customer lifetime value

1

Chapter 29

CUSTOMER LIFETIME VALUE V. Kumar, University of Connecticut

Introduction

In the past two decades, the firms tended to focus on either cost management or

revenue growth. When a firm adopts one of these approaches it looses out on the other

(Rust, Lemon, & Zeithaml, 2004). For instance, if a firm focuses only on revenue growth

without emphasis on cost management, it fails to maximize the profitability. Similarly,

cost management without revenue growth affects the market performance of the firm.

What is needed is an approach which balances the two, creating market-based growth

while carefully evaluating the profitability and return on investment (ROI) of marketing

investments. Optimal allocation of resources and efforts across profitable customers and

cost effective and customer specific communication channels (marketing contacts) is the

key to the success of such an approach. This calls for assessing the value of individual

customers and employing customer level strategies based on customers’ worth to the

firm.

The assessment of the value of a firm’s customers is the key to this customer-

centric approach. But what is the value of a customer? Can customers be evaluated based

only on their past contribution to the firm? Which metric is better in identifying the future

worth of the customer? These are some of the questions for which a firm needs answers

before assessing the value of its customers. Many customer oriented firms realize that the

customers are valued more than the profit they bring in every transaction. Customers’

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

2

relationship with the firm. In simple terms, the value of a customer is the value the

customer brings to the firm over his/her lifetime. Some recent studies (Reinartz &

Kumar, 2003) have shown that past contributions from a customer may not always reflect

his or her future worth to the firm. Hence, there is a need for a metric which will be an

objective measure of future profitability of the customer to the firm (Berger & Nasr,

1998). Customer lifetime value takes into account the total financial contribution—i.e.,

revenues minus costs—of a customer over his or her entire lifetime with the company and

therefore reflects the future profitability of the customer. Customer lifetime value (CLV)

is defined as the sum of cumulated cash flows—discounted using the Weighted Average

Cost of Capital (WACC) — of a customer over his or her entire lifetime with the

company.

In this chapter, we first discuss the importance and the relevance of CLV and

compare it with other traditionally used metrics. Two approaches for measuring CLV,

namely the aggregate approach and the individual level approach, are explained in the

following section. The concept of P (Active) as the probability of customer being active

in the future is also introduced in this section. In the subsequent section, we discuss the

antecedents of CLV followed by a detailed discussion about how CLV measure can be

used for developing customer-centric strategies with specific applications of using CLV

to maximize ROI and/or profitability. We also present organizational challenges in

implementing CLV-based framework and we conclude the chapter by discussing the

future of CLV.

Why Is CLV Relevant and Important?

3

CLV is a measure of the worth of a customer to the firm. Calculation of CLV for

all the customers helps the firms to rank order the customers on the basis of their

contribution to the firm’s profits. This can be the basis for formulating and implementing

customer specific strategies for maximizing their lifetime profits and increasing their

lifetime duration. In other words, CLV helps the firm to treat each customer differently

based on their contribution rather than treating all the customers same.

Calculating CLV helps the firm to know how much it can invest in retaining the

customer so as to achieve positive return on investment. A firm has limited resources and

ideally wants to invest in those customers who bring maximum return to the firm. This is

possible only by knowing the cumulated cash flow of a customer over his or her entire

lifetime with the company or the lifetime value of the customers. Once the firm has

calculated CLV of their customers, it can optimally allocate its limited resources to

achieve maximum return. CLV framework is also the basis for purchase sequence

analysis and customer specific communication strategies. CLV can be considered as the

metric which guides the allocation of resources for ongoing marketing activities in a firm

adopting customer-centric approach.

Traditionally Used Metrics

Some of the commonly used metrics for computing customer value include RFM,

Share-of-Wallet and Past Customer Value.

RFM Method

RFM stands for Recency, Frequency, and Monetary Value. This technique

utilizes these three metrics to evaluate customer behavior and customer value.

4

1. Recency is a measure of how long it has been since a customer last placed an order

with the company.

2. Frequency is a measure of how often a customer orders from the company in a

certain defined period.

3. Monetary value is the amount that a customer spends on an average transaction.

Two methods are generally used for computing RFM. The first method involves

sorting customer data from the customer database, based on RFM criteria and grouping

them in equal quintiles and analyzing the resulting data.

The second method involves the computation of relative weights for R, F, and M

using regression techniques and then the use of those weights for calculating the

combined effects of RFM. RFM can be considered as the sum of the weighted recency,

frequency, and monetary value scores for a customer.

Example

Three customers have a purchase history calculated over a 12-month period. For

every customer numerical points have been assigned to each transaction according to a

historically derived R/F/M formula. The relative weight based on the importance

assigned to each of the three variables, R, F and M on the basis of an analysis carried out

on past customer transactions is as follows:

Recency-50%, Frequency- 20%, Monetary Value– 30%

Table 29.1a about Here Table 29.1b about Here Table 29.1c about Here Table 29.1d about Here

5

In the above example MAGS has highest RFM score (i.e. 30.4) and is preferred to

other customers for resource allocation if we use RFM method. RFM technique can be

applied only on historical customer data available and not on prospects data.

Share-of-Wallet (SOW)

Share-of-Wallet at an aggregate level is defined as the proportion of category

value accounted for by a focal brand or a focal firm within its base of buyers. At an

individual customer level, SOW is defined as the proportion of category value accounted

for by a focal brand or a focal firm for a buyer from all brands that the buyer purchases in

that category. It indicates the degree to which a customer meets his needs in the category

with a focal brand or firm (Kumar & Reinartz, 2005).

It is computed by dividing the value of sales (S) of the focal firm (j) to a buyer in

a category by the size-of-wallet of the same customer in a time period. SOW is measured

in percentage.

Individual Share-of-Wallet (%) of firm to customer (%) = Sj / ∑=

J

j 1 Sj (3)

Where:

S = sales to the focal customer

j = firm

∑=

J

j 1represents the summation of the value of sales made by all the J firms that sell a

category of products to a buyer.

For instance, if a consumer spends on an average $500 per month on groceries

and $300 of her purchases is with Supermarket A, then supermarket A’s share-of-wallet

for that consumer is 60% in that month.

6

The information about a customer’s spending with competitors is not normally

available with the firms. This is obtained from primary market research or surveys

administered to a representative sample of firm’s customers. The results are then

extrapolated to the entire buyer base. However, in certain B-to-B contexts firms can infer

the size of wallet for certain products especially when the number of players in the

market is few.

Past Customer Value

This model is built on the assumption that the past performance of the customer

indicates their future level of profitability and an extrapolation of the results of past

transactions is a measure of customer’s value in the future. The value of a customer is

determined based on the total contribution (towards profits) provided by the customer in

the past. The contributions from past transactions are adjusted for the time value of

money and the cumulative contribution till the present period is the past customer value

(PCV) of a customer. PCV can be computed using the following formula,

Past Customer Value of a customer

Where i = number representing the customer

r = applicable discount rate (for example 15% per annum or 1.25% per

month)

T = number of time periods prior to current period when purchase was made

GCit = Gross Contribution of transaction of the ith customer in time period, t.

Example: Consider an electronic retailer BB Corp. is interested in calculating the

past customer value of all its customers to identify their best customers. They have data

∑=

+=T

t

tit rGC

1)1(*

7

on the products purchased by various customers over a period of time, the value of the

purchases and the contribution margin. They can compare the value generated by each

customer by computing all transactions in terms of their present value. The spending

pattern by one of their customer is given below. The gross margin is 30% of the purchase

amount and discount rate is 15% per year or 1.25% per month.

Table 29.2 about Here

The Past customer value of this customer is then computed as follows;

The above customer is worth $302.01 in contribution margin, expressed as net

present value in May in dollars. By comparing this score among a set of customers we

arrive at a prioritization for directing future marketing efforts. The customers with higher

values are normally the customers deserving greater marketing resources.

