, an analytical model for assessing the overall value of a service encounter is developed.

A stochastic parameter is identified, characterizing the level of quality to compute the long‐term value of a given customer and stochastic ordering properties to determine the relative value of different customers.

The implications discussed are analytical to help service managers shaping their thought process in decision making. Future research can empirically test the model proposed.

The theorem specifies the optimal solutions to determine: how much capacity should be committed to a given customer; and how to choose a customer in the first place. These are important and useful tools for managers in making their managerial decisions in service marketing.

A general model of resource allocation is provided, under which those seminal models such as CALLPLAN, DETAILER are special cases. This is particularly valuable as key account management has become more important in globally operated businesses.

The calculations which underlie efforts to balance marketing spending on customer acquisition and customer retention are usually based on either single‐period customer profitability or forecasts of customer lifetime value (CLTV). This paper argues instead for risk‐adjusted CLTV, which is termed the economic value (EV) of a customer, as the means for marketing to assess both customer profitability and shareholder value gains.

Reports on the empirical measurement of EV of customers through a collaborative case study analysis of business‐to‐business relationships in the financial service industry.

One direct consequence of measuring this risk and the EV of key account customers was a customer portfolio review which led to changes in their relationship marketing strategies and improvements in shareholder value for the firm.

Selective customer retention through lifetime value analysis and a risk‐adjustment process may be the means for developing relationship marketing strategies.

This paper contributes to the field by extending the discussion on customer risk and demonstrating a method that managers can readily adopt to evaluate the risk of their customers.

Customers differ widely in the long‐term value they represent to a company, and the “best” customers are often many times more valuable than the average ones. Cites four customer value components: acquisition cost, revenue stream, cost stream and length of relationship. Argues that by understanding and managing lifetime customer value, a company not only allocates resources to its customers more effectively, but also becomes better able to focus on developing long‐term customer relationships. Examines ways to calculate lifetime customer value and use it as the basis for strategy development.

The aim of this study is to develop an applicable and detailed model for customer lifetime value (CLV) and to highlight the most important indicators relevant for a specific industry – namely the banking sector.

This study compares the results of the least square estimation (LSE) and artificial neural network (ANN) in order to select the best performing forecasting tool to predict the potential CLV. The performances of the models are compared by the hit ratio, which is calculated by grouping the customers as “top 20 per cent” and “bottom 80 per cent” profitable.

Due to its higher performance; LSE based linear regression model is selected. The results are found to be highly competitive compared with the previous studies. This study shows that, beside the indicators mostly used in the literature in measuring CLV, two additional groups, namely monetary value and risk of certain bank services, as well as product/service ownership-related indicators, are also significant factors.

Organisations in the banking sector have to persuade their customers to use certain routine risk-bearing transaction-based services. In addition, the product development strategy has a crucial role to increase the CLV of customers because some of the product-related variables directly increase the value of customers.

The proposed model predicts potential value of current customers rather than measuring current value considered in the majority of previous studies. It eliminates the limitations and drawbacks of the majority of models in the literature through simple and industry-specific method which is based on easily measurable and objective indicators.

The purpose of this study is to show that the use of CAM (cognitive analytics management) methodology is a valid tool to describe new technology implementations for businesses.

Starting from a dataset of recipes, we were able to describe consumers through a variant of the RFM (recency, frequency and monetary value) model. It has been possible to categorize the customers into clusters and to measure their profitability thanks to the customer lifetime value (CLV).

After comparing two machine learning algorithms, we found out that self-organizing map better classifies the customer base of the retailer. The algorithm was able to extract three clusters that were described as personas using the values of the customer lifetime value and the scores of the variant of the RFM model.

The results of this methodology are strictly applicable to the retailer which provided the data.

Even though, this methodology can produce useful information for designing promotional strategies and improving the relationship between company and customers.

Customer segmentation is an essential part of the marketing process. Improving further segmentation methods allow even small and medium companies to effectively target customers to better deliver to society the value they offer.

