FH Schmalkalden

FB Wirtschaft

„Customer Lifetime Value Calculation and Maximization“

von

Kathleen Paeger

Matrikel-Nr.: 241801

Erstellt im Rahmen des Schwerpunktes Controlling:

Prof. Dr. Peter Schuster

Sommersemester 2009

Anschrift der Bearbeiterin:

Ernst-Thälmann-Straße 16

98593 Floh-Seligenthal

Abgabe: 14. Juli 2009

Table of Content

1. Introduction........................................................................................1

2. Basic Model.......................................................................................2

2.1. “Lost-for-good” model...................................................................3

2.2. “Always-a-share” model ...............................................................4

3. Extension of the Model ......................................................................6

3.1. Market potential ...........................................................................7

3.2. Resource potential .......................................................................9

4. CLV Maximization............................................................................13

4.1. Customer Selection....................................................................13

4.2. Customer Segmentation ............................................................ 13

4.3. Optimal Resource Allocation...................................................... 15

4.4. Purchase Sequence Analysis.....................................................16

4.5. Targeting Profitable Prospects ...................................................18

5. Evaluation of the Model ...................................................................20

6. Conclusion....................................................................................... 22

References............................................................................................23

1

1. Introduction

Customers are critical assets of any company. Without customers a firm has no

revenues, no profits and no market value.

Recently, the concept of customer lifetime value is gaining increasing importance in

literature and practice. Companies have had enormous success after implementing

customer lifetime value calculation and corresponding database techniques.

(Gupta/Lehmann, 2008 p.255)

Companies have realized that with an increasing duration of a customer relationship

the generated profit per customer and year is increasing as well. To build long-term

relationships companies operate systems for customer retention. However, the

question arises, how much should a company invest in a customer relationship and

which customer is worth spending money. On the one hand the amount spending is

dependent on available resources; on the other hand the expected future surplus is

of vital importance. Therefore, a company has to know how valuable their existing

customers are before they are able to predict their potential.

This thesis will answer important questions of companies for example what value a

customer has for a company, the influence of specific key drivers, and which

qualitative determinants have to be considered beside the common quantitative

determinants. Can customers be evaluated based only on their past purchase

behavior and if not which measure is the most suitable in identifying the future value

of the customer? Oftentimes customer oriented companies realize that their

customers are valued more than the profit they bring in every transaction. In fact their

value has to be based on their contribution to the company across the duration of

their relationship with the firm. In other words, the value of a customer is the value

the customer brings to the company over his or her lifetime. Some recent studies

(Reinartz/Kumar, 2003) have already proven that past contributions from customers

may not always reflect their future worth to the company. Hence, there is a need for a

metric which will display future profitability of the customer to the firm (Berger/Nasr,

1998): Customer lifetime value calculation.

2. Basic Model

Kumar (2006, p.9) defines customer lifetime value (CLV) as the sum of cumulated

cash flows – discounted using the weighted average cost of capital – of a customer

over his or her entire lifetime with the company.

Customer lifetime value is increasingly recognized as one of the most important

measures of the worth of a customer, as it takes into account not only the customer’s

current value but the expected value over their projected lifetime with a company. So,

it can be defined as the potential value of a customer for a company over the

relationship duration related to a specific point in time, usually the present day (Bruhn

et al. 2000, p.172). For this purpose the expected cash flows generated by a

customer during his or her relationship are discounted by a determined discount rate

to present day. This results in a perspective’s movement from single transactions to

a long-term relationship between supplier and customer within the relationship

framework. CLV is mainly determined by the following factors:

Number of transaction per period

Value of single transactions

Discount rate

Relationship duration (customer retention)

We differentiate between two basic models to calculate customer lifetime value: The

so-called “lost-for-good” model (resp. customer retention model) and on the other

hand the “always-a-share” model (resp. customer migration model) (Dwyer 1997).

Both models will be described below.

