rajkumar venkatesan & v. kumar a customer lifetime value …elmadaga/mkt/principles of...

106 / Journal of Marketing, October 2004Journal of MarketingVol. 68 (October 2004), 106–125

Rajkumar Venkatesan & V. Kumar

A Customer Lifetime ValueFramework for Customer Selectionand Resource Allocation Strategy

The authors evaluate the usefulness of customer lifetime value (CLV) as a metric for customer selection and mar-keting resource allocation by developing a dynamic framework that enables managers to maintain or improve cus-tomer relationships proactively through marketing contacts across various channels and to maximize CLV simulta-neously. The authors show that marketing contacts across various channels influence CLV nonlinearly. Customerswho are selected on the basis of their lifetime value provide higher profits in future periods than do customersselected on the basis of several other customer-based metrics. The analyses suggest that there is potential forimproved profits when managers design resource allocation rules that maximize CLV. Managers can use theauthors’ framework to allocate marketing resources efficiently across customers and channels of communication.

Rajkumar Venkatesan is Assistant Professor in Marketing (e-mail:[email protected]), and V. Kumar is ING Chair Professor inMarketing and Executive Director, ING Center for Financial Services (e-mail: [email protected]), School of Business, University of Connecticut.This research study was supported partially by a grant from the TeradataCenter for Customer Relationship Management at Duke University, theMarketing Science Institute, and the Institute for Study of Business Mar-kets, and the authors owe special thanks to them. The study greatly ben-efited from audience discussions at the National Center for DatabaseMarketing conference in Philadelphia and the Marketing Science Instituteconference at INSEAD, as well as at Michigan State University, CurtinUniversity, Tilburg University, State University of New York–Buffalo, andthe 2003 Marketing Science Conference. The authors thank Munshi Mah-fuddin and Rajendra Ladda for research assistance. Special thanks aredue to Don Lehmann, Rick Staelin, Greg Allenby, and John Lynch for theircomments on the proposal version of this study. The authors thank abusiness-to-business firm for providing the data for this study. They alsothank the anonymous JM reviewers for providing suggestions to enhancethe contribution of this study. They thank Renu and Andrew Peterson forcopyediting the manuscript.

Maximizing CLVSome researchers have recommended CLV as a metric forselecting customers and designing marketing programs(Reinartz and Kumar 2003; Rust, Zeithaml, and Lemon2004). However, there is no empirical evidence as to theusefulness of CLV compared with that of other customer-based metrics. Table 1 compares the CLV framework pro-posed in this study with the existing literature on CLV anddatabase marketing. A comparison of the studies listed inTable 1 shows that most of the previous studies provideguidelines for calculating CLV and return on investment atthe aggregate level. Some recent studies (Reinartz andKumar 2003; Rust, Zeithaml, and Lemon 2004) provideempirical evidence for the existence of a relationshipbetween marketing actions and CLV at the aggregate level.However, as Berger and colleagues (2002) state, none of thestudies has proposed and tested a framework that providesrules for resource allocation across various channels ofcommunication for each individual customer and acrosscustomers.

On the basis of the comparisons in Table 1, we summa-rize the significant contributions of our study as follows:We provide a framework for measuring CLV that links theinfluence of communications across various channels onCLV. We also evaluate the usefulness of CLV as a metric forcustomer selection and develop a framework for marketingresource allocation that maximizes CLV. Given the assumedlink between CLV and firm profitability (Hogan et al.2002), these are important issues.

In this study, we use customer data from a largebusiness-to-business (B2B) manufacturer to illustrate theproposed framework empirically. The customer database ofthe organization focuses on B2B customers. Our analysesshow that marketing communications across various chan-nels influence CLV nonlinearly. The results from our analy-ses suggest that customers selected on the basis of CLV pro-vide higher profits than do customers selected on the basis

Customer lifetime value (CLV) is rapidly gainingacceptance as a metric to acquire, grow, and retainthe “right” customers in customer relationship man-

agement (CRM). However, many companies do not useCLV measurements judiciously. Either they work withundesirable customers to begin with, or they do not knowhow to customize the customer’s experience to create thehighest value (Thompson 2001). The challenge that mostmarketing managers currently face is to achieve conver-gence between marketing actions (e.g., contacts across vari-ous channels) and CRM. Specifically, they need to take allthe data they have collected about customers and integratethem with how the firm interacts with its customers. In theacademic literature, Berger and colleagues (2002) supportthe allocation of resources to maximize the value of the cus-tomer base, and they strongly argue that such resource allo-cation models are needed.

Customer Lifetim

e Value Framew

ork / 107

TABLE 1Comparing the Proposed CLV Framework with Existing Models

Return onInvestment How Market Resource Resource Comparison

Modeled ing Communi- CLV-Based Allocation Allocation of Customer-Type of Representa- and CLV cation Affects Resource for Each Across Based StatisticalModel tive Research Calculated Calculation CLV Allocation Customer Channels Metrics Details

CLV Berger and No Yes Yes No No No No YesNasr (1998)

Berger et al. Yes Yes Yes Yes No Yes No No(2002)

Customer Blattberg and Yes Yes No Yes No No No Yesequity Deighton

(1996)

Libai, Yes Yes Yes No No No No NoNarayandas, and Humby

(2002)

Database Reinartz and Yes Yes No No No No No Yesmarketing Kumar (2000)

Bolton, Lemon, Yes Yes Yes No No No No Yesand Verhoef

(2004)

CLV Reinartz and Yes Yes Yes No No No Yes Yesantecedents Kumar (2003)

Rust, Zeithaml, Yes Yes Yes No No No Yes Yesand Lemon

(2004)

CLV-based Berger and Yes Yes Yes Yes No No No Noresource Bechwati allocation (2001)

Present Study Yes Yes Yes Yes Yes Yes Yes Yes

108 / Journal of Marketing, October 2004

of other widely used CRM metrics. In addition, there is thepotential for substantial improvement in profits when man-agers design resource allocation rules that maximize CLV.

In the next section, we develop the framework for themeasurement and maximization of CLV. We then proposehypotheses about the influence of supplier-specific factorsand customer characteristics on the various CLV compo-nents. In the subsequent section, we explain the models anddata we used to estimate CLV. We then discuss the resultsfrom our analyses and explain the comparison of CLV withother metrics for customer selection. Specifically, we com-pare the aggregate profits provided by high-CLV customerswith those of customers who score high on several othercustomer-based metrics. In the section “Resource Alloca-tion Strategy,” we provide details on allocating resourcesthat maximize CLV. Our objective there is to evaluate theextent to which CLV, and thus profits, can be increased byallocating marketing resources across channels of contactfor each customer so as to maximize his or her respectiveCLVs. Finally, we derive implications based on the results,discuss the limitations of our study, and identify areas forfurther research.

CLV Measurement andMaximization

The various components of CLV include purchase fre-quency, contribution margin, and marketing costs (however,the various CLV components can vary depending on theindustry). Some of the antecedents of purchase frequencyand contribution margin (e.g., marketing communications)are under management’s control and affect the variablecosts of managing customers. We use these antecedents tomaximize CLV.

Objective Function: CLV

Typically, CLV is a function of the predicted contributionmargin, the propensity for a customer to continue in a rela-tionship (customer retention), and the marketing resourcesallocated to the customer. In general, CLV can be calculatedas follows:

where

i = customer index,t = time index,n = forecast horizon, andr = discount rate.

In contractual settings, managers are interested in pre-dicting customer retention, or the likelihood of a customerstaying in or terminating a relationship. However, in non-contractual settings, the focus is more on predicting futurecustomer activity because there is always a chance that thecustomer will purchase in the future. Therefore, managerswho calculate CLV in noncontractual settings are interestedin predicting future customer activity and the predicted con-

( )(

1 CLV =Future contribution margin

iit − Future costit )

( ),

11

+=∑ r tt

n

tribution margin from each customer. Previous researchershave used the variable P(Alive), which represents the prob-ability that a customer is alive (and thus exhibits purchaseactivity) given his or her previous purchase behavior(Reinartz and Kumar 2000), to predict future customeractivity in noncontractual settings. However, the measureassumes that when a customer terminates a relationship, heor she does not return to the supplier. This is also called the“lost-for-good” scenario (Rust, Zeithaml, and Lemon2004). If a customer is won back after termination, the com-pany treats the customer as a new customer and ignores itshistory with the customer.

