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The aim of this chapter is to develop a performance measurement framework for understanding the relationships among drivers of customer profitability in internet companies.

We recognize an opportunity to improve management control systems for internet companies, where performance measurement systems currently focus on measuring web data, such as number of customers, cost of service, cost of acquisition (CoA), and churn rate. However these indicators, taken separately, do not provide useful information to make decisions.

To fill this gap we developed a framework, which we designate as the Lifetime Value Scorecard, to investigate the relationships between customer data and financial data, providing an early indication as to whether or not the marketing strategies being implemented are successful. We then offer an application of the Lifetime Value Scorecard to the mobile value-added services industry, where content and services are provided to consumer cell phones, mainly using wireless networks.

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.

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.

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.

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.

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.

Building on the relationship marketing and stimulus-organism-response (SOR) theory, the purpose of this paper is to study the impact of the integrated store service quality (ISSQ) on the omnichannel customer lifetime value (CLV). The mediating role of customer commitment (affective, normative and continuance) and relationship program receptiveness with the moderating role of customer relationship proneness were relied upon to better understand the omnichannel customer profitability metric (CLV).

The study is descriptive and relies upon the cross-sectional data collected using the self-administered structured questionnaires from 785 omnichannel shoppers. A purposive sampling technique was performed in the study. Structural equation modeling was performed using the SMART-PLS 4.0 software to analyze the data.

The results indicate that omnichannel customer commitment (affective, normative and continuance) differentially mediates the relationship between ISSQ and relationship program receptiveness, subsequently impacting the omnichannel CLV. The customer relationship proneness significantly and positively moderated the relationships between different dimensions of customer commitment and relationship program receptiveness.

The study relied upon the cross-sectional data from the Indian population aged above 18 years for testing the proposed model. Further studies could test the model across different populations to generalize the study results.

This study addresses the need to investigate the omnichannel retail store customer profitability and their relationship performance with the store. By testing the customer relationship management model in the omnichannel retail store context, this study is the first to show that ISSQ will impact the customer profitability and relationship performance metric (CLV) through omnichannel customer commitment and relationship program receptiveness. The moderating effect of customer relationship proneness on a few proposed hypotheses was also tested to give managerial recommendations.