© 2017, American Marketing Association

Journal of Marketing
PrePrint, Unedited
All rights reserved. Cannot be reprinted without the express
permission of the American Marketing Association.

A Theory of Customer Valuation:

Concepts, Metrics, Strategy, and Implementation

V. Kumar

Revised: September 2017

*V. Kumar (VK) is the Regents Professor, Richard and Susan Lenny Distinguished Chair, &
Professor in Marketing, and Executive Director of the Center for Excellence in Brand and
Customer Management, and the Director of the PhD Program in Marketing at the J. Mack
Robinson College of Business, Georgia State University in Atlanta, GA 30324; Phone: 404-413-
7590, Email: vk@gsu.edu. VK is also honored as the Chang Jiang Scholar at Huazhong
University of Science and Technology in Wuhan, China; Fellow, Hagler Institute for Advanced
Study, Texas A&M University, College Station, TX, USA; and the Senior Fellow, Indian School
of Business, Hyderabad, India.

I thank the senior editor, Robert Meyer, the area editor, and the three anonymous reviewers for
their valuable comments during the review process. This study has been presented in many
universities worldwide over the past few years and has benefitted significantly from the
audience feedback. I also thank Bharath Rajan for his assistance in the preparation of this
manuscript, and Renu for copyediting the manuscript.
 2

Abstract

Customer value refers to the economic value of the customer’s relationship with the firm. This
study approaches the topic of customer value for measuring, managing, and maximizing
customer contributions by proposing a customer valuation theory (CVT) (based on economic
principles) that conceptualizes the generation of value from the customers to the firms. We
review the established economic theories for valuing investor assets (e.g. stocks) and draw a
comparison to valuing customer contributions. Further, we recognize the differences in the
guiding principles between valuing stocks and valuing customers in proposing CVT. Using
CVT, this study discusses the concept of customer lifetime value (CLV) as the metric that can
provide a reliable, forward-looking estimate of direct customer value. Additionally, economic
models to estimate CLV, ways to manage CLV using portfolio management principles, and
strategies to maximize CLV are discussed in detail. We extend the customer value concept by
discussing ways a customer can add value to the firm indirectly through incentivized referrals,
social media influence and feedback. Finally, the benefits of CVT to multiple constituencies are
offered.

Keywords: Customer valuation theory, Customer lifetime value, Customer Engagement,

Customer profitability, Stock Valuation
 Introduction

Any sustainable business, first creates value for its customers through firm offerings1, and in the

process derives value from its customers in the form of profit2. It is this duality of roles

performed by the firms and customers comprising of deriving and delivering value that best

summarizes the firm-customer relationship from a value standpoint. However, this value is

distributed heterogeneously across customers. As it is the firms/decision-makers who allocate

resources to markets, customers, and products, the challenge in this context for firms is to

dynamically align resources spent on customers and products in order to simultaneously

generate both value to customers and value from customers. Additionally, the volatility and

vulnerability in customer cash flows differentially impacts overall firm profitability. These

changes can be due to both customer actions and firm actions. For instance, especially in

business markets, leadership change in the customer organization impacts procurement decisions

that can result in changes in future cash flows. Similarly, life events for customers (e.g., divorce,

becoming empty nesters) can also impact future cash flows. Therefore, firms look for ways to

better manage cash flows (Srivastava et al. 1999).

To better understand/manage the value creation and cash flow management process, this

paper proposes a theory to value future customer contributions. We refer to this theory as the

customer valuation theory (CVT). The CVT focuses on two aspects of customer financial

contributions – the nature (i.e., direct and indirect), and the scope (i.e., breadth and depth). In

doing so, the CVT informs firms about (a) the conceptualization of value generation from

customers, and (b) the ways and means available to generate and maximize value from

customers. In this regard, the power of the customer lifetime value (CLV) metric to accurately

1
Firm offering refers to physical goods, services, brands, or a combination thereof.
2
Customer denotes both an end consumer as well as a business customer.
 2

value a customer’s future contributions is established. By applying the CLV metric, this study

demonstrates how firms can use CVT to (a) value customer assets, (b) manage customer

portfolios, and (c) nurture profitable customers. The robustness of the CVT is also highlighted

through its successful implementation across various types of markets (consumer and business),

scenarios (contractual and non-contractual business settings), contexts (domestic and global),

and industries (e.g., insurance, airline, retail). Further, the widespread positive impact of

implementing CVT on multiple constituents of the market such as the firms, the customers, the

employees, the society, and the environment is also identified.