Difference Between CLV and the Traditionally Used Metrics

Though RFM, Past Customer Value, and Share-of-Wallet are commonly used for

computing customer’s future value, they suffer from the following drawbacks. These

methods are not forward looking and do not consider whether a customer is going to be

active in the future. These measures consider only the observed purchase behavior and

extrapolate it to the future to arrive at the future profitability of a customer. RFM assumes

that the recency, frequency, and monetary value of a customers purchase explain the

future value of the customer. It fails to account for other factors which help in predicting

302.01486 5)0125.01(2404)0125.01(15

3)0125.01(152)0125.01(9)0125.01(6

Scoring ValueCustomer Past

0.3 Amount Purchase (GC)on Contributi Gross

=++++

+++++

=

×=

8

customer’s future purchase behavior and his/her worth to the firm. Also, the weights

given for R, F, and M greatly influence the computation of customer’s worth. PCV

technique also fails to account for factors influencing future purchase behavior of

customers. It also does not incorporate the expected cost of maintaining the customer in

the future. Since SOW measure is based on responses from a representative sample of

customers, it is unable to provide us a clear indication of future revenues and profits that

can be expected from a particular customer. This limits its use as a valuable input in

designing customer level marketing strategies.

On the other hand, CLV measure incorporates both the probability of a customer

being active in the future and the marketing costs to be spent to retain the customer. As

discussed above, one goal of calculating the value of a customer is to design customer

level strategies so that firms can maximize their return. To effectively do this, we need to

know whether the customer is going to purchase in future time periods and the expected

value of profits he/she brings to the firm. We should also know the effort or marketing

costs to be spent to retain the customer. RFM, PCV, and SOW approaches do not take

into account the probability of being active in the future and the costs whereas CLV

approach incorporates both these aspects in the calculation as can be seen in the next

section. CLV can be effectively used as a metric in allocating resources optimally and

developing customer level marketing and communication strategies.

Measuring CLV

Lifetime value of a customer can be either calculated as an average CLV or

individual level CLV.

9

An Aggregate Approach

In the aggregate approach, average lifetime value of a customer is derived from

the lifetime value of a cohort or segment or even the firm. Three approaches to arrive at

average CLV are explained here. In the first approach, the sum of lifetime values of all

the customers, called Customer Equity (CE) of a firm is calculated as;

tT

tit

I

i

CMCE ∑∑==

⎟⎠⎞

⎜⎝⎛

+=

11 11

δ (1)

where

CE = customer equity of customer base in $ (sum of individual lifetime values)

CM = Contribution margin in time period t

δ = discount rate.

i = customer index

t = time period

T = the number of time periods for which CE is being estimated.

In this case, the CE measure gives the economic value of a firm and we can

calculate average CLV by dividing CE by the number of customers.

In another approach (Berger & Nasr, 1998; Kumar & Ramani, 2004) the average

CLV of a customer is calculated from the lifetime value of a cohort or customer segment.

The average CLV of a customer in the first cohort or cohort 1 can then be expressed as;

( )( )∑

=

−⎥⎦

⎤⎢⎣

⎡

+−

=T

t

tt Ar

dMGCCLV

01 1

(2)

where

r = rate of retention

10

d = discount rate or the cost of capital for the firm.

t = time period

T = the number of time periods considered for estimating CE.

GC = the average gross contribution.

M = marketing cost per customer

A = the average acquisition cost per customer

This approach takes into account only the average gross contribution (GC), the

average acquisition cost per customer (A), and marketing cost (M) per customer. The

retention rate, r is the average retention rate for the cohort and is taken as a constant over

a period. However this is not the case in reality. Customers leave the relationship with

the firm in different points in time the retention probabilities vary across customers. This

means that we have to account for retention probabilities in the calculation for CE.

In another approach, (Blattberg, Getz, & Thomas, 2001) customer equity of the

firm is first calculated as the sum of return on acquisition, return on retention and return

on add-on selling. This is expressed in a mathematical equation as follows;

( ) ( ) ( )∑ ∑ ∏=

∞

=++++

=+

⎥⎥⎦

⎤

⎢⎢⎣

⎡⎟⎠⎞

⎜⎝⎛

+−−−⎟⎟

⎠

⎞⎜⎜⎝

⎛+−−=

I

i k

k

ktAOiktriktikti

k

jktjtititaitititititi d

BBcSNBNcSNtCE0 1

,,,,,,1

,,,,,,,,,, 11ραα

where

CE(t) = the customer equity value for customers acquired at time t

Ni,t = the number of potential customers at time t for segment i

ti ,α = the acquisition probability at time t for segment i

ti ,ρ = the retention probability at time t for a customer in segment i

Bi,a,t = the marketing cost per prospect (N) for acquiring customers at time t for

segment i

11

Bi,r,t = the marketing in time period t for retained customers for segment i

Bi,AO,t = the marketing costs in time period t for add-on selling for segment i

d = discount rate

Si,t = sales of the product/services offered by the firm at time t for segment i

ci,t = cost of goods at time t for segment i

I = the number of segments

I = the segment designation

t0 = the initial time period.

Average CLV can then be arrived at by dividing CE by the number of customers.

One of the important application of average CLV (Gupta & Lehmann, 2003;

Kumar & Ramani, 2004) is for evaluating competitor firms. In the absence of

competitors’ customer level data, firms can deduce information from published financial

reports about approximate gross contribution margin, marketing and advertising spending

by competing firms to arrive at reasonable estimates of average CLV for competitors.

This gives an idea of how profitable or unprofitable are competitors’ customers. Average

CLV approach can also be used for assessing the market value of the firm. Gupta and

Lehmann demonstrated that for high growth companies, aggregate CLV of a firm or

customer equity may be used as surrogate measure of firm’s market value.

However, average CLV has limited use as a metric for allocation of resources

across customers because it does not capture customer level variations in CLV, which is

the basis for developing customer specific strategies. Hence it is necessary to calculate

CLV of individual customers in order to design individual level strategies.

Individual-level Approach

12

At an individual level, customer lifetime value is calculated as the sum of

cumulated cash flows—discounted using the Weighted Average Cost of Capital (WACC)

— of a customer over his or her entire lifetime with the company. It is a function of the

predicted contribution margin, the propensity for a customer to continue in the

relationship, and the marketing resources allocated to the customer. In its general form,

CLV can be expressed as;

( )( )∑

= +−

=T

tt

iti d

tFutureFutureCLV1

it

1cosmarginon contributi

(4)

where

i = customer index,

t = time index

T = the number of time periods considered for estimating CLV, and

d = discount rate.

The CLV has two components, future contribution margin and future costs both

adjusted for the time value of money. To calculate the future contribution from a

customer in a non-contractual setting, a firm should know the probability that the

customer continues to do business with the firm in future time periods or probability of

customer being active, P (Active). Taking into account this probability, we can first get

the net present value (NPV) of expected Gross Contribution (EGC) as (Reinartz &

Kumar, 2003);

NPV of EGCit = ( )( )∑

+

+= +×

xt

tnnit

in dAMGC

ActiveP1 1

AMGCit = average gross contribution margin in period t based on all prior purchases

i = customer index

13

t = the period for which NPV is being estimated

x = the future time period

n = the number of periods beyond t

d = Discount Rate

P (Active) in = the probability that customer i is active in period n

Example

The spending pattern by a customer of an IT company, AMC Inc. is given as

follows. For instance, the customer purchased a desktop PC in January for $800. In the

next four months he purchased some software, flash memory, and DVDs. The average

gross margin is 30% of the purchase amount and discount rate is 15% per year or 1.25%

per month.


If the probability of customer being active, P(Active) in June is 0.40 and that in

July is 0.19, then the NPV of EGC for June and July for this customer can be calculated

as follows;

AMGC = (240+15+15+9+6)/5 = 57

( ) ( )82.28

125.015719.0

125.01574.0 21 =

+×+

+×=EGCofNPV

Costs include acquisition cost (A) and the marketing costs (M) in future time

periods. Marketing costs in future time period need to be discounted with appropriate

discount rate, d to arrive at the present value of these costs. The discounted marketing

costs (M) and the acquisition cost (A) are then subtracted from the NPV of ECG to get

the CLV of a customer. If the marketing costs are accounted at the beginning of a given

time period and the gross contribution at the end of time period, we can express CLV as;

14

CLV of customer i = ( )( )

Ad

Md

AMGCActiveP

nx

nin

xt

tnnit

in −⎟⎠⎞

⎜⎝⎛

+×−

+×

−

=

+

+=∑∑

1

11 11

1

Average Monthly Gross Contribution (AMGC)

The average monthly gross contribution, AMGC is the average monthly revenue

obtained from a customer minus the average cost of goods sold. This is calculated based

on his/her past purchases.