Another method for predicting future customer activityis to predict the frequency of a customer’s purchases givenhis or her previous purchases. The assumption underlyingthis framework is that customers are most likely to reducetheir frequency of purchase before terminating a relation-ship. This assumption is in accordance with theories aboutthe different phases in a relationship and relationship lifecycles (Dwyer, Schurr, and Oh 1987; Jap 2001). In addition,such a methodology enables a customer to return to the sup-plier after a temporary dormancy in a relationship. Thus, inthis framework, we measure CLV by predicting the pur-chase pattern (purchase frequency or interpurchase times)over a reasonable period. This is also called the “always-a-share” scenario. The lost-for-good approach is questionablebecause it systematically understates CLV (Rust, Zeithaml,and Lemon 2004). Thus, we use the always-a-shareapproach in this study. Given predictions of contributionmargin, purchase frequency, and variable costs, the CLVfunction we use can be represented as follows:

where

CLVi = lifetime value of customer i;CMi,y = predicted contribution margin from cus-

tomer i (computed from a contributionmargin model) in purchase occasion y,measured in dollars;

r = discount rate for money (set at 15%annual rate in our study);

ci,m,l = unit marketing cost for customer i in chan-nel m in year l (the formulation of CLVdoes not change if l is used to representperiods other than one year);

xi,m,l = number of contacts to customer i in chan-nel m in year l;

frequencyi = predicted purchase frequency forcustomer i;

n = number of years to forecast; andTi = predicted number of purchases made by

customer i until the end of the planningperiod.

In addition to accurate measurement of CLV for eachcustomer, our objective is to allocate resources so as tomaximize CLV. Thus, we model the purchase frequency and

( )

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CLV

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r( ),

Customer Lifetime Value Framework / 109

1In this study, we do not use a budget constraint on the totalresources available for contacting customers. Therefore, we areinterested only in allocating resources across channels for eachindividual customer (i.e., within each customer across channels).However, our framework can be applied to allocate resourcesacross channels with each individual customer and across cus-tomers in the presence of a budget constraint.

contribution margin of customers as a function of marketingresource variables such as channel contact. We then use thecustomer responsiveness to marketing actions, obtainedfrom the purchase frequency and contribution margin mod-els, to develop resource allocation strategies that maximizeCLV. In summary, the objective is to identify the resourceallocation rules across various channels of communicationfor each individual customer such that the respective CLVs(as provided in Equation 2) are maximized.1 Our objectivefunction is subject to the following constraints: frequency >0 ∀ i, t, and xi,m,l ≥ 0 ∀ i, m, l.

Discounting contribution margin. We first focus on thediscounting of contribution margin over a period of time.Assume that it is currently year l = 1 and that we need toforecast the contribution margin from each customer for thenext n years (i.e., until l + n). It is possible that a customermakes several purchases in a given year. Berger and Nasr(1998, Equation 2) and Rust, Zeithaml, and Lemon (2004)provide guidelines for discounting contribution marginfrom customers when there is more than one purchase occa-sion (y) per year. In this approach, the discount rate from acustomer is scaled according to his or her frequency of pur-chase (as is shown in Equation 2). For example, considerwhen the planning horizon is one year and the frequency ofpurchases is two times (frequency = 2). The first purchaseoccasion (y = 1) occurs after 6 months; therefore,y/frequency = .5 (in other words, we use the square root ofthe discount rate). The second purchase occasion (y = 2)occurs after 12 months; therefore, y/frequency = 1.

Discounting cost allocations. The discounting of costallocations is straightforward if we assume that there is ayearly allocation of resources (as is the case in most organi-zations) and that the cost allocation occurs at the beginningof the year (the present period). Thus, the cost allocation inthe first year need not be discounted, the cost allocation inthe second year needs to be discounted for one year, and soon. Thus, we raise the denominator in the cost function cal-culation to current year – 1 (i.e., l – 1).

Discussion of model constraints. The constraints ensurethe nonnegativity of the predicted purchase frequency andcommunication levels for each customer i during period l.

Antecedents of PurchaseFrequency and Contribution MarginPurchase FrequencyAn objective of relationship marketing is to ensure futurepurchase activity. Purchase frequency is also a componentof our CLV calculation. Therefore, as the basis for selectingantecedents to predict purchase frequency, we use thecommitment–trust theory of relationship marketing (Mor-

gan and Hunt 1994) as well as previous research in cus-tomer equity and CLV (Bolton, Lemon, and Verhoef 2004;Bowman and Narayandas 2001; Reinartz and Kumar 2003;Rust, Zeithaml, and Lemon 2004) and channel communica-tions (Grewal, Corner, and Mehta 2001; Mohr and Nevin1990; Morgan and Hunt 1994; Rindfleisch and Heide1997).

The overall theoretical framework that we used is pro-vided in Figure 1. We summarize the antecedents of pur-chase frequency, their operationalization, expected effects,and the rationale for our expectations in Table 2. Next, weprovide a detailed discussion for a few of the hypothesesthat are unique to our study.

Supplier-Specific Factors: ChannelCommunications

In this study, we classify channels of communication intothe following contact modes: rich (e.g., face-to-face, tradingevent meetings), standardized (e.g., direct mail, telephone),and Web based (Mohr and Nevin 1990). Although weexpect the relationships between different channels of com-munication and predicted customer activity to be similar,we need to analyze customer responses separately acrossdifferent channels because the costs of serving customersacross different channels are different, and customers mightexhibit different responsiveness across the various channels.The costs of communication in each channel can influencemanagers’ frequency of communication in each channel.

Frequency of rich and standardized modes. Face-to-facecommunications and trading event meetings are the richestand most direct mode of communication possible amongchannel members (Mohr and Nevin 1990). Relational cus-tomers tend to have high commitment and trust with theirsuppliers, which results in less uncertainty, more coopera-tion, and less complexity in their relationships than in thoseof transactional customers (Morgan and Hunt 1994). Richmodes of communication are preferred to standardizedmodes when issues in the channel structure are complexand when there is a high degree of uncertainty in the rela-tionship. Rich modes of communication are also effective inconverting transactional customers to relational ones (Gane-san 1994).

Direct mail and telephone communication are the moststandardized and cost-effective modes of individual-levelcommunication available to an organization. Standardizedmodes are also the most cost-effective method for identify-ing customers who are interested in an organization’s cur-rent promotion (Shepard 2001). For transactional cus-tomers, direct mail can be used in combination withtelephone sales to generate interest in products while simul-taneously improving the return on investment (Nash 1993).For relational customers, direct mail serves to maintaincommitment and trust by communicating relationship bene-fits (Morgan and Hunt 1994) and to inform the best cus-tomers about new product offerings. Therefore, althoughthe purpose of standardized communication may be differ-ent for transactional customers than for relational ones, weexpect that the marginal response for increased frequency isthe same across segments.


FIGURE 1 A Conceptual Framework for Measuring and Using CLV

Customer Characteristics: Switching Costs•Upgrading •Cross-buying

Customer Characteristics•Lagged contribution margin •Establishment size •Industry category •Total quantity of purchases

Predicted Purchase Frequency

Contribution Margin

Total Profit

Net Present Value (Future Profits)

= CLV

Marketing Costs

Discount Rate

Allocate Appropriate Resources

Generate allocation rules

Supplier-Specific Factors:Channel Communication •Level of rich modes •Level of standardized modes •Intercontact time

Supplier-Specific Factors•Total marketing communication

Customer Characteristics: Involvement •Bidirectional communication •Number of returns •Number of Web-based contacts

Customer Characteristics: Previous Behavior•Product category purchased

However, it has been proposed that too much communi-cation causes a relationship to be dysfunctional (Fournier,Dobscha, and Mick 1997). In addition, the marginalresponse to a higher level of rich modes of communicationneed not always be higher; sometimes, it even can be nega-tive. Although the utility of marketing contacts is not ques-tioned, too much contact can overload buyers and have dys-functional consequences (e.g., ubiquitous junk mail). Thus:

H1: An inverted U-shaped relationship exists between the fre-quency of rich and standardized modes of communicationand a customer’s predicted purchase frequency.

Intercontact time. Following the theory that leads to H1,we expect that there exists an optimal level of intercontacttime between suppliers and buyers. Higher levels of previ-ous communications lead to trust with the supplier and actas glue that holds together a communication channel (Mor-gan and Hunt 1994). However, too much communicationmay be dysfunctional. In addition, the marginal utility of anadditional piece of information from a supplier firm in ashort period is low. To maximize the effect of each contact,

supplier firms need to pace their communication scheduleto suit customer needs. Thus:

H2: An inverted U-shaped relationship exists between inter-contact time and a customer’s predicted purchasefrequency.