2.1. “Lost-for-good” model

The first approach, “lost-for-good”, assumes that when a customer terminates a

relationship, he or she does not come back to the company and has to be acquired

again. This model especially applies to relationships with a contractual setting as in

financial services or relationships based on subscriptions. A very important factor

within this model is the customer retention rate. The retention rate describes the

probability that a customer relationship persists in the next period. If the retention rate

equals to zero, the relationship ends after the considered period. With a customer

retention rate of one the continuity of a relationship is assured for example within a

contractual setting (Bruhn et al. 2000, p.174). The following formula demonstrates the

basic approach for a CLV calculation of a “lost-for-good” model:

where

CLV = customer lifetime value

i = customer

t = time period

d = discount rate

CMi,t = predicted contribution margin from customer i in period t

Mi,t = predicted marketing costs for customer i in period t

Ri = retention rate for customer i.

The length of the considered planning period depends heavily on the considered

branch. Oftentimes five periods are suggested for consideration, because with a

longer planning period the uncertainty of forecast increases (Dwyer 1997, p.9). In

branches with long transaction cycles ten periods for a planning period are

appropriate (Berger/Nasr 1998, p.21). In case of a long planning period Blattberg et

al. (2001, p.210) suggest a compromise to choose a relatively high discount rate so

that cash flows far in the future have a lower impact, which is appropriate to

uncertainty.

The used imputed discount rate is consequently of vital importance for a forecast.

The level of discount rate is determined by the level of capital market interest,

inflation rate, cycle period and individual risk assessment. As mentioned before the

customer retention rate is of vital importance for CLV calculation in the “lost-for-good”

model.

An estimation of the retention rate can be carried out in two ways: On the one hand

based on customer surveys within marketing research (behavior tendency) and on

the other hand based on monitored customer behavior in the past (actual behavior

figures). Actual figures referring to the monitored customer behavior in the past allow

often more accurate forecasts. Following determinants are mentioned as indicators

for customer retention (Reinecke/Dittrich 2006, p. 333):

Purchase intensity (number of transactions per period)

Frequency of contacts (number of contacts from customer per period)

Relative duration since last transaction

Average sale per transaction

Share of wallet (percentage of customer’s expenses for a product that goes to

the company selling the product)

Number and type of customer complaints

Number of recommendations

Because of the assumption that when a customer terminates a relationship, he or she

does not come back to the company the approach is questionable. The approach

systematically understates CLV. To resolve this problem, researchers use the

“always-a-share” model.

2.2. “Always-a-share” model

In contrast to the “lost-for-good” model the “always-a-share” approach assumes, that

a customer frequently switches between different suppliers without terminating the

relationship definitely. Therefore, the term “customer migration model” is

synonymously used. Future purchase decisions of a customer are considered as a

stochastic process. It is assumed that the customer’s choice of a supplier follows a

probability mechanism. Instead of the customer retention rate within the “lost-for-

good” model the possibility of choosing the supplier for each transaction is of vital

importance in the “always-a-share” approach. Hence, the “always-a-share” approach

calculates CLV with the predicted frequency of a customer’s purchases given his or

her previous purchases. It is a better way of projecting future customer activity. A

CLV function, which incorporates predicted frequency, contribution margin, and

variable costs, can be expressed as follows: (Venkatesan/Kumar 2004, p.108):

where

CLVi = lifetime value of customer i,

CMi,y = predicted contribution margin from customer i in purchase occasion y,

d = discount rate

ci,m,l = unit marketing cost for customer i in channel m in year l,

xi,m,l = number of contacts to customer i in channel m in year l,

frequencyi = total number of transactions within planning period

n = number of years to forecast, and

Ti = predicted number of purchases made by customer i until the end ofthe planning period

In contrast to the “lost-for-good” model the periods are not cumulated, but the number

of transactions. Therefore, y is the predicted total number of transactions within the

planning period. So the discount rate’s exponent considers the number of transaction

per period. And if frequencyi = 1 (one transaction per period) the above formula

equals the traditional CLV-formulas (Rust et al. 2004, p. 114.)

