V. Kumar & Werner Reinartz Creating Enduring Customer Valueproudtobu.com/wp-content/.../02/jm.15.0414-creating...Creating Enduring Customer Value One of the most important tasks in

V. Kumar & Werner Reinartz

Creating Enduring Customer ValueOne of the most important tasks in marketing is to create and communicate value to customers to drive theirsatisfaction, loyalty, and profitability. In this study, the authors assume that customer value is a dual concept. First, inorder to be successful, firms (and the marketing function) have to create perceived value for customers. Toward thatend,marketers have tomeasure customer perceived value and have to provide customer perceptions of value throughmarketing-mix elements. Second, customers in return give value through multiple forms of engagement (customerlifetime value, in the widest sense) for the organization. Therefore, marketers need to measure andmanage this valueof the customer(s) to the firm and have to incorporate this aspect into real-time marketing decisions. The authorsintegrate and synthesize existing findings, show the best practices of implementation, and highlight future researchavenues.

Keywords: customer value, perceived value, customer lifetime value, CLV models, customer engagement

A1991 BusinessWeek article describes the notion of“perceived value” as “the new marketing mania” and“the way to sell in the ’90s” (Power 1991). Morris

Holbrook (1994, p. 22) wrote, “despite this obvious importanceof customer value to the study ofmarketing in general and buyerbehavior in particular, consumer researchers have thus fardevoted surprisingly little attention to central questionsconcerning the nature of value.” Moving forward to 2014, theMarketing Science Institute (MSI) specifies in its biannualResearch Priorities (among its top priorities), “One of the mostimportant tasks inmarketing is to create and communicate valueto customers to drive their satisfaction, loyalty, and profitability.Any insights in this area have significant implications for thelong-term financial health of an organization. It truly is at theheart of what marketing is all about.” Out of the 30 Dow Jones(United States) and 30 DAX (Germany) companies, 50% of thecompanies’ vision ormission statements explicitlymention thenotion of value creation for customers and/or stakeholders.For example, American Express’s mission statement includesthe sentence, “We provide outstanding products and un-surpassed service that, together, deliver premium value to our

customers.” Taken together, these high-level observationsserve to underline the importance that the customer valueaspect has had in the past and is continuing to have.

Clearly, business is about creating value. The purpose of asustainable business is, first, to create value for customers1and, second, to extract some of that customer value in theform of profit, thereby creating value for the firm. Thus, thekey underlying premise of this article is that customer valueis a dual concept. First, in order to be successful, firms (andthe marketing function) have to create perceived value forcustomers. In that sense, value is defined as overall assess-ment of the utility of an offering2 according to perceptions ofwhat is received and what is given (Zeithaml 1988). Second,customers provide value (customer lifetime value [CLV], inthe widest sense) for the organization. For the firms/decisionmakers who allocate resources to markets, customers, andproducts, the challenge is to dynamically align resourcesspent on customers and products in order to simultaneouslygenerate value both to and from customers.

We believe that aligning the customer-perceived valuewith customer-generated value vis-a-vis resource allocation is aresearch challenge that needs careful and comprehensive atten-tion. In this article, we focus on this alignment by examining thespecifics of this resource allocation challenge. More specifically,our goal is to answer the following concrete research questions:

1.What is value to the customer? What do we know?2. How should (customer perceptions of) product and servicevalue bemeasured? How can key drivers of customer value beidentified and calibrated?

3. How should the value from the customer be measured andmanaged?

4.What are the drivers of customer value? How can they beincorporated in real-time marketing decisions?

V. Kumar (VK) is Regents Professor, Richard and Susan Lenny Dis-tinguished Chair, Professor in Marketing, Executive Director of the Centerfor Excellence in Brand and Customer Management, and Director of thePhD Program in Marketing, J. Mack Robinson College of Business, GeorgiaState University; V. Kumar is also honored as the Chang Jiang Scholar,Huazhong University of Science and Technology, China; TIAS Fellow,Texas A&M University, College Station, TX; and ISB Senior Fellow, IndianSchool of Business. (e-mail: [email protected]). Werner Reinartz is Professor ofMarketing and Director of the Center of Research in Retailing, Universityof Cologne (e-mail: [email protected]). The authors thank theMarketing Science Institute and Kevin Keller for providing them the opportunityto contribute to this topic. They thank the guest editors of this special issue, aswell as Sarang Sunder, Maren Becker, Manual Berkmann, Mark Elsner,Vanessa Junc, Monika Kauferle, Annette Ptok, Julian Wichmann, andthree anonymous reviewers for their valuable suggestions on an earlierversion of this manuscript. They thank Bharath Rajan for his valuableassistance in this study and Renu for copy editing an earlier version ofthis manuscript.

1We use the term “customer” to denote a consumer as well as abusiness customer.

2We use the notion of “offering” to indicate physical products,services, brands, or a combination thereof.

© 2016, American Marketing Association Journal of Marketing: AMA/MSI Special IssueISSN: 0022-2429 (print) Vol. 80 (November 2016), 36–68

1547-7185 (electronic) DOI: 10.1509/jm.15.041436

mailto:[email protected]

mailto:[email protected]

http://dx.doi.org/10.1509/jm.15.0414

What Is Value to Customers?What Do We Know?

Regardless of whether a physical good or a service is demandedby individuals for final consumption (direct demand) or as aninput factor used in providing other goods and services (deriveddemand), the fundamental economic principle of utility max-imization remains the same. Whereas the utility maximizationtheory is used to study productsmeant forfinal consumption, theprofit maximization principle is used to study the inputs used inthe production of other products. The traditional model assumesthat the customer has perfect information about product char-acteristics and associated costs, as well as stable preferences,and thus is able to perfectly construct his/her final objectivefunction—an assumption that has of course been relaxed inmultiple ways (e.g., Hauser and Shugan 1983).

Given the central role of the notion of value to the customerin the marketing literature, it is not surprising that the subjecthas been dealt with repeatedly (Anderson 1998; Anderson, Jainand Chintagunta 1993; Monroe 1971; Wilson 1995; Zeithaml1988). In various definitions of “value,” there is a reasonablevariety in the spirit of describing the trade-off between “give”elements and “get” elements (Anderson, Kumar and Narus2007; Sawyer and Dickson 1984). For the purpose of thisarticle, we define perceived value as customers’ net valuation ofthe perceived benefits accrued from an offering that is basedon the costs they are willing to give up for the needs they areseeking to satisfy.

Perceived customer value of an offering is the aggregationof benefits that the customer is seeking, expecting, or expe-riencing and the undesired consequences (Gutman 1982) thatcome with them. Benefits and undesired consequences are theresults of buying and consuming the offering, and these mayaccrue directly or indirectly and be immediate or delayed. Thecentral aspect of this conceptualization is that customers chooseactions that, ceteris paribus, maximize the desired consequencesand minimize concurrent undesired consequences. Benefits (andundesired consequences) are generated through offering attri-butes. Benefits differ from attributes in that people receive ben-efits, whereas offerings have attributes (Gutman 1982). Thus,attributes are features or properties of an offering. De facto,customers will never perceive all objective attributes (clearly) butwill form a composite perception that recognizes the respectiveattribute salience.

Although much of the literature conceptualizes price asone of many potential attributes, it is of course necessary tosubsume all cost components separately. Besides price, thesetypically include a vast array of different transaction costs, aswell as learning cost and risk. Interestingly, much of themarketing literature typically accounts for price as the onlycost component of an offering, thereby neglecting the manydifferent immediate and delayed types of costs that customersmight incur.

Perceived attributes then are aggregated by customersthrough a categorization process into more abstract benefits inorder to reduce information overload and to facilitate furtherprocessing. Themeans-end chainmodel (Gutman 1982;Howard1977) and the Gray benefit chain (Young and Feigin 1975) areprobably the most widely used conceptualizations behind this

effect chain from concrete offering attributes mapping ontoabstract benefits generated. The “house of quality” concept(Hauser and Clausing 1988) is another very successful attempt,originating from the operations and quality control domain, tomap objective attributes with customer perceived benefits. In-terestingly, this mapping process has been considered virtuallyonly for the offering’s attributes, but an expansive considerationof monetary and nonmonetary cost aspects (price, transactioncosts, risks, privacy) seems absent from the literature.

Once customers construct an aggregate assessment of anoffering’s perceived value, this value acquires meaning in twoways. First, as a necessary condition for customers to perceivean offering to be of positive value, perceived benefits have tooutweigh undesired consequences. Second, once this is the case(and assuming the customer has the required willingness andmeans to transact), the assessment of value of a single offeringamong a set of different offers (e.g., from a consideration set)requires a comparison standard. This comparison standardmay be concurrent competitive offerings, expectations, or pastexperience (similar to Golder, Mitra, and Moorman’s [2012]nomenclature, a “value stock” perspective). Once the customerdetermines an offer to be of highest value, behavioral (choice,loyalty, CLV) and attitudinal (satisfaction, loyalty) outcomesensue.

To summarize, we can observe that customer perceivedvalue is a central mediating construct, separate from quality,perceived benefits, and satisfaction. Moreover, despite therelevance of this construct in practice, the literature has for themost part not been looking at the customer perceived valueconstruct per se and has either omitted it from conceptualmodels (e.g., Golder, Mitra, and Moorman 2012) or hasused proxies, such as customer satisfaction, instead. Finally,the aspects of perceived cost and undesired consequencesin customer value models are arguably underspecified andunderstudied.

How Should (Customer Perceptionsof) Product and Service Value BeMeasured? How Can Key Driversof Customer Value Be Identified

and Calibrated?Measuring customer perceived value is the natural startingpoint before one can even consider giving recommendationswith respect to the value-oriented management.

Customer Value Measurement

The key tasks to be completed are (1) measure overall per-ceived value, (2) measure the associated underlying attributesand benefits, and (3) determine the relative weights that linkattributes/benefits to overall perceived value. Customers areseeking offerings that yield the highest expected value orutility. Because utility cannot be measured or observeddirectly, market researchers, psychologists, and economistshave devised ways to proxy these utilities. There is a longtradition of utility/preference measurement in marketing thatcommonly uses compositional and decompositional methodsto model consumer preferences (see Table 1).

Creating Enduring Customer Value / 37

TABLE1

Ove

rview

ofValueMea

suremen

tApproac

hes

withExe

mplary

Studies

Mea

suremen

tApproac

h

Exe

mplary

Applic

ations/

Sources

Focu

s/Starting

Point

Outcome

Role/Treatmen

tofAttributes

Role/Treatmen

tofBen

efits

Role/Treatmen

tofUndes

ired

Conse

quen

ces

DataInput

Other

Compositional

Multia

ttribute

metho

dGale(199

4)Presp

ecified

quality

andprice

attributes

Ove

rallva

lue

asse

ssmen

tPresp

ecified

Typ

ically

not

cons

idered

Presp

ecified

,typica

llyprice

only

Attributes

are

gene

rated

throug

hfocu

sgrou

psor

man

agerial

intuition

;attribute

ratin

gsan

dim

portan

ceweigh

tsare

collected

directly

bycu

stom

ersu

rvey

Exp

licit

cons

ideration

ofco

mpe

tition

poss

ible

(com

paris

onwith

compe

titive

offerin

g)

Con

sumer

perceive

dva

lue

multiitem

scale

Swee

neyan

dSou

tar(200

1)(PREVALsc

ale)

Presp

ecified

high

er-le

vel

dimen

sion

s(emotiona

lvalue

,so

cial

value,

func

tiona

lvalue

)

Ove

rallva

lue

scoreas

wella

sun

derly

ing

dimen

sion

s

Individu

alpres

pecified

itemsto

mea

sure

high

er-le

velvalue

dimen

sion

s

Sev

eral

pres

pecified

itemsallude

tobe

nefits

ofthe

offerin

g

Individu

alpres

pecified

itemsto

mea

sure

valueformon

ey/

price(broad

erco

stco

ncep

t)

Cus

tomer

survey

Doe

sno

tallow

forhe

teroge

neity

Relations

hip

valueinde

xUlaga

andEgg

ert

(200

6)Presp

ecified

high

er-le

vel

bene

fit

dimen

sion

s

Value

ashigh

er-

orde

rco

nstruc

t,includ

ingweigh

tsthat

determ

ine

theinflue

nceof

bene

fitan

dco

stdimen

sion

s

Individu

alpres

pecified

itemsto

mea

sure

bene

fit

dimen

sion

s

Individu

alpres

pecified

itemsto

mea

sure

vario

usco

stdimen

sion

s

Cus

tomer

survey

Exp

licit

cons

ideration

ofco

mpe

tition

poss

ible

(com

paris

onwith

seco

nd-bes

tsu

pplier)

Dec

ompositional

Con

joint

analys

isTou

bia,

Hau

ser,

andSim

ester

(200

4)(Polyh

edral

adap

tivech

oice

-ba

sedco

njoint

analys

is)

Stated

preferen

cesfor

hypo

thetical

prod

uct

confi

guratio

ns(attributes

and

price)

Part-worth

estim

ates

for

each

attribute

leve

l

Presp

ecified

bymarke

trese

arch

ers

(pric

eis

trea

ted

asan

attribute)

Typ

ically

not

cons

idered

,bu

tco

uldtech

nica

llybe

inco

rporated

Typ

ically

price

only

(tec

hnically,

othe

rco

stdimen

sion

sco

uld

beintegrated

)

Cus

tomer

survey

(relev

ant

attributes

have

tobe

iden

tified

qualita

tively

throug

hprior

stud

ies)

Allowsfor

custom

erhe

teroge

neity

segm

ents

Agg

rega

tepe

rceive

dva

lue

Sinha

and

DeS

arbo

(199

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rallpe

rceive

dva

lue

Offe

ringloca

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onpe

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dva

luedimen

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Impliedwith

out

beingsp

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neity/

segm

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38 / Journal of Marketing: AMA/MSI Special Issue, November 2016

TABLE1

Continued

Mea

suremen

tApproac

h

Exe

mplary

Applic

ations/

Sources

Focu

s/Starting

Point

Outcome

Role/Treatmen

tofAttributes

Role/Treatmen

tofBen

efits

Role/Treatmen

tofUndes

ired

Conse

quen

ces

DataInput

Other

Auc

tion(W

TP)a

Wan

g,Ven

katesh

,an

dCha

tterje

e(200

7)(in

centive-

compa

tible

elicita

tionof

the

rang

ein

aco

nsum

er’s

rese

rvation

prices

;ICERANGE)

Con

sumers

indica

temax

imum

WTP

andstateatwhich

pricepo

intthey

areindiffe

rent

(ince

ntive-

aligne

d)

Con

sumer’s

rese

rvationprice

rang

e

Presp

ecified

bygive

nprod

uct(no

varia

tion)

Pric

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lyAuc

tion/

expe

rimen

t(w

eb-bas

edor

person

alinterview)

Ince

ntive-aligne

d

Pay

wha

tyo

uwan

t(W

TP)a

Kim

,Natter,an

dSpa

nn(200

9)Con

sumers

directlystatetheir

WTP(sellerha

sto

acce

ptan

yam

ount)

Insigh

tinto

individu

alleve

lof

valuepe

rcep

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varia

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sumer

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aftertran

saction

a The

semea

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imateva

lueby

mea

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nsum

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essto

pay(W

TP).Strictly

spea

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,the

yon

lyyieldan

overallv

alue

asse

ssmen

tand

dono

tprovide

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ssibility

ofdise

ntan

glingtheva

lueattach

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unde

rlyingch

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teris

tics(excep

tin

theca

seof

ave

ryelab

oratestud

yde

sign

).