Customer Characteristics: Customer Involvement

Bidirectional communication. Research on channelcommunications shows that highly relational channel struc-tures are associated with large bidirectional communica-tions among channel members (Mohr and Nevin 1990).Although customer-initiated contacts are associated primar-ily with complaints in business-to-consumer settings, thesame is not necessarily true for B2B settings. In a B2B set-ting, customers can initiate contacts with suppliers for sev-eral reasons, such as if they have new needs that the sup-plier may be able to fulfill, if they want the supplier toconduct training programs at the customer’s site (Cannonand Homburg 2001), or if the supplier invites the customerto participate in new product development sessions. Onmost occasions, bidirectional communication in channelsstrengthens a relationship, indicates customer involvement,


Variable

Upgrading

Cross-buying

Bidirectionalcommunica-tion

Returns

Frequency ofWeb-basedcontacts

Relationshipbenefits

Frequency ofrich modesof communi-cation

Frequency ofstandardizedmodes ofcommunica-tion

Intercontacttime

Operationalization

Number of product purchase upgradesuntil an observed purchase

Number of different product categoriesa customer has purchased

Ratio of number of customer-initiatedcontacts to total number of customercontacts (customer initiated andsupplier initiated) between two observedpurchases

Number of products the customerreturns between two observedpurchases

Number of times in a month thecustomer contacts the supplier throughthe Internet between two observedpurchases

Indicator variable of whether a customeris a premium service member (basedon revenue contribution in the previousyear)

Number of customer contacts by thesupplier in a month (through salespersonnel) between two observedpurchases

Number of customer contacts by thesupplier in a month (through telephoneor direct mail) between two observedpurchases

Average time between two customercontacts by the supplier across allchannels of communication betweentwo observed purchases

ExpectedEffect

+

+

+

�

+

+

�

�

�

Rationale

Customers who upgrade have higherswitching costs with each upgrade,which can lead to lower propensity toleave and higher recurrent needs(Bolton, Lemon, and Verhoef 2004).

Customers who purchase acrossseveral product categories have higherswitching costs and recurrent needs(Bowman and Narayandas 2001;Reinartz and Kumar 2003).

Two-way communication betweenparties strengthens the relationship andensures that the focal firm is recalledwhen a need arises (Morgan and Hunt1994).

Returns provide an opportunity for firmsto satisfy customers and ensure repeatpurchases (Reinartz and Kumar 2003),but too many purchases can bedetrimental to the relationship and canindicate that the firm has not used thereturn opportunities appropriately.

Customers who use onlinecommunication want transactionefficiencies, and customers who want tocreate efficiencies are highly relationaland have recurring needs (Grewal,Corner, and Mehta 2001; Rindfleischand Heide 1997).

Acknowledgment of customers withrelationship benefits reduces thepropensity of customers to quit andincreases the probability that the focalfirm is recalled when a need arises(Morgan and Hunt 1994).

Timely communication between partiesreduces the propensity of a customer toquit a relationship (Mohr and Nevin1990; Morgan and Hunt 1994), but toomuch communication can be detrimentalto the relationship (Fournier, Dobscha,and Mick 1997; Nash 1993); thus, thereis an optimal communication level.

A long time between contacts can leadto forgetfulness, but contacts that aretoo soon can cause dysfunction.

TABLE 2Antecedents, Covariates, and Expected Effects

Purchase Frequency Model

Antecedents


Variable OperationalizationExpected

Effect Rationale

TABLE 2Continued

Productcategory

Laggedcontributionmargin

Total marketingefforts

Total quantitypurchased

Size of firm

Industrycategory

Two indicator variables: one indicates ahardware purchase; the other indicatesa software purchase

Customer’s contribution margin from theprevious year

Total number of customer contactsacross all channels

Total quantity of products the customerpurchased across all product categories

Number of employees in the customerfirm

Standard industrial classification–basedindustry category to which the customerfirm belongs

+

+

+

+

A customer’s purchase patterns maydepend on the product categorypurchased.

Previous revenue is a good predictor ofcurrent revenue and accounts for anymodel misspecification (Niraj, Gupta,and Narasimhan 2001).

Previous marketing communicationsand depth (quantity) of purchasespositively affect contribution margin(Gupta 1988; Tellis and Zufryden 1995).

Control variables that accommodate forcustomer heterogeneity (Niraj, Gupta,and Narasimhan 2001).

Contribution Margin Model

Antecedents

Covariate

Covariates

Notes: For the purchase frequency model, the dependent variable is purchase frequency; for the contribution margin model, the dependentvariable is contribution margin.

and increases interdependence among channel members(Ganesan 1994; Mohr and Nevin 1990). Thus:

H3: The higher the level of bidirectional communication, thehigher is a customer’s predicted purchase frequency.

Frequency of Web-based contacts. In this study, we ana-lyze Web-based contacts separately from other channels ofcommunication because Web-based communication is cus-tomer initiated (i.e., a passive mode of operation for thesupplier). However, there are several advantages to trackingWeb-based contacts in a B2B setting. First, Web-basedcommunication between buyers and suppliers is the mostcost-effective method of communication. Second, Web-based contacts from the buyers provide some important sig-nals to the supplier about the buyer’s relationship orienta-tion. Grewal, Corner, and Mehta (2001) find thatorganizations enter and actively participate in electronicmarkets if their motivation is to improve efficiency in trans-actions. In addition, participation in electronic markets (oruse of Web-based initiatives) improves transaction effec-tiveness and efficiency (Rindfleisch and Heide 1997). Effi-ciency of communication and transactions among channelmembers is associated with a relational structure and highercustomer involvement (Mohr and Nevin 1990; Sheth andParvatiyar 1995). Thus:

H4: The higher the number of Web-based contacts from a cus-tomer, the higher is the customer’s predicted frequency ofpurchase.

Contribution Margin

The antecedents that we adopt to predict contribution mar-gin are based on findings from previous research onantecedents of customer revenue (Niraj, Gupta, andNarasimhan 2001) and purchase quantity (Gupta 1988; Tel-lis and Zufryden 1995). As with purchase frequency, weclassify the antecedents of contribution margin as supplier-specific factors (total marketing efforts) and customer char-acteristics (lagged contribution margin and purchase quan-tity). We use size of an establishment and industry categoryas covariates in our model. In Table 2, we provide a descrip-tion of the antecedents that we propose influence customerpurchase frequency and contribution margin. In the data-base, we also provide the operationalization of theantecedents, their expected effects, and the rationale for ourexpectations based on previous research. Because all theantecedents we use in our contribution margin model arebased on previous research and findings, we do not discussthe hypotheses in detail. In Table 2, we provide a descrip-tion of the covariates we use as control variables in the pur-chase frequency and contribution margin models.

Modeling CLV and DataModel DevelopmentTo predict CLV, we need a stochastic model to predict eachcustomer’s purchase frequency and a panel-data model that


predicts contribution margin. In this study, we assume thatthe amount a customer spends is independent of purchasetiming. This is a rather restrictive assumption for frequentlybought consumer goods (Tellis and Zufryden 1995). How-ever, in our product category, we find that the correlationbetween purchase frequency and contribution margin is notsignificant.

Purchase Frequency

We model a customer’s purchase frequency using the gener-alized gamma model of interpurchase timing that Allenby,Leone, and Jen (1999) developed. The generalized gammamodel also accommodates the commonly used exponentialdistribution for interpurchase times (Reinartz and Kumar2003). The likelihood function for the purchase frequencymodel is given as follows:

where

f(tij|α, λi, γ) = the density function for the generalizedgamma distribution (i.e., the probabilityof the jth purchase for customer i occur-ring at period t, given α, λi, γ);

S(tij|α, λi, γ) = the survival function for the generalizedgamma distribution (i.e., the probabilityof the jth purchase for customer i occur-ring at a given period is greater than t,given that the jth purchase has notoccurred until time t, given α, λi, γ);

cij = the censoring indicator, where cij = 1 ifthe jth interpurchase time for the ith cus-tomer is not right-censored, and cij = 0 ifthe jth interpurchase time for the ith cus-tomer is right-censored;

Φijk = the probability of observation j for the ithcustomer belonging to subgroup k; and

α, λi, γ = the parameters of the generalized gammadistribution.

Because we use a generalized gamma distribution to modelinterpurchase time and the likelihood function in Equation3, the expected time until next purchase is given as follows:

The ratio of 12 (because we use months as the unit ofanalysis) to the expected time until next purchase (whichwe obtain by modeling a generalized gamma distribution onthe interpurchase times, as is shown in the work of Allenby,Leone, and Jen [1999]) gives the predicted purchase fre-quency. The parameters α and γ establish the shape of theinterpurchase time distribution, and λi is the individual-specific purchase rate parameter. We assume that the popu-lation consists of k subgroups, and Φik provides the masspoint (i.e., weight) for each subgroup. We model the proba-

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2We also used lagged interpurchase time instead of the log ofthe lagged interpurchase time, and we did not find any differencein the substantive conclusions. We used log of the lagged interpur-chase times because lagged interpurchase times can have a thresh-old effect on the influence of current interpurchase times (Allenby,Leone, and Jen 1999). The log of the lagged interpurchase timeachieved this objective in scaling the tail of the lagged interpur-chase time distribution.

bility of a customer belonging to each subgroup Φik as aprobit function of the antecedents and covariates of pur-chase frequency. Specifically, we represent the link functionas Φik = f(xijβi), where xij represents the antecedents andcovariates of purchase frequency for customer i in purchaseoccasion j, and βi represents the customer-specific responsecoefficients.