Valuing Assets – A Contextual Background

What is an asset? Despite the conflicting viewpoints on the role and constituents of an asset or firm

resource (Mahoney and Pandian 1992), an asset can be broadly defined as any physical,

organizational, or human attribute that enables the firm to generate and implement strategies that

improve its efficiency and effectiveness in the marketplace (Barney 1991). Given the prominence

of assets in deploying firm strategies and gaining competitive advantage, the next question is what

makes them valuable? Here too, scholars from diverse fields such as finance, industrial

organization, management, and economics have presented several approaches to decode an asset’s

value. With specific reference to marketing, academicians and practitioners consider customers as

assets and have instated strategies that manage and nurture customers, rather than use them only for

specific marketing actions. In this regard, studies have investigated customer asset valuation from

various viewpoints such as firm valuation (Gupta et al. 2004; Kumar and Shah 2009), customer

management (Berger et al. 2002), financial performance (Hogan et al. 2002), among others.

Overall, the differing approaches to asset valuation notwithstanding, the true value of an asset is

most often observed in its interaction in the external marketplace. This leads to the next question –
 3

how are assets valued? To better understand the approach to value assets, let us consider two

scenarios – an investor investing in stocks, and a firm investing in its customers.

In the case of the investor (applies to both individual and institutional investors), the

valuation of stocks forms a crucial component. Based on the valuation, the investor typically

performs three routine actions – (a) selects and invests in stocks that show potential for growth, (b)

constructs a portfolio of stocks and bonds, and (c) constantly rebalances the portfolio to ensure

maximum future gains. An important feature that integrates all the above actions is the element of

risk. This is because, risk impacts the volatility and vulnerability of cash flows, which in turn,

impacts the stock value, and ultimately reflects in the overall firm value.3 As a result, investors

seeking to avoid risk would ideally want to (a) invest in stocks that indicate a steady stream of cash

flow, (b) construct a portfolio of similar stocks so that their overall future value is secured and

maximized, and (c) constantly evaluate the earnings potential of the stocks in their portfolio and

reconfigure the portfolio by selling risky stocks and buying robust stocks. To assist investors in the

stock valuation process, several approaches such as the discounted cash flow (DCF) models, capital

asset pricing models (CAPM), arbitrage pricing theory (APT) among others are available. The

resulting stock valuation would then inform investors on the configuration of the portfolio using

approaches such as the modern portfolio theory (MPT) (Elton et al. 2014).

In the case of the firm investing in its customers, the valuation of customers too forms a

crucial component. If the principles of the stock valuation approach were applied to manage

investments in customers, firms would ideally like to perform three actions – (a) identify and invest

in the “right” customers, (b) form a customer portfolio (or customer base) consisting of favorable

3
We recognize that valuation of a company is affected by many other factors (including non-monetary factors such
as competition, mergers & acquisitions) than just the focal firm’s cash flows. However, this example is designed to
illustrate the importance of cash flows in determining firm value. We thank an anonymous reviewer for raising this
distinction.
 4

customers, and (c) constantly re-evaluate the portfolio to ensure that the firm is maximizing its

future gains. In reality, each of these three actions are not always possible due to certain challenges.

The above comparison between the case of the investor and the firm shows the similarity in

managing their respective assets (stocks vs. customers). So, can the principles that guide the

valuation of stocks be applied in valuing customers? To answer this question, let us first understand

the challenges in valuing customers.

First, firms would need a reliable method to identify and invest in the “right” customers.

While it may seem straightforward to say that the “right” customers are the ones who exhibit the

highest value potential, the specifics may not be so apparent. Traditionally, value measures have

focused on repeat purchases, acquisition cost, retention cost, tenure, and share-of-wallet, among

others. So a valuation approach that can accurately capture these intricacies for firms to identify the

most valuable customers is essential.

Second, configuring a portfolio of the most valuable customers is easier said than done. The

customer characteristics (e.g., consumption pattern, lifestyle habits) that determine their value

potential has been found to change over time (Kumar 2013). As a result, periodic evaluation of

customer value measures (e.g., profitability) is necessary, and a reliable method to help managers

keep track of changing customer characteristics is of vital importance. Further, regulated

monopolies such as telephone and municipality services are often required to cross-subsidize one

group of customers with another (e.g., rural and urban customers). In such cases, firms may not be

able to build the ideal portfolio of customers.

Third, rebalancing of customers is not a feasible strategy. While cases have been reported

where companies have fired customers due to profitability concerns4, it is not a common practice.

4
Marguerite Reardon, “Sprint breaks up with high-maintenance customers,” July 5, 2007, accessed from
https://www.cnet.com/news/sprint-breaks-up-with-high-maintenance-customers/
 5

For instance, many banks are unable to “fire” the unprofitable customers especially if they are from

lower socioeconomic and minority groups. Additionally, churn is a challenge that firms constantly

face. While a firm may want to have a profitable customer, holding on to that customer typically

poses challenges for the firm. Therefore, firms need a reliable way to discern how to manage

unprofitable customers, and ways to nurture profitable customers.