This paper shows the application of CAM methodology to guide the implementation and the adoption of a new customer segmentation algorithm based on the CLV.

Word‐of‐mouth is a rarely quantified phenomenon, in spite of its importance for service firms. Therefore, referrals remain a neglected determinant of customer lifetime valuation, although some authors claim them to be the astronomical part of customer equity. The paper discusses different approaches to the calculation of positive word‐of‐mouth, leading to a monetary referral value of a company’s customers.

Customer lifetime value (CLV) scoring is highly effective when applied to marketing databases. Some researchers have extended the traditional association rule problem by associating a weight with each item in a transaction. However, studies of association rule mining have considered the relative benefits or significance of “items” rather than “transactions” belonging to different customers. Because not all customers are financially attractive to firms, it is crucial that their profitability be determined and that transactions be weighted according to CLV. This study aims to discover association rules from the CLV perspective.

This study extended the traditional association rule problem by allowing the association of CLV weight with a transaction to reflect the interest and intensity of customer values. Furthermore, the authors proposed a new algorithm, frequent itemsets of CLV weight (FICLV), to discover frequent itemsets from CLV-weighted transactions.

Experimental results from the survey data indicate that the proposed FICLV algorithm can discover valuable frequent itemsets. Moreover, the frequent itemsets identified using the FICLV algorithm outperform those discovered through conventional approaches for predicting customer purchasing itemsets in the coming period.

This study is the first to introduce the optimum approach for discovering frequent itemsets from transactions through considering CLV.

One of the salient challenges in customer-oriented organizations is to recognize, segment and rank customers. Customer segmentation is usually based on customer lifetime value (CLV) measured by three purchase variables: “Recency,” “Frequency” and “Monetary.” However, due to the ambiguity of these variables, using deterministic approach is not appropriate. For tackling this matter, the purpose of this paper is to propose a new method of customer segmentation and ranking by combining fuzzy clustering (as a segmentation method) and fuzzy AHP (as a ranking method).

First, customers are classified based on purchase variables using fuzzy c-means clustering algorithm. Second, the variables are weighed applying an optimized version of AHP method. Considering the derived weights and customer groups, this paper follows to ranks segments based on CLV. The developed methodology has been implemented for a large IT company in Iran.

The results show a tremendous capability to the company to evaluate his customers by dividing them into nine ranked segments. The validity of clusters has been submitted.

For researchers, this study provides a useful literature by combining FCM and an optimized version of fuzzy AHP in order to cover the limitations of previous methodologies. For organizations, this study clarifies the procedure of customer segmentation by which they can improve their marketing activities.

Managers can consider the proposed CLV calculation methodology for selling the next best services/products to the group of customers that are more valuable, by calculating the entire lifetime value of the customers.

This study contributes to the process of customer segmentation based on CLV, proposing a new method which covers the limitations of previous customer segmentation methods.

In previous studies, historical information of customer had been used for determining customer lifetime value (CLV). The purpose of this paper is to modify CLV estimation to be applied before producing a new product.

In this study, the CLV estimation has been modified using Kano satisfaction coefficient. The Kano satisfaction coefficient has been assumed as loyalty indicator in estimating CLV and related equations have been developed for allocating Kano requirements to various phases of product life cycle. The proposed approach has been examined in two new product options of the automobile industry. Finally, by using customers’ purchase records during three years, CLV has been calculated for both product development options.

Findings indicate that CLV of the first development option is equal to 407 million and 500,000 toumans and of the second option is equal to 392 million toumans, this difference is related to different requirements of the Kano model, and as a result, to different satisfaction coefficients. Therefore, the first option has been suggested for investing in developing new product.

Application of the proposed approach is limited to short time periods. The findings are limited to the automobile industry.

The modified approach of estimating CLV can be applied for prospective new product development in addition to traditional approaches in which, only the historical data of sold products are used. In addition, using Kano satisfaction coefficient in estimation of CLV in short periods, seems an appropriate approach for competitive industries that focus on dynamic needs of customers.