Comparing both approaches it is to state, that neither of them is to favor over the

other. The appropriateness of the approaches primarily depends on the considered

branch. Especially in branches characterized by a contractual setting like financial

services or telecommunications, normally the lost-for-good-model is better suited for

calculation. In case of consumer goods on the contrary the always-a-share approach

depicts reality the best (Calciu/Salerno 2002, p.126; Dwyer 1997, p.9.).

With the calculation of the customer lifetime value it has to be considered, that

besides of the monetary components within the formulas there exist additional

determinants, which influence the value of a customer. These will be explained in

detail in the following chapter.

3. Extension of the Model

The customer’s economic relevance for a company is not certainly only limited to his

or her profit generated today or in the future. Customer’s contribution to company’s

profit turns out to be more complex. On the one hand there is the profit resulting from

the business transactions with the customer referred to as market potential of a

customer in the following. And on the other hand the customer virtually acts as

resource of the company. Thus, it is described as resource potential of a customer.

Accordingly, for an extensive evaluation of a customer the quantitative criteria are not

sufficient. Sales volume and contribution margin are available for the past and the

present, but there will be forecast problems for an estimation of the future value.

Tomczak and Rudolf-Sipötz (2006) point out that with an appraisal of customer

lifetime values it is to consider, that besides the quantifiable components of the basic

model there exist more components, soft factors. These factors are not measureable

in terms of monetary criteria, but have to be considered within the analysis because

they make a significant contribution to the customer value in excess of financial

importance of a customer relationship and provides valuable information, if individual

customers are evaluate in comparison with each other. Hence, a customer

relationship can not only be determined by monetary facts, but soft facts have to be

incorporated into customer valuation with adequate criteria even though they do not

affect customer’s contribution margin directly. Because these additional components

are caused by customer behavior, they must be added to the customer lifetime value.

So the basic calculation of customer lifetime value has to be expanded by information

about customer behavior and necessitated to consider all determinants of potentials

(Table 1).

Table 1: Determinants of customer value (Source: Tomczak/Rudolf-Sipötz, 2006, p. 132)

It should be mentioned that indeed within the literature diverse potentials are

discussed but there is a lack of adequate conceptualization and operationalization of

these determinants.

3.1. Market potential

The so far considered monetary potential of a customer in the basic model is also

referred to as market potential. The market potential of a customer is the sales

success, which a customer generates for a company at present and in the future as a

purchaser of goods and services in a business relationship. Both in past and today

companies still orientate their decision mainly on actual data.

Potential yield: The potential yield of a customer represents the current monetary

contribution of a customer to company’s success. A suitable figure is the customer

profitability and customer contribution margin calculation, which considers the

proceeds and costs resulting from the relationship. Although this figure is not

convincing as the value of a customer for a company, it provides the only basis,

which investments for a relationship are undertaken. Still problematic in practice is

the costs-by-cause principle. The reasons for differences in costs and revenues are

manifold. As cost drivers are to name e.g. amount of orders and frequency of

purchase (Tomczak/Rudolf-Sipötz, 2006, p. 132).

Development potential: The expected future revenues are more important than the

potential yield. As a description of the development potential the expected

contribution margin potential of a customer as sole information is not sufficient. In fact

the development of a customer – his or her growth – is of larger interest. Because a

customer who contributes negatively at present to the success of a company can

achieve very well a positive contribution margin in the future. Here, reference is made

to the customer life cycle. Similar to the product life cycle business relations pass

through several phases, which help to predict the demand over time. But at the same

time situational influencing factors show the limitations of the concept. Therefore it is

difficult, almost impossible to predict the development of an individual customer. But

even if the total demand is known or determined, the following question is about

which share of wallet a company is able to realize because of its strengths and

weaknesses and which is covered by the competitor. The probability of how high the

share can be depends on the loyalty potential of a customer and underlies a specific

forecast uncertainty (Tomczak/Rudolf-Sipötz, 2006, p. 133).

Cross-buying potential: The development potential discussed before is very closely

related to a customer’s cross-buying potential. It describes the extent to which a

customer makes additional dealings from other than the previous fields of the

provider’s service offering.