Therefore, in this study, we present the case for a valuation approach that is specifically

designed to value customer assets. In doing so, we show why the stock valuation approach is not

readily applicable to valuing customer assets, and how customer assets are uniquely positioned to

provide value to the firm.

Theoretical Approach to Valuing Assets

The theoretical underpinnings to an investor valuing stocks and a firm valuing its customers can be

understood through the following questions.

• How do firms view customer assets?

• Why financial theories are not appropriate for valuing customers?
• How does customer valuation work?
How do firms view customer assets?

Research on customer asset management dates back to models that explored how consumers

make purchase decisions (Howard and Sheth 1969). The research insights generated since then

have led to the consideration of customers as integral to organizations (Gupta and Lehmann

2005; Shah et al. 2006). In this regard, studies have considered customers to be intangible assets

of a firm (Hunt and Morgan 1995; Srivastava et al. 1998), and have proposed approaches to

value and manage their contributions to the firm (Bolton et al. 2004; Reinartz and Kumar 2000).

Studies have also focused on applying customer value to enhance firm performance from

various perspectives such as the role of customer acquisition strategies (Lewis 2006), customer

retention strategies (Reinartz et al. 2005), customer loyalty (Reinartz and Kumar 2002),
 6

customer satisfaction (Anderson and Mittal 2000), resource allocation (Petersen and Kumar

2015), and customer metrics (Petersen et al. 2009; Srinivasan and Hanssens 2009), among

others. Such attempts continue to shape profitable customer management (based on future

customer profitability) for both contractual and non-contractual business settings.

Research on customer value has also explored the volatility and uncertainty in future

revenue contributions. In this regard, studies have identified that certain behavioral drivers on

the part of customers (e.g., level of purchases, product returns behavior) determine the level and

volatility of cash flows (Kumar et al. 2006a; Reinartz and Kumar 2003). Therefore, the

management of behavioral drivers is critical in valuing customers.

Why financial theories are not appropriate for valuing customers?

Prior studies have investigated the application of financial theories to marketing decisions. For

instance, Cardozo and Smith (1983) proposed an approach for making product portfolio

decisions by applying the financial portfolio theory. However, Devinney et al. (1985)

highlighted critical differences regarding applying a financial theory in a product decision

setting. Similarly, Tarasi et al. (2011) demonstrated the application of financial portfolio theory

for making customer portfolio decisions, which subsequently, attracted critical review in the

literature (Billett 2011; Selnes 2011). Other efforts that have incorporated concepts from

financial theories into marketing applications include among others, the introduction of customer

beta that measures the riskiness of customers (Dhar and Glazer 2003), a customer relationship

scorecard based on customers’ risk-return characteristics (Ryals 2003), and the management of

customer segments using portfolio theory (Bolton and Tarasi 2015; Buhl and Heinrich 2008;

Groening et al. 2014). In light of these efforts to apply financial theories for valuing customers,

the following principal differences between finance and marketing must be noted. These
 7

differences, and how they ultimately impact firm value, are illustrated in Figure 1.

[Insert Figure 1 here]

• It is possible for an investor to invest as much money into stocks and achieve a higher amount
in return. However, this is not possible in the case of customers. Firms know that beyond a
certain point, investing more money in their customers will yield a lower rate of return. In other
words, while the investment-to-earnings relationship can be linear in the case of stocks, it is
nonlinear in the case of customers.
• Investors can be fairly certain about a stock’s ‘life expectancy’ and the survival of that firm.
However, firms that invest in customers can make no such conclusion about their customers. In
other words, investors have relatively more information about how long the stocks they have
invested will remain in trading, as compared to the firms’ knowledge of how long their
customers will remain their customers.
• In the case of widely held stocks (i.e., not closely controlled by investors and fund owners), if a
stock starts to perform poorly, the investor has the option of quickly divesting that stock. After
divesting, the investor has the option of either buying another stock, or just holding on to the
cash. However, firms typically do not have prior information about how much value a customer
is going to bring in. Further, changes in customer lifestyle may make customers less profitable,
or even make losses. In such a case, the firm typically does not ‘divest’ of such loss-making
customers, but has to find ways to manage them appropriately. This is also true in certain
business settings (e.g., oil industry) where it is difficult for suppliers to exit a customer
relationship. In some cases, such situations can lead to speculative business practices such as
stockpiling. In other words, the value and volume of investments in stocks is easily scalable, as
compared to the investments in customers.
• Investors routinely buy and sell stocks that will increase the value to the portfolio and/or
minimize the risk of losing value. However, firms that invest in customers do not have the
luxury of hiring and firing customers. As a result, a low value customer is still likely to be part
of a firm’s customer portfolio, and firms will have to find ways to manage these customers in
such a manner that they do not lose firm value. In other words, rebalancing a stock portfolio is
easier, as compared to rebalancing a customer portfolio.
• While investors value their investments in widely held stocks based on the projected cash flows
 8