Marketing Cost (M)

This includes the development and retention costs. It can be the cost of programs

to increase the value of existing relationship, cost of loyalty or frequent flyer programs,

cost of campaigns to ‘win back’ the lost customers, and the cost of serving the customer

accounts. One main component of these costs is the cost of marketing contacts through

various channels of communication. The contacts through different channels have

different costs to the firm. For example, a face-to-face meeting with customer costs much

higher than communication through direct mail or e-mail. To arrive at marketing costs

specific to a customer, firms need to estimate the number of contacts required to retain

the customer and the cost of contact through various channels. Once firms have such cost

accounting, calculation of marketing cost is straightforward. Estimation of marketing cost

is important in arriving at optimal customer specific communication strategies.

Discount Rate (d)

The revenue or gross contribution from the customer comes at different time

periods in the future, accounted yearly, monthly, or weekly. The value of money is not

constant across time and since the money received today is more valuable than the

received in future time periods, the GC and marketing costs have to be discounted to the

15

present value of money. This is achieved by dividing the cash flow in time period i by

(1+d)i, where d is the discount rate. The discount rate, d depends on the general rate of

interest and is normally proportional to the Treasury bill or the interest that banks pay on

savings accounts. It can also vary across firms depending upon the cost of capital to the

firm.

Time Period (n)

The number of future time periods (n) for which the gross contribution and the

marketing costs are considered for calculation of CLV refers to the natural ‘lifetime’ of

the customers. For most businesses it is reasonable to expect that the customers will

return for a number of years (n). There are no strict guidelines to decide on the value of n.

The word “lifetime” must be taken in many circumstances with a grain of salt. While the

term makes little sense with one-off purchases (say, for example, a house), it also seems

strange to talk about LTV of a grocery shopper. Clearly, there is an actual lifetime value

of a grocery shopper. However, given the long time span, this actual value has not much

practical value. For all practical purposes, the lifetime duration is a longer-term duration

that is managerially useful. For example, in a direct marketing general merchandise

context, managers consider maximum 4-year time span, sometimes only 2 years. Beyond

that, any calculation and prediction may become difficult due to so many uncontrollable

factors (the customer moves, a new competitors moves in, and so on) It is therefore

important to make an educated judgment as to what is a sensible duration horizon in the

context of making decisions.

P (Active) in is the probability that the customer continues to be active in

subsequent time period. For CLV calculation to be at an individual level, this probability

16

of retaining customer has to be calculated at an individual customer level rather than the

average rate of retention at the firm level. Each customer is likely to have different

purchase patterns and their active and inactive periods vary as shown in the Figure 29.1.

Figure 29.1 about Here

Given their purchase behavior in the past, one can predict the probability of

individual customers being active or P (Active) in subsequent time periods. A Simple

formula to calculate P (Active) is

P (Active) = (T / N)n

Where n is the number of purchases in the observation period, T is the time elapsed

between acquisition and the most recent purchase, and N is the time elapsed between

acquisition and the period for which P (Active) needs to be determined. For illustration, if

indicates a purchase, then for customer 1,

P (Active) in month 12 = (8/12)4 = 0.197 where n=number of purchase = 4

P (Active) for customer 2 in month 12 = (8/12)2 = 0.444 where n=2

In the above case, for a customer, who bought four times in the first eight months and did

not buy in the next four months, the probability of purchase after 4 months (i.e. at the end

of month 12) is less than that of customer 2 who purchased only two times in the first

eight months. The formula introduced here for calculation of P (Active) is very basic.

However, other sophisticated methods are employed for the calculation of the probability

of a customer purchasing in future time periods.

One drawback of using P (Alive) to predict customer’s future activity is that it

assumes that when a customer terminates a relationship, he/she does not come back to the

firm. This approach called “lost-for-good” is questionable because it systematically

17

underestimates CLV (Rust, Lemon, & Zeithaml, 2004). To overcome this, researchers

use “always-a-share” approach, which takes into account the possibility of a customer

returning to the supplier after a temporary dormancy in a relationship (Venkatesan &

Kumar, 2004). In this case, predicting the frequency of a customer’s purchases given his

or her previous purchase is a better way of projecting future customer activity. This

predicted frequency can be used to calculate CLV. The CLV function which incorporates

predicted frequency can be expressed as follows1;

( ) ( )∑ ∑∑=

−= +

×−

+=

n

ll

m lmilmiT

y frequencyy

yii d

xc

r

CMCLV

i

i 11

,,,,

1

,

11

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 = predicted purchase frequency for customer i,

n = number of years to forecast, and

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

planning period.

Example

Suppose the predicted contribution from a customer in purchase occasions in next

two years, number of marketing contacts and the marketing costs in different channels are

as follows:

18

Time period Jan ‘05 May‘05 Nov‘05 Feb ‘06 Jul ‘06 Oct ‘06

Predicted contribution ($) 100 70 50 90 65 30

Number of direct mails: Year 1 = 4 Year 2 = 4

Number of contacts via telephone: Year 1 = 2 Year 2 = 3

Cost per direct mail ($) 2.50

Cost per contact via telephone ($) 3.00

If the discount rate is taken as 15%, then CLV of this customer can be calculated as given

below.

Predicted purchase frequency = 3

( ) ( )( ) ( ) ( )

( ) ⎭⎬⎫

⎩⎨⎧

+×+×

+×+×−+

+++

=15.01

3345.22345.215.01

30.........15.01

1003631CLV = $319.05

Various supplier-specific factors (channel communication) and customer

characteristics (involvement, switching costs, and previous behavior) are first identified

as the antecedents of purchase frequency and contribution margin. Purchase frequency

and contribution margin are then modeled separately using suitable models. In the

framework developed by Venkatesan and Kumar (2004) a generalized gamma

distribution is used to model interpurchase time and panel-data regression methodologies

are employed in modeling the contribution margin.

The CLV model described above can be employed to identify the responsiveness

of customers to marketing communication through different channels of communication,

which is the basis for optimal allocation of marketing resources across channels of

contact for each customer so as to maximize his or her respective CLVs. In addition to

using the CLV framework for resource allocation strategy, it can also be used for

formulating other customer-level strategies such as customer selection, purchase

sequence analysis, and for targeting right customers for acquisition.

19

As can be seen from the CLV calculations, the lifetime value of a customer

depends to a great extent on whether the customer is going to be active in the future time

periods or not. This is especially important in a non-contractual setting because customer

has the freedom to leave the relationship anytime. Hence it is very important for a firm to

understand the factors influencing the profitable duration of customer with the firm or the

drivers of profitable lifetime duration.

Drivers of CLV

While firms are interested in knowing the lifetime value of their customers, they

are also keen on identifying the factors that are in their control that could increase the

value of their customers. Reinartz and Kumar (2003) identified the factors which explain

the variation in the profitable lifetime duration among customers. The antecedents of

profitable lifetime duration are grouped as exchange characteristics and customer

heterogeneity. The exchange characteristics define and describe the nature of customer-

firm exchange where as demographic variables capture customer heterogeneity. Different

exchange characteristics that are identified as positive drivers of profitable lifetime

duration in a B-to-C and B-to-B contexts include customer spending level, cross buying

behavior, focused buying, customer’s ownership of loyalty instrument and the mailing

efforts by the firm. The relationship of these drivers with CLV as observed in the above

mentioned study is given in Table 29.4:


The average interpurchase time for customers exhibited an inverse U-shaped

relationship with profitable lifetime duration. Customers living in areas with lower

20

population density or businesses operating in lower population density had higher

profitable lifetime duration. Also, the income of the customer (B-to-C) or the firm (B-to-

B) had positive relationship with profitable lifetime duration.

Identification of antecedents of profitable lifetime duration enables managers to

take specific actions to improve the drivers and thereby the profitability from the

customers. Managers can also identify customers who are likely to be profitable in the

future and decide when it is worthwhile to stop investing in a customer by analyzing the

antecedents of profitable lifetime duration with respect to specific customer. Drivers of

profitable lifetime duration/CLV are important inputs for resource allocation strategy and

purchase sequence analysis.