Our model framework, as presented in Equation 3,resembles a hierarchical Bayes formulation of the concomi-tant continuous mixture model. To address the issue ofendogeneity, we use the one-period lagged value for all theantecedents and covariates in our analysis (Villas-Boas andWiner 1999). However, to account for any extraneous fac-tors, we also use the log of the lagged interpurchase.2 Thespecification of the model enables us to estimate individualcustomer-level coefficients for the influence of the covari-ates on the probability of a customer belonging to a particu-lar subgroup and thus interpurchase times.

Contribution Margin

We model the contribution margin from a customer usingpanel-data regression methodologies. We needed to addressendogeneity issues while using lagged contribution marginas an independent variable in our model. In panel-data mod-els with lagged dependent variables, the endogeneity in for-mulation can be alleviated with a one-period difference inthe dependent variable and a two-period lagged dependentvariable as an independent variable (Baltagi 1998). We usethe growth in contribution margin from period t – 1 to t asthe dependent variable and the contribution margin inperiod t – 2 as an independent variable. The other indepen-dent variables we used are specific to period t – 1 (this alsoaccommodates the issue of endogeneity). The independentvariables in the contribution margin model are thus laggedcontribution margin, lagged total quantity purchased,lagged firm size, industry category, and lagged total market-ing efforts. Thus, the contribution margin model is

(5) ∆CMi,t = β0 + β1CMi,t – 2 + β2Quantityi,t – 1 + β3Sizei,t – 1

+ ΣjβjIndustryj + β4Totmarki,t – 1 + ei,t,

where

∆CMi,t = difference in contribution margin fromperiod t – 1 to period t for customer i,measured in dollars;

Sizei,t – 1 = firm size for customer i in period t – 1,measured as number of employees;

Industry = indicator variable for industry categoryof the customer firm;

Totmarki,t – 1 = total number of contacts made to cus-tomer i in period t – 1;


Quantityi,t – 1 = total quantity of products bought bycustomer i in period t – 1;

ei,t = error term;i = index for the customer; andt = index for time.

Data

We used data from a large multinational computer hardware(servers, workstations, and personal computers) and soft-ware (integration and application) manufacturer for theempirical application of our framework. The company’sdatabase focuses on business customers. The product cate-gories in the database represent different areas of high-technology products. In addition, for these product cate-gories, it is the choice of the buyer and seller to developtheir relationships, and there are significant benefits forboth parties to maintain a long-standing relationship. Thechoice of vendors for the products is normally made aftermuch deliberation by the buyer firm. Even though the firm’sproducts are durable goods, they require constant mainte-nance and frequent upgrades, which provides the variancerequired in modeling customer response. For our analyses,we used two cohorts of customers: Cohorts 1 and 2. Weassigned customers to Cohort 1 (Cohort 2) if their first pur-chase with the manufacturer occurred in the first quarter of1997 (first quarter of 1998). In our samples, we removedcustomers who had missing values for either rich or stan-dardized modes of communication. We also restricted oursample to customers who had made at least five purchases.Overall, we removed 20% of the original cohort of cus-tomers for our analyses, which resulted in an effective sam-ple size of 1316 and 873 observations for customers inCohorts 1 and 2, respectively. The interpurchase time forcustomers in Cohort 1 ranges from 1.5 to 23 months; forCohort 2, it ranges from 1 month to 20 months.

Purchase frequency model. We used each observed pur-chase for a customer as an observation in the purchase fre-quency model. For Cohort 1, we selected customers whomade their first purchase in the first quarter of 1997. Foreach customer, we omitted the first observed purchase inour analysis sample because the first observed purchase isrestricted to be within three months for all customers in thecohort, and theoretically the customer retention phasebegins after the first purchase. The antecedents and covari-ates we used can be classified as cumulative and currenteffects. The cumulative effects antecedents include cross-buying and upgrading, and their values represent the totalnumber of different products (for cross-buying) or upgradesthat the customer has purchased since the first purchaseuntil the current observed purchase.

The current-effects antecedents include bidirectionalcommunication; returns; relationship benefits; frequency ofrich, standardized, and Web-based contacts; and intercon-tact time. The covariates in the purchase frequency model(type of product purchased) can be classified as currenteffects. We calculated the current-effects antecedents andcovariates on the basis of the activities of the customer orthe supplier (in the case of channel communications)between the previous observed purchase (j – 1) and the cur-rent observed purchase (j). To assess the inverted U-shaped

relationships, we used a quadratic conversion (including thesquare covariate term in Equation 3) of the respectiveantecedent. For all customers, we used the interpurchasetimes until the end of 2000 as our calibration sample. Weused the 2001 data as a holdout sample and to comparestrategies. All the antecedents and covariates we used in ouranalyses are lagged variables. Specifically, for observedpurchase j, the cumulative-effects antecedents represent thecustomer’s activity since relationship initiation untilobserved purchase j – 1. Similarly, for observed purchase j,the current effects antecedents and covariates represent thecustomer’s (or supplier’s) activity between observed pur-chases j – 2 and j – 1.

Contribution margin model. To model contribution mar-gin from a customer, we used the annual sales from variouspurchases of each customer. For customers in Cohort 1, weused the annual sales from each customer from 1997 to2000. Given our model structure in Equation 5, there aretwo observations per customer in our analysis sample.Specifically, for each customer, for Observation 1 thedependent variable is the difference in contribution marginbetween 2000 and 1999, and the independent variablesinclude the contribution in 1998, the firm size in 1999, theindustry category of the customer, the total number of con-tacts made to the customer in 1999, and the total quantity ofproducts purchased in 1999. Similarly, for Observation 2,the dependent variable is the difference in contribution mar-gin between 1999 and 1998, and the independent variablesinclude the contribution margin in 1997. As we stated previ-ously, we used the 2001 data as a holdout sample and tocompare strategies. The descriptive statistics for the dataand the correlation matrix of the antecedents are provided inTable 3.

Results from Estimation of CLVPurchase Frequency ModelAs we discussed previously, we used an effective samplesize of 1316 and 873 observations that belong to Cohort 1(first purchase in 1997) and Cohort 2 (first purchase in1998), respectively, for our analyses. We discuss the resultsfrom Cohort 1 in detail in the text. The results from Cohort2 are quite similar to those of Cohort 1 and are providedalong with the results for Cohort 1 in the correspondingtables. We censored our data set in 2000 and used the 2001data as our validation (or holdout) sample in both cohorts.We estimated the purchase frequency model in Equation 3using Markov chain Monte Carlo (MCMC) methods. Theresults from the purchase frequency model for Cohorts 1and 2 are provided in Table 4 (for details on the model esti-mation, model selection, model performance, and supportfor hypotheses, see Appendix A). The coefficients of theantecedents reported in Table 4 are the means from the pos-terior samples of βi, and the signs for the coefficients repre-sent their influence on a customer’s purchase frequency.There are several insights that we derive from Table 4,which we discuss subsequently.

Model fit. The results show that the generalized gammamodel with two subgroups provides a good fit to the data

Customer Lifetim

e Value Framew

ork / 115

Mean

5(4.5)

1.32(1.16)

2.58(3.13)

.84(.62)

.91(.86)

3.88(4.37)

.09(.12)

1.79(1.52)

20.74(22.81)

15.3(16.1)

StandardDeviation

8.4(6.8)

.89(.91)

1.7(1.5)

2.41(3.52)

3.7(2.81)

25.81(24.94)

.29(.25)

5.69(5.84)

47.75(45.81)

13.8(14.2)

PurchaseFrequency

1

.62***

.53***

.68***

.36***

.40***

.41***

.45***

.44***

.51***

Upgrading

1

.39*

.51*

.09

.21

.31**

.15**

.22*

–.08

Cross-Buying

1

.48*

.13

.15

.36**

.29**

.34*

.06

Bidirec-tional

Communi-cation

1

.11*

.17**

.22

.32*

.24**

–.01

Returns

1

.06

–.21*

.05

.07

–.01

Frequencyof Web-Based

Contacts

1

.25

.34*

.32*

–.04

Relation-ship

Benefit–PremiumServiceLevel

1

.07

.19*

–.03

Frequencyof RichModes

1

.30**

–.11

Frequency of Stan-dardizedModes

1

.06

Intercon-tact Time

(Days)

1

TABLE 3Descriptive Statistics and Correlation Matrix

Variable

Purchasefrequency

Upgrading

Cross-buying

Bidirectionalcommuni-cation

Returns

Frequency ofWeb-basedcontacts

Relationshipbenefit–premiumservicelevel

Frequency ofrich modes

Frequency ofstandard-izedmodes

Intercontacttime(days)


116/ Journal of M

arketing,October 2004

TABLE 3Continued

*Significant at α = 10%.**Significant at α = 5%.***Significant at α < 1%.Notes: The frequency of rich, standardized, and Web-based contacts represents the frequency of the respective mode of communication between two consecutive purchases. Values in paren-

theses for the descriptive statistics represent Cohort 2. The correlation matrix for Cohort 2 is similar to that of Cohort 1 and is available on request from the authors. We do not report thecorrelation matrix of the covariates because it does not have any substantive interpretation.