Determining factors of the cross-buying potential are the customer’s willingness to

cross-buy besides of the customer needs. Therefore a customer, who does not want

to be dependent of a single supplier, shows low willingness to cross-buy. In addition

other factors can be critical as well for customers, who do not want to purchase from

the same supplier, e.g. the desideratum of variety-seeking.

There is a broad consent about the necessity of improving exhaustion of a customer

throughout cross-selling. And although the concept of cross-selling is anchored in the

everyday language of managers and sales department it is deficient in

implementation. Determination and concretization of the cross-buying potential is

very difficult in practice. Also within the literature only a few approaches are

discussed (Cornelsen, 2000, p. 172).

Loyalty potential: Oliver (1997, p.392) defines loyalty as a deep commitment to

repurchase a product or inquire service consistently in the future despite situational

influences and marketing efforts having the potential to cause switching behavior. So

this driver defines, if a customer inquires services in the future.

In general two dimensions of loyalty potential are identified: dedication-based

relationship maintenance and constraint-based relationship maintenance.

(Bendapudi/Berry 1997, p.17) The first view comprehends trust, commitment, and

customer satisfaction whereas the constraint-based relationship is defined by

dependency within the relationship and the existence of alternatives.

Despite of the positive effect of dependency on relationship continuity is has to be

considered, that a customer will try to drop out of the relationship dependence which

feels negative according to this high dependence. So trust is crucial for loyalty

potential and it reduces the opportunistic behavior. Also commitment is considerable.

Commitment describes the strong inner bond, which covers stability (willingness to

maintain the relationship) and readiness to make sacrifices (acceptance of temporary

disadvantages). In particular, customer satisfaction and the confidence of the

customer should be named as key determinants.

3.2. Resource potential

The market potential describes the profit-oriented view of customers and his or her

direct contribution to the success of a company. The second determining factor refers

to the customer as a resource of the company and comprises the indirect contribution

margin by acting often active and passive as company’s resource. Reference,

information, cooperation and synergy potential are related to the resource potential.

Reference potential: The reference potential of a customer is determined by the

number of potential customers reached in a specific period because of his or her

recommendation, ability of influencing, frequency and intensity of contact, and

degree of social networks. The customer is able to influence with positive, neutral or

negative information about a supplier or product. And we have to differentiate

between active (e.g. recommendations) and passive reference potential (e.g. acting

as lead user).

Indicators, which are adequate to represent the reference potential of a customer, are

manifold and related to the number of reference recipient, personality and know-how

of the reference bearer, his or her willingness to recommend and effect of the

reference (Cornelsen, 2000, p.200).

Information potential: The information potential contains all information, which a

customer offers a supplier and can be used by the company. This information is of

strategic as well as operative relevance. A substantial criterion is that the term

information potential always refers to an information stream from a customer to the

company, while the reference potential is based on the communication between a

customer and his social environment (Cornelsen, 2000, p.224).

In the same way information are manifold, so are source of generation. The simplest

and in practice most frequent established method is the customer survey. Within

almost all companies data warehouse respectively marketing information systems

provide the opportunity to collect information of the customers, to evaluate them and

to use them for company’s decision. In daily business an active complaint

management is helpful to use continuous such information potentials.

The information potential is not only limited to the communication between end

customer and company. Quite the contrary, especially the business-to-business area

represents a lot of possibilities. For example an involvement of the customer in the

product development enables a company to tap the full information potential.

For an evaluation of the customer’s information potential we have to take into

account the willingness to provide information and to give feedback, as well as the

content and quality of the information. Objective of a customer value management is

to name and structure the different areas of information and to support the

information stream actively. Therefore, an intensified exhaustion of the information

potential of a customer can be achieved (Kleinaltenkamp/Dalke, 2006, p.236).

Cooperation potential: The cooperation or integration potential of a customer

describes the readiness and ability to contribute production factors in the production

of goods and services for limited time. Part of the cooperation potential are all

synergies and potentials to enhance value, which can be realized throughout an

intensified cooperation and integration into the value-added chain of suppliers and

demanders within a limited period.