Compositional methods begin with a set of explicitlychosen attributes/benefits and use them as the basis fordetermining overall value evaluations. In the compositionalapproach, expected utility is a function of the productattributes or benefits and corresponding costs multiplied bytheir respective importance weights. The key premise here isthat relevant attributes and their respective relevant levels areknown to the decision maker, and customers follow largely arational decision-making approach. This approach has foundreasonable entrance into the managerial domain (e.g., Gale1994; Sweeney and Soutar 2001; Ulaga and Eggert 2006).

In contrast, decompositional techniques attempt to inferunderlying utilities from observed choice, that is, revealedpreferences. They start with measures of preference forofferings or attribute bundles and use them to infer thevalue attached to underlying characteristics. The goal is toapproximate offering value from the customer’s willingnessto pay. A vast literature covers the use of this approach and,therefore, the interested reader should consult those sources(Rao 2014). The key approaches include survey approaches,auctions, conjoint analysis, and field experiments, with someof these methods having been widely used. Moreover, in thepast 30 years, there has been a strong recognition that nor-mative decision models are usually violated (Huber, Payne,and Puto 1982), and an entire stream of behavioral decisiontheory research has unfolded (Simonson 2015) that detailsthe wide range of systematic deviations that can occur whencustomers attempt to assess value.

From the firm’s perspective, the focus of the measurementapproaches pertains mostly to tangible product attributes. Theonly intangible aspects that has seemingly found entry intothese measurement models are the brand name dimension(e.g., Sinha and DeSarbo 1998) and aesthetic design aspects(Kumar 2015). Other intangible aspects seem absent. For ex-ample, in the context of digitization, what seems interesting ishow an individual’s perception of what constitutes value isinfluenced by others (i.e., network effects).

Treatment of Undesired Consequences (Costs)

The vast majority of attention in customer value measurementhas been focused on the “get” side of the offering, namely,documenting the range and depth of attributes and benefits thatare associated with the offering. The situation is starkly differentwith respect to the undesired consequences and cost aspects.Although perceived sacrifice is multidimensional, it is de factooperationalized as a unidimensional aspect in existing research(Teas and Agarwal 2000)—namely, on the price dimension. Forexample, inmuch of the conjoint analyses and logit models, priceis introduced as another (linear) attribute variable (Hauser andUrban 1986, p. 448).

However, besides the mere cost (transaction price) of theoffering to the customer, there is a large set of associatedtransaction costs, learning cost, and maintenance and life cyclecost, which are for the most part overlooked in existing models.In many situations, the monetary value of the sum of thesenonprice costs easily outsizes the transaction price, typical inmany business-to-business (B2B) settings (Anderson, Narus,and Narayandas 2009). Even more importantly, purchasing andconsumption models that are based on usage (i.e., customer

solutions)will becomemuchmore prevalent in the future (Ulagaand Eggert 2006; Ulaga and Reinartz 2011; Vargo and Lusch2004). Therefore, the conceptualization and operationalizationof a broader set of cost attributes is required in future customervalue models.

Furthermore, in the context of digitization, a new cost-related aspect has been emerging. For many online services(e.g., Google Maps, Facebook), customers are not expected topay in monetary terms. The core benefit is free of monetarycharge from the end user’s perspective. The monetizationcomes mainly from advertising revenues, with ads targetedat narrow segments or personal individual profiles. Here, wehave a new situation wherein the monetary component withinthe undesired consequences has entirely vanished. Customersnow have to understand the value of the personal informationthat they will give up in this exchange. Thus, customers pay interms of less privacy instead of monetary outlays. In fact, somecustomers value privacy of personal information privacy somuch that they would be willing to pay to preserve privacy—this then creates a market for privacy (Rust, Kannan, and Peng2002). Moreover, Koukova, Kannan, and Kirmani (2012) hintat new option value that digitization may provide. They showhow different formats of media (e.g., offline newspaper sub-scription vs. online formats) may provide complementary andincremental value to customers, depending on usage situation,as opposed to mere substitution. In other words, digitizationwill provide interesting and important new facets to the valuedebate. Table 2 presents a summary of related findingsregarding the measurement of value and value drivers.

How Should Value from CustomersBe Measured and Managed?

Until now we have reviewed (1) how firms can create value tothe customer, (2) the ways to measure customer perceptions ofvalue, and (3) the treatment of undesired consequences whenmanaging customer value. However, creating and communi-cating perceived value to customers is better servedwhen firmsalign perceived value with the resources they spend on cus-tomers, to ensure that the right amount of resources go towardmanaging perceived value. To identify the right amount ofresources, it is important to ascertain the value that customersprovide to the firm. Figure 1 illustrates the approach firms canadopt to derive value from customers.

As seen from Figure 1, the first step in deriving value is torealize the need for a forward-looking metric rather than abackward-looking one. Traditionally, firms have used metricssuch as recency–frequency–monetary value (RFM), past cus-tomer value (PCV), share of wallet (SOW), and tenure/durationto measure the value of customers. The guidance from thesemetrics has driven decisions pertaining to the allocation ofmarketing resources. However, many of the traditional metricsfocus on a backward-looking approach that only takes intoconsideration past activity of a customer, which leads to out-dated information being used for customer selection andresource allocation. In contrast, the CLV metric is a forward-looking metric that takes into account the variable nature ofcustomer behavior and enables firms to treat individual cus-tomers differentially and distinctly from each other depending


on their contributions to the company. Amid the backward-versus-forward-looking distinction, it is also important tonote the role of big data. Using big data that contains pastinformation (e.g., sales, revenue, marketing spending), it ispossible to accurately predict future behavior of customersand, thus, compute their value to the firm. The use of big data,however, does come with the challenges relating to the qualityof data, data visualization, computing capabilities, and ability

to obtain meaningful results. Nevertheless, the point to benoted here is the importance of being able to look into thefuture in determining the future value of customers, as op-posed to relying on insights from past value contributions.

Realizing the need for a forward-looking metric is themost critical step for a firm. Once this has been achieved, thesubsequent steps only strengthen the firm’s position in cultivatinga customer-centric organization. The following sections describe

TABLE 2Summary of Findings Pertaining to the Measurement of Value and Value Drivers

Scholarly Contributions Thus Far Tasks for Future Research

Perceived attributesand benefits

• Deriving underlying choice-relevant attributesusing decompositional approaches

• Understanding of longitudinal, situational, andcross-sectional heterogeneity.

• Understand whether and how attributessimultaneously pay into a given benefit

• Broaden conceptualization and measurementof intangible attributes and benefits

Perceived costs andundesired consequences

• Inclusion of price as key (cost-related) variable • Define conceptualization and operationalizationof a broader set of cost-related attributes

• Include new cost-related aspects due todigitization

Perceived value • Deriving customer perceived value usingcompositional approaches

• Measurement of customer perceived valuethrough willingness to pay

• Accommodating for incentive alignment andheterogeneity across time, customers, andproduct categories

• Recognition of systematic violations of valuemaximization principle

• Examine value of personal information (contextof digitization)

• Examine influence of value perception by socialnetwork

FIGURE 1Approach to Deriving Value from the Customer

Move from backward-looking metrics to a forward-looking metric

Introduce CLV

Understand the drivers of

CLV

Decide on a modeling approach (e.g., individual vs. aggregate)

Collect data for measuring CLV

Choose a type of model (e.g., deterministic vs. probabilistic)

Develop and implement strategies to maximize CLV

Extend the customer value concept to an engagement framework

Rev

ise

and

Ree

stim

ate

Estimate the model (e.g., Bayesian, econometric)

Realize enhanced firm value and shareholder value


the other steps identified in Figure 1 that put in place a cyclicalprocess involving data collection, deciding on the modelingapproach and the type ofmodel, estimating themodel, identifyingthe drivers of CLV, developing and implementing CLV-basedstrategies, extending CLV into a customer engagement frame-work, and reaping the benefits through higher firm value.

Firms have realized that just as customers derive valuefrom the products/services being offered, firms, too, derivevalue from the customer base. Kumar and Reinartz (2012)define this value from the customer as “the economic value ofthe customer relationship to the firm—expressed on the basisof contribution margin or net profit” (p. 4). When firmsidentify the value provided by customers, they will be able to(1) better manage their costs, (2) post increases in revenuesand profits, (3) realize better return on investment (ROI), (4)acquire and retain profitable customers, and (5) realign mar-keting resources to maximize customer value.

More so than customers’ past and current contributions tothe firm, a crucial factor is their contribution in future periods.It is this future component that is of immense interest toacademicians and practitioners. The concept of future valuecontribution has been conceptualized in the form of CLV. TheCLV metric has been conceptualized as the present value offuture profits generated from a customer over his or her lifetimewith the firm (Venkatesan and Kumar 2004). Estimating CLVhelps the firm to treat each customer differently according tohis or her contribution, rather than treating all customers in asimilar fashion. Furthermore, the sum total of lifetime value ofall customers of the firm represents the customer equity (CE)of the firm. In other words, CLV is a disaggregate measure ofcustomer profitability, and CE is an aggregate measure. Aftercomputing the CLV of its customers, a firm can developstrategies such as optimally allocating its limited resources,identifying the next products that customers are likely topurchase, and balancing acquisition and retention efforts,among others, to achieve maximum return.

Modeling Approaches to CLV

InmeasuringCLV,marketing literature presents a rich variety ofmeasurement approaches. However, the main objective acrossall approaches is clear—identify, maintain, and nurture profit-able customers. The approaches can be categorized into twobroad categories: the aggregate approach and the individualapproach. In the aggregate approach, the average lifetime valueof a customer is derived from the lifetime value of a cohort orsegment, or even a firm. This level of measurement helps firmsin evaluating the overall effectiveness of the marketing plan butnot in customizing strategies for customers. In the individualapproach, the CLV of one customer over his or her entirelifetime with the firm is computed. This level of measurementhelps firms personalize strategies according to customer needsand the future profitability potential of the customer. WebAppendix A lists the aggregate and individual approaches tomeasuring CLV. Table 3 provides an overview of the selectliterature from the extensive field of CLV.

Types of CLV Models

Over the past two decades, research onCLVhas covered awiderange of business conditions through the modeling approaches.

This coverage has expanded the scope and application ofCLV-based models for a multitude of industries and markets.This section discusses some of the popularmodeling approachesfrom the extant literature.

Estimating models independently. Most models, barringfew (Niraj, Gupta, and Narasimhan 2001; Venkatesan, Kumar,and Bohling 2007), focus on predicting future revenue andapply a constant gross margin and retention cost. Venkatesanand Kumar (2004) use a generalized gamma distribution tomodel interpurchase time and employ panel-data regressionmethodologies to model the contribution margin. They con-sider various supplier-specific factors (channel communication)and customer characteristics (involvement, switching costs, andprevious behavior) as the antecedents of purchase frequency andcontribution margin. Web Appendix B provides details on theindependent models. Given the factors identified, purchase fre-quency and contribution margin are modeled separately, andthe equation of CLV is specified as

CLVit = �Ti

t=1

GCit

ð1 + rÞt=fi- �

n

l=1

�mMCi,m,l

ð1 + rÞl ,(1)

where GCi,t is the gross contribution from customer i in pur-chase occasion t;MCi,m,l is the marketing cost for customer i incommunication channel m in time period l; fi, or frequency, is12/expinti (where expinti is the expected interpurchase time forcustomer i); r is the discount rate; n is the number of years toforecast; and Ti is number of purchases made by customer i.

Estimating models simultaneously. Endogeneity is a sta-tistical issue in the CLVmodel that relates directly to causation.When purchase frequency, marketing cost, and gross con-tribution are predicted independently, it becomes unclearwhether it is current MC that leads to future GC or current GCthat leads to future MC. In addition, the issue of heterogeneityrelates to the customer profiles and has been found to influencepurchase quantity and timing significantly (Allenby, Leone,and Jen 1999). When different customers respond differentlyto marketing messages, the contribution margin model mustreflect this variation by allowing the regression weights to bedifferent for each customer. Simultaneously modeling pur-chase frequency, MC, and GC solves both these issues andprovides more accurate results.

Venkatesan, Kumar, and Bohling (2007) use Bayesian de-cision theory to address the uncertainty in customer responseto marketing actions in a B2B setting. In this regard, theymodel the three parameters simultaneously. In the business-to-consumer (B2C) setting, studies have developed a joint modelfor purchase timing and quantity. For instance, Boatwright,Borle, and Kadane (2003) use the Conway–Maxwell–Poissondistribution to jointly model the purchase timing and purchasequantity for an online grocery retailer. Similarly, Chintagunta(1993) models the incidence of purchase at each time intervaland the purchase quantity for grocery purchases by customerswho make regular and frequent visits to a grocery store. Bysimultaneously modeling the parameters, it is possible toobtain an early-warning indication of abrupt changes ininterpurchase times and purchase quantities for a customer.When individual customers/customer segments that exhibit


TABLE3

SummaryofSelec

tCLVLiterature

Rep

rese

ntative

Studies

StudyFocu

sStudy

Typ

eModel

Typ

eStudySetting

Insights

Rus

t,Kum

ar,a

ndVen

katesa

n(201

1)

Use

Mon

teCarlo

simulationalgo

rithm

topred

ict

custom

erpu

rcha

seprop

ensity,profi

t,an

dfirm

marke

tingac

tions

.

Empiric

alMon

teCarlo

simulation

High-tech

nology

services

The

prop

osed

mod

elprov

ides

large

improv

emen

tsin

pred

ictio

nov

erthesimpler

mod

elssh

ownin

literature.

Pfeife

r(201

1)Use

compa

ny-rep

ortedda

tato

estim

atethetotal

CLV

ofafirm

’scu

rren

tcu

stom

ers.

Empiric

alAutoreg

ress

ive

(first-orde

r)Netflix

The

stud

yoffers

guidan

ceab

outh

owto

estim

ate

retentionrate

andreve

nuepe

rrene

wal

whe

nthe

repo

rtingpe

riodsp

ansmultip

lerene

wal

perio

ds.