Growth in Lagged Total TotalVariable Mean Standard Deviation Contribution Margin Contribution Margin Marketing Efforts QuantityPurchase

Contribution Margin ModelGrowth in contribution margin 4955 417,270 1.00***

(4827) (381,297)Lagged contribution margin 31,143 323,139 –.78*** 1.00**

(32,825) (318,867)Total marketing efforts 3.49 17.26 .61*** .08** 1.00

(4.5) (16.24) .00Total quantity purchased 1.89 13.40 .71*** .05** .07 1

(2.01) (14.02)


TABLE 4Coefficients for the Generalized Gamma Purchase Frequency Models

Model 2 Model 3Model 1 (Generalized Gamma (Generalized Gamma

(Generalized Gamma with Mixture, Without with Mixture andVariable Without Mixture) Temporal Variation) Temporal Variation)

Component 1α1 3.05 (4.12)** 4.1 (4.2)** 3.27 (3.59)**υ1 26.18 (25.08)** 42.97 (43.82)** 47.05 (48.90)**θ1 .007 (.002)** .01 (.08)** .04 (.02)**γ1 1.2 1.2 (1.3) 1.2 (1.5)Mass point .54 (.55) .54 (.56)

Component 2α2 .58 (1.57)** 1.48 (3.61)**υ2 38.21 (34.28)** 32.07 (49.02)**θ2 .008 (.012)** .005 (.001)**γ2 .9 (.9) .9 (.9)Mass point .46 (.45) .46 (.44)

Coefficientsβ01 .89 (–1.52)** –2.05 (–2.51)*Lagged log interpurchase

time –2.09 (–2.85)**

Antecedents: Customer CharacteristicsUpgrading 5.01 (5.62)**Cross-buying 6.08 (6.92)**Bidirectional communication 1.49 (1.01)**Returns 10.52 (9.98)**(Returns)2 –3.84 (–4.01)*Frequency of Web-based

contacts 3.52 (2.38)**

Antecedents: Supplier-Specific FactorsRelationship benefits 9.78 (7.65)**Frequency of rich modes of

communication 4.50 (5.65)**(Frequency of rich modes of

communication)2 –1.30 (–1.28)**Frequency of standardized

modes of communication 6.53 (7.02)**(Frequency of standardized

modes of communication)2 –.28 (–.53)**Intercontact time 9.64 (8.56)**(Intercontact time)2 –3.21 (–4.05)**

Log-likelihood –3298.91 (–3627.08) –2908.66 (–3297.04) –2237.82 (–2437.91)AIC –6606 (–7262) –5825 (–6602) –4484 (–4884)BIC –6639 (–7295) –5910 (–6687) –4683 (–5083)Relative absolute error .95 (.91) .84 (.80) .51 (.53)

*Posterior sample values between the 2.5th and 97.5th percentile do not contain zero.**Posterior sample values between the .5th percentile and 99.5th percentile do not contain zero.Notes: Values in parentheses represent Cohort 2. The product category variable was not significant in our analysis, and thus we do not include

it here. Relative absolute error is with respect to a moving average model. The significance levels apply to the coefficients of Cohorts 1and 2.

and is better than other models for modeling purchase fre-quency (log-likelihood for Model 3 = –2237.82, Akaikeinformation criterion [AIC] = –4484, and Bayesian infor-mation criterion [BIC] = –4683). We also used a hazardmodel with the finite mixture framework (Kamakura andRussell 1989) to model purchase frequency, and we foundthat our proposed model fits the data better and has betterpredictive capabilities.

3The parameter estimates of the purchase frequency model arebased on mean values from 50 repeats with random starting valuesfor each repetition. We adopted such a procedure to ensure that theparameter estimates are global optimal values and are not affectedby any local maxima.

Distribution parameters.3 The mean expected purchasefrequency for Subgroup 1 is 4.2 purchases in a year, and the


4We also estimated the contribution margin model at themonthly, quarterly, and semiannual levels, but we did not find any

mean expected purchase frequency for Subgroup 2 is 1.01purchases in a year. Given the variation in expected pur-chase frequencies in each subgroup, we term Subgroup 1the “active state” and Subgroup 2 the “inactive state.” Thecomponent masses for Subgroup 1 (ϕ1) and Subgroup 2(ϕ2) are .54 and .46, respectively. This implies that weexpect 54% of the customers to be active in the predictionwindow and 46% of the customers to be inactive in the pre-diction window.

Supplier-specific factors. Our analyses indicate that asupplier’s contact strategy and provision of relationshipbenefits significantly affect a customer’s predicted purchasefrequency. We find that the frequency of contacts affectspurchase frequency nonlinearly. Specifically, we find aninverted U-shaped relationship. This leads us to believe thatthere is an optimal level of marketing communication foreach customer. A firm’s increasing communication beyonda certain threshold may result in diminishing returns interms of customer purchase frequency. This finding alsoprovides the reasoning to determine the optimal level ofresources that needs to be allocated across channels to max-imize CLV in Phase 2.

The coefficients of the marketing contacts reveal a dif-ference in the influence of various channels on customerpurchase frequency. The coefficient of the quadratic termfor rich modes of communication (–1.30 for Cohort 1) ishigher than the coefficient of the quadratic term for stan-dardized modes of communication (–.28 for Cohort 1).Thus, we can infer that the rate of diminishing returns (afterexceeding the threshold) is much higher for the rich modeof communications than for standardized modes. Therefore,although the rich mode of communication is interactive andeffective, firms should use it with great caution.

Customer characteristics. The results indicate thatupgrading and cross-buying positively influence a cus-tomer’s purchase frequency. This is in line with the findingsof Reinartz and Kumar (2003), who also find that breadth ofpurchase positively affects a customer’s duration in a prof-itable relationship. We also find that the higher the bidirec-tional communication between the customer and supplier,the higher is the customer’s purchase frequency. Thus, inaddition to timely communication from the supplier to thecustomer (Morgan and Hunt 1994), communication from acustomer can be a good indicator of a customer’s activity.

With respect to returns from a customer, our analysissuggests that managers need to exercise caution. We findsupport for an inverted U-shaped relationship between pur-chase frequency and returns. This indicates that customerswho return products within a threshold are a good asset tothe firm. The results highlight the importance of firms’ rec-ognizing the customers who establish contact through theonline channel in their CRM strategies.


We estimated the contribution margin model with annualdata from 1997 (t – 4) to 2000 (t) for Cohort 1 and from1998 to 2000 for Cohort 2.4 The revenue in 2001 (t + 1) acts

significant differences in model performance. Thus, to maintainsimplicity, we used the contribution margin model with the annualdata.

as a holdout sample for Cohorts 1 and 2. The coefficients ofthe contribution margin model are provided in Table 5. Themain insight from Table 5 is that the contribution marginmodel provides an adjusted R2 of .68 and thus can explainsignificant variation in contribution margin from customers.We derive several other insights from Table 5, which wediscuss subsequently.

Supplier-specific factors. Total lagged marketing effortscontribute significantly to an explanation of variation incurrent contribution margin, which implies that a supplier’scontact strategy affects both purchase frequency and contri-bution margin.

Customer characteristics. The two-period lagged contri-bution margin provides the highest contribution to an expla-nation of current-period contribution margin. In addition,lagged quantity of goods is significant in explaining varia-tion in contribution margin. Firm size and industry categoryexplain variation in current-period contribution margin.Among the various industry categories, firms in the finan-cial services, technology, consumer packaged goods, andgovernment industry categories provide, on average, ahigher contribution margin than do firms in other industrycategories. However, firms in the education industry pro-vide a lower contribution margin than do firms in otherindustries.