There are various areas of application along the value-added chain for example

research, development, logistics, organization and marketing. In the course of the

increasing movement of the value-added chain towards the customer the cooperation

potential is particularly to find in the business-to-business and service sector,

because customer and supplier are often involved in the production of goods and

services at the same time.

In the presented conceptualization of the customer value especially the exchange of

tangible resources (real capital and human resource) mostly defines the cooperation

potential. By contrast the information streams are a constitutive characteristic of the

information potential.

Despite of the importance of the factor cooperation potential in the business-to-

business sector there are more applications in business-to-customer business. For

example, an implementation of an online banking service depends on the willingness

of a customer to cooperate. This includes the will and the ability to use it and the

customer’s know how (Tomczak/Rudolf-Sipötz, 2006, p. 137).

Synergy potential: Besides the external synergy possibilities of the cooperation

potential there might be internal company synergies relevant, too. Focusing on key

accounts might result from the need of internal coordination or from an expectation of

general relief of internal coordination. The internal synergy potential covers all

economies of scope of the customer base, where a customer – active or passive –

causes interactions.

A significant indicator for a high synergy potential is a high share of wallet of a

customer with the company. Therefore a strong dependency of the customer is given,

whereby changes within the relationship affect company’s profit significantly.

An important economy of scope is not only a high share of wallet for each customer,

but also important sales relationships of a customer to subsidiaries, even if the

customer has a marginal economic relevance for the supplier. Characteristics of such

a high synergy potential are for example centralized decision processes for

investments, production or procurement. (Belz/Senn, 1995, p.47)

Another example is economies of scale. This effect results from an increasing size of

the company respectively increasing number of customers with the possibility of cost

reduction in several business sectors e.g. manufacturing and distribution.

As a conclusion of the basic and extended model and as mentioned before, in the

literature there are several CLV approaches discussed, but to this day no single CLV

approach and, most importantly, no single calculation model exists which covers all

relevant CLV components that have been identified so far. For example some of the

approaches include effects of the resource potential while neglecting retention rates

or vice versa. (Bauer et al., 2003, p.53)

4. CLV Maximization

4.1. Customer Selection

Several customer valuation techniques (e.g. ABC analyses, scoring models,

customer portfolio methods) have shown that not all loyal customers are profitable.

So it is important to figure out which customers bring maximum profit, because these

are worth to retain and improve company’s profitability. Valuing each customer and

identifying customer- and firm-specific drivers of profitable long-term relationships are

the first steps to determine profitable customers. Studies of Reinartz and Kumar

(2000, 2003) and Venkatesan and Kumar (2004) have shown that CLV calculation is

superior to scoring models when it comes to customer selection. The superiority of

the CLV model was thereby explained, that the predictions about future profits and

customer purchase behavior based on past data most likely correspond to the actual

future results and were more precisely than other scoring methods. These studies

clearly show that CLV-method is a better metric in selecting the most profitable

customers. These results from two separate studies using database from business-

to-customer and business-to-business firms provide substantial support for the

superiority of CLV framework over other metrics for customer scoring and customer

selection.

4.2. Customer Segmentation

Segmentation is a strategy to manage marketing efforts directed at customers. It

marks a change from mass marketing to targeting marketing activities to specific

groups of customer. Incorporating customer profitability into segmentation strategies

enables an organization to improve the effectiveness and efficiency of their marketing

campaigns. (Lemon/Mark, 2006, p.56) So a development of customized marketing

strategies for each segment, which means differential treatment of customers, is one

way to manage customers according to their profitability.

Companies are building on traditional bases of segmentation such as geographic,

demographic, socioeconomic, attitudinal, and behavioral to incorporate customer

past and projected purchase data into customer segmentation models. In order to

understand why certain customers are more profitable than others, firms need to

understand the variables, which differentiate each segment from the other and

explain why certain customers are more profitable than others. Some of the

exchange and customer demographic variables are amount of purchase, degree of

cross-buying, average interpurchase time, number of product returns, ownership of

loyalty instrument, mailing effort by the firm, location and income of customers.