Fad

eran

dHardie

(201

0)Dev

elop

amod

elof

relatio

nshipdu

ratio

nthat

can

beus

edto

pred

ictretentionratesbe

yond

the

obse

rved

retentionrates.

Empiric

alShifte

dbe

tage

ometric

Any

indu

stry

Failingto

acco

untforco

hort-le

velreten

tion-rate

dyna

micslead

sto

bias

edes

timates

ofthe

residu

alva

lueof

acu

stom

er.

Kum

ar(201

0)Study

amultim

edia

multicha

nnel

commun

ication

fram

eworkba

sedon

CLV

.Con

ceptua

l—

Retailing

Max

imizingfirm

’sprofi

tabilityis

critica

lfor

unde

rstand

ingdriversof

CLV

andCRV.

Kum

aran

dSha

h(200

9)Link

custom

ereq

uity

(determined

bytheCLV

metric

)to

marke

tca

pitalization.

Empiric

alMultin

omiallog

itHigh-tech

nology

services

,retailing

Marke

tingstrategies

targeted

atmax

imizingCLV

canincrea

sefirm

valuean

d,thus

,ultim

ately,

stoc

kprice.

Kum

aret

al.

(200

8)Sho

wtheim

plem

entatio

nof

CLV

atIBM

andits

effectson

profi

tabilityan

dreso

urce

alloca

tion.

Empiric

alSee

mingly

unrelated

regres

sion

High-tech

nology

services

CLV

-bas

edrealloca

tionof

marke

tingreso

urce

syielde

da$2

0millionreve

nueincrea

sewith

out

anyad

ditio

nalres

ourceinve

stmen

t.

Ben

oitan

dVan

denPoe

l(200

9)

Ana

lyze

CLV

bymea

nsof

quan

tileregres

sion

and

compa

retheeffectsof

theco

varia

tes.

Empiric

alQua

ntile

regres

sion

Finan

cial

services

The

stud

yprov

ides

insigh

tsinto

theeffectsof

the

cova

riateson

theco

ndition

alCLV

distrib

utionthat

may

bemisse

dby

trad

ition

alleas

tsq

uares.

Don

kers,

Verho

ef,an

dDeJo

ng(200

7)

Predict

CLV

inmultiservice

indu

stry.

Empiric

alProbit,

parametric

duratio

n,no

nparam

etric

duratio

n

Insu

ranc

ese

rvices

Sim

plemod

elspe

rform

well.Foc

usingon

custom

erretentionis

noten

ough

;cros

s-bu

ying

also

need

sto

beac

coun

tedfor.

Fad

er,Hardie,

andJe

rath

(200

7)

Dem

onstrate

theus

eof

Pareto/NBD

mod

elsof

repe

atbu

ying

forco

mpu

tingCLV

.Empiric

alPareto/NBD

Cas

ean

alys

isDifferen

ttype

sof

econ

omic

efficien

cies

for

acqu

isition

strategies

andcu

stom

erse

gmen

tatio

nca

nbe

mad

ethroug

hthis

mod

el.

Fad

er,Hardie,

andLe

e(200

5)Propo

seane

wmod

elthat

links

RFM

metric

with

CLV

usingisov

alue

curves

.Empiric

alPareto/NBD

Onlinemus

icweb

site

Isov

alue

curves

arehigh

lyno

nlinea

rbe

caus

ecu

stom

erswith

low

rece

ncyva

lues

buthigh

freq

uenc

ypres

entlow

erCLV

than

custom

erswho

have

lower

freq

uenc

y.

Rus

t,Le

mon

,an

dZeitham

l(200

4)

Prese

ntafram

eworkthat

enab

lesco

mpe

ting

marke

tingstrategy

optio

nsto

betrad

edoffo

nthe

basisof

projec

tedfina

ncialreturns

.

Empiric

alLo

git

Airline

The

fram

eworken

ablesa“w

hat-if”

evalua

tionof

marke

tingROI,give

napa

rticular

shiftincu

stom

erpe

rcep

tions

.


TABLE3

Continued

Rep

rese

ntative

Studies

StudyFocu

sStudy

Typ

eModel

Typ

eStudySetting

Insights

Ven

katesa

nan

dKum

ar(200

4)Dev

elop

ady

namic

fram

eworkto

maintain/

improv

ecu

stom

errelatio

nships

throug

hop

timal

alloca

tionof

marke

tingreso

urce

san

dmax

imize

CLV

simultane

ously.

Empiric

alGen

eralized

gamma

distrib

ution,

gene

ticalgo

rithm

s

High-tech

nology

services

Marke

tingco

ntac

tsov

erva

rious

chan

nels

influe

nceCLV

nonlinea

rly.CLV

-bas

edcu

stom

erse

lectionprov

ides

high

erprofi

tsin

thefuture

compa

redwith

othe

rmetric

s.

Reina

rtzan

dKum

ar(200

3)Dev

elop

afram

eworkto

compu

teCLV

and

iden

tifyfactorsthat

explaintheva

riatio

nin

profi

tablelifetim

edu

ratio

n.

Empiric

alNBD/Pareto

Catalog

retailer,

high

-tec

hnolog

yse

rvices

The

stud

yde

mon

stratesthesu

perio

rityof

CLV

fram

eworkby

compa

ringitwith

othe

rtrad

ition

alfram

eworks

.

Liba

i,Naray

anda

s,an

dHum

by(200

2)

Introd

ucease

gmen

t-ba

sedap

proa

chfor

custom

erprob

ability

analys

is.

Con

ceptua

l—

Retailer

The

prop

osed

approa

chretainstheac

tiona

ble

inform

ationas

sociated

with

individu

al-le

vel

analys

iswhile

also

maintaining

thesimplicity

ofthemoreag

greg

ate-leve

lmod

els.

Reina

rtzan

dKum

ar(200

0)Propo

seametho

dof

compu

tingCLV

totest

the

prop

osition

sbe

twee

ncu

stom

erloya

ltyan

dprofi

tability.

Empiric

alNBD/Pareto

High-tech

nology

services

Alth

ough

loya

lcon

sumersde

man

dprem

ium

servicean

dbe

lieve

they

dese

rvelower

prices

,they

areno

talway

sprofi

table,

nordo

they

contrib

utemoreto

profi

tsin

thelong

run.

Pfeife

ran

dCarraway

(200

0)

Introd

ucetheus

eof

Marko

vch

ainmod

elsfor

mod

elingcu

stom

errelatio

nships

andCLV

.Empiric

alMarko

vch

ain

Catalog

compa

nyMarko

vch

ainmod

elsca

nha

ndle

complicated

custom

errelatio

nshipsituations

.The

yca

nalso

hand

leso

meco

mplicated

situations

that

alge

braicso

lutio

nsca

nnot.

Berge

ran

dNas

r(199

8)Prese

ntmathe

maticalmod

elsford

eterminationof

CLV

.Con

ceptua

l—

Illus

trative

exam

ples

The

stud

yintrod

uces

thege

neralclass

esof

CLV

mod

els.


such a warning are identified, firms can implement appropriatecustomer relationship management (CRM) initiatives and cus-tomized marketing actions to each individual or to each cus-tomer segment.

Another setting in which simultaneousmodeling has beenoffered is in winning back lost customers. Losing customersfor good can be a significant setback for a firm’s customermanagement efforts. While studies have demonstrated themerits of preventing customers from defecting (Bolton 1998,Bolton and Lemon 1999, Lemon, White, and Winer 2002),it is also important to look into ways of winning back lostcustomers. Kumar, Bhagwat, and Zhang (2015) studywhether firms should chase a lost customer by investigatingthe impact of the reason for defection on reacquisition andsecond-lifetime duration and profitability. They achieve thisby jointly estimating customer reacquisition, second-lifetimeduration, and second-lifetime profitability per month. WebAppendix B provides details on these simultaneous models.

Brand-switching approach. Using information about thefocal brand and the competing brands, Rust, Lemon, andZeithaml (2004) model acquisition and retention of customers inthe context of brand switching. This approach requires collectinginformation on the brand purchased in the previous purchaseoccasion, the probability of purchasing different brands, andindividual-specific CE driver ratings from the customers.Through a Markov switching matrix, the individual customers’probability of switching from one brand to another based onindividual-level utilities is modeled. The probability thus cal-culated ismultiplied by the contribution per purchase to arrive atthe customer’s expected contribution to each brand for eachfuture purchase. The summation of expected contribution over afixed time period, after adjustments are made for the time valueof money, produces the CLV for the customer. Web AppendixB provides more details on the CLV model specification.

Monte Carlo simulation algorithm. Another approachadopted by researchers to investigate the value created bycustomers is the simulation method. While managerial heu-ristics and empirical models have uncovered important in-sights on customer value, studies have also investigated theuse of simulation models. The reason for exploring simulationmethods stems from relatively unsuccessful attempts at pre-dicting future customer profitability, with very simple modelsoften performing just as well as more sophisticated ones. Forinstance, Campbell and Frei (2004) find that it is easier topredict future profitability for some customers than for others,even for customers within the same profit tier. Similarly,Malthouse and Blattberg (2005) find that their best modelsmisclassify most customers who are predicted to have highprofitability. Furthermore, Donkers, Verhoef, and De Jong(2007) show that the simplest model they test performs thebest and provides better predictions than other models. Thesestudies make the case for alternative approaches, such as asimulation method.

Rust, Kumar, andVenkatesan (2011) present aMonteCarlosimulation method that can accurately predict future customerprofitability and performs better than existing methods. Theyaccomplish this in an “always-a-share” setting wherein there isno dormancy in a customer–firm relationship and customers

never completely terminate their relationship with a firm. Thus,in this approach, firmsmeasure future profitability of a customerby predicting his/her purchase pattern over a prediction period,but they do not predict when a customer will terminate therelationship with the firm. In this model, the profitability of acustomer is measured in terms of total profits and net presentvalue of profit. The predictions regarding customer behaviorinclude (1) the propensity for customer i to purchase in eachfuture time period t and (2) the profit provided by customer igiven purchase in future time period t. While the predictions ofcustomer behavior capture the revenue aspect, the marketingactions by the firm (e.g., number of sales calls, direct mail sentto a customer) capture the cost aspects; these aspects need to bepredicted for accurate CLV measurement. Toward this end, thestudy proposes a singlemodel framework that predicts customerpurchase incidence (Purit), customer gross profit (Pit) condi-tional on purchase, and firm marketing contacts (Xit) and alsomodels the potential correlations among these factors. WebAppendix B provides more details on the joint probabilitymodel.

Customer migration model. Dwyer (1997) presents a cus-tomermigrationmodel for CLVanalysis that is applicable for the“always-a-share” typology. Accordingly, customer behavior canbe predicted on the basis of historical probabilities of purchase,depending on recency and the current recency state in which thecustomer is located. Further generalizations include segmenta-tion variables such as the RFM metric and other demographicvariables. In several situations, RFM is used as a segmentationtool by classifying segments as “low RFM” and “high RFM.”Other segmentation approaches include SOW and customer lifecycle.WebAppendixB provides the approach to expressingCE.

Deterministic model. Deterministic models preciselymodel outcomes as determined by parameter values, rela-tionship states, and initial conditions. These models focus oninputs and outputs and leave little room for variations. Typ-ically, these models are used to study firm actions such ascustomer acquisition, customer retention, customer profit-ability, cross-buying behavior, and product return behavior(Reinartz and Kumar 2000, 2002, 2003). The basic form of thedeterministic approach used tomodel CLV is described by Jainand Singh (2002) (see Web Appendix B for the basic model).While this model does not account for acquisition costs, othermodels assume a constant gross contribution margin andmarketing costs (Berger and Nasr 1998). Several variationsof this basic model have been proposed (e.g., Dwyer 1997).Blattberg, Getz, and Thomas (2001) provide a comprehensiveway to calculate customer equity by accounting for the numberof prospects, acquisition spending, and consumer segments.

Probabilistic model. In a probabilistic model, the observedbehavior is viewed as the realization of an underlying stochasticprocess governed by latent (unobserved) behavioral charac-teristics, which, in turn, vary across individuals. The focus ofthis type ofmodel is on describing (and predicting) the observedbehavior instead of trying to explain differences in observedbehavior as a function of covariates (as is the case with anyregression model). In other words, this type of model assumesthat consumers’ behavior varies across the population accord-ing to some probability distribution (Gupta et al. 2006). Web


Appendix B provides details on the basic probabilistic model.Literature provides many models to estimate the CLV of acustomer using the internal data of a company. One such ap-proach defines CLV as a function of the time interval betweene-mail contacts sent to a customer (Dreze and Bonfrer 2009).Using the data from the entertainment industry, this modelestimates the relationship between the time interval and CLV.The objective is to find the optimal time interval for permission-based e-mails to a customer base. Apart from internal data,survey data can also be used to estimate the CE of a firm (seeRust, Lemon, and Zeithaml 2004; Rust, Zeithaml, and Lemon2001).

Structural model. The issue of multiple discreteness(wherein consumers may purchase more than one brand inone purchase occasion) in studies of CLV has received at-tention in the literature (e.g., Kim, Allenby, and Rossi 2002;Manchanda, Ansari, and Gupta 1999). However, the resultshave been suboptimal because conclusions regarding thequantity decision could not be made effectively. Sunder,Kumar, and Zhao (2016) adopt a direct utility approach tostructurally model multiple discreteness while accounting forvariety-seeking behavior in the demand model in assessingCLV of consumers in the consumer packaged goods (CPG)setting. Furthermore, the study is conducted on a longitudinaltransaction database and allows the budget to vary deter-ministically with time. In addition, this is the first study tounify choice, timing, and quantity decisions in a singleequation, thereby providing a direct approach for assessingCLV. Web Appendix B details this approach.

As a summary, Table 4 provides a comparison of theapproaches discussed in the previous sections, according totheir merits and shortcomings.

What Are the Drivers of CustomerValue? How Can Customer Value BeIncorporated in Making Real-Time

Marketing Decisions?Until now, we have discussed various models proposed forunderstanding and measuring value from customers. Thechoice of model is ultimately decided by the availability ofdata (volume and variety), time, and technical resources, aswell as the intended use of the CLV measure. Furthermore,information on competitive actions brings marketplacerealities into the decision-making process through game-theoretic approaches. The final choice of model notwith-standing, the results have to lead to managerial decisionmaking that is also conducive to real-time modifications. Thisinvolves understanding the drivers of customer value. Indiscussing the real-time applications, we adopt a relativeapproach wherein we focus more on time intervals (e.g.,frequency of buying, pattern of buying cycles, the need torevise CLV scores periodically) rather than immediate actions.In this regard, we survey and present research that hasexamined this aspect of real-time applications and providerelated insights for implementation. This, we believe, willpresent a new perspective on the real-time nature of decisionmaking.