Customer Selection StrategyIn this section, we compare the customer selection capabili-ties of the following: CLV, our proposed metric; previous-period customer revenue (PCR), a simple metric; past cus-tomer value (PCV), which is widely considered a good

TABLE 5Regression Results from the Contribution Margin

Model

ParameterIndependent Variable Estimate

Intercept N.S. (N.S.)Contribution in t – 2 .83*** (.85***)Lagged total quantity purchased .02** (.03**)Size .02** (.02**)Aerospace N.S. (N.S.)Financial services .02* (.02**)Manufacturing N.S. (N.S.)Technology .03** (.02**)Consumer packaged goods .03*** (.03***)Education, K–12 –.03*** (–.02***)Travel N.S. (N.S.)Government .02* (.02*)Lagged total level of marketing effort .04*** (.06***)

*Significant at α = .10.**Significant at α = .05.***Significant at α < .01.Notes: The reported coefficients are standardized estimates; the

values in parentheses represent Cohort 2. N.S. = notsignificant.


5We also compared the metrics with a censoring time at 18months and prediction window of 30 months. The substantiveresults of the study hold even for a 30-month prediction window.The results are available on request from the authors.

predictor of future customer value; and customer lifetimeduration (CLD), a forward-looking metric that is also usedas a proxy for loyalty. (We also compared the customerselection capabilities of CLV with other customer-basedmetrics, such as share of wallet and recency, frequency, andmonetary value. The results were similar to the comparisonwith PCR, PCV, and CLD.) In general, organizations indirect marketing situations rank-order their customers onthe basis of a particular metric and prioritize their resourcesfrom best customers to worst customers on the basis of therank order (Roberts and Berger 1999). Descriptions of thevarious customer-based selection metrics used in our analy-ses are provided in Appendix B.

Performance of the Customer Selection Metrics

To compare the performance of the four metrics, we rank-ordered customers from best to worst according to eachmetric and then compared the sales, costs, and profits fromthe top 5%, 10%, and 15% of customers. We used the datafrom the first 30 months to score and sort the customers oneach metric, as do Reinartz and Kumar (2003). We thencompared the actual sales, variable costs of communication,and profits for the top 5%, 10%, and 15% of customersfrom the censoring period (30 months) until the end of theobservation window (48 months).5 To select customers forcontact, in general organizations choose the top 5% to 15%of their customers, rank-ordered on the basis of a scoringmetric. Selection of more customers to contact may not befeasible because of limited time and resources. Thus, toreflect industry practice, we compared the performance ofour metric among the top 5%, 10%, and 15% of customers.The results are provided in Table 6, and the reported values

are cell medians. We subsequently summarize the resultsfrom our comparison.

Overall, Table 6 shows that the proposed metric betteridentifies profitable customers than do other metrics wecompared in the study, such as PCR, PCV, and CLD. On thebasis of the 18-month prediction window, we expect theaverage net profits of customers selected from the top 5%using the proposed CLV metric to be $143,295 (afteraccounting for cost of goods sold [70%] and variable costsof communication), whereas the average net profits are$70,929, $130,785, and $106,389 for the top 5% of the cus-tomers selected on the basis of PCR, PCV, and CLD,respectively. These findings hold across all the percentagesubgroups. The results provide substantial support forincorporating the responsiveness of each individual cus-tomer across various communication channels and for theusefulness of CLV as a metric for customer scoring and cus-tomer selection. Although the difference in profits from useof PCR, PCV, CLD, and CLV is, on average, approximately$40,000 for a customer in the top 5% sample, the differencein total profit across the top 5% to 15% of the entire cus-tomer base can easily yield more than $1 million.

Resource Allocation StrategyHaving evaluated the usefulness of using CLV for customerselection, we now describe our methodology for designingresource allocation strategies that maximize CLV. Theframework also provides managers a tool for assessingreturn on marketing investments by identifying avenues foroptimal resource allocation across channels of communica-tion for each individual customer (and possibly across cus-tomers), so as to maximize CLV. The marketing literaturehas provided guidelines for optimal resource allocation inacquisition and retention decisions (Blattberg and Deighton1996; Blattberg, Getz, and Thomas 2001), promotionexpenditures (Berger and Bechwati 2001; Berger and Nasr1998), and marketing actions when future brand switching

TABLE 6Comparisons of CRM Metrics for Customer Selection

Percentage of Cohort (Selected from Top) CLV PCR PCV CLD

5%Gross profit ($) 144,883 71,908 131,735 107,719Variable costs ($) 1,588 00,979 000,950 000,790Net profit ($) 143,295 70,929 130,785 106,389



Notes: All metrics are evaluated at 30 months, with an 18-month prediction window. Cohort 2 provides similar results. The reported values arecell medians. Gross profit is residual revenue after removing cost of goods sold. In general, for the firm that provided the database, thecost of goods sold is approximately 70%; thus, gross profit = revenue × .3.


6In Equation 2, the level of contacts in each channel for eachcustomer is varied each year. However, to simplify our optimiza-tion routine, we assumed that the level of contacts is equal acrossthe prediction period. Our assumption can be viewed as taking theaverage level of contacts in the prediction periods.

is considered (Rust, Zeithaml, and Lemon 2004). Theseguidelines represent a significant step toward incorporationof long-term customer profitability effects into firm-levelmanagerial decision making. However, the models provideless insight into decisions about how to manage individualcustomers in a way that accounts for the heterogeneity, andthey do not provide a mechanism for dynamic updating ofprofitability assessment (Libai, Narayandas, and Humby2002).

Our resource allocation algorithm uses Equation 2 asthe objective function, and the purpose of the optimizationis to find the level of contacts across various channels witheach individual customer that would maximize CLV. Equa-tion 2 is a function of predicted purchase frequency (basedon Equation 4), predicted contribution margin (based onEquation 5), and marketing costs. We first estimated theresponsiveness of customers (coefficients, βs) to marketingcontacts from the purchase frequency model and the contri-bution margin model. To design a CLV-based resource allo-cation strategy, we held the coefficients constant and identi-fied the level of covariates (i.e., level of channel contacts)for each customer that would maximize CLV. In summary,in Equation 2, the contacts made to a customer across vari-ous channels are under the supplier’s control and thus canbe used to maximize CLV, depending on the cost of eachmode of communication and the responsiveness of the cus-tomer (in terms of both purchase frequency and contribu-tion margin) to each channel of communication.6 In otherwords, with respect to marketing resources for a firm, thecustomer contact levels across different channels appear inthe revenue and cost sides of Equation 2 and thus avoid thescope of corner solutions.

We used a genetic algorithm to derive the levels of con-tact desired for each individual customer that maximizeCLV. Genetic algorithms (Goldberg 1989) are simulation-based, parallel-search algorithms that have been used ineconometrics (Dorsey and Mayer 1995; Liang and Wong2001) to obtain optimal solutions when the complexity ofthe optimization function tends to be intractable and multi-dimensional. In our study, support for a purchase frequencydistribution with two subgroups led us to believe that theoptimization surface is multimodal. In addition, weintended to allocate resources for each customer on thebasis of individual responsiveness. These issues made ouroptimization problem extremely complex and intractablewith traditional analytical methods. Thus, we resorted to asearch algorithm to find the optimal resource allocation lev-els. In addition, the multimodal nature of the optimizationsurface (given the support for a mixture distribution for pur-chase frequency) motivated us to use a parallel-search tech-nique, which is not susceptible to local minima (common inmultimodal distributions) (Venkatesan, Krishnan, andKumar 2004). Appendix C explains how we used a parallel-search technique for resource allocation purposes.

Aggregate Results

The total net present value of future profits from a resourceallocation strategy that maximizes CLV (with the predictedcontribution margin) is approximately $44 million. We alsocomputed the total net present value of future profits whenthe organization uses its current resource allocation strat-egy. Specifically, for each customer, we maintained theresource allocation levels for the most recent year and cal-culated CLV over a three-year period. We find that, basedon this status quo resource allocation strategy, the total netpresent value of future profits is approximately $24 million.Therefore, we find that a resource allocation strategy thatmaximizes CLV results in an increase in profits by approxi-mately 83%. The total cost of communication (over threeyears), based on the resource allocation strategy that maxi-mizes CLV, is approximately $1 million. The total cost ofcommunication in the organization’s current strategy isapproximately $716,188. We find that the organizationimproves profits by increasing costs of serving customers(rather than cost of communication in the previous year) by48%. The return on marketing communication to the orga-nization, based on its current strategy, is approximately $34million ($24 million/$716,188). With a communicationstrategy that maximizes CLV, the return on marketing com-munication to the organization is approximately $44 million($44 million/$1 million). Thus, it is possible to improveprofits and return on marketing communication by appro-priately identifying customers for target communicationsand by matching the channel of communication with cus-tomer preferences. The aggregate results suggest that giventhe improvement of approximately $20 million among asample of 216 customers, there is a potential for the firm toincrease its revenue by at least $1 billion across its entirecustomer base. However, such benefits may not be realizedimmediately because the firm also needs to incur costs tomove toward a customer-centric view and to train itsemployees to manage customers on the basis of CLV.