(Kumar, 2006, p. 614)

Reinartz and Kumar examined in their study (2003) the different impact of each

variable on the customer lifetime duration and how they affect CLV. The customers

are grouped into segments based on these exchange and demographic/firmographic

variables and afterwards the profile of the segments are analyzed. Profiling helps

firms to better understand the customer composition of each segment, the

characteristics of the most valuable customers, what are the best marketing channels

to reach them, and how frequent do they purchase. The analysis can be used to

identify the customer segments on which companies should concentrate on and to

evaluate the efficiency and effectiveness of their marketing programs. For instance, if

number of marketing touches is found to be a key driver of high CLV, firms can easily

identify segments which are low on the number of touches on an average and target

those segments in increasing the number of marketing touches through the most

effective channels thereby improving the profitability of the segment.

Thus, a mixture of segment level marketing to improve the key drivers and customer

level strategies on marketing communication can improve the customer lifetime value

of a segment.

For managing for example loyalty and profitability simultaneously, a segment-based

approach is used to tailor the most suitable marketing messages to these segments.

But this is only one example of an approach a firm can follow. While designing and

implementing a customer segmentation firms can use CLV with any other metric that

fits to their type of business (Kumar, 2006, p. 615).

4.3. Optimal Resource Allocation

After evaluating the usefulness of CLV for customer selection and the help of

customer segmentation to identify the most profitable customer, the strategy is

described how to allocate resource optimal to maximize CLV. Most companies are

constrained by a restricted budget and the resources are not sufficient to allocate to

all customers. Because of customer selection and segmentation the firm knows in

which customer they have to invest to get the highest outcome. However, many firms

continue mass marketing and spend their resources on unprofitable customers as

well because they have not yet identified how much resource they have to spend on

each customer (Venkatesan/Kumar, 2004). With the introduction of CLV framework it

is now feasible to allocate resource on an individual customer level. Venkatesan and

Kumar (2004, p.23) have developed a framework that enables firms to determine

optimal marketing strategies across various channels to each customer within the

database, while at the same time continuing to maximize financial performance of the

firm. The model allows customer level actions and help manager choosing the right

channel to communicate to a customer and expending the right amount of resources

across channels of communication for each customer to improve the result.

Venkatesan’s and Kumar’s model (2004) to optimize resource allocation uses the

CLV equation as the objective function and the purpose of the model is to find the

level of contacts across various channels with each individual customer that would

maximize CLV. Their first step was to estimate the responsiveness of customers to

marketing contacts whereas each CLV is related to the cash flow of each customer,

the expected interpurchase time and costs and frequency of marketing contacts.

Using these coefficients, the level of channel contacts for each customer which would

maximize the CLV can be determined. The frequency of channel contacts to a

customer across various channels is under the supplier’s control and therefore can

be used to maximize CLV with respect to marketing costs and responsiveness of the

customer in terms of purchase frequency and contribution margin.

(Venkatesan/Kumar, 2004, p.120).

The results from Venkatesan and Kumar’s study (2004) demonstrate the

effectiveness of resource allocation strategies and the importance of considering

individual customers’ responsiveness to marketing activities as well as marketing

costs across various channels when making resource allocation decisions. It

illustrates that there is potential for increasing CLV, and therefore profit, by proper

customer selection and optimal resource allocation strategies. Manager can make

use of the resource allocation model to design more effective marketing strategies

and it can be a basis for evaluating the potential benefits of customer relationship

management and it provides accountability for strategies geared toward managing

customer as assets (Kumar, 2006, p. 618).

4.4. Purchase Sequence Analysis

While optimizing resource allocation to increase the profitability of the firm, it is still

possible to refine the marketing strategy further and determine what exactly the

customers are looking to buy and when they might be purchasing each type of

product or service.

Firms offer a range of products, so it is almost impossible to determine what product

a particular customer is going to buy next. It would be useful for a firm to predict the

relative probabilities of different product categories being bought at different times

from a given firm, given the varying purchase patterns of each of its customers. This

information are very valuable, because the firm would be able to contact customers

at appropriate time intervals, about products they are more likely to be purchased in

the near future, resulting in a customer specific communication strategy. Some

companies try to predict the future purchase behavior given the past purchases and

preferences and then make recommendations for products most suitable to the

customer. These recommendations are based on the products purchased in the past

by a particular customer who bought the same products. The more accurately the

product recommendations match with customer’s preferences, the more likely the

customer is to make another purchase. A firm having this knowledge has a significant

advantage over the competition (Kumar, 2006b, p.24).