The drivers of CLV determine the nature of the rela-tionship between the firm and the customer, and they helpestimate the level of profitability and the CLV of eachcustomer. They have been classified into two types: exchangecharacteristics and customer heterogeneity (Reinartz andKumar 2003). Exchange characteristics encompass the setof variables that define and describe relationship activitiesin the broadest sense. Customer heterogeneity refers to thedemographic and psychographic indicators that help a firmin segmenting customers and managing customer–firm rela-tionships. As expected, the nature of a business (whether B2Bor B2C) determines the exchange characteristics and customerheterogeneity factors. Figure 2 lists the classification of thesedrivers in B2B and B2C settings.

Similarly, Palmatier et al. (2006) provide a synthesis of theseveral empirical studies that have investigated the driversthat lead to stronger customer–firm relationships, as observedthrough WOM, customer loyalty, sales-related perfor-mance, customer likelihood to repurchase, and customer–firm cooperation. These studies clearly identify the driversthat have the greatest impact on customer–seller relation-ships. However, as more investigations on the nature ofcustomer–firm relationships are uncovered, we can expect thelist of drivers to change.

Strategies for Maximizing CLV

Once the computation of CLV is completed, firms proceed tomaximize this metric in order to reap its full benefits. TheCLV metric assists marketers to increase future profitabilityof not just current customers but also prospects. Furthermore,the CLV metric is not just about the dollar value of futurecustomer profitability. It extends beyond that and aids instrategy development on one or more of the following: cus-tomer acquisition, customer retention, balancing customeracquisition and retention, customer churn, and customer win-back.3 While the importance of these five tasks in ensuringprofitability is duly noted, this does not mean that “max-imizing” each individual metric is the correct recipe forsuccess. Firms can look into maximizing CLV from anoptimization perspective wherein the elasticities of each ofthese factors can be studied. Such an endeavor might be apromising avenue for future research. This section highlightsthe importance of understanding these five tasks in devel-oping the CRM playbook of an organization.

Customer acquisition. The expansive literature on cus-tomer acquisition has probed several important questions, suchas the following:

• How likely is it that prospects will respond to our acquisitionpromotion?

• How many new customers can we acquire in this campaign?• How many orders will each of our newly acquired customers

place?• How do the marketing variables, such as shipping fee, WOM

referral, and promotion depth, influence prospects’ responsebehavior?

3For more details, see the “Wheel-of-Fortune” strategies in Kumar(2008).


TABLE4

ComparisonofSelec

tCLVModels

CLVModel

Typ

eMerits

Shortco

mings

Typ

ical

DataReq

uirem

ents

Exe

mplarStudySetting

Agg

rega

teap

proa

ch•Aidsin

evalua

tingov

erall

effectiven

essof

marke

tingac

tions

•Not

able

tocu

stom

izemarke

ting

strategies

•Pub

liclyav

ailablefirm

-leve

ldatathat

includ

esinform

ationon

margin,

disc

ount

rate,an

dretentionrate

•Bothbrick-an

d-mortarfirm

san

don

linefirm

sac

ross

allind

ustriesthat

arepu

blicly

trad

ed

Inde

pend

ent

estim

ation

•Eas

yto

use

•Aidsincu

stom

er-le

veland

firm

-leve

lstrategy

deve

lopm

ent

•Req

uiresen

d-us

ertran

sactionda

ta•Creates

endo

gene

ityan

dhe

teroge

neity

issu

es•Doe

sno

tinclud

eco

mpe

tition

•Cus

tomer-le

veldataon

tran

sactions

andcu

stom

er-firm

interactions

from

firm

’sinternal

reco

rds

•Hightech

nology

Sim

ultane

ous

estim

ation

•Acc

ountsforen

doge

neity

and

heteroge

neity

•Moreac

curate

resu

ltsthan

inde

pend

entes

timation

•Mod

elde

velopm

entan

des

timation

ismoreco

mplex

•Cus

tomer-le

veld

atafrom

firm

’sinternal

reco

rdsthat

includ

esinform

ationon

purcha

sequ

antity,

prod

uctup

grad

es,cros

s-bu

ying

,marke

tingco

mmun

ication,

prod

uct

returns,

andfreq

uenc

yof

contac

ts

•Telec

ommun

ications

•Hightech

nology

•Groce

rystores

Brand

-sw

itching

approa

ch

•Can

beus

edwhe

nthefirm

has

cros

s-se

ctiona

land

long

itudina

lda

taba

se•Acc

ountsforalltyp

esof

marke

ting

expe

nditu

res

•Can

acco

mmod

ateco

mpe

tition

•Sam

plese

lectionca

nplay

anim

portan

trolein

theac

curacy

ofthe

metric

•Ofte

nrelieshe

avily

onsu

rvey

-bas

edda

ta,thus

lead

ingto

anincrea

sein

samplingco

stan

dsu

rvey

bias

es

•Surve

yda

tafrom

asa

mpleof

custom

ersthat

includ

esda

taon

purcha

sefreq

uenc

y,co

ntrib

ution

margin,

andbran

dspu

rcha

sed

rece

ntly

•Airlines

•Ren

talc

ars

•Electronicstores

•Groce

rystores

Mon

teCarlo

simulation

algo

rithm

•Betterp

redictivepo

wer

over

simpler

compe

tingmod

els

•Betterun

derstand

ingof

custom

erprofi

tabilityan

dfirm

value

•Can

notbe

used

inalost-for-goo

dse

tting

•Hea

vyrelianc

eon

long

purcha

sehistories

•Individu

al-le

veld

atafrom

firm

’sinternal

reco

rdsthat

includ

esinform

ationon

purcha

seprop

ensity,

marke

tingco

ntac

ts,a

ndgros

sprofi

t

•Hightech

nology

(can

also

beus

edforac

tual

andsimulated

data)

Cus

tomer

migratio

nmod

el

•Con

side

rsprob

abilistic

nature

ofcu

stom

erpu

rcha

ses

•Can

beus

edon

lyinlim

itedbu

sine

ssse

tting

s•Datafrom

firm

’sinternalreco

rdsthat

includ

espu

rcha

seprop

ensitie

san

drece

ncyof

purcha

se

•Catalog

mailing

•Dire

ctmarke

ting


TABLE4

Continued

CLVModel

Typ

eMerits

Shortco

mings

Typ

ical

DataReq

uirem

ents

Exe

mplarStudySetting

Deterministic

mod

el•Highe

rpred

ictiveac

curacy

•Aidsin

firm

-leve

lstrateg

yde

velopm

ent

•Req

uireshu

geam

ountsof

individu

alcu

stom

erda

ta•Doe

sno

tco

nsider

therelatio

nship

betwee

nmod

elpa

rameters

•Des

criptive,

butn

otpres

criptive,

and

thereforeless

helpfulinman

agerial

decision

mak

ing

•Doe

sno

tac

coun

tforco

mpe

tition

•Seg

men

t-leve

ldataon

marke

ting

costs,

acqu

isition

rate,retention

rate,an

dco

ntrib

utionmarginfrom

firm

’sinternal

reco

rds

•Finan

cial

services

•Hightech

nology

•Insu

ranc

e•App

arel

•Catalog

retailing

Proba

bilistic

mod

el•Can

beus

edwhe

nthefirm

does

not

have

along

itudina

ldatab

ase

•Iden

tifica

tionof

subd

riversaids

inbe

tterreso

urce

alloca

tion

•Ass

umes

purcha

sevo

lumean

dinterpurch

asetim

eto

beex

ogen

ous

•Calls

forfreq

uent

upda

tingof

the

mod

el•Hea

vyrelianc

eon

data

andless

errelianc

eon

man

agerialins

ight

•Individu

al-le

veld

atafrom

firm

’sinternal

reco

rds,

such

asrece

ncyof

purcha

ses,

freq

uenc

yof

purcha

ses,

andva

lueof

tran

sactions

•Mag

azines

/catalog

s•Entertainmen

t•Internet

•Hightech

nology

•Airlines

Struc

tural

mod

el•Mod

elba

sedon

theo

retical

unde

rpinning

sof

cons

umer

beha

vior

•Can

acco

untforva

rious

salient

aspe

ctsof

cons

umer

beha

vior

(e.g.,

multip

ledisc

retene

ss,bu

dgeting)

that

cann

otbe

addres

sedby

othe

rmetho

ds•Aidsin

accu

rate

out-of-sam

ple

pred

ictio

nan

dman

agerialp

olicy

simulations

•Mod

elde

velopm

entan

des

timation

isve

ryco

mplex

•Relieshe

avily

onac

ross

andwith

inva

riatio

nin

custom

erpu

rcha

ses

•Sca

nner

pane

ldataon

bran

dspu

rcha

sedin

aninstan

ce,im

puted

priceinform

ation,

hous

ehold-leve

lmarke

tsh

areof

compe

tingbran

ds,

andbu

dgetaryco

nstraints

•Con

sumer

pack

aged

good

s


• How long will the newly acquired customers stay with ourcompanies?

• How much profit or value will this acquisition campaignbring to our companies?

To understand customer acquisition, it is important to payattention to the business setting: noncontractual or contractual.In a noncontractual setting, customers can and do split theirspending across several firms. As a result, observing when acustomer ceases to be a customer becomes difficult for thefirm.However, situations wherein customers develop strong rela-tionships with firms do exist (e.g., strong preference towarda particular brand of coffee). In a contractual setting, firmsenjoy a relatively continuous future cash flow for a period oftime, and they know when customers terminate the relation-ship. Even in such settings, it is possible for customers to defectwithout notifying the firm (e.g., failure to renew a magazinesubscription). Studies have developed different models tostudy these two business settings regarding factors such as theexpected duration or time of the relationship with customers,the likelihood of a customer continuing the relationship, andindicators of defection at the end of a service period, amongothers. Table 5 lists a representative set of studies that haveconsidered these issues and accounted for many of theproblems that might occur in the model-building process.

A fundamental research interest in understanding cus-tomer acquisition is in identifying the probability of a cus-tomer being acquired. Several studies have explored thisquestion and have uncovered valuable insights (Lix et al.1995; Reinartz, Thomas, and Kumar 2005). For instance,Von Wangenheim and Bayon (2007) find that customersatisfaction influenced the number of WOM referrals, whichhad an impact on customer acquisition, and that the recep-tion of a WOM referral had an increased marginal effect onthe likelihood of a prospect to purchase. This is achievedby studying (1) whether prospects’ purchase likelihood is afunction ofWOM referrals, and (2) whether the characteristics

of the WOM referral source influence the probability that re-ceived WOM induces a purchase behavior. In a B2B setting,studies have investigated the role of referrals in customeracquisition. For instance, Hada, Grewal, and Lilien (2010)recognize the types of referrals (customer–to–potential cus-tomer referrals, horizontal referrals, and supplier-initiatedreferrals) and have developed the concept of referral equity tocapture the net effect of all referrals for a supplier firm inthe market. These authors further propose a framework formanaging supplier-initiated referrals that incorporates thesupplier and the supplier’s management of the communi-cation between the referrer and the potential customer. Godes(2012) explores the conditions and subsequent impact offirms launching referral programs. The study demonstratesthat launching such programs increases the willingness to payof the early adopters in the high-technology B2B market. Inaddition, Kumar, George, and Leone (2013) develop and testan approach to compute business reference value (BRV),identify the behavioral drivers of BRV, and offer strategies totarget and manage the most promising customers on the basisof their CLV and BRV scores.

In addition to customer acquisition, the number of newlyacquired customers and their initial order quantity are alsoimportant. While Lewis (2006b) identifies that shipping feesinfluence the customer acquisition process and the initialorder size, Villanueva, Yoo, and Hanssens (2008) show thatmarket channels also influence the customer acquisitionprocess and the value that newly acquired customers willbring to the company. The latter study develops two func-tions: (1) the value-generating function, which links newlyacquired customers’ contributions to the firm’s equitygrowth, and (2) the acquisition response function, whichexpresses the interactions between marketing spending andthe number of acquisition. Using a three-variable vectorautoregression modeling technique for data from an onlineretailer, the study finds that marketing-induced customers add

FIGURE 2Drivers of CLV in B2B and B2C Settings

• Past customer spending level• Cross-buying behavior• Purchase frequency• Recency of purchase• Past purchase activity• Marketing contacts by the firm

• Past customer spending level• Cross-buying behavior• Focused buying behavior• Average interpurchase time• Participation in loyalty programs• Customer returns• Customer-initiated contacts• Frequency of marketing contacts• Type of marketing contacts• Multichannel shopping• Consumer deal usage intensity• Coupon usage intensity

Includes variables such as age, gender, spatial income, and

physical location of the customers

Includes variables such as industry, annual revenue, and

location of the business

B2B Firm B2C Firm

Exchange

Characteristics

Customer

Heterogeneity


TABLE5

SummaryofSelec

tCustomer

Acq

uisitionStudies

Rep

rese

ntative

Studies

StudyFocu

sStudy

Typ

eModel

Typ

eStudySetting

Insights

Had

a,Grewal,an

dLilien(201

4)Propo

seafram

eworkthat

inco

rporates

the

supp

lieran

dtheirman

agem

entof

commun

icationbe

twee

nthereferrer

andthe

potentialc

ustomer.

Empiric

alTria

dic

commun

ication

Bus

ines

san

alytic

solutio

nsInflue

nceof

thesu

pplier-se

lected

referral

onpo

tentialc

ustomersde

pend

son

supp

lier

unce

rtainty,

andpe

rceive

dbias

sign

ifica

ntly

redu

cespo

tentialc

ustomers’

supp

lier

evalua

tion.

Kum

ar,Geo

rge,

and

Leon

e(201

3)Mea

sure,un

derstand

,an

dman

ageBRV.

Empiric

alBinarych

oice

mod

elTelec

ommun

ications

,fina

ncials

ervice

sThe

stud

yde

fine

stheco

ncep

tof

BRV,

determ

ines

thedriversof

BRV,a

ndillum

inates

therole

ofmea

suresin

drivingBRV.

God

es(201

2)Und

erstan

dwhe

nan

dwhy

abu

sine

sssh

ould

anno

unce

areferral

prog

ram.

Empiric

alGam

etheo

ryHigh-tech

nology

B2B

Referen

ceprog

ram

canse

rveas

apa

rtial

subs

tituteforan

exclus

ive-us

eco

ntract.

Villan

ueva

,Yoo

,an

dHan

ssen

s(200

8)Propo

sean

dtest

anem

piric

almod

elthat

captures

long

-term

effectsof

custom

erac

quisition

onCEgrow

th.

