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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.
Table 29.3 about Here
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:
Table 29.4 about Here
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.
Table 29.6 about Here
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.
Table 29.7 about Here
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
Table 29.8 about Here
‘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.
Table 29.92 about Here
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.
Table 29.10 about Here
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 about Here
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 about Here
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)
Customer Purchases (Number)
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
Customer Purchases (Number)
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
50% (n=2101) Revenue
Profit
361,125
61,636
186,267
36,380
210,860
41,729
70% (n=2941) Revenue
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