A purchase sequence model developed by Kumar et al. (2006) captures the

differences in the durations between purchases for different product categories by

incorporating cross-product category variables. The basic concept of this framework

is that often times there are interdependences in product purchases and similarities

in purchase pattern of customers. Purchases of certain products are dependent on

the product purchases in the past and a customer who bought a particular product is

unlikely to buy the same product immediately. So there is a natural ordering of

product purchases, for example purchases of accessories follow the main product.

After recognizing the interdependences of product purchases and purchase

propensities across product categories as well as in the purchase timing, customer

level predictions can be developed to target the right customer, with the right product

at the right time.

Customers also seem to follow purchase pattern similar to other customers, because

they observe other customer, whom they trust or because of word-of-mouth effects

according to their reference potential and resulting from communication with other

customers. In either case, the customer purchases a product relying on the

information processed by customers whom they trust. As a result the firm is able to

model behavior and predict the probability of purchase timing and sequence.

The study by Kumar et al. (2006) illustrates the effectiveness of the purchase

sequence model using data from a business-to-business firm. The results indicate

that the model is able to prioritize customers by indicating the probability to purchase

different products and to predict the expected profits. Therefore, knowing the

sequence and timing of purchases by individual customers, a company is able to

develop the best and more effective marketing strategy to contact customers. It helps

in increasing cross-buy ratio and revenue, apart from decreasing marketing costs,

thereby leading to a higher profitability and an incremental return on investment

compared to firm’s traditional contact strategies. This also helps firms to develop

cross- sell and up-sell strategies (Kumar, 2006b, p. 25).The main advantage of this

analysis is that a company can therefore contact customers with time and product

specific offerings rather than contact the customers with multiple product offerings in

each time period.

4.5. Targeting Profitable Prospects

It is already explained how companies can maximize their profitability from existing

customer by prioritizing, selecting and implementing individual level strategies.

However, a company who wants to grow and be competitive has to target profitable

prospects, acquire them and nurture relationship with them. The challenge is to

acquire only profitable customers, because acquiring unprofitable customers will only

cause additional costs in the long run and not acquiring a profitable customer will be

a lost opportunity. The first step is to determine profitable potential customers and

also which passive customers are worthwhile to win back. The question is how to

determine these customers with only limited information about their prospects and

which marketing campaigns is the most effective to acquire the profitable customers.

Customer profile analysis and segmentation of the existing customers answers this

question. They provide as the information about demographic variables, what

channels of communication are most suited to them, and what marketing campaigns

are most effective to win them. Profiles of existing customer should be the basis for

the prospect pool and archived customer information should be used to find potential

customers with matching profiles as those customers who currently have a positive

CLV. There is a high probability that these prospects with characteristics similar to

the existing customers become high-value customers in the future (Kumar/Petersen,

2005, p.514).

The findings of the former profile analysis and optimal resource allocation help to

identify the right marketing activities and to efficiently manage their restricted

marketing budget when attracting new prospects. Another important aspect is to

consider acquisition and retention as two independent activities. Firms need to link

acquisition expenditures to retention expenditures to avoid underspending and

overspending, whereas underspending is more detrimental and results in smaller

return on investments than overspending (Reinartz et al., 2005, p.77).

Blattberg and Deighton (1996) show that firms need to link acquisition efforts to

retention efforts to avoid underspending and overspending on acquisition or

retention. The research by Thomas et al. (2004) show a balanced allocation of

acquisition expenses and retention expenses leads to higher customer profitability.