Empiric

alVec

tor

autoregres

sion

mod

elus

ing

threeva

riables

Web

hostingco

mpa

nyMarke

ting-indu

cedcu

stom

ersad

dmoresh

ort-

term

value,

butWOM

custom

ersad

dne

arly

twiceas

muc

hlong

-term

valueto

thefirm

.

Von

Wan

genh

eim

andBay

on(200

7)Exa

minethelinks

betwee

ncu

stom

ersa

tisfaction,

WOM,an

dcu

stom

erac

quisition

.Empiric

alLo

gistic

regres

sion

Ene

rgymarke

tThe

satisfaction–

WOM

linkis

nonlinea

ran

dis

mod

erated

byse

veralc

ustomer

invo

lvem

ent

dimen

sion

s.

Lewis

(200

6a)

Exa

minetherelatio

nshipbe

twee

nac

quisition

disc

ount

depthan

dtheva

lueof

custom

eras

sets.

Empiric

alSurviva

lan

alys

isNew

spap

eran

don

line

groc

erAcq

uisitio

ndisc

ount

depthisne

gativelyrelated

torepe

at-buy

ingratesan

dcu

stom

eras

set

value.

Lewis

(200

6b)

Study

how

shipping

feesc

hedu

lesaffect

custom

erac

quisition

,cu

stom

erretention,

and

orde

rsize

.

Empiric

alSys

tem

oflinea

rregres

sion

Onlinegroc

erHighe

rsh

ipping

fees

areas

sociated

with

redu

cedorde

ringrates,

andpe

nalties

forlarge

rorde

rslead

toredu

cedorde

rsize

.

Tho

mas

,Reina

rtz,

andKum

ar(200

4)Determinewhe

ther

max

imizingcu

stom

erac

quisition

andcu

stom

erretentionse

parately

max

imizes

profi

ts.

Empiric

alStand

ardrig

ht-

cens

ored

Tob

itB2B

high

-tec

hnolog

yman

ufac

turer,

pharmac

euticals,

and

catalogretailer

Dec

reas

ingmarke

tingsp

ending

fortheB2B

firm

andca

talogretailer,an

dincrea

sing

spen

ding

tocu

stom

ersof

theph

armac

eutical

firm

,wou

ldincrea

setotalcus

tomer

pro fi

tability.

Han

sotia

andWan

g(199

7)Determinewhich

pros

pectssh

ould

beco

ntac

ted,

andpres

entmod

elsto

estim

ate

resp

onse

andcu

stom

erva

lueat

theindividu

alleve

l.

Empiric

alProbitan

dTob

itmod

els

Motor

club

mem

bership

The

authorsreco

mmen

dthat

firm

sad

opta

long

-term

view

bylook

ingbe

yond

resp

onse

totheac

tual

beha

vior

ofcu

stom

erson

cethey

are

acqu

ired.

Lixet

al.(199

5)Link

purcha

se-in

tentioninform

ationfrom

survey

data.

Empiric

alLine

arregres

sion

and

log-linea

r

Retailer

The

stud

yprov

ides

ametho

dology

toac

hiev

eefficien

cyin

othe

rdirect

maile

fforts.


more short-term value, but WOM customers add nearly twiceas much long-term value to the firm. The study also dem-onstrates the long-term impact of different resource alloca-tions for acquisition marketing using dynamic simulations.

Of course, making marketing decisions using the responseprobability, initial order quantity, and duration is not enough.Companies should select prospects to acquire according totheir lifetime contribution, which can be termed lifetime value,CLV, or CE. For instance, Reinartz, Thomas, and Kumar (2005)estimate customer profitability with a standard right-censoredTobit model using a set of exogenous variables and including thepredicted duration and response probability. The authors findthat decreasing marketing spending for a B2B firm and catalogretailer and increasing spending to customers for a pharma-ceutical firm leads to increases in total customer profitability.

Customer retention. Aside from customer acquisition,firms devote large amounts of resources toward customerretention practices. To gain a better understanding of cus-tomer retention and to advise firms in a better manner, studieshave typically focused on answering the following questions:

• Will the recently acquired customer repurchase or not?• What will be the lifetime duration of the customer (i.e., when

will the customer churn)?• Given that the customer is going to repurchase, (1) How

many items is that customer going to purchase? (2) Howmuch is that customer likely to spend? (3) Will the customerpurchase in multiple product categories?

• What will firms have to do in order to retain a customer worthretaining?

• What is the long-term impact of this customer’s purchasebehavior on firm value?

Various studies have been conducted and many models havebeen developed to answer these questions. Table 6 shows arepresentative set of studies that have considered these issuesand accounted for many of the problems that might occur inthe model building process.

When to engage in the activity of retaining a customer canbe a highly misunderstood and undervalued component incustomer retention. Monitoring a customer’s purchasing andattitudinal behavior is vital in understanding when a firmshould aggressively and actively pursue his or her retention.This is important for three reasons. First, firms can often losesight of a customer’s loyalty and lose their profitable cus-tomers, thereby creating undue financial stress. Second,monitoring customer behavior allows the firm to identify theattitudinal changes in a customer. This is important becauseunderstanding the attitudinal changes of a customer withregard to the firm’s brand advises the firm on how and whento be aggressive in its retention strategies for that particularcustomer. Finally, a defecting customer can cause harm tothe firm’s brand through negative WOM if the customer’sdefection is due to unmet needs. If such unmet needs prevailover a large set of customers, and if this possibility is notaddressed, the negative WOM may quickly snowball into aserious issue, thereby damaging both the brand and firm value.

Determining how much to spend on a customer is an im-portant assessment involved in identifying who and when toretain. Innovations in statistical modeling now allow firms to

measure a customer’s future value and profitability to the firm,whichmakes it easier tomake decisions on howmuch to spendas compared with the future value. This logical approach tocustomer retention calls for data pertaining to several aspects ofcustomer transactions over a period of time. With respect tomodeling techniques required to understand customer reten-tion, simulated maximum likelihood estimation is the mostcommonly used procedure. However, more advanced esti-mation techniques, such as Markov chain Monte Carlo,Bayesian estimation, and generalized method of moments,have been used. Understanding customer retention also callsfor accounting for customers’ responsiveness to retention effortsbecause this determines the method of communicating theintention to retain and the costs related to it.

Researchers and managers alike are placing higher im-portance on the study of customer retention and its impact oncompany profits. In both B2B and B2C firms, model-basedapproaches are becoming increasingly available, and thusnecessary, in both contractual and noncontractual relation-ships. In developing strategies for firms, research studies havedrawn insights from (1) explaining customer retention ordefection (Bolton and Lemon 1999; Borle, Singh, and Jain2008); (2) predicting the continued use of the service rela-tionship through the customer’s expected future use andoverall satisfactionwith the service (Lemon,White, andWiner2002; Lewis 2006a); (3) predicting renewal of contracts usingdynamic modeling (Bolton, Kannan, and Bramlett 2000), (4)modeling the probability of a member lapsing at a specific timeusing survival analysis (Bhattacharya 1998); (5) modelingthe duration of relationship using the negative binomial dis-tribution (NBD)/Pareto model and the proportional hazardmodel (Fader, Hardie, and Lee 2005; Reinartz and Kumar2000; Schmittlein, Morrison, and Colombo 1987); (6) use ofloyalty and rewards programs for retention (Leenheer et al.2007;Meyer-Waarden 2007); and (7) assessing the impact of areward program and other elements of the marketing mix(Anderson and Simester 2004; Schweidel, Fader, and Bradlow2008).

An important strategy for retaining customers is tonurture their cross-buying behavior. It has been shown thatwhen customers purchase more products or services fromthe same firm, they extend the duration of their relationshipwith the firm (Reinartz and Kumar 2003) and increasepurchase frequency (Reinartz, Thomas, and Bascoul 2008).Cross-buying results not only in an increase in revenue con-tribution for the firm but also in more engagement with thefirm, higher profit contribution, and higher switching costs(Kumar, George, and Pancras 2008). Although cross-buyinghas the potential to increase profitability, some firms (e.g.,financial services firms) have encountered unprofitable cus-tomers despite taking efforts to promote cross-buying (Brown2003). Greater customer cross-buying does not alwaysresult in higher customer profitability. Shah et al. (2012)study this phenomenon of unprofitable cross-buying andfind that (1) customer cross-buying is not necessarily prof-itable for all customers of the firm and can in fact adverselyaffect a firm’s bottom line, (2) persistent adverse customerbehavior drives unprofitable customer cross-buying overtime, and (3) a company’s marketing policies and practices


TABLE6

SummaryofSelec

tCustomer

Reten

tionStudies

Rep

rese

ntative

Studies

StudyFocu

sStudy

Typ

eModel

Typ

eStudySetting

Insights

Borle,S

ingh

,and

Jain

(200

8)Estim

atepu

rcha

se-le

velC

LVby

jointly

mod

eling

purcha

setim

ing,

purcha

seam

ount,an

dris

kof

custom

erde

fection

Empiric

alDiscrete-ha

zard,

log-no

rmal

Mem

bership-ba

sed

direct

marke

ting

compa

ny

Long

erinterpurch

asetim

esareas

sociated

with

larger

purcha

seam

ountsan

dgrea

terris

kof

leav

ingthefirm

.

Sch

weide

l,Fad

er,an

dBradlow

(200

8)

Mod

elcu

stom

erretentionthat

acco

unts

for

duratio

nde

pend

ence

,prom

otiona

leffe

cts,

subs

criber

heteroge

neity,cros

s-co

horteffects,

andse

ason

ality.

Empiric

alPropo

rtiona

lha

zard

Telec

ommun

ications

service

Inclus

ionof

prom

otiona

leffe

ctsim

prov

esthe

foreca

stac

curacy

ofretentionbe

havior,w

hereas

includ

ingcros

s-co

horteffectsdo

esno

tsign

ifica

ntly

improv

eit.

Fad

eran

dHardie(200

7)Provide

analternativeap

proa

chto

survivor

analys

isfores

timatingcu

stom

ertenu

re.

Empiric

alShifte

dbe

tage

ometric

Con

trac

tual

subs

criptio

n-ba

sed

busine

ss

Propo

sedmod

eloffers

useful

diag

nostic

insigh

tsan

dis

very

easy

toim

plem

entus

ingMicroso

ftExc

el.

Leen

heer

etal.

(200

7)Dev

elop

amod

elon

therelatio

nbe

twee

nloya

ltyprog

ramsan

dpu

rcha

sebe

havior.

Empiric

alTyp

eIITob

itGroce

ryretailing

Mod

elac

coun

tsforen

doge

neity

ofloya

ltyprog

rams.

Predictiveva

lidity

oftheprop

osed

mod

elismuc

hbe

ttertha

nthat

ofthena

ıvemod

el.

Mey

er-W

aarden

(200

7)Exa

minetheim

pact

ofloya

ltyprog

ramson

custom

erlifetim

edu

ratio

n.Empiric

alPropo

rtiona

lha

zard

Sup

ermarke

tThe

high

erthesh

areof

cons

umer

expe

nditu

resin

astore,

thelong

erthelifetim

edu

ratio

nwillbe

.

Bolton,

Lemon

,an

dBramlett

(200

6)

Mod

elafirm

’srepa

tron

agebe

havior

forse

rvice

contracts.

Empiric

alRan

dom

intercep

tsEnterprise-leve

lsy

stem

sMod

elsof

custom

erretentionsh

ould

inco

rporate

theex

tent,va

riability,

andtim

ingof

asu

pplier’s

servicede

liveryov

ertim

e.

Verho

efan

dDon

kers

(200

5)

Inve

stigatetheim

pact

ofch

anne

lsfirm

sfreq

uently

useon

custom

erloya

ltyan

dcros

s-bu

ying

.

Empiric

alProbit

Finan

cial

services

prov

ider

Dire

ct-m

aila

cquisitio

nch

anne

lperform

spo

orly

onretentionan

dcros

s-se

lling

;radioan

dTV

perform

poorly

forretentionon

ly;an

dweb

site

performswellfor

retention.

And

erso

nan

dSim

ester

(200

4)

Inve

stigateho

wthede

pthof

acu

rren

tprice

prom

otionaffectsfuture

purcha

sing

offirst-tim

ean

des

tablishe

dcu

stom

ers.

Empiric

alPoiss

on,linea

rregres

sion

Mail-o

rder

durable

good

sDee

perpricedisc

ountsin

thecu

rren

tpe

riod

increa

sedfuture

purcha

sesby

first-tim

ecu

stom

ersbu

tredu

cedfuture

purcha

sesby

establishe

dcu

stom

ers.

Verho

ef(200

3)Inve

stigatethediffe

rentiale

ffectsof

custom

errelatio

nshippe

rcep

tions

andrelatio

nship

marke

tinginstrumen

tson

custom

erretentionan

dsh

areov

ertim

e.

Empiric

alTob

it,prob

itFinan

cial

services

prov

ider

Affe

ctiveco

mmitm

entan

dloya

ltyprog

ramsthat

prov

ideec

onom

icince

ntives

positivelyaffectbo

thcu

stom

erretentionan

dsh

arede

velopm

ent.


TABLE6

Continued

Rep

rese

ntative

Studies

StudyFocu

sStudy

Typ

eModel

Typ

eStudySetting

Insights

Lemon

,White,

andWiner

(200

2)

Exa

minetheinflue

nceof

custom

erfuture-foc

used

cons

iderations

,ov

eran

dab

ovetheeffectsof

satisfaction,

onthecu

stom

er’s

decision

todisc

ontin

uease

rvicerelatio

nship.

Empiric

alLo

git

Interactivetelevision

entertainm

ent

service

Con

sumersaresign

ifica

ntly

forw

ard-look

ing

whe

nthey

mak

ethede

cision

toco

ntinue

(or

discon

tinue

)ase

rvicerelatio

nship.

Bolton,

Kan

nan,

andBramlett

(200

0)

Inve

stigatetheco

ndition

sin

which

aloya

ltyprog

ram

willha

veapo

sitiveeffect

oncu

stom

erev

alua

tions

,beh

avior,an

drepu

rcha

seintentions

.

Empiric

alLo

git

Finan

cial

services

Loya

ltyprog

ram

mem

bers

overlook

nega

tive

evalua

tions

oftheco

mpa

nyvis-a-visco

mpe

tition.

Reina

rtzan

dKum

ar(200

0)Tes

twhe

ther

long

-life

custom

ersarealway

sprofi

table.

Empiric

alNBD/Pareto

Catalog

retailer

Long

-life

custom

erskn

owtheirva

lueto

the

compa

nyan

dde

man

dprem

ium

service;

they

believe

they

dese

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could facilitate (or deter) the persistence of adverse customerbehavioral traits associated with unprofitable cross-buying.Using data from five firms, the study finds that the firm withthe most liberal product-return policy had the maximumlevel of unprofitable cross-buying due to excessive productreturns.

