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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.

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.

An electrolytic capacitor is extensively used as filtering devices in various power supplies and audio amplifiers. Low cost and higher value of capacitance make it more well known. As environmental stress and electrical parameters increase, capacitors degrade on accelerated pace. The paper aims to discuss these issues.

This paper focusses on the impact of thermal stress on electrolytic capacitors using accelerated life testing technique. The failure time was calculated based on the change in capacitance, equivalent series resistance and weight loss. The experimental results are compared with the outcome of already available life monitoring methods, and the accuracy level of these methods is accessed.

The results of all the three methods are having maximum 55 per cent accuracy. To enhance the accuracy level of theoretical methods, modifications have been suggested. A new method has been proposed, whose outcome is 92 per cent accurate with respect to experimentally obtained outcomes.

To assess the capacitor’s reliability using an experimental and modified theoretical method, failure prediction can be done before it actually fails.

A new method has been proposed to access the lifetime of capacitor.

Compensation of funds managers increasingly involves elements of profit sharing that entitle managers to option-like payoffs. An important example is the compensation of private equity fund managers. Compensation of private equity fund managers typically consists of a fixed management fee and a performance-related carried interest. The fixed management fee resembles common compensation terms of mutual funds and hedge funds, while the performance-related carried interest is uncommon among most mutual funds. Moreover, the performance-related carried interest typically differs from variable hedge fund fees. In this chapter, we derive the value of the variable components of private equity fund managers’ compensation based on a risk-neutral option-pricing approach.

This exercise is one in a series intended to help students learn how to perform financial calculations in marketing contexts.

Maru Keitou, a decorated former collegiate softball player with a PhD from Oxford University, ran Maru Batting Center in the Roppongi district of Tokyo's Minato ward. She had a deep knowledge of the game and of her customers, but she lacked a marketing background. She had recently signed up for a hosted customer relationship management service that would allow her to track the cost of acquiring and serving each of her four main customer segments. Using this data, she could determine which segments to target in the upcoming year.

The exercise describes the use of calculations of customer acquisition cost, retention rates, and customer lifetime value in picking between market segments and various options for activities to acquire customers.

Maru Keitou, a decorated former collegiate softball player with a PhD from Oxford University, ran Maru Batting Center in the Roppongi district of Tokyo's Minato ward. She had a deep knowledge of the game and of her customers, but she lacked a marketing background. She had recently signed up for a hosted customer relationship management service that would allow her to track the cost of acquiring and serving each of her four main customer segments. Using this data, she could determine which segments to target in the upcoming year.

The exercise describes the use of calculations of customer acquisition cost, retention rates, and customer lifetime value in picking between market segments and various options for activities to acquire customers.

After completing the exercise, students should be able to:

• Calculate customer acquisition cost

• Determine customer break-even

• Calculate and explain customer lifetime value

Calculate customer acquisition cost

Determine customer break-even

Calculate and explain customer lifetime value

As Lindsay (1994, 1995) encourages validation of existing results, this research replicates Guilding and McManus (2002) in a New Zealand (NZ) context. The usage and perceived merit of customer accounting practices were lower in NZ than in the Australian study. Few of the regressions where customer accounting usage and perceived merit were dependent variables revealed a statistically significant role for competition intensity and market orientation. There was some minor support for the perceived merit of customer accounting being higher in companies experiencing medium levels of competition intensity.

For any given customer, his/her profitability for a business enterprise can be estimated by the so-called customer lifetime value (CLV). One specific goal for many enterprises consists in maximizing the aggregated CLV associated with its set of customers. To achieve this goal, a company uses marketing resources (e.g. marketing campaigns), which are usually expensive.

This paper proposes a formal model of the Customer Life Value problem inspired by the uncapacitated facility location problem.

The computational experiments conducted by the authors illustrate the potential of the approach when compared with a standard (non-algorithm-supported) one.

The approach leads up to the economic trade-off between the volume of the employed resources and the aggregated CLV, i.e. the higher the number of resources utilized, but also the higher the cost of achieving this level of lifetime value. Hence, the number of resources to be “activated” has to be decided, and the effect of each of these resources on each CLV will depend upon how “close” the resource is from the corresponding customer (i.e. how large will the impact of the active resource on the customer).