How Can CLV Measure be Used for Developing Customer-

centric Strategies?

Calculation of CLV for all its customers is only the first step firms can take to

implement customer level strategies. CLV is a metric, which can be a basis for firm’s

investments in infrastructure and ongoing marketing activities. Firms can use CLV

framework to identify which customers are most likely to bring maximum profit to the

firm in the future, what are the factors leading to higher CLV, and the optimal level of

resource allocations to various channels of communication. Dynamic customer

management based on CLV can improve the shareholder value. Customer management

from the perspective of CLV can be defined as “the process for achieving a continuing

dialogue with customers, across all available touch points, through differentially tailored

treatment, based on the expected response from each customer to available marketing

21

initiatives, such that the contribution from each customer to overall profitability is

maximized.” (Kumar & Ramani, 2003). The success of a firm in exploiting a CLV

framework lies in firm’s ability in identifying and implementing the most effective

customer level marketing decisions based on CLV metric so that the future profit from

the customer is maximized. These strategies will have a strategic impact of increasing the

customer lifetime duration and the lifetime value.

Specific Applications of Using CLV to Maximize ROI and/or Profitability

Recent academic literature (Kumar & Petersen, 2005) have shown evidence that

CLV can be used to generate customer level strategies and optimize firm performance.

Specifically these strategies include: (1) customer selection, (2) customer segmentation,

(3) optimal resource allocation, (4) purchase sequence analysis, and (5) targeting

profitable prospects. These strategies help to maximize the profitability and customer

equity of the firm, thereby increasing the shareholder value. They also have strategic

impact on profitable lifetime duration of the customers.

Customer Selection

Recent research (Dowling & Uncles, 1997; Reinartz & Kumar, 2000) has shown

that not all loyal customers are profitable. This research questions the reasoning that

retaining more number of customers increases the overall profitability of the firm. This is

because the contributions from many customers are far less than the cost incurred by the

firm to retain them. Acquiring and retaining such unprofitable customers can only act as a

drain on the overall profitability. Selection of right customers to retain, who bring

maximum profits to the firm, is then an important step in improving the profitability.

22

How can then a firm identify the right customers to retain? Are they the ones who

bring maximum revenue to the firm? Research shows that this need not be the case. Firms

need a measure of profitability of each customer to decide who their best customers are.

CLV calculation, which takes into account the future profits from a customer, comes in

handy here. Reinartz and Kumar (2000, 2003) have shown that determining lifetime

value of each customer and the customer and firm specific drivers of profitable customer

lifetime duration help the firms to identify the right customers to retain. These studies

have also showed that CLV is superior to RFM method in predicting future profits and

purchase behavior of customers. Reinartz and Kumar (2003) used data from a U S

general merchandise catalog retailer for 11,992 households over 36 months. Based on

information up to 30 months, they ranked the customers using three methods: NPV of

ECM (CLV method), advanced RFM, and Past Customer Value (PCV). These three

customer selection methods are then compared based on the actual revenue and profit

generated in the remaining time period by the top 30%, 50%, and 70% of customers

selected by each method. The results are given in Table 29.5.

Table 29.5 about here

CLV method (in this case NPV of ECM) selected the most profitable customers.

This is explained by the fact that the profit generated by top 30% customers selected by

CLV method ($62,991) was much higher than profits from top 30% customers selected

by either advanced RFM ($27,582) or PCV ($35,916). The results were similar for other

two groups (top 50% and top 70%) also. This clearly shows that CLV is a better metric in

selecting the most profitable customers. The support for superiority of CLV in customer

selection is further strengthened by a recent study by Venkatesan and Kumar (2004)

23

using data from a large multinational computer hardware and software manufacturer.

They compared the customer selection capabilities of the following: CLV, previous

period customer revenue (PCR), past customer value (PCV), and customer lifetime

duration (CLD). The study was similar to the earlier study by Reinartz and Kumar

(2003). The actual sales, variable costs of communication, and profits for the top 5%,

10%, and 15% customers (selected using different customer selection methods) for 18

months prediction window are compared and the results are provided in Table 29.6.


The average net profits of top 5% customers selected using CLV was $143,295,

compared to the average net profits of $70,929, $130,785, and $106,389 for the top 5% of

the customers selected on the basis of PCR, PCV, and CLD. The results were similar for

top 10% and 15% of customers as well. These results from two separate studies using

database from B-to-C (catalog retailer) and B-to-B (computer hardware and software

manufacturer) firms provide substantial support for the superiority of CLV framework

over other metrics for customer scoring and customer selection.

Customer Segmentation

Differential treatment of customers is the key to manage the customer relationship

profitably. Though customer level marketing actions are the desired outcome of CLV

computation it is also worthwhile to look at specific segments of customers based on

CLV and develop strategies for each segment. In order to do customer segmentations,

firms need to understand the exchange variables and customer demographic variables

which differentiate each group from the other. These variables explain why certain

customers are more profitable than others. Reinartz and Kumar (2003) studied the

24

exchange and demographic variables that affect the lifetime duration of customers in a

non-contractual setting. Some of the key variables found in the study were amount of

purchase, degree of cross buying, degree of focused buying, average interpurchase time,

number of product returns, ownership of loyalty instrument, mailing effort by the firm,

location and income of customers. Each of these variables has different impact on the

customer lifetime duration and possibly on CLV. For instance, in a study of catalog

retailer, degree of cross buying was found to have a positive relationship with customer

lifetime duration, number of returns had an inverted U-shape relationship with lifetime

duration, and the relationship between average interpurchase time and profitable lifetime

duration was inverted U-shape.

We can therefore profile the customers based on various exchange and

demographic/ firmographic variables, which are drivers of customer lifetime duration and

CLV. In practice, the customers are first grouped into deciles or demideciles on the basis

of their CLV scores. The profile of these deciles/demideciles or a segment (a set of

deciles/ demideciles) are then analyzed. Profiling helps to better understand the customer

composition of each segment. Profiling helps the firms to understand the characteristics

of their best customers, how do they want to do business with the firm, what are the best

means of communication or touch channel to reach their best customers, and how

frequent their best customers buy from them. The customer profile analysis can be used

to identify the segments on which firm should concentrate on their marketing efforts and

to tailor the most suitable marketing messages to these segments. For instance, if number

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

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

25

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

channels thereby improving the profitability of the segment. Such segment level

marketing actions to improve the drivers of customer lifetime value coupled with

customer level strategies on marketing communication can thus improve the CLV of the

segment.

CLV along with other customer value metrics can be used to segment customers

into four different groups as shown in two segmentation schemes discussed below. First

of this segmentation schemes groups customers into four distinct cells based on

High/Low values for customer lifetime profits (CLV) and customer relationship duration.

Table 29.7 contains the description of each group and the actionable marketing strategies

to maximize CLV for customers in each group.


The ‘Butterflies’ may become ‘True Friends’ or ‘Barnacles’ in the long run.

Hence companies should be watchful of the inflection point beyond which investing on

them may result in overspending. It is not worthwhile to spend marketing dollars on

‘Strangers’ or ‘Barnacles’ with small size-of-wallet. ‘True Friends’ is the segment which

firms should identify to spend maximum of their marketing resources in order to nurture

and strengthen the customer relationship. Firms should aim for achieving attitudinal and

behavioral loyalty of this segment through consistent intermittently spaced marketing

communications.

Another useful segmentation for the firms is grouping based on historical profits

and future profitability of customers. Table 29.8 shows the customer segments as per this

segmentation scheme.

26


‘True loyalists’ are customers who have high PCV or historical profits and have

high profit potential in the future (High CLV) as well. Firms have to reward them

proactively, invest in them to strengthen the relationship, to retain them, and to achieve

high positive attitudinal loyalty. ‘Rising Stars’ displays high future profit potential (high

CLV) even though their historical profits are low. The relationship with them needs to be

strengthened. Firms should target them for cultivating attitudinal loyalty and should up-

sell or cross-sell to them so that they can be converted into ‘True Loyalists’ and not

“Falling Angels’ in the long run. ‘Falling Angels’ are customers who contributed

significantly to the profitability of the firm in the past but are not expected to do so in the

future for various reasons. Firms should be wary of investing too much on them based on

their past profits but should try to optimize (minimize) marketing cost by transacting

through low-cost channels. Identifying specific up-sell or cross-sell opportunities may

help to bring some of them back to the high profitability path once again. ‘Total Misfits,’

whose contribution to the firm’s profitability is low in the past and in the future should be

dealt with very cautiously. Firm’s aim should be to extract maximum profit from every

transaction probably by migrating them to low cost channels. It is not worth investing on

developing strong relationship with them.