Even though several studies have highlighted theimportance of customer retention as a single link in the chainof CRM, many firms have not yet understood the largercomprehensive view of the CRM process. For instance, somemanagers still view customer acquisition and retention asseparate processes. Although most studies assess acquisitionand retention separately, the literature provides an abundanceof direction through which firms can link the two together inorder to improve their CRM process.

Balancing acquisition and retention. Apart from iden-tifying the balance in marketing effort between acquisitionand retention in order to maximize profitability, studies haveaddressed questions such as the following:

• What are the drivers of customer acquisition?• After being acquired, how long can the customer be expected

to stay with the firm?• How much investment is required in order to keep the

firm–customer relationship alive?• Given the resource constraints, how much should be spent on

acquisition efforts versus retention efforts to maximize long-term profitability?

In investigating such topics, researchers have modeledacquisition and retention both independently and jointly.

Table 7 lists a select set of studies that have consideredbalancing customer acquisition and retention.

In modeling independently, Lewis (2006b) investigateswhether shipping fees differentially influence customeracquisition and retention. In a system of simultaneousequations, the study examines the effects of shipping feesand other marketing variables on the number of newcustomers acquired, the average order size for new cus-tomers, and the number of daily orders and the averageorder size for established customers. Furthermore, toaccount for the possible correlation between various equationsand the possibility of endogenously determined explanatoryvariables, the author estimates the equations using three-stageleast squares.

In another study, Lewis (2006a) investigates the influ-ence of customer acquisition promotion depth on customerretention, including repeat purchasing and duration. In anonline retailing setting, the study adopts a logistic regressionto model whether the customer makes a subsequent pur-chase within the next three quarters, using acquisitiondiscount as an explanatory variable. Using data from thenewspaper industry, the study adopts accelerated failuretime models to model the time as a subscriber, usingacquisition discount and its quadratic form as an explan-atory variable. The study also examines the effects ofacquisition discount on customer asset value.

Berger and Bechwati (2001) optimize the allocation ofpromotion budget between acquisition spending and reten-tion spending. When companies are considering one marketsegment and using one promotion method, such as direct

TABLE 7Summary of Select Balancing Customer Acquisition and Retention Studies

RepresentativeStudies Study Focus

StudyType Model Type

StudySetting Insights

Reinartz, Thomas,and Kumar (2005)

Develop a modelingframework for balancingresources betweencustomer acquisition effortsand customer retentionefforts.

Empirical Probit two-stageleast squares

B2B high-technology

manufacturer

Firms can manage theircustomer bases profitablythrough resource allocationdecisions that involve trade-offs between acquisition andretention initiatives.

Thomas (2001) Establish that the customeracquisition process affectsthe customer retentionprocess.

Empirical Tobit Airplane pilotmembership

The proposed methodologycorrects for biases in thecustomer retention analysisthat result from assumingthat customer acquisitionand retention areindependent processes.

Berger and Bechwati(2001)

Create a framework for theoptimal allocation ofpromotion budget forcustomer acquisition andretention.

Conceptual Decision calculus — The study discussesdecisions about expenditureallocation betweenacquisition and retention indifferent market conditions.

Blattberg andDeighton (1996)

Find the optimal balancebetween acquisition andretention that maximizescustomer equity.

Conceptual Decision calculus — The authors recommendthat once managers havedetermined the balancebetween acquisition andretention, they plan for eachtask separately.


mailing, managers decide the allocation of existing budgetbetween acquisition and retention to maximize CE.

In modeling acquisition and retention jointly, Thomas(2001) proposes a method known as a Tobit model withselection to account for the impact of the customer acquisitionprocess on the retention process. The proposed model suc-cessfully links customer acquisition to retention because thelength of a customer’s lifetime is observed conditional onthe customer being acquired. The model also establishes thatthe error term in the acquisition equation is possibly corre-lated with that in the retention equation. Finally, Reinartz,Thomas, and Kumar (2005) model customer acquisition,retention, and profitability together as a system of equations,using a probit two-stage least squares model and estimate itas a system of equations.

In summary, customer acquisition modeling is actuallya probability prediction, and customer retention modelingessentially concerns the duration of customer lifetime.Acquisition probability can be estimated by a probit or logitmodel; hazard models can also be used if the timing ofincidence is concerned. Duration data are usually right-censored, so Tobit or hazard models are by nature suit-able estimation techniques. Researchers link acquisition andretention modeling either by specifying the correlationof the error terms in probability and duration models or byspecifying the joint distribution of acquisition and reten-tion for estimation.

Customer churn. Determining who is likely to churn isan essential step. This is possible by monitoring customerpurchase behavior, attitudinal response, and other metricsthat help identify customers who feel underappreciated orunderserved. Customers who are likely to churn demonstrate“symptoms” of their dissatisfaction, such as fewer purchases,lower response to marketing communications, longer timebetween purchases, and so on. It is important to note thatwhile customer retention and customer churn are similarconcepts, the need to study customer churn separately isrooted in the business setting. Whereas customer retentionapplies to both noncontractual and contractual businesssettings, customer churn is relevant only in a contractualsetting. Therefore, understanding customer churn becomescrucial in certain customer–firm relationships. In this regard,the CLV-based churn models provide directions on severaltopics, including the following:

• When are the customers likely to defect?• Can we predict the time of churn for each customer?• When should we intervene and prevent the customers from

churning?• Howmuch dowe spend on churn prevention with respect to a

particular customer?

Table 8 shows a representative set of studies that investigatethe customer churn process.

Researchers have strong interest in the causes of customerchurn and switching behaviors. They have developed variousmodels that include (1) modeling churn with time-varyingcovariates (Jamal and Bucklin 2006; Van den Poel andLariviere 2004), (2) analyzing themediation effects of customerstatus and partial defection on customer churn (Buckinx

and Van den Poel 2005), (3) modeling churn using twocost-sensitive classifiers (Glady, Baesens, and Croux 2009;Lemmens and Croux 2006), (4) determining the factors thatinduce service switching (Capraro, Broniarczyk, and Srivastava2003), and (5) analyzing the impact of price reductions onswitching behavior (Danaher 2002).

Customer win-back. It is no surprise that it is not pos-sible for firms to retain all acquired customers. When cus-tomers churn, managers usually consider this indicative of theend of the customers’ life cycle with the firm. However, itdoes not have to be: firms can still win the lost customers backand give them a second life. Unlike new customers, lostcustomers have certain knowledge about the products andservices of the company and have their own judgment onthe attributes and functions of the products and services.While it is easier to approach lost customers because they havefamiliarity with the firm, lost customers often switch firmsbecause they are not satisfied with the product, which makesit difficult for the reacquiring firm to change customers’attitudes and persuade them to come back. Furthermore, firmsalso have to consider whether it is worth trying to bringcustomers back to the firm—not all customers are worthchasing after they leave the firm.

Once a customer is likely to churn, the firm response vis-a-vis letting a customer go or winning the customer back iscritical. Researchers have studied this phenomenon fromthree aspects:

• Should the firm intervene in customer churn?• If so, how should the firm approach customers to win them

back?• What elements would help firms in reacquiring lost

customers?

The answers to these questions will drive the optimal cus-tomer win-back strategy. Table 9 lists select literature that hasexplored the topic of customer win-back.

Stauss and Friege (1999) provide a conceptual frameworkfor regaining lost customers that consists of analysis, actions,and controlling. The study contends that to determine cus-tomer value in the context of regain management, the lifetimevalue of the terminated relationship is not an appropriatemeasurement. Furthermore, Griffin and Lowenstein (2002)highlight the importance of highly trained win-back teamsand provide a general outline for winning back lost cus-tomers. This study proposes that not all churned customersare contenders to be won back. Instead, firms should calculatesecond-lifetime value (SLTV), segment customers on thebasis of SLTV, and evaluate customers in each segment todetermine why they defected. In addition, Thomas, Blattberg,and Fox (2004) investigate the best price strategy for reac-quisition of lapsed customers, using a split hazard model (orcensored duration model). Using data from the newspaperpublishing industry, this study finds that lowering reac-quisition prices to increase the likelihood of reacquisition isan optimal strategy. Finally, Kumar, Bhagwat, and Zhang(2015) investigate whether lost customers are worth theinvestment in reacquisition and whether they will remainprofitable if reacquired. The study finds that (1) the lostcustomers’ first-lifetime experiences and behaviors, (2) the


TABLE 8Summary of Select Customer Churn Studies

RepresentativeStudies Study Focus

StudyType Model Type Study Setting Insights

Glady, Baesens, andCroux (2009)

Present a frameworkusing a profit-sensitiveloss function for theselection of the bestclassificationtechniques withrespect to theestimated profit.

Empirical Logistic regression,neural network,

decision tree, cost-sensitive classifier

Retail financialservices

Cost-sensitiveapproaches achievegood results in terms ofthe defined profitmeasure and overallclassification.

Jamal and Bucklin(2006)

Study the empirical linkbetween customerchurn and factors suchas customer serviceexperience, failurerecovery, and paymentequity.

Empirical Weibull hazard Satellite television Prediction of customerchurn is significantlyimproved whenheterogeneity is addedto the churn rates andto the responseparameters.

Lemmens and Croux(2006)

Investigate whetherbagging andstochastic gradientboosting can improvechurn predictionaccuracy.

Empirical Bagging and boostingclassification trees

Wirelesstelecommunications

Bagging and boostingtechniquessignificantly improvethe classificationperformance, andbalanced calibrationsample reduces theclassification errorrate.

Buckinx and Van denPoel (2005)

Identify which of thecurrently behaviorallyloyal customers arelikely to (partially)churn in the future.

Empirical Logistic regression,automatic relevancedetermination, neuralnetwork, random

forests

CPG retailer RFM variables are thebest predictors ofpartial customerdefection; variablessuch as customerrelationship duration,mode of payment,cross-buying behavior,usage of promotions,and brand purchasebehavior aremoderately useful inattrition models.

Van den Poel andLariviere (2004)

Develop acomprehensiveretention modelincluding time-varyingcovariates related tocustomer behavior.

Empirical Proportional hazard Financial services Demographiccharacteristics,environmentalchanges, andinteractive andcontinuous customerrelationships affectretention.

Capraro,Broniarczyk, andSrivastava (2003)

Study repurchasedecisions that involvean information-basedevaluation ofalternatives.

Empirical Hierarchical logisticregression

Health insurance After satisfaction levelis accounted for,objective andsubjective knowledgeabout alternativesdirectly affectsdefection likelihood.

Danaher (2002) Derive a revenue-maximizing strategyfor subscriptionservices.

Fieldexperiment

Time-seriesregression

Cellular service Access and usageprices have differentrelative effects ondemand and retention.


reason for defection, and (3) the nature of the win-back offermade to lost customers are all related to the likelihood of thecustomers’ reacquisition, their second-lifetime duration, andtheir second-lifetime profitability per month.

Customerwin-back is a critical componentwithin the overallCRM strategy, but it has received relatively less researchattention than customer acquisition, retention, and churn. Firmsmust first be able to understand the drivers of customer

TABLE 9Summary of Select Customer Win-Back Studies

RepresentativeStudies Study Focus Study Type Model Type Study Setting Insights

Kumar, Bhagwat,and Zhang(2015)

Demonstrate how lostcustomers’ first-lifetimeexperiences andbehaviors, the reasonfor defection, and thenature of the win-backoffer made to lostcustomers are relatedto reacquisitionlikelihood, their second-lifetime duration, andsecond-lifetimeprofitability per month.

Empirical Probit, right-censoredTobit, regression

Telecommunicationsservice

The stronger acustomer’s first-lifetime relationshipwith the firm, the morelikely the customer isto accept the win-backoffer.

Homburg, Hoyer,and Stock(2007)

Test the relationshipbetween customers’first-lifetime satisfactionand their reacquisition.

Empirical Logistic regression Telecommunicationsservice

Health of the first-lifetime relationship ispositively related toreacquisitionlikelihood; customerperceptions offairness regardingwin-back offer arepositively related toreacquisition.

Tokman et al.(2007)

Identify the factorsdriving win-back offereffectiveness.

Quasi-experimental

design

Analysis of variance Auto repair andmaintenance service

Price and servicebenefits provided inthe win-back offer,social capital, andservice importanceare important inshaping customerswitch-backintentions, regardlessof previoussatisfaction, regret, ordelight with the newservice provider.

Thomas,Blattberg, andFox (2004)

Study the probability ofcustomer reacquisitionand the duration of thesecond lifetime, with afocus on the impact ofthe depth of the pricediscount.

Empirical Split-hazard,Bayesian Markovchain Monte Carlo

Newspaper industry For the win-back offerto be effective, itshould consist of lowpromotional prices;successfulreacquisition shouldbe followed by priceincreases.

Stauss andFriege (1999)

Conceptual basis for“regain management”aimed at winning backcustomers who eithergive notice to terminatethe businessrelationship or whoserelationship has alreadyended.

Conceptual — — Retention policy inservice companiesneeds to becomplemented byregain management.


reacquisition, customer duration in a second lifetime, andsecond-lifetime customer profitability before they can makeaccurate strategic decisions regarding which customers to try towin back, and what offers to provide to maximize customerprofitability.

Extending CLV to Engage with Customers

Typically, customer contribution to firm profitability oc-curs (1) directly, through their purchases, and (2) indirectly,through their nonpurchase reactions, which include referringpotential customers, influencing current and potential cus-tomers in their social network, and offering review/feedbackfor improvements. In this regard, Kumar et al. (2010)propose the customer engagement value framework, whichcan be used to identify and evaluate the right customer,who is successfully engaged with the firm and who generatesvalue and positively contributes to the profits of the firm. Thefollowing discussion elaborates on customers’ indirect con-tribution to value.

Role of referrals. Studies in this area have found thatcustomers acquired through referrals are not only lessexpensive to acquire but also more valuable for the firm, ascompared with customers acquired through traditional meth-ods (Schmitt, Skiera, and Van den Bulte 2011; Villanueva,Yoo, and Hanssens 2008). For instance, Schmitt, Skiera, andVan den Bulte (2011) examine the difference in value derivedfrom each customer depending on acquisition method, usingcustomer data from a financial services firm. The study findsthat referred customers are more profitable than nonreferredcustomers. However, Villanueva, Yoo, and Hanssens (2008)find that customers acquired through traditional techniques aremore valuable in the short term to some extent, but referredcustomers are twice as valuable in the long term. In addition,the value differential becomes even largerwhen the disparity inacquisition costs is considered, due to the considerably lowercosts of referrals. Table 10 summarizes the other key contri-butions of past research in this area.