Implications, Limitations, andFurther Research

The objective of our study was to analyze the usefulness ofCLV as a metric for customer selection and resource alloca-tion strategy. First, we developed and estimated an individ-ual customer-level objective function, the goal of which isto measure CLV. Second, we demonstrated the superiorityof selecting customers for contact on the basis of CLV com-pared with commonly used metrics such as PCR, PCV, andCLD. Third, we evaluated the benefits of designing market-ing communications that maximize CLV. We now discussthe implications of our study and how managers can usethis knowledge to design efficient marketing programs. Wealso provide an outline for future researchers to build on theframework proposed herein.

Implications

Antecedents of purchase frequency and contributionmargin. The theoretical implications of the purchase fre-quency model are also related to the CUSAMS customer


asset management framework (Bolton, Lemon, and Verhoef2004). In this study, we tested parts of the CUSAMS frame-work and found empirical support for the parts we tested.Specifically, the CUSAMS framework proposes that mar-keting instruments (e.g., direct mailings, reward programs)affect a customer’s price perceptions, satisfaction, and com-mitment. In turn, these affect the length, depth, and breadthof a relationship, which then ultimately influence CLV. Wefind empirical support for marketing instruments’ effects onpurchase frequency (rich and standardized modes of com-munication and relationship benefits) and contribution mar-gin (total marketing efforts), both of which ultimately influ-ence CLV. We also find that breadth of purchases(cross-buying) and depth of buying (upgrading) affect pur-chase frequency, which ultimately influences CLV. In addi-tion, we find support for a nonlinear relationship betweensupplier communications and purchase frequency. This sup-ports Fournier, Dobscha, and Mick’s (1997) expectationsthat too much communication between suppliers and cus-tomers can be disruptive. Thus, our results indicate thatmanagers need to be cautious when designing marketingcommunication strategies across different channels andneed to be wary of contacting customers too many times,especially through rich modes of communication.

We find an inverted U-shaped relationship betweenreturns and purchase frequency. A possible benefit fromcustomers who return products could be the opportunity tounderstand the reasons for dissatisfaction. In addition, cus-tomers who return products within a certain period may doso because they have inherent trust in the supplier andbecause they expect future benefits, such as improvementsin the quality of the product. However, a customer’s return-ing too many times may indicate erosion of trust with thefirm or a lower level of future activity. We also find thatcustomers that establish contact through the online channelof communication exhibit high frequency of purchases andhave high involvement. Therefore, the online channel canprovide an ideal setting for B2B firms to enhance their cus-tomer relationships. We find that in addition to influencingpurchase frequency, marketing communications influencethe expected contribution margin from a customer. Also inthe B2B scenario, industry category and size seem to beimportant factors that influence the magnitude of contribu-tion margin.

Enhancement of marketing productivity. Rust, Zeithaml,and Lemon (2004) propose that firm strategies and tacticalmarketing actions affect the marketing productivity chain.Our analyses of customer selection investigate how firmscan enhance strategies, and our analysis of optimal resourceallocation investigates how firms can improve tactical mar-keting actions.

Customer selection. A CLV metric better identifies cus-tomers that provide higher future profits than do PCR, PCV,and CLD. Our analyses indicate that CLV is preferred toincorporate the dynamics of customer purchase behaviorinto the customer selection process. Managers can substan-tially improve their return on marketing investments byusing a dynamic, customer-level measure of CLV for scor-

ing rather than using the other metrics suggested in the lit-erature and by prioritizing contact programs.

Resource allocation strategy. The results from our studyhighlight the importance of firms’ considering individualcustomers’ responsiveness to marketing communication aswell as the costs involved across various channels of com-munication when making resource allocation decisions. Ouranalyses suggest that there is a potential for substantialimprovement in CLV through appropriate design of market-ing contacts across various channels. When firms designresource allocation rules, they can realize the increase inprofits by incorporating the differences in individual cus-tomer responsiveness to various channels of communicationand the potential value provided by the customer. The pro-posed resource allocation strategy can be a basis for evalu-ating the potential benefits of CRM implementations inorganizations, and it provides accountability for strategiesgeared toward managing customer assets.

To summarize, the major conclusions that we derivefrom our study are the following:

•Marketing communication across various channels affectsCLV nonlinearly;

•CLV performs better than other commonly used customer-based metrics for customer selection such as PCR, PCV, andCLD; and

•Managers can improve profits by designing marketing com-munications that maximize CLV.

Limitations and Further Research

The study has limitations that further studies can address.The results of this study are from a customer database in thehigh-technology industry. Further studies need to investi-gate whether the results are generalizable to other industriesand settings. In addition, further research needs to developmodels that combine forecasts of aggregate competitiveresponses to marketing actions and customer brand switch-ing with individual-level models of direct marketing. Wealso consider only the average levels of optimal communi-cation strategy in a channel. However, firms can furtherimprove the efficiency of communication strategy by appro-priately sequencing their customer contacts across differentchannels. In addition, in our study, we provide a frameworkfor maximizing CLV with marketing communications.However, note that we do not compare our proposedresource allocation strategy (that focuses on maximizingCLV) with a strategy that focuses on allocating resources tocustomers for which the increment in CLV from appropriatedesign of marketing communication is highest. For exam-ple, with an appropriately designed marketing strategy, it ispossible that customers that previously had high CLV con-tinue to have high CLV in the future, irrespective of thelevel of marketing communications, and that somecustomers that have had low CLV transform to high-CLVcustomers. Further research can investigate whether thecustomers selected for high levels of marketing communi-cations are the same when the resource allocation strategyfocuses on maximizing CLV or on maximizing incrementalCLV. Finally, the sum of optimal CLVs for each individualcustomer need not lead to the optimal customer equity in


the case of a budget constraint. We performed our optimiza-tion with a budget constraint, and there were no changes inthe substantive results of the study. In addition, ouroptimization algorithm is flexible enough to allow for inclu-sion of the budget constraint without any substantialadjustments.

The customer- and supplier-specific antecedents used inthe customer response model also can directly affect costsand thus margins. However, because we estimated the cus-tomer response model in a single step, which we thenincluded in a net present value function (Equation 1) thatincludes both costs and margins, we assessed the indirecteffect of the covariates on both costs and margins. Furtherstudies can develop and test hypotheses that directly relateCRM efforts to costs and margins. In addition, it can beexpected that margins change over time. In this case, thevalue that a customer provides to a firm is a function ofboth the expected time frame until the next purchase andthe contribution margin at that particular period. We treatedseveral antecedents used in our framework (e.g., upgrading,cross-buying, bidirectional communication, number ofreturns, number of Web-based contacts) as exogenous vari-ables in our analysis. We used the lagged variables of theseto account for potential endogeneity. Further research caninvestigate more sophisticated techniques that explicitlytreat these variables as endogenous. Finally, a notable issuethat arises from our analyses is whether the recommenda-tions from an optimization framework pan out when imple-mented in the real market. Although our study is a step inthe right direction to assess the accountability of marketingactions, a field experiment that tests the recommendationsof such a framework on a test group against a control groupthat is managed according to existing norms would providea stronger justification for CRM-based efforts.

Appendix AResults from Data Analyses


Model estimation. For Cohorts 1 and 2, the results arebased on 50,000 samples of the MCMC algorithm (fordetails of the algorithm, see Allenby, Leone, and Jen 1999,Appendix). We simulated the posterior distribution usingfive parallel chains with overdispersed starting values. Ineach chain, we used the initial 40,000 iterations as burn-inand used the last 10,000 iterations to obtain posterior statis-tics. We used “slice-sampling” (Neal 2000) to obtain ran-dom samples. We assessed the autocorrelations of the poste-rior samples to perform thinning. The autocorrelationfunctions revealed that every fifth sample is unrelated toevery other fifth sample. Thus, the posterior statistics arebased on 2000 samples (using every fifth sample from10,000 samples). We assessed the convergence of the algo-rithm from the line plots of the posterior sample and byevaluating Gelman and Rubin’s (1992) √ statistic. Valuesof √ that are closer to 1 reveal that all the chains have con-verged to true posterior distribution. In our analyses, wefound that the value of √ ranged from 1.2 to .9 (in previ-

�R

�R

�R

ous research [Cowles and Carlin 1996], these values havebeen found to indicate convergence of the MCMC chains).Our approach to investigating within-chain autocorrela-tions, computing Gelman and Rubin’s statistic, and visuallyinspecting the sampling plots has been recommendedwidely for convergence diagnostics (Cowles and Carlin1996). We implemented the estimation algorithm using theGAUSS software package, and we performed the conver-gence diagnostics using CODA. We also used multiple priorvalues for the estimates and did not find a significant impacton the posterior samples; this is because we used diffuseprior values and because of the large sample size of our dataset.