But when firms have to trade off between expenses for both, a suboptimal allocation

of retention expenses will have a greater impact on long-term customer profitability

than suboptimal acquisition expenditures will have. Even a small deviation of the

optimum spending can result in extensive consequences on the firm’s overall

profitability. Their study reveals that acquisition and retention costs of profitable

customers can be either high or low having compared the profits generated by the

customer with the expenditures of acquisition and retention. With this approach it is

easy to determine at which point extra spending on customer retention starts to reap

diminishing returns. A customer segmentation into four groups (high/low acquisition

costs and high/low retention costs) illustrated that profitable customers are present in

each field whether acquisition costs are high(low) and retention costs are low(high) or

both costs are low(high). Thus, to maximize financial performance, firms need to

carefully pick customers from each of these four cells rather than going after only

customers who are inexpensive to acquire or retain.

5. Evaluation of the Model

In comparison to static investment appraisals this dynamic approach for customer

valuation enables a company to consider the development of a relationship over a

customer lifetime and to quantify the monetary value. Only over time it is evident if

customer retention actions are able to generate the expected profit or in other words

if a retention of a customer increases the customer value in the long run or if a

retention leads to losses. An isolated static calculation is not able to reflect a

customer’s future potential in an adequate way.

But problems of this model have to be mentioned as well. The accurate allocation of

costs and revenues on different periods of a relationship constitutes an obstacle to

overcome as well as the consideration of non-monetary determinants.

(Hempelmann/Lürwer, 2003, p.336)

Accountants should not have a problem with familiarization in the method because

similar approaches are already known for example net present value calculations of

product investments. The most important obstacles are the level of the discount rate,

reinvestment assumption and uncertainty of cash flow estimation. One difference to

the common approaches is that the duration of a business relationship is a lot harder

to estimate than expected useful life of machines. But be aware, that with an

approach of very long relationship duration not every relationship will finally end up

with a positive contribution margin. It is more essential than ever to get information

about the course of a relationship instead of rely on past figures. (Weber/Willauer,

2000, p. 30)

Prior research has typically relied on past purchase behavior and marketing actions

to predict future behavior. They overcome the obstacle of future uncertainty through

incorporating retention rate and discount rate. For most companies it is impossible to

perfectly predict the cash flows associated with an individual customer, but they can

calculate the expected value of the cash flows (adjusting for risk) associated with an

individual customer conditional on the customer’s characteristics, the company’s

planned marketing strategies and environmental factors (Zeithaml et al., 2006).

Kumar et al. (2006b, p. 90) agree with the last statement. The current CLV models

mainly factor customers‘ past purchase behavior and firms’ efforts to build long term

relationships in to predict future customer contribution. But this supposes a relatively

stable past and predictable future customer behavior. Under certain circumstances

this assumption may be true for the majority of customers, but others does not

continue past purchase behavior therefore a prediction of future behavior based on

the past is hardly possible. One possible approach to improve the CLV model is to

identify measures, which are more forward-looking and more effective in helping

firms anticipate customer changes before they occur and thereby provide

opportunities to build and maximize CLV. This comes along with an improvement of

customer behavior prediction. Currently available customer metrics may be

insufficient, because they only reflect the past.

6. Conclusion

Although the CLV approach is classified as relative useful and its superiority is

proven only a few companies use the approach to evaluate customers. Obviously in

practice there is a lack of feasible calculation methods for lifetime valuation while

another reason is the inertia to move away from the accepted practices. But

nevertheless the achievement of customer lifetime value calculation is to view

customers in terms of long-term relationships rather than transaction because a

short-term view could lead to misinterpretations. Hence, CLV concept is the

appropriate tool to help companies developing the long-term perspective and deriving

the best suited marketing strategies to get the most out of their customer.

Nevertheless, further research is expected in the fields of CLV measurement and

improvement of the CLV calculation, better understanding of CLV key drivers and

further convincing evidence concerning the importance of using CLV for optimal

marketing concepts. However, considering the dynamic nature of the purchase

behavior of customers future models are also expected to draw a better picture of the

impact of customer potentials in determining the lifetime value of customers.

But with more and more companies implementing the CLV concept for resource

allocation and other customer specific strategies to increase company’s profit, CLV is

expected to gain wide spread acceptability as the preferred metric for resource

allocation.

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