Kumar, George, and Leone (2007) introduce the customerreferral value (CRV) metric, which captures the net presentvalue of the future profits of new customers who purchased thefirm offerings as a result of the referral behavior of the currentcustomer. Simply put, it represents the value of how firm-initiated and firm-incentivized customer referral programs canimprove the profitability of the customer base by acquiringcost-effective prospects (Kumar, George, and Leone 2010).Most notably, this metric makes a distinction between thecustomers that initiated a relationship with the firm solelybecause of the referral and those that would have becomecustomers even in the absence of a referral. Therefore, CRVincludes both the acquisition savings and the profits of thefuture transactions of customers who join as a result of thereferral. However, it includes only the acquisition savings ofcustomers who would join anyway, because their futuretransactions would happen even in absence of the referral, sothey cannot be attributed to the referrer.

Research in this area has focused on (1) developing modelsthat use only a customer’s actual past referral behavior tocompute an individual customer’s referral value (Kumar,

George, and Leone 2007), (2) the identification of thebehavioral drivers of CRV and the methods of solicitingreferrals according to each customer’s CLV and CRV scores(Kumar, George, and Leone 2010), and (3) studying referralbehavior in a business setting, that is, BRV (Kumar, George,and Leone 2013). The BRV is defined as the ability of anddegree to which a client’s reference influences prospects topurchase. The BRV of each referencing client and the CLV ofeach referencing client and newly acquired client from theprospect pool are measured using a three-step methodcomprising (1) determining whether client referencesinfluenced the prospect’s decision to adopt, (2) determiningthe influence each client reference had on the prospect’sdecision to adopt, and (3) computing the CLV of theconverted prospect. Furthermore, Kumar, George, andLeone (2013) empirically determine the key drivers of BRVand recommend that a firm using a client-referencingstrategy needs to leverage a portfolio of client referencesthat includes clients with varied characteristics in order tomatch the client to the prospect successfully.

Valuing customer influences. The spread of informationbyWOM is a critical component in converting prospects intocustomers. To better understand the impact of WOM, severalstudies have called for its inclusion in customer value models(Hogan, Lemon, and Libai 2003; Libai et al. 2010). Researchhas investigated WOM propagation in the context of onlinereferrals (Trusov, Bucklin, and Pauwels 2009) and the evo-lution of the network as a whole with every consecutiveinstance of WOM (Robins et al. 2007). Kumar et al. (2013)develop a framework to capture the value of an individual’sWOM in terms of both its viral impact and the net sales thatit facilitates through two key metrics—customer influenceeffect (CIE) and customer influence value (CIV).Whereas theCIE measures the net spread and influence of a messagefrom a particular individual; the CIV calculates the monetarygain or loss realized by a firm that is attributable to a cus-tomer, through that customer’s spread of positive or negativeinfluence. The study implements the framework by creatingand deploying a social media strategy at an ice creamretailer. By tracking these two metrics for the retailer, thisstudy is able to demonstrate a 49% increase in brandawareness, 83% increase in ROI, and 40% increase in salesrevenue growth rate. While most firms are still strugglingwith social media accountability, this study effectivelyshows the importance of CIE and CIV. Although theseearly studies illustrate the importance of valuing customerinfluences, more research is required to get a deeperunderstanding of this phenomenon.

Valuing customer knowledge contributions. Customerscan add value to the company by helping elucidate customerpreferences and participating in the knowledge developmentprocess. In this regard, beta site customers that use/reviewproducts and provide valuable feedback to firms are a greatexample of customer knowledge contribution. Studies haveinvestigated the amount of value added by customers throughtheir knowledge contributions. For instance, Fuller, Matzler,and Hoppe (2008) find that brand community members whohave a strong interest in the product and in the brand usually


TABLE10

SummaryofSelec

tCustomer

ReferralBeh

aviorStudies

Rep

rese

ntative

Studies

StudyFocu

sStudy

Typ

eModel

Typ

e

B2B vs.

B2C

StudySetting

Insights

Kum

ar,Geo

rge,

andLe

one

(201

3)Und

erstan

dtherole

andva

lueof

client

referenc

esin

abu

sine

ssco

ntex

t.

Empiric

alLo

git

B2B

Telec

ommun

ications

service,

fina

ncial

services

Firm

sca

neffectivelyus

ebu

sine

ssreferenc

esto

pullin

newcu

stom

ers.

Richmed

iaan

dsimilarco

mpa

nies

aretheke

ydriversof

succ

ess.

Sch

mitt,Skiera,

andVan

den

Bulte

(201

1)Determinetheex

tent

ofprofi

tability

andloya

ltyof

referred

custom

ers.

Empiric

alReg

ress

ion,

prop

ortio

nal

haza

rd

B2C

Ban

king

services

Referredcu

stom

ers(1)ha

vea

high

erco

ntrib

utionmargininitially,

(2)h

aveasu

staine

dhigh

erretention

rate,a

nd(3)a

remoreva

luab

leinthe

shortan

dlong

run.

Kum

ar,Geo

rge,

andLe

one

(201

0)Determinetheop

timal

custom

ertargetingforreferral

marke

ting

campa

igns

.

Empiric

al,field

stud

yBay

esianTob

itB2C

Finan

cial

services

,retailing

Firm

sca

neffectivelyse

lect

custom

erswith

inse

gmen

tsof

high

andlow

CLV

/CRVforreferral

marke

tingca

mpa

igns

usingthe

driversof

CRV(dyn

amic

targeting).

Villan

ueva

,Yoo

,an

dHan

ssen

s(200

8)Estab

lishava

lue-ge

neratin

gproc

essby

mod

elingtheinteractions

betwee

nne

wcu

stom

erac

quisition

andthegrow

thof

firm

value.

Empiric

alVec

tor

autoregres

sion

B2B

Web

-hos

ting

compa

nyMarke

ting-indu

cedcu

stom

ersad

dmoresh

ort-term

value,

butWOM

custom

ersad

dne

arlytwiceas

muc

hlong

-term

valueto

thefirm

.

Kum

ar,Geo

rge,

andLe

one

(200

7)Com

pare

custom

erse

gmen

tatio

nac

cordingto

CLV

andCRV.

Con

ceptua

l—

B2C

Telec

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service,

fina

ncial

services

Cus

tomerswith

high

CLV

areno

tthe

sameas

custom

erswith

high

CRV.

Ryu

andFeick

(200

7)Determinetheeffectiven

essof

rewards

forreferral

marke

ting.

Field

stud

yAna

lysisof

cova

rianc

eB2C

MP3play

ers

Offe

ringarewarddo

esincrea

sereferral

likelihoo

d,bu

tthe

size

ofthe

rewardis

notsign

ifica

nt.

Hog

an,Le

mon

,an

dLiba

i(200

4)Qua

ntify

thead

vertisingrip

pleeffect.

Con

ceptua

l—

—Hairstylingse

rvices

The

WOM

gene

ratedafteran

adve

rtising-motivated

purcha

seca

nbe

sign

ifica

nt.

Hog

an,Le

mon

,an

dLiba

i(200

3)Dev

iseametho

dto

acco

untfor

social

effectsin

theman

agem

entof

custom

ers.

Empiric

alNon

linea

rleas

tsq

uares

regres

sion

B2C

Internet

bank

ing

The

valueof

alost

custom

erch

ange

sas

afunc

tionof

thetim

ein

theprod

uctlifecy

cle.


have extensive product knowledge and engage in product-related discussions and support each other in solving prob-lems and generating new product ideas. With respect to theroles played by customers in this process, studies havecategorized them into two categories: information providersand codevelopers (Fang 2008). In capturing the value con-tribution through knowledge sharing, Kumar et al. (2010)introduce the customer knowledge value (CKV) metric asthe value a customer adds to the firm through his or herfeedback. The need for computing the knowledge valuecontributed by customers has been recognized by studiesthat have posited that (1) tracking a customer’s product orservice expertise (which is correlated with the customer’sCLV) can be valuable in assessing CKV (Von Hippel 1986),and (2) defected customers might contribute to CKV bysharing their reasons for leaving, allowing the firm toidentify service improvement opportunities and increase itscapability to detect at-risk customers (Stauss and Friege1999; Tokman, Davis, and Lemon 2007). These studiesconstitute a nascent stream of research that has muchpromise.

Realizing Enhanced Firm and Shareholder Value

Customer equity represents the sum of the lifetime valuesof all customers of the firm. This topic has been a subjectof constant attention; it has been shown to maximize thereturn on marketing investments and guide the allocationof the marketing budget (Blattberg, Getz, and Thomas2001; Reinartz, Thomas, and Kumar 2005; Rust, Lemon,and Zeithaml 2004). For instance, Gupta, Hanssens, andStuart (2004) show across multiple firms that customervalue can be a method of firm valuation that is as good as abetter than traditional accounting. They also quantify theimpact of firm value resulting from improvements inretention, margin, and acquisition costs. Customer sat-isfaction has also been linked to higher firm value in that itincreases immediate cash flows and creates future growthoptions (Fornell et al. 2006; Gruca and Rego 2005; Malsheand Agarwal 2015).

Kumar and Shah (2009) propose a framework to linkthe outcome of marketing initiatives (as measured by CE)to the firm’s market capitalization (MC) (as determined bythe stock price of the firm). The intuition behind thisframework is that the stock price of the firm is based on theexpected future cash flows of the firm. If cash flow isprimarily generated from customers, an increase in CE (orcash flow from customers) should relate to an increase inMC (or the stock price of the firm). Such an approachwould answer three important questions: (1) Can marketingstrategies that increase CE also increase the stock price of thefirm? (2) If so, can such increases in stock price be predictedon the basis of changes in CE? (3) Can the increases in thestock prices of the two firms be attributed to shareholder valuecreation?

To answer these questions, Kumar and Shah (2009) im-plement CLV-based strategies that are designed to strengthenthe CRM practices of a firm. The implementation was doneover a nine-month period in a B2B firm and a B2C firm, bothFortune 1,000 companies. At the end of the implementation, the

study finds that the increases in stock price for the B2B firm andthe B2C firm were approximately 32.8% and 57.6%, respec-tively. Furthermore, when the average monthly values of thestock price are plotted over time and compared with the actualaverage values of the stock prices of the two firms, the resultsindicate that it is possible to track the actual movement of stockprices within a maximum deviation range of 12%–13%. It isalso found that the stock price of the B2B firm outperformedthe S&P 500 by 200%, and the B2C firm outperformed theS&P 500 by 360%. Finally, the study also finds that theB2B firm’s stock increases by 32.8% during the observationperiod, while its three closest competitors’ stocks go up byan average of 12.1% over the same period. Similarly, theB2C firm’s stock increases by 57.6%, while its three closestcompetitors’ stocks go up by an average of 15.3% over thesame period. These findings clearly demonstrate the link-age between CE and MC.

The studies discussed here sufficiently demonstrate thepower of real-time decision making that firms would gainwhen the drivers of customer value and timely marketinginterventions are carefully understood and coordinated. Thisimplementation would not only refine marketing actions butalso enhance value to the firm.

Key Insights and Future ResearchDirections

Extant marketing literature has investigated the concept ofcustomers providing value to firms and have sought tounderstand the process of deriving value from customers,ways of measuring it, and strategies for maximizing it. Thecurrent study has discussed the knowledge created byresearchers thus far in understanding customer value. Thisresearch has shown that value is created to and from thecustomers. While this creation happens fairly regularly, thisprocess is beneficial only if it is long-lasting, which is pos-sible only when the customer perceived value (i.e., value tocustomers) and firm value (i.e., value from customers) arealigned. This alignment, however, only happens over time.Furthermore, the alignment process is constantly shaped bywhat we know about value and the knowledge we still seek.When these two value sources are aligned, it leads to thecreation of enduring customer value, as opposed to just value.The alignment is materialized through the appropriate settingof prices in a manner that reflects customer and competitivefactors. In effect, there is a constant reevaluation from thefirm’s side vis-a-vis prices to ensure the value alignment toand from the customers. The setting of prices is subsequentlyreflected in the development of products and services, as wellas in the designing of marketing campaigns. Figure 3 presentsan organizing framework that describes the process of valuealignment.

This study has uncovered some valuable insights that willbe helpful in understanding the creation and communicationof value. With regards to value to customers, this study hasdefined customer perceived value that incorporates both the“give versus get” perspective and the value-communicatedaspect. Such an approach to defining and understanding per-ceived value highlights the importance of thinking beyond


the value component, to include the costs given up for thebenefits that are being sought. Following this definition, thisstudy has identified three tasks for measuring customerperceptions of value: measuring overall perceived value,measuring the associated underlying attributes and benefits,and determining the relative weights of the attributes/benefitslinked to overall perceived value. With respect to value fromthe customers, this study has identified that a forward-lookingmetric such as CLV is ideal in metricizing customer valuecontributions. Furthermore, this study has observed that tocreate net value for the firm, the perceived value that cus-tomers receive must be aligned with the resources spent onthe customers, through the adoption of forward-lookingmetrics.To this end, CLV is identified as the metric (amongthe popularly used metrics) that most accurately com-putes the net present value of a customer according to his orher future transactions with the firm, after accounting forrevenue, expense, and customer behavior. Various CLV-based strategies have been proposed that firms can use tomaximize value from customers after computing CLV.Table 11 lists the key insights from this study.

Looking ahead, we would like to offer three key areas thatcould spur future research. First, we believe the Internet willattain even more dynamism in terms of the uses, abilities, andopportunities it presents. In this regard, the Internet of Things(IoT) is bound to offer several avenues for researchers andpractitioners to identify and maximize ways to create value forfirms and customers. With its origins in the supply chaincontext (Ashton 2009), the IoT now encompasses uses invirtually all areas, including health care, urban planningand management, emergency services, construction, envi-ronment monitoring, lifestyle, and sports management. Theready applicability to a wide range of industries is largely dueto the connectivity to media and transportation channels theIoT provides to firms.

For instance, consider the case of a professional sportsteam. Sports fans would like to get to the sporting ven-ues in time for the game and the other attractions offered.However, a game day brings with it the problems of trafficand changing weather conditions, which create challengesin finding the fastest route to the venue. With a key goal ofensuring fan satisfaction, a sports team could considerproviding a wearable device to fans that would inform themof traffic conditions, road closures, and weather updates,in addition to team-related and game-related information.Such a device from the team management, apart from being apiece of fan merchandise, would also keep fans engaged withthe team. The technology to create such a device is availablethrough IoT, but how can firms create value from such anoffering? To begin with, what value-based metric can be usedto identify the fans who would receive such a device? Cansuch a device capture fan satisfaction effectively? Howwouldthe team view fan satisfaction at the venue and fan sat-isfaction getting to and coming from the venue? In otherwords, would an unpleasant experience away from the venueadversely influence a fan’s satisfaction at the game andsubsequently lead to a decline in loyalty? Furthermore, whatinsights could the team gain regarding fan experience ongame day and the ways to mitigate any loss in following?How could the team capture these fan sentiments and use thedevice to offer a valuable proposition that both engages thefan with the team and ensures value to the team throughconsistent ticket sales?