Model selection. Table 4 shows that a simple general-ized gamma model without the probit link function or time-varying covariates (Model 1) provides a log-likelihood of–3298.91 (we calculated the log-likelihood using Newtonand Raferty’s [1994] log marginal density measure). A gen-eralized gamma model with the probit link function (twosubgroups) but no time-varying covariates (Model 2) pro-vides a log-likelihood of –2908.66. Finally, the log-likelihood for the generalized gamma model with the probitlink function (with two subgroups) and time-varying covari-ates (Model 3) is –2237.82. Increasing the number of sub-groups in Models 2 or 3 did not result in significantlyhigher log-likelihoods. We also computed the AIC and BICfor model selection. For both measures, a higher value indi-cates a better model. We find that Model 3 has the highestvalue for both AIC and BIC (AIC = –4484 for Model 3,–5825 for Model 2, and –6606 for Model 1; BIC = –4683for Model 3, –5910 for Model 2, and –6639 for Model 1).In addition, a latent-class finite mixture model with twosegments provides a likelihood of –2938. Overall, theresults show that the generalized gamma model with twosubgroups provides a good fit to the data.

Distribution parameters. The values of γ in the general-ized gamma model are γ1 = 1.2 and γ2 = .9 for Subgroups 1and 2, respectively. The component masses for Subgroup 1(ϕ1) and Subgroup 2 (ϕ2) are .54 and .46, respectively. Allthe distribution parameters (αk, νk, and θk) have more than99% of their samples different from zero. The meanexpected interpurchase time for Subgroup 1 is 4.2 pur-chases in a year, and the mean expected frequency for Sub-group 2 is 1.01 purchases in a year. Given the variation inexpected frequencies in each subgroup, we term Subgroup1 the “active state” and Subgroup 2 the “inactive state.”

Influence of antecedents and covariates. Our empiricalanalyses support all the proposed effects of the antecedentson purchase frequency.

Out-of-sample forecasting accuracy. We used relativeabsolute error (RAE) to evaluate the forecasting accuracy ofthe generalized gamma model compared with that of anaive moving average model (the moving average model isimplemented as an updated average of every consecutiveinterpurchase time for a customer). We used all but the lastobservation for each customer (calibration sample) to esti-mate the parameters of each model. We then used the meanposterior values to compute the expected time until next


purchase from Equation 3. We then compared the forecasttime until the next purchase with the holdout sample(formed from the last observation for each customer) toassess the mean absolute deviation (MAD). The RAE isgiven by the ratio of the model MAD to the MAD based onthe moving average measure. Based on the RAE measure,the generalized gamma model with time-varying covariates(Model 3) has an RAE of .51, compared with that of a naivemoving average technique. The MAD from Model 3 is 5.21months, compared with 10.24 months for the moving aver-age measure. Model 3 provides the best improvement inforecasting accuracy compared with Model 2 (RAE = .84)and Model 1(RAE = .95). We also assessed the predictivecapability of the purchase frequency model using hit rate. Inother words, we assessed the number of purchases in theholdout sample that the model also correctly predicted as apurchase. We observe that among customers who boughtwithin 12 months, the model currently identifies 89% ofthem, and among customers who did not buy within 12months, the model currently identifies 90% of them.


Estimation and influence of antecedents and covariates.We estimated the contribution margin model on annual datafrom 1997 (t – 4) to 2000 (t) for Cohort 1 and from 1998 to2000 for Cohort 2. The revenue in 2001 (t + 1) is a holdoutsample for Cohorts 1 and 2. The coefficients of the contri-bution margin model are provided in Table 5. The contribu-tion margin model provides an adjusted R2 of .68. Overall,the results of the analyses support all the hypothesizedrelationships.

Out-of-sample forecasting accuracy. As with typicaltime-series models, we advanced the independent variablesby one period to forecast the dependent variable in periodt + 1. Specifically, when the contribution margin model isused to predict period t + 3, the contribution margin inperiod t + 1 is an independent variable and is obtained fromthe prediction in period t + 1. The contribution marginmodel predicts the growth in revenue from period t to t + 1.We obtained the magnitude of revenue in period t + 1 byadding the predicted value from the contribution marginmodel to the base revenue in period t. We evaluated the per-formance of the contribution margin model in the holdoutsample on the basis of our estimates from the calibrationsample. Table A1 provides the descriptive statistics of theobserved contribution margin and the predicted contribution

margin in the holdout sample (period t + 1). In the holdoutsample, the mean predicted contribution margin in periodt + 1 is approximately $67,729, and the mean observed con-tribution margin is approximately $64,396.

Appendix BDescription of Customer Selection

MetricsCLVWe entered predictions from the purchase frequency (Equa-tion 3) and contribution margin (Equation 4) models intoEquation 2 to obtain the net present value of future profits(period t + 1) from each customer. The purchase frequencymodel predicts the expected time in months until next pur-chase for each customer. We assigned a 30% margin afteraccounting for cost of goods sold (the managers who pro-vided the database informed us that 30% was a nominalmargin for most of their products), and we computed thevariable costs using costs of communication. The mean unitcost of standardized modes of communication is approxi-mately $3, and the mean unit cost of rich modes of commu-nication is $60. We computed the unit cost of communica-tion for each customer as the ratio of the total contacts for agiven channel in a given year to the total cost of contact fora given mode in a given year. Finally, we used an annualdiscount rate of 15% for each customer, which is based onthe lending rate that is appropriate for the time of the study.

PCR and PCV

We define PCR as the revenue provided by the customer inthe most recent observed purchase. We define PCV as thecumulative profits obtained from a customer until the cur-rent period. The cumulative profits are calculated annuallyfrom a customer’s initiation until the current period. Weprojected the profit in each year to current terms using adiscount factor. The PCV calculation is as follows:

where CMi,t is the contribution margin for customer i inperiod t; MCi,t denotes marketing costs for customer i inperiod t; t is an index for time period (t = 0 for the period ofcustomer initiation; for example, t = 0 for 1997 for Cohort 1customers, and t = 0 for 1998 for Cohort 2 customers); T is

( ) ( )B PCV CM MC ri it itT t

t

1 1= −( ) × + −

= 00

T

∑ ,

TABLE A1Comparison of Descriptive Statistics Between Observed Contribution Margin and Predicted Contribution

Margin in the Holdout Sample

Mean Standard Deviation Minimum Maximum

Observed 50,199 23,850 –171 1,010,881(49,229) (24,836) (–181) (1,420,981)

Predicted 57,729 24,538 –178 1,897,257(74,283) (22,598) (–159) (1,938,458)

Notes: All reported values are in dollars and are rounded to the nearest integer. Values in parentheses represent Cohort 2.


the current period; and r is the discount rate, which we set at15%.

CLD

In our analysis, we evaluated the probability that a customeris alive or dead in the planning window using the P(Alive)measure that Schmittlein and Peterson (1987) and Reinartzand Kumar (2002) recommend. The P(Alive) measure usesthe previous purchase pattern to predict the probability thata customer is still alive at each period in the prediction win-dow. Higher values of P(Alive) indicate longer lifetimeduration.

Appendix CGenetic Algorithms to DevelopResource Allocation Strategies

Researchers in marketing have only recently begun to rec-ognize the potential benefits of using genetic algorithms(Balakrishnan and Jacob 1996; Midley, Marks, and Cooper1997; Naik, Mantrala, and Sawyer 1998; Venkatesan,Krishnan, and Kumar 2004) in deriving optimal strategiesfor complex marketing problems. The genetic algorithmproceeds by searching for the optimal level of contact foreach customer that maximizes CLV. The sum of the optimalCLVs from each individual customer provides the optimalcustomer equity of the analysis sample.7 Specifically, we

varied the contact levels for each customer and then calcu-lated the sum of CLV of all customers in the sample. Ourobjective was to calculate the maximum value for this sumof the CLVs. In this case, our optimization algorithm maxi-mized the objective function by varying 432 parameters inCohort 1 (216 customers and 2 parameters for each cus-tomer [levels of rich and standardized modes]). Followingresearch in customer equity (Rust, Zeithaml, and Lemon2004), we set the time frame for our optimization frame-work as three years. Our database provides information onthe approximate unit cost of communication through richmodes and standardized modes to each customer. On aver-age, the unit marketing cost through standardized modes(average of direct mail and telephone sales) is $3, and theunit marketing cost through rich modes (salesperson con-tacts) is $60. Thus, the cost of communication through richmodes is approximately 20 times the cost of communica-tion through standardized modes. Such a cost index is com-monly encountered. We set the parameters in the geneticalgorithm as follows: population size = 200, probability ofcrossover = .8, probability of mutation = .25, and conver-gence criteria = difference in solution in the last 10,000 iter-ations should be less than .01%. We ran the genetic algo-rithm at least 50 times and used the mean of the resourcelevels corresponding to the maximum CLV from each runas the resource reallocation rule for each customer.

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