These are only some of the segmentation schemes firm can follow. Firms can use

CLV with any other loyalty metric and come up with customer segmentation most

suitable to the firm or type of business.

Optimal Resource Allocation

27

In most cases, the firms are constrained by a limited budget and the resources are

not adequate to allocate to all its customers. Ideally, firms should be investing only on

customers who are profitable. However many companies continue to spend resources on

large number of unprofitable customers (Venkatesan & Kumar, 2004). They would either

be investing on customers who are easy to acquire but are not necessarily profitable or

are trying to increase the retention rate of all their customers, thereby leading to wastage

of limited resources. One reason for this is that these firms have not identified who their

most profitable customers are, and how much resource to be spent on them to maximize

the profitability. We addressed the first issue in the customer selection section. The

second issue, optimal resource allocation, can also be addressed using CLV metric.

Optimal allocation of resources on an individual customer level was not feasible

before the introduction of the customer value framework. Previous research on optimal

resource allocation have addressed the resource allocation in acquisition and retention

decisions (Blattberg & Deighton, 1996; Blattberg, Getz & Thomas, 2001; Venkatesan &

Kumar, 2004), promotion expenditures (Berger & Bechwati, 2001; Berger & Nasr, 1998),

marketing actions when future brand switching is considered (Rust, Lemon, & Zeithaml,

2004). By utilizing the customer value framework, researchers have now come up with

models that allow customer level actions. This model will help a manager to know the

extent to which he/she should use various contact channels to communicate to a customer

and optimize the allocation of resources across channels of communication for each

customer, so as to maximize CLV. As discussed in CLV measurement section, the

equation for calculating CLV is a function of predicted purchase frequency, predicted

contribution margin and marketing costs. The Inter-purchase time for a customer is

28

influenced by marketing initiatives that a firm takes. The purchase frequency model

calculates the inter-purchase time as a function of nature of marketing and

communication efforts. The contribution margin model predicts the cash flows from each

customer in the future time periods and the marketing costs to be spent on the customer.

The CLV of a customer is then related to the cash flow from each customer, the expected

Inter-purchase time and the cost and frequency of the marketing contacts employed. A

recently developed model for optimizing resource allocation (Venkatesan & Kumar,

2004) uses this CLV equation as the objective function to arrive at the optimal level of

contacts across various channels with each individual customer that would maximize

CLV. The first step in optimization is estimating the responsiveness of customers to

marketing contacts on CLV with respect to individual customers. Using these

coefficients, the level of channel contacts for each customer which maximizes the CLV

can be determined. A manager can determine the frequency of each of the available

marketing and communication strategies such that the NPV objective function is

maximized. An optimization technique can be utilized to accurately arrive at the

differential allocation of strategic resources to individual customers across a variety of

integrated marketing strategies (Venkatesan & Kumar, 2004). The objective function is

thus based on three elements:

1. A probability based model that predicts the inter-purchase time of each

customer, as a function of marketing communication inputs and the

customers’ past purchase behavior observed over time.

29

2. A panel data model that predicts the cash flows from each individual

customer, also as a function of marketing communication inputs and the

customers’ past purchase behavior observed over time

3. An optimization algorithm that maximizes the profits from each individual

customer by examining the impact of various levels of marketing

communication inputs

The study by Venkatesan and Kumar (2004) illustrates the effectiveness of

resource allocation strategy. They compared the net present value of future profits for a

large Business-to-Business (B2B) manufacturer when the resource allocation strategy is

employed vis-a vis the NPV of future profits when the firm used the current resource

allocation strategy. CLV calculated for three years based on the current resource

allocation strategy among a sample of 216 customers, was $24 million whereas when the

optimal resource allocation strategy as explained above was used the CLV for three years

was $44 million, an increase of 80%. The total cost of communication in the current

strategy was approximately $716,188 and in the optimal resource allocation strategy it

was $1 million. The increase in profit was 48% and the return on marketing

communication increased from 34 ($24 million/$716,188) in the current strategy to 44

($44 million/$1 million) in the optimal strategy. This illustrates that it is possible to

increase the profit and return on marketing communications by proper customer selection

and by optimal allocation of resources across different channels of communication for

each customer based on CLV.

Managers can therefore make use of the optimal resource allocation algorithm to

design more effective marketing communication strategies across various channels and to

30

improve the CLV of their customers. The resource allocation strategy can be a basis for

evaluating the potential benefits of implementing CRM and it provides accountability for

strategies geared toward managing customer assets.

Purchase Sequence Analysis

In a multi-product firm it is not easy to speculate what product a particular customer

is going to buy next. But from the firm’s point of view this is a very valuable piece of

information because firm can then decide the message and timing of customer specific

communication strategy. An ideal contact strategy is one where the firm is able to deliver

a sales message that is relevant to the product that is likely to be purchased in the near

future by a customer. Companies such as Amazon try to predict what you are most likely

to buy given your past purchases and preferences and then make suitable product

recommendations to customers. These recommendations are based on the products

purchased in the past by a particular customer by customers who bought same products.

The more accurately these product recommendations match customer’s preferences, the

more likely the customer is to make another purchase with Amazon. Therefore, a firm

that knows when and what a customer is likely to purchase next can have a significant

advantage over the competition. In order to predict customer’s future purchase, a firm

should find answers to the following questions about its customers:

What is the sequence in which a customer is likely to buy multiple products or

product categories?

When is the customer most likely to make the next purchase?

What is the expected revenue from that customer?

31

A purchase sequence model developed by Kumar, Venkatesan, and Reinartz (2005)

offers a framework to analyze the purchase sequence and timing of each customer. The

basic theory behind this framework is that often times; there are interdependence in

product purchases and similarity in purchase pattern of customers. Purchases of certain

products are dependent on the product purchases in the past. For example, a printer and

software purchases follow that of a computer; purchases of accessories follow the main

product and the like. In other words there is a natural ordering of purchasing in some

cases. Therefore, companies can to a certain extent incorporate this natural sequencing of

purchases to draw inferences about what a customer is likely to buy next given the logical

path of purchasing. Consumers also seem to follow purchase patterns similar to other

consumers. This is either because they observe purchasing by other customers, whom

they trust, or because of word-of-mouth effects (Bikhchandani et al., 1992, 1998)

resulting from communication with other customers. In either case, the consumer chooses

to purchase a product or a series of products relying on the information processed by

customers whom they trust. As a result, they follow similar purchase sequence as past

customers, allowing the firm to model behavior and predict the likelihood of purchase

timing and sequence.

Using customer data from a B2B firm, which markets multiple categories of

products; Kumar, Venkatesan, and Reinartz (2005) were able to demonstrate the

effectiveness of purchase sequence model. The results indicate that the model is able to

prioritize customers by indicating the propensity to purchase different products for each

of its customers. It also predicts the expected profits and there were significant

improvements in both profitability and ROI over the firm’s routine contact strategy. The

32

following table is an illustration of the improvement or growth in profit for the selected

product category, over the previous year, generated by the test group of sales persons

who adopted strategies based on the Purchase Sequence Model versus the control group

of sales persons who were not provided the predictions given by the model.


These findings were validated by Kumar and Petersen (2005) by applying the

model to a B-to-C setting and achieving similar results. They computed the purchase

propensities of different customers for three products spanning across four quarters

within a year. The purchase sequence for each customer can then be predicted using these

propensities to purchase. Based on these predicted purchase sequence firms can develop

the marketing contact strategy. For example, if customer A has high propensity to

purchase products #1 in quarter 2, it is optimal to contact customer A in quarter 2

offering information regarding product #1. They were also able to show that by

implementing this targeted strategy (i.e. contacting the right customer with the right

product at the right time) versus using a traditional strategy, there was an incremental

gain in ROI of $2 for every $1 spent.

