Chapter 29 CUSTOMER LIFETIME VALUEV. Kumar, University of Connecticut

IntroductionIn 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 firms customers is the key to this customercentric 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 1

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 contributioni.e., revenues minus costsof 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 flowsdiscounted 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?

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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 firms 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 MetricsSome of the commonly used metrics for computing customer value include RFM, Share-of-Wallet and Past Customer Value.

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

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

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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.

ExampleThree 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

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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 / Where: S = sales to the focal customer j = firm

j =1

J

Sj

(3)

j =1

J

represents 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 As share-of-wallet for that consumer is 60% in that month.

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The information about a customers 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 firms 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 ValueThis 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 customers 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

= GCit * (1 + r ) tt =1

T

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

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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;

Gross Contribution (GC) = Purchase Amount 0.3 Past Customer Value Scoring = 6(1 + 0.0125) + 9(1 + 0.0125) 2 + 15(1 + 0.0125) 3 + 15(1 + 0.0125) 4 + 240(1 + 0.0125) 5 = 302.01486The 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 MetricsThough RFM, Past Customer Value, and Share-of-Wallet are commonly used for computing customers 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 7

customers future purchase behavior and his/her worth to the firm. Also, the weights given for R, F, and M greatly influence the computation of customers 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 CLVLifetime value of a customer can be either calculated as an average CLV or individual level CLV.

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An Aggregate ApproachIn 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;CE = i =1 I

1 CM it 1 + t =1T

t

(1)

where CE = customer equity of customer base in $ (sum of individual lifetime values) CM = Contribution margin in time period t = 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;

(GC M ) t CLV1 = r A t 1 t =0 ( + d ) T

(2)

where r = rate of retention 9

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;k I k 1 CE (t ) = N i ,t i ,t (S i ,t ci ,t ) N i ,t Bi , a ,t + N i ,t i ,t j ,t + k (S i ,t + k ci ,t + k Bi ,r ,t + k Bi , AO ,t + k ) 1+ d i =0 k =1 j =1

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 = the acquisition probability at time t for segment i = the retention probability at time t for a customer in segment i = the marketing cost per prospect (N) for acquiring customers at time t for segment i

i,t i,tBi,a,t

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Bi,r,t d Si,t ci,t I I t0

= the marketing in time period t for retained customers for segment i = discount rate = sales of the product/services offered by the firm at time t for segment i = cost of goods at time t for segment i = the number of segments = the segment designation = 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;

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

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 firms 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

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At an individual level, customer lifetime value is calculated as the sum of cumulated cash flowsdiscounted 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;

CLVi = where

(Future contribution marginit Future cos tit ) (1 + d )t t =1T

(4)

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 =

n = t +1

P( Active)

t+x

in

AMGCit

(1 + d )n

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

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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

ExampleThe 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 = 57NPV of EGC = 0.4

(1 + 0.125)

57

1

+ 0.19

(1 + 0.125)2

57

= 28.82

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;

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CLV of customer i =

n = t +1

t+x

P( Active)in

AMGCit

(1 + d )n

x 1 M in 1+ d n =1

n 1

A

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

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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

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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 customers 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

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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 customers 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;

CLVi = y =1

Ti

CM i , y

(1 + r )

y frequencyi

l =1

n

m

(1 + d )l 1

ci , m,l xi , m,l

where CLVi = lifetime value of customer i, CMi,y = predicted contribution margin from customer i in purchase occasion y, d ci,m,l xi,m,l = discount rate, = unit marketing cost for customer i in channel m in year l, = number of contacts to customer i in channel m in year l,

frequencyi = predicted purchase frequency for customer i, n Ti = number of years to forecast, and = predicted number of purchases made by customer i until the end of planning period.

ExampleSuppose 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: 17

Time period Predicted contribution ($) Number of direct mails:

Jan 05 100

May05 70 Year 1 = 4 Year 1 = 2 2.50 3.00

Nov05 50

Feb 06 90

Jul 06 65

Oct 06 30

Year 2 = 4 Year 2 = 3

Number of contacts via telephone: Cost per direct mail ($) Cost per contact via telephone ($) below. Predicted purchase frequencyCLV =

If the discount rate is taken as 15%, then CLV of this customer can be calculated as given =33063

(1 + 0.15)

100

13

+ ......... +

(1 + 0.15)

(2.5 4) + (3 3) = $319.05 (2.5 4) + 3 2 + (1 + 0.15)

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.

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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 CLVWhile 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 customerfirm 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, customers 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

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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-toB) 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 Customercentric 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 firms 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

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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 firms 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 ProfitabilityRecent 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 SelectionRecent 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.

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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)

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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 SegmentationDifferential 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 23

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

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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.

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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 upsell 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 firms profitability is low in the past and in the future should be dealt with very cautiously. Firms 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

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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

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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.

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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

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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 AnalysisIn a multi-product firm it is not easy to speculate what product a particular customer is going to buy next. But from the firms 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 customers 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 customers 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?

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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 firms routine contact strategy. The

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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 ProspectsWe discussed how firms can, using CLV framework, prioritize, select, and implement individual level strategies in order to maximize the profitability from its 32

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

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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.

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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 OrganizationCollection 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 35

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 ManufacturerA 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

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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 ManufacturerA 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 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