Second, health and fitness consciousness has been on therise, with fitness studios offering varied programs to suitevery customer need. These paid options provide severalcustomization options for users to design their visit andstructure their fitness regimen. These options includescheduling visit times (some studios are even open 24 hoursa day), personal training, designing meal plans, and studio

FIGURE 3Ensuring Value Alignment: An Organizing Framework

What We Know

Alignment

What We Know

What We Would Like to Know

• Impact of products’ intangible aspects on perceived value• Value of privacy• Source of perceived value

Productsand

ServicesPerceived

CostUndesired

Consequences

BenefitsPerceivedAttributes

FirmValue

CustomerPerceived

Value

Marketing Programs

CustomerChurn

Customer Win-Back

Customer Retention

Customer AcquisitionProfitable

Customer Engagement

What We Would Like to Know

• CLV for a basket of goods• Effect of customer attitudes on CLV• Impact of macroeconomic trends on CLV• Nature of relationship on value creation• Customer engagement and privacy• Level of engagement and successive generation of products

Pricing Decisions


amenities, among others. In addition, fitness studios nowhave a strong online presence through blogs and socialnetworking sites. They now interact with customers andfitness enthusiasts by exchanging fitness-related information.Consumers also have access to unpaid options such as fitnessvideos and predesigned fitness routines available on theInternet. While this option lacks customization, beingavailable for free compensates for this dearth. Perhaps themajor attraction of the unpaid option is the availability evenduring travel. However, some fitness studios also now havededicated members’ websites that offer health tips, alongwith tools to manage workout schedules through videos andinstruction manuals, in addition to regular studio admission.From the perspective of a fitness studio, the challenge ofsuch a site is twofold. First, how does the firm present itsvalue proposition vis-a-vis other competing studios (e.g.,www.my360gym.com)? Second, how does it fight the com-petition from the unpaid fitness options? In such cases,what other novel and innovative programs can be offered byfitness studios to attract customers? Furthermore, how canstudios design, price, and offer online fitness material (text,audio, and video) that can effectively competewith the existingunpaid options? In addition, can studios exist only in the onlineformat (e.g., www.booyafitness.com) without any physicalpresence, and if so, how would the value then be determinedfor a fitness customer?

Finally, household purchase decisions have receivedsubstantial research attention (e.g., Epp and Price 2008;Gupta, Hagerty, andMyers 1983). The decision-making rolestypically include influencers, gatekeepers, deciders, buyers,users, and disposers. In situations in which the head of thehousehold is the decider and buys a good, the product isintended either for personal use or common use. Householdconsumption is also known to be affected by scale econo-mies, wherein the utilization rate of a good can be raised byincreases in family size (Lazear and Michael 1980). In suchconditions, for the purposes of identifying value and designingcustomer strategies, firms would benefit if they had answers toquestions such as the following: (1) Is the good being pur-chased for personal use or common use? (2) Who will bepaying for the good—the individual or the family? (3) Will atrade-off be necessary to buy the good, either by the individualor the family? (4) In the case of a common good, will thegood hold the same value to all members of the household,and if not, how can the disparity be alleviated? (5) In the caseof a common good, will there be any design changes necessaryto satisfy all the users? (6) How should the media mix be de-signed such that the influencers of the household are reachedwith the right message, in the right format, and at the righttime? (7) Given these questions regarding the decision-makingroles, the usage of the good, and the awareness of the good,should firms compute the value as a comprehensive amount

TABLE 11Key Insights from This Study

Value to Customers Value from Customers

• Customer perceived value is different from quality, perceivedbenefits, and satisfaction. This article defines perceived valueas the customer’s net valuation of the perceived benefitsaccrued from an offering that is based on the costs they arewilling to give up for the needs they are seeking to satisfy.

• Benefits and undesired consequences are the results of buyingand consuming the offering (i.e., the attributes of the offering),and these may accrue directly or indirectly and may beimmediate or delayed.

• Perceived value is measured according to attributes, whichmay be objective attributes (i.e., produced attributes) orperceived attributes (i.e., experienced attributes).

• Approaches to modeling consumer preferences have adoptedtwo types of methods: compositional and decompositional.While the former is a set of explicitly chosen attributes/benefitsthat are used as the basis for determining overall valueevaluations, the latter attempts to infer underlying utilities fromobserved choice.

• Firms can create perceived value for customers through (1)leveraging their own capabilities, (2) aligning with customers’perception of what is valuable for them, and (3) claiming adifferential advantage (e.g., premium or margin) overcompetitive offerings.

• Customers form a judgment of value as a function of perceived,not actual, benefits and costs.

• Measuring customer perceptions of value involves three keytasks: (1) measuring overall perceived value, (2) measuring theassociated underlying attributes and benefits, and (3)determining the relative weights of the attributes/benefits linkedto overall perceived value.

• Firms need to align the perceived value customers receive withthe resources spent on them through the adoption of forward-looking metrics, to create net value for the firm.

• Unlike backward-looking metrics, forward-looking metricsfocus on the customer, rather than the product, and can beused to understand current clients as well as prospects.

• CLV calculates the net present value of a customer accordingto his or her future transactions with the firm, after accountingfor revenue, expense, and customer behavior.

• CLV can be modeled at the aggregate level or the individualcustomer level.

• CLV-based strategies can be used not only to maximizerevenue, minimize costs, or both, but also help with customeracquisition and retention, balancing acquisition and retention,customer churn, and customer win-back.

• The customer contribution to firm profitability occurs directly,through customer purchases, and also indirectly, throughactions that might include referrals or influencing others viasocial networks, customer reviews, and feedback to the firm.

• The concept of customer engagement value helps in theidentification and evaluation of the right customer, who issuccessfully engaged with the firm, who generates value, andwho positively contributes to the profits of the firm.


http://www.my360gym.com

http://www.booyafitness.com

TABLE 12Future Research Directions

Value to the Customers Value from the Customers

• The effect of customer costs and consumer needs onperceived value has been studied largely from the viewpoint ofa product’s tangible features. Intangible aspects, such as, forinstance, the network effect on perceived value, have not beenexplored. Network effects have been addressed in the “valuefrom customers” perspective (e.g., Kumar et al. 2013; Robinset al. 2007) to strengthen profitable customer–firmrelationships. Future research could explore individuals’perception of value and its constituents in a network settingthat exhibits influences across customers.

• Studies of undesired consequences have explored the realmof privacy concerns primarily in an online business setting.However, a more comprehensive treatment of the value ofprivacy is required to better our understanding in terms of (1)quantifying the overall value of privacy, (2) determining thevalue of personal information that customers will be willing togive up for products with lower prices, and (3) establishing theoffline product categories that privacy costs apply tocustomers.

• In identifying the source of perceived value, while usage, costs,and profits involved have been considered, the nature ofproduct has not been considered. Future studies could focuson the source of perceived value—simple product design orsophisticated product design.

• While Sunder, Kumar, and Zhao (2016) have bridged the gap inliterature by proposing a structural approach tomeasuring CLVthat incorporates the choice, timing, and quantity decisions ofconsumers to assess CLV in the CPG setting, future studies inthis area can look into expanding the analysis for a basket ofgoods, and including stochastic shocks to the system thatmight influence the consumption.

• Literature has shown that customer behavior influences CLV(Reinartz and Kumar 2003) and that customer attitudesinfluence customer behavior (e.g., Anderson 1998; Hogan,Lemon, and Libai 2003), which in turn influences CLV. But docustomer attitudes directly influence CLV? Answering thisquestion is bound to provide insights into the reasoning behindcustomer behavior and how they affect customer profitability.

• While most CLV implementation studies account for marketingand financial variables, the macroeconomic trends remainunaccounted for. What approaches can we adopt to accountfor macroeconomic factors such as GDP growth rate, rate ofunemployment, and so on, in the CLV implementation to makeit more accurate and reflective of market realities?

• Although the benefits of implementing customer engagementhave been demonstrated, we need to know the nature of therelationship between level of engagement and value creation(e.g., linear or inverted U shaped).

• Furthermore, what is the role of customer engagement incustomer privacy issues? That is, if customers are engagedmore, will they be less sensitive to sharing private information?

• In realizing value through customer engagement, is it possibleto identify which form of engagement will work with a certaintype of customer? That is, can a customer provide value in allforms of engagement?

• Research has identified other forms of engagement, such asgifting behavior. Bhagwat and Kumar (2015) propose thatencouraging customers to take part in gifting behavior is oneway to effectively engage them with the firm and toconsequently see profitable outcomes. In this regard, are thereany other forms of engagement that can lead to profits for thefirm?

• When firms launch products, the performance of the previousgeneration of products and the overall firm performance arechallenged. For instance, whenever Apple launches a product(e.g., iPhones), the prices of the previous-generation productsare lowered, prompting many consumers to wait for such aprice drop to buy the earlier version. In this regard, does thelevel of engagement facilitate the adoption of successivegeneration of products, and if so, how?

• CLV maximization can also be viewed from an optimizationstandpoint. Firms often plan and change their resource inputs.When the elasticities of factors such as customer acquisition,retention, and win-back are considered, the resultingoptimization may lead to better-informed resource planning.

• Despite the prevalence of coalition loyalty programs, they havereceived little attention in academic research. Future studiescould investigate the profitability drivers of such loyaltyprograms.

• The identification of the lifetime value for dealers (e.g., dealerlifetime value) would help firms such as car dealerships to finda reliable method of (re)allocating scarce marketing resourcesto the “best”-performing dealerships.


that accounts value derived by all the users of the household,or as a summation of individual values of all the users?

This study has also presented specific research areasregarding value to and value from the customers.With respectto value to the customers, for instance, this study recom-mends looking into the network effects because the effect ofcustomer costs and consumer needs on perceived value hasbeen studied largely from the viewpoint of a product’stangible features, while the intangible aspects, such as thenetwork effects, have been explored by only a few studies(Kumar et al. 2013; Robins et al. 2007). Therefore, a moredetailed investigation into this is required to broaden ourunderstanding on delivering customer value.

Another issue that requires research attention is the caseof privacy. In order to use online services, customers arerequired to share their personal information instead of beingcharged a fee. In effect, while customers get to use theservices for free, the cost incurred is sharing personalinformation. Amid this growing digitization climate, firmsnow will have to understand (1) the value of shared privatedata, (2) the business settings in which such an option will(and will not) work, and (3) how the value proposition can becommunicated to customers. Future research along theselines will be helpful in understanding the drivers of value in adigitized format and in customizing offerings to maximizevalue to and from customers.

With respect to value from customers, this study hasidentified several areas for future research. For instance,while a structural approach to measuring CLV that incor-porates the choice, timing, and quantity decisions of con-sumers to assess CLV in the CPG setting has been proposed(Sunder, Kumar, and Zhao 2016), more studies in this areaare needed to formalize this learning. Specifically, studiesthat can expand the specific product-level categorization toinclude a basket of goods will facilitate the study of CLVfrom a retailer’s perspective. Such studies will likely result in(1) further refinements to the drivers of customer and retailerprofitability, (2) better customization of product offerings,and (3) targeted store-level product promotions.

Another area that could be fruitful for research on CLVinvolves accounting for macroeconomic trends and newproduct introductions by competitors. Specifically, futurestudies should look into identifying approaches that can beadopted to account for macroeconomic factors such as GDPgrowth rate, rate of unemployment, inter- and intracountryvariations, consumption and investment spending, and inter-national trade balance in the CLV implementation to make itmore accurate and reflective of market realities. In this regard,Kumar and Pansari (2016) have shown that national culturaldimensions affect the drivers of purchase frequency andcontribution margin and that economic factors influence thecomponents of CLV directly. In effect, the relative effects ofthe drivers of CLV are influenced by the differences in themacroeconomic trends of the countries as well as in the cul-tures of the countries. However, more studies across countrieswith different levels of economic stability/performance areneeded to more thoroughly understand this result. Theseinsights will be of use to firms that have international oper-ations to plan their resource allocation well ahead of time.

Future studies could also focus on identifying the natureof the relationship between level of customer engagement andvalue creation (e.g., linear or inverted U shaped). This willhelp in refining the engagement strategies aimed at enhanc-ing customer value creation. Furthermore, identifying newerforms of engagement would expand the number of ave-nues through which a firm could establish engagement. Forinstance, research has determined that encouraging cus-tomers to take part in gifting behavior effectively engagesthem with the firm and consequently leads to higher profits(Bhagwat and Kumar 2016). Future studies could investigateother forms of engagement that, when encouraged amongcustomers, can lead to profits for the firm.

An area of loyalty that is gaining significant traction iscoalition loyalty. Such a program brings together two or morecompanies to offer loyalty incentives across a wide spectrumof retail and service offerings (e.g., Star Alliance in the air-line industry). These programs provide significant advan-tages such as shared operational costs, higher cross-sellingpotential, and a symbiotic relationship among participatingcompanies (Lee, Lee, and Sohn 2013). Limited research inthis area has found that service failure by one partner has aspillover effect on other partners (Schumann, Wunderlich,and Evanschitzky 2014), and positive spillover effects fromone partner are reflected in cross-buying of other partners(Lemon and Von Wangenheim 2009). Future research couldlook into the profitability drivers of these programs to gain abetter understanding of the outcomes.

Another area of research opportunity lies in determiningthe optimal balance of resource allocation between tradi-tional and new media, and the role of each in maximizingROI. The challenge here for researchers lies in determiningthe optimal mix of media options and how they should beprioritized among the customer deciles in order to max-imize ROI. When the rebalancing of resources is linkedback to customer acquisition, retention, and win-back ini-tiatives, the results would lead to better informed resourceplanning.

The identification of lifetime value of distributors/dealers(e.g., car dealerships) is another area with opportunities forfuture research. In the case of U.S. car dealerships, automanufacturers sell their cars through exclusive dealershipsand/or dealerships that carry multiple brands. Here, the automanufacturers need to find a reliable method of (re)allocatingscarcemarketing resources to the “best-”performing dealerships.In this regard, creating a “dealer lifetime value,” classifyingdealers as “high-value” or “low-value” and placing them inappropriate deciles, would help manufacturers solve thereallocation problem. Table 12 provides a compilation oftopics that might be considered for future research.

This study has discussed the major developments in theCLV area of research and highlighted potential future re-search directions. Specific refinements and improvementscan be expected in (1) approaching measurement of CLV, (2)understanding the drivers of CLV, and (c) gathering moreempirical evidence regarding the various business applica-tion of CLV. When these developments feed into real-timedecision making for marketers, marketing’s accountability tothe corporate boardroom will have been enhanced.


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