These results show that knowing the sequence and timing of purchases by

individual customers will help the firm to develop more effective marketing strategy. The

firm can now contact customers with time specific and product specific offerings rather

than having to contact the customers with multiple product offerings in each time period.

Targeting Profitable Prospects

We discussed how firms can, using CLV framework, prioritize, select, and

implement individual level strategies in order to maximize the profitability from its

33

existing customers. However, for a firm to grow it has to target prospect, acquire them

and nurture relationship with them. The challenge here is to identify the best prospects,

who when acquired will bring maximum value to the firm. This is very important because

acquiring an unprofitable customer will only add to the cost in the long run while on the

other hand, not acquiring a profitable customer will be a lost opportunity. Firms therefore

need to determine which prospects are worth chasing and also which dormant customers

are worthwhile to win back (Kumar & Petersen, 2005). How can firms do this with

limited information about their prospects? What are the most effective marketing

campaigns to acquire profitable customers? The answer lies in the profile analysis of

existing customers. Customer profile analysis and segmentation tell us who our best

customers are, what their demographic variables are, what channels of communication

are most suited for them, and what marketing campaigns are most effective to win them.

Once a firm has profiled its existing customers, it can profile its prospect pool and use

archived customer information to find potential customers with matching profiles as

those customers who currently have positive lifetime values with the firm. These

prospects with characteristics similar to the existing high CLV customers are most likely

to become high-value customers in the future. Firms can also use the profile analysis and

the optimal resource allocation strategy to identify the communication strategy and

marketing campaign and to efficiently manage their marketing budget when attracting

new prospects.

Most firms consider that acquisition and retention are two independent activities.

Thomas (2001) showed that firms need to link acquisition efforts to retention efforts to

avoid underspending and overspending on acquisition or retention. Blattberg and

34

Deighton (1996) show that optimizing the resources spent on marketing to maximize

either the retention rate or the acquisition rate may not result in maximization of profits.

It is the balancing of acquisition and retention spending and acquiring the customers who

are most likely to provide future profits that help to maximize long-term profitability and

customer equity. Further research by Thomas, Reinartz, and Kumar (2004) shows that

firms can maximize profitability by balancing acquisition and retention. Thomas,

Reinartz, and Kumar show that a small deviation of even 5% away from the level of

optimum spending (either above or below) can have significant consequences on the

overall profitability of the firm. Using their ARPRO (Allocating Resources for Profits)

model, they were able to determine the point at which extra spending on customer

retention starts to reap diminishing returns. The results from their study using data from a

pharmaceutical company are presented in Tables 29.10 and 29.11.


We can see from Table 29.10 that highest rate of retention (in terms of

relationship duration) is achieved with an investment of $70 per customer.


Table 29.11 shows that the maximum profitability is achieved when company

spends $10 on acquisition and $60 on retention per customer. The recommended budget

split between acquisition and retention in this case is 14% (i.e. 10/70) on acquisition and

86% (i.e.60/70) on retention. The above tables clearly show that firms can maximize

profitability by optimal allocation of resources between acquisition and retention.

35

In order to balance acquisition and retention appropriately, Thomas, Reinartz, and

Kumar (2004) have shown that firms need to realize that the acquisition or retention costs

of profitable customers can be either low or high. They compared the profits generated by

customers in a mail-order company and the cost and effort required to acquire and retain

them. The results are provided in Table 29.12.


Table 29.12 shows that 32% of all customers were easy to acquire and retain

(Casual customers) but they accounted for only 20% of the total profits. Largest profit

contribution (40% of profits) came from the smallest group (15% of customers), the

customers who are expensive to acquire but cheap to retain (Low-maintenance

customers). Customers who were expensive to acquire and retain (Royal customers)

contributed 25% of the total profits. Customers who are cheap to acquire but expensive to

retain (High-maintenance customers) contributed only 15 % of the total profits. This

illustrates that profitable customers are present in all four cells - Retention cost

(High/Low) Vs Acquisition cost (High/Low). 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.

Implementing CLV Framework in a B-to-C Organization

Collection of transaction data for all the end consumers poses a great challenge

for a B-to-C organization. The data collection can be very expensive because of relatively

large number of customers. In some cases getting transaction data on all the customers is

impossible because the firm is not in direct contact with the end-consumers. This is true

in the case of an FMCG manufacturer who sells through the intermediary channels. In

36

such cases, the computation and application of CLV need to be modified to make

maximum use of the framework. This can be illustrated using following case studies.

Case Study 1: CLV Framework Applied to Software Manufacturer

A software manufacturer who sells through intermediaries has limited information

about the transactions by the end consumers. In this case, the manufacturer cannot

calculate the value of the end consumer using the data available with the company.

Instead it can rely on survey data. Company can conduct a survey of a large number of

end consumers (say 2000) and collect information on what products and upgrades have

been bought by each customer in the past, and their demographic/firmographic variables.

This gives us information on transactions for consumers in the sample. Based on this

information, the firm can calculate the value of each customer. For example, survey data

gives us a measure of purchase frequency, measure of purchase value and thereby a

measure of the contribution margin, types of products purchased and marketing costs.

Marketing cost in this case may not be available at an individual customer level.

However the firm can allocate mass communication costs to individual customer level.

The basis for allocation can be either the share value of purchase or the contribution.

Based on this information, the firm can make projections on future frequencies,

contribution margin and market costs and assess the value of the customer. Once the

customer values are calculated, the customers can be grouped into deciles or segments

based on the customer value. The firm can then profile the customers in different

segments / deciles. This will help the firm to identify the profile of high value customers.

The firm can therefore identify high potential customers who have matching profiles with

existing high value customers and create marketing strategy to reach out to these

37

prospects. This will ensure targeting and acquiring prospects who have high customer

lifetime value which in turn will help to maximize the customer equity of the firm.

Case Study 2: CLV Framework Applied to Soft Drink Manufacturer

A soft drink manufacturer usually sells through its intermediary channels. Though

the company may have the data on sales to its intermediaries, it is unlikely to have

transaction data for all the end consumers. Also the number of end-consumers will be

unmanageably large. The contribution from each customer may be low and hence

managing business at an individual level may not be the right strategy because of high

touch cost relative to the contribution from an individual customer. Instead, the firm will

be interested in knowing the drivers of consumption at different age groups so that it can

improve the drivers of CLV to maximize the customer value from that age group (Kumar

& George, 2005). In order to identify the drivers, the firm needs to gather information on

consumption and demographic variables from a large number of respondents from

different age groups. For example customers can first be grouped into 6 age groups. The

age groups can be <13Yrs, 13-18yrs, 18-29yrs, 30-39yrs, 40-50yrs, and >50yrs. Then

select randomly a sample of customers within each group for all the age groups and

collect information about the quantity of soft drink (specific brand) consumed by each

respondent, and the demographic variables using a questionnaire survey. In the case of

<13yrs age group, information can be collected by contacting the head of the household.

Based on this data the firm can arrive at a rough estimate of the lifetime value of a

customer in each age group. It is expected that the consumption pattern in one age

segment may be quite different from that in another segment. The average consumption

and the variation within each age segment may vary as given in Figure 29.2.

38

Figure 29.2 about Here

Figure 29.2 will help us to understand how the average yearly consumption varies

across different age groups and the variation within each age group. If a firm computes

the average yearly consumption of a specific brand of soft drink for different age groups,

it can calculate the total consumption of that brand of soft drink by an average consumer

in his/her lifetime. For instance, suppose that the consumption figures for each age group

are as given in Figure 29.2. If we assume a typical consumer starts consumption at the

age of 5 and the average life expectancy is 75years, we can compute the total

consumption by an average consumer as;

Average lifetime consumption = 8*1000 + 8*1500 + ……+ 25*1600 = 123,000 oz

However, the variation in consumption within an age group may be high.

Therefore the average consumption will not help us in developing strategies for the age

segments. Instead, the firm should identify the demographic variables which explain the

variation in consumption pattern of customers within an age group either by regressing

the average monthly consumption quantity on different demographic variables or by

using other suitable statistical techniques. The average monthly consumption quantity

(CQ) can be expressed as a function of demographic variables as given below:

CQi = f (Age, Education, Income, Occupation, Gender, Ethnicity, Religion,…)

These drivers of consumption pattern help the firm to predict the lifetime value of

customers in that age group across a heterogeneous group of individuals. Firms can then

formulate suitable marketing strategy for each age group to maximize the customer value

from each age group. It can make use of publicly available data such as census to collect

39

information on demographic variables of customers in different age group as well as the

growth in each age segment of the population. Such information along with the drivers of

lifetime value can be used to predict the lifetime value of customers in each group (i.e.

total of lifetime values of all the customers in that segment). This will help the firm to

direct its marketing efforts to the high value customer segment. It can also use the profile

information of high value customer groups to target high potential prospects. These two

strategies collectively will maximize the customer equity of the firm.

Organizational Challenges in Implementing a CLV-based

Framework

Firms can no doubt benefit from a CLV-based framework in terms of acquiring

and retaining the profitable customers, developing the right communication and

marketing strategies, and allocating resources optimally so that the profits are maximized.

However, the firms face many organizational challenges in implementing such a

framework. CLV based approach calls for classification of customers as high and low

value and differential treatment to customers based on their value to the firm. Customer

differentiation can potentially lead to consumer backlash unless the process is carefully

managed by the firm (Diane, 2000). The important challenges faced while implementing

a customer-centric approach are discussed in this section.

Transformation from Product Centric to Customer Centric Marketing

Traditionally, firms followed a brand or product centric marketing approach.

When firms adopt brand or product management structure, the emphasis is on new

product development, brand building, and brand equity. Each product or brand is

40

managed by different brand managers and the marketing and sales activities planned by

one group are independent of those by other product groups. Often the same customer is

contacted by different groups with possibly different messages. In customer centric

approach, the customer is the focus and the organizational activities are centered on them.

For the successful implementation of CLV framework, firms need to move from a

product centric to customer centric approach. Firms have to consider customers as

sources of value rather than only brands / products as sources of value. Building customer

equity rather than brand equity should be the central goal of resource allocation and

strategic marketing expenditures. However, transformation from a product centric to

customer centric marketing may not be always easy. It requires concerted effort by the

top management to change the organizational level philosophy of doing business. It may

also involve realignment of organizational roles and integration of different functions.

Firms effectively managing this transition have laid down the foundation for

implementing CLV based customer management.

Challenges in Data Collection and Management

Firms need to collect individual level data about all its customers on a large

number of variables in order to compute CLV. Some key informational needs are

demographic/ firmographic information, the amount of purchase, products purchased in

each occasion, the number, time, and type of the marketing contacts. Though the cost of

data collection and storage has decreased over the years, many firms face challenges in

identifying the right informational needs, integrating the data and making use of the

available information. Before start collecting the information, firms should ask the

relevant questions. What should be the outcome of implementation of CLV framework

41

specific to the firm? In the context of your organization what are the possible drivers of

CLV on which you need to collect information? Answers to these questions help the

firms to manage the data more effectively.

Another area in which firms face challenge is in gathering information about

prospects and competitor’s customers. This information is important for the acquisition

process. One way to obtain this information is to cooperate with the competition like the

catalog retailers and global airline industry (Bell et al., 2002). But the firm should

evaluate the benefits of gaining information about prospects vis-à-vis the disadvantage of

loosing the private customer information.

How to Make the Most of the CLV Framework?

Firms often get in to the trap of calculating CLV for its customers once and not

using it to maximize the firm’s profitability (Bell et al., 2002). They limit the use of CLV

scores only to segment the customers but not to implement customer specific

communication and marketing strategies which maximizes the customer equity of the

firm. Organizations have to understand CLV as a dynamic measure which changes as a

result of customer-specific marketing actions. As discussed earlier, CLV can be used to

optimally allocate resources, predicting future purchase of customers, and reaching the

right customers with right message through the most apt channels. Unless an organization

is effectively using CLV to achieve these results and maximizing the profitability, it is

not making the most of the CLV framework.

Future of CLV

42

CLV framework relies on customers’ personal and behavioral information. There

is growing concern among customers about privacy of their information. Firms, while

gathering and using customer level information, should be aware of this and take steps to

gain the confidence of customers. CLV framework is also expected to undergo further

sophistication and improvement. Improvements are expected in: (1) measuring CLV, (2)

a better understanding of the antecedents or drivers of CLV, and (3) emergence of the

evidence regarding the importance of using CLV as the metric for Resource Allocation.

The formula for calculation of CLV has improved in the past two years

significantly. However, considering the dynamic nature of the purchase behavior of

customers more sophisticated models that incorporate the conditional effects (Reinartz &

Kumar, 2003) of changes in the amount and quality of marketing mix need to be

developed. The future models are also expected to incorporate the impact of Word-of-

Mouth in determining the lifetime value of customers. Identification of other meaningful

antecedents of CLV in addition to the ones discussed in this chapter and understanding

their relationships with lifetime value is another area where improvements are expected.

Though recent studies have shown the impact of using CLV as better metric for resource

allocation, many firms continue to use traditional metrics. One possible reason may be

the inertia to move away from the accepted practices while another reason is the lack of

empirical evidence supporting the impact of use of CLV on profitability. With more and

more firms adopting CLV framework for resource allocation and other customer specific

strategies, CLV is expected to gain wide spread acceptability as the preferred metric for

resource allocation.

43

ACKNOWLEDGMENT

The author sincerely thanks the assistance of Morris George in the preparation of this

chapter.

ENDNOTES

1For details please refer “Venkatesan, Rajkumar and V. Kumar (2004). A Customer

Lifetime Value Framework for Customer Selection and Optimal Resource Allocation

Strategy. Journal of Marketing, 68(4), 106-125.

2All figures have been altered by a constant multiplier due to confidentiality reasons.

44

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46

Venkatesan, R., & Kumar, V. (2004). A customer lifetime value framework for customer

selection and optimal resource allocation strategy. Journal of Marketing, 68(4),

106-125.

47

Table 29.1a

RFM Method (Recency Score)

Customer Purchases (Number)

Recency (Number)

Assigned Points

Weighted Points

1 2 20 10 JOHN 2 4 10 5 3 9 3 1.5 SMITH 1 6 5 2.5 1 2 20 10 MAGS 2 4 10 5 3 6 5 2.5 4 9 3 1.5

Points for Recency : 20 if within past 2 months; 10 if within past 4 months; 05 if within past 6 months; 03 if within past 9 months; 01 if within past 12 months; Relative weight = 50%

Table 29.1b

RFM Method (Frequency Score)


Frequency Assigned Points

Weighted Points

1 1 3 0.6 JOHN 2 1 3 0.6 3 1 3 0.6 SMITH 1 2 6 1.2 1 1 3 0.6 MAGS 2 1 3 0.6 3 2 6 1.2 4 1 3 0.6 Points for Frequency: 3 points for each purchase within 12 months; Maximum = 15 points; Relative weight = 20%

Table 29.1c

RFM Method (Monetary Value Score)

48


Monetary Assigned Points

Weighted Points

1 $40 4 1.2 JOHN 2 $120 12 3.6 3 $60 6 1.8 SMITH 1 $400 25 7.5 1 $90 9 2.7 MAGS 2 $70 7 2.1 3 $80 8 2.4 4 $40 4 1.2 Monetary Value: 10 percent of the $ Volume of Purchase with 12 months; Maximum = 25 points; Relative weight = 30% Source: (for Tables 29.1a, 29.1b, and 29.1c) Marketing Research”, Eighth edition, David A.Aaker, V.Kumar, George S. Day (2003), John Wiley & Sons, Inc., New York

Table 29.1d

RFM Score

Customer Recency score*

Frequency score*

Monetary value score*

RFM score

JOHN 16.5 1.8 6.6 24.9

SMITH 2.5 1.2 7.5 11.2

MAGS 19.0 3.0 8.4 30.4 * Recency, frequency, and monetary value scores are sum of weighted points for Recency, frequency, and monetary value for each customer.

Table 29.2

Spending Pattern of a Customer (for Calculation of PCV)

January February March April May Purchase Amount ($) 800 50 50 30 20 GC 240 15 15 9 6

Table 29.3

Spending Pattern of a Customer (to Calculate NPV of EGC)

January February March April May

49

Purchase Amount ($) 800 50 50 30 20 Gross Margin 240 15 15 9 6

Figure 29.1

Variation in Inter Purchase Time

Customer 1

Customer 2

Month 1 Month 8 Month 12

Source: Kumar, V., Girish Ramani, and Timothy Bohling (2004). Customer Lifetime Value Approaches and Best Practice Applications. Journal of Interactive Marketing, 18(3), 60-72.

Table 29.4

Drivers of Profitable Lifetime

Drivers Description Impact on Profitable lifetime

Spending Level Average monthly spending level over a given period

(+)

Cross-buying Number of different product/categories purchased

(+)

Focused buying Purchase within one category (-) Average Interpurchase Time

Number of days between purchases (average)

(∩)

Loyalty instrument Customer’s ownership of company’s loyalty instrument (B-to-C) or availability of line of credit (B-to-B)

(+)

Mailing Effort by the company

Number of mailing efforts of the company(B-to-C) or the number of contacts (B-to-B)

(+)

Income Income of the customer (B-to-C) or income of the firm (B-to-B)

(+)

Population density Number of people in a two-digit zip code (only B-to-C)

(-)

Source: Reinartz and Kumar (2003), “The Impact of Customer Relationship Characteristics on Profitable Lifetime Duration,” Journal of Marketing, 67(1), 77-99

Observation period

50

Table 29.5

Actual Revenues and Profits for the Selected Group of Customers Based on NPV of

ECM (CLV), RFM, and Past Customer Value Selection (Cohort 1*)

Percentage of Cohort (Selected from Top)

NPV of ECM (CLV method)

Advanced RFM Past Customer Value (PCV)

30% (n=1260) Revenue

Profit

318,831

62,991

140,781

27,582

179,665

35,916


Profit

361,125

61,636

186,267

36,380

210,860

41,729


Profit

380,855

60,305

216,798

42,839

225,910

44,738 * Cohort 1 had 4202 observations. Notes: Results were similar for cohort 2 (4965 observations), and cohort 3 (n=2825)

Source: Adapted from Reinartz, Werner J., and V. Kumar (2003). The Impact of Customer Relationship Characteristics on Profitable Lifetime Duration. Journal of Marketing, 67(1), 77-99.

Table 29.6

Comparisons of CRM Metrics for Customer Selection

Percentage of Cohort (Selected from Top)

CLV PCR PCV CLD

5% Gross profit ($) Variable costs ($) Net profit($)

144,883 1,588 143,295

71,908 979 70,929

131,735 950 130,785

107,719 790 106,389

10% Gross profit ($)

78,401

27,981

72,686

55,837

51

Variable costs ($) Net profit($)

1,245 77,156

943 27,038

794 71,892

610 55,227

15% Gross profit ($) Variable costs ($) Net profit($)

56,147 807 55,340

15,114 944 14,170

52,591 809 51,782

44,963 738 44,225

Notes: All metrics are evaluated at 30 months, with an 18-month prediction window. The reported values are cell medians. Gross profit for the firm which provided the database is approximately 30% of the revenue. Source: Venkatesan, Rajkumar, and V. Kumar (2004). A customer Lifetime Value Framework for Customer Selection and Resource Allocation Strategy. Journal of Marketing, 68(4), 106-125.

Table 29.7 Segmentation of Customers Based on Customer Lifetime Profits and Relationship

Duration BUTTERFLIES

• Good fit between company’s offerings and customers’ needs

• High Profit potential • Action

o Aim for transactional satisfaction, not attitudinal loyalty

o Maximize profits from these accounts as long as they are active

o Stop investing once inflection point is reached

TRUE FRIENDS • Excellent fit between company’s

offerings and customers’ needs • Highest profit potential • Action

ο Consistent intermittently spaced communication

ο Achieve attitudinal and behavioral loyalty

ο Invest to nurture/defend/retain

STRANGERS • Little fit between company’s offerings

and customers’ needs • Lowest profit potential • Action

ο Make no investment in these relationships

ο Make profit on every transaction

BARNACLES • Limited fit between company’s

offerings and customers’ needs • Low profit potential • Action:

ο Measure size and share of wallet ο If share-of-wallet is low, focus on

specific up and cross selling ο If size of wallet is small, impose

strict cost controls Source: Reinartz, Werner and V Kumar (2002),”The Mismanagement of Customer Loyalty,” Harvard Business Review, July, 1-13.

High

Low

Cus

tom

er L

ifetim

e Pr

ofits

Low High Relationship Duration

52

Table 29.8

Segmentation of Customers Based on Past and Future Profitability

RISING STARS Action

ο Invest to deepen relationship ο Identify specific up-sell/ cross-sell

opportunities ο Cultivate attitudinal loyalty

TRUE LOYALISTS Action

ο Cultivate attitudinal loyalty ο Invest to nurture/defend/retain ο Reward proactively

TOTAL MISFITS Action

ο No relationship investment ο Aim to extract profit from every

transaction by migrating the customer to low cost channels

FALLING ANGELS Action

ο Identify specific up-sell/ cross-sell opportunities

ο Transact through low-cost channels

ο Optimize (Minimize) Marketing costs

Table 29.9

Change Between Current Year and Previous Year

Test Group Control Group Revenue ($) 1050 (18,130) 1033 (17,610) Cost of Communication ($) -750 (3,625) 75 (4,580) # of attempts before purchase -4 (15) 1 (18) Profits ($) 3,000 (9080) 637 (6,275) Return on Investment (%) 504 (3.7) 2.2 (2) * The reported values are unit values per customer Number indicates change from base level (previous year). Base level is in parentheses. Source: Kumar, V., Rajkumar Venkatesan and Werner Reinartz (2005), “A Purchase Sequence Analysis Framework for Targeting Products, Customers and Time Period’, forthcoming; Journal of Marketing

Table 29.10

Average Customer Relationship Duration (as a Function of Retention Spending)

Futu

re P

rofit

abili

ty

(CL

V)

Low

High

Low High Historical Profits (PCV)

53

Retention spending (per customer)

$40 $50 $60 $70 $80

Estimated relationship duration (days)

122 135 142 143 138

Table 29.11

Average Customer Profitability (as a Function of Acquisition and Retention

Spending)

Retention Spending

$40 $50 $60 $70 $80

$1 $1,423 $1,543 $1,583 $1,543 $1,423

$5 $1,437 $1,557 $1,597 $1,557 $1,437

$10 $1,443 $1,563 $1,603 $1,563 $1,443

$15 $1,437 $1,557 $1,597 $1,557 $1,437

Acquisition spending

$20 $1,418 $1,538 $1,578 $1,538 $1,418

Source: (for Table 29.10 & 19.11) Thomas, Jacquelyn S., Werner Reinartz, and V. Kumar (2004). Getting the Most out of All Your Customers. Harvard Business Review, July-August, 116-123.

54

Table 29.12:

Customer Segments Based on Acquisition and Retention Costs

Source: Thomas, Jacquelyn S., Werner Reinartz, and V. Kumar (2004). Getting the Most out of All Your Customers. Harvard Business Review, July-August, 116-123.

High-maintenance customers 25% of customers 15% of profits

Royal customers 28% of customers 25% of profits

Casual customers 32 % of customers 20% of profits

Low-maintenance customers 15% of customers 40% of profits

High Acquisition cost Low

Hig

h Lo

w

Ret

entio

n co

st

55

Figure 29.2 Soft Drink Consumption Pattern Across Age Groups

Age <13Yrs

µ1 = 1000 oz

13 – 20 Years

µ2 = 1500 oz

31 – 40 Years

µ4 = 2500 oz

> 50 Years

µ6 = 1600 oz µ5 = 1800 oz

41 – 50 Years

Average Yearly Consumption (oz)

Freq

uenc

y

Note: The average yearly consumption figures are for illustration purpose only.

21 – 30 Years

Variation in consumption pattern

µ3 = 2000 oz

customer lifetime value

Documents

cumulated

ongoing marketing

average interpurchase

resource allocation

optimal resource

profitable

average gross

weighted average