of the respective stocks5, firms that have invested in their customers assess the value of the
customers based on customer contributions to firm revenue that subsequently determines the
stock price, and ultimately the firm value. Recognizing this chain of impact is important in
developing a metric that can effectively track the firm’s value creation. In other words, whereas
the impact of investing in customers can be seen on the value of stocks (and ultimately on firm
value), investing in widely held stocks has a limited observable impact on the value of
customers.
• Using financial theories, it is possible for investors to identify the type of risks that stocks are
exposed to (e.g., given the economic cycles), and recognize the ones that can be diversified
from the ones that cannot be. However, firms that have invested in their customers cannot
readily identify the risks from customer contributions, and their impact on profitability. In other
words, it is relatively easier to identify (and therefore manage) risks arising from investments in
stocks, as compared to the investments in customers.
• Investor sentiments play an important role in investment decisions (Weber and Johnson
2009). Stock market operations involve investor sentiments regarding a firm’s future
performance expectations, which in turn, determines the level of attractiveness of that firm in
the industry. On the contrary, the influence of investor sentiment is not a significant force
when valuing a direct contribution of the customer. However, for customers who are based
in politically unstable regions, the valuation is different more due to sentimental reasons than
economic reasons. In other words, the importance of investor sentiment is higher in the
valuation of stocks than in the valuation of customers.
• Speculation also plays an important role in investment decisions. For instance, speculation
is based on a rational betting decision that is known to stabilize asset prices (Friedman
1953), and is sometimes based on insider trading (Kyle 1985). Further, it is known that if
the investment actions of rational speculators trigger the buying of securities when prices
rise and selling when prices fall, then, an increase in the number of forward-looking
speculators can increase volatility about the asset fundamentals (de Long et al. 1990). On
the contrary, speculation does not play a major role when assessing the value of a
customer. In other words, the importance of speculation is higher in the valuation of stocks

5
An exception to this are initial public offerings (IPOs) wherein the stock value increases as new customers are
added.
 9

than in the valuation of customers.

• Based on known future discounted cash flows, an investor typically decides on his/her
choice of investment. That is, the options are limited to either investment or divestment.
As a result, investors generally cannot influence the future cash flow patterns of a firm to
change the course of their own actions. In other words, financial theories offer a passive
approach to managing investments, while customer management requires an active
management approach.
• The volatility and vulnerability of stocks make it difficult for investors to predict stock returns
in the short run. Short run returns are difficult to predict because of their random walk
feature (Jensen 1978; Malkiel 1995). Kumar et al. (2000) also observe that daily returns are
sensitive to random disturbances in the market. To predict stock movements, they offset the
effect of random disturbances by considering a longer time period such as a month. Using
the principles advanced by CAPM and APT, the study developed a multistage model to
study variation in stock returns. The study found that the addition of significant factors other
than the market factors (i.e., cost and supply of money) increases the level of risk, which
results in a decrease in the price of the firm. In this regard, appropriately designed marketing
actions directed at the environmental uncertainties (i.e., macroeconomic factors) can lessen
the impact on the firm’s cash flows, since predictions of customer value are accurate in the
short run. Therefore, estimating the value of customers and pairing the value with
appropriately designed marketing strategies can place firms on the path to increased
financial returns. The level of predictive accuracy of customer value however, declines only
in the long run. In other words, a more accurate prediction of stock returns is possible only
in the long run, as compared to the prediction of customer value, which is more accurate in
the short run than in the long run.
In addition to the financial theories, knowledge from behavioral finance is also relevant

here. Behavioral finance argues that some financial phenomena can reasonably be understood

using models in which some agents are not fully rational (Barberis and Thaler 2003). With

specific reference to investor behavior, behavioral finance has explained how certain investors

groups behave, the types of portfolios held, and their trading pattern over time. In this regard,
 10

Barberis and Thaler (2003) trace investor behaviors such as insufficient diversification of

portfolios (Baxter and Jermann 1997), opting for simpler diversification strategies (Benartzi

and Thaler 2001), high trading volumes (Barber and Odean 2000), holding on to stocks even if

they are going down in value (Odean 1998), and considering “attention-getting” stocks for

their purchase decisions (Barber and Odean 1999) to demonstrate that investors do not always

display rationality in their investment decisions. This stream of literature establishes that

investors’ attitudes and behaviors are of vital importance.

In light of the above mentioned differences, we develop the CVT as a robust theory for

valuing customer assets by adopting a different approach than valuing stocks.

How does customer valuation work?

Based on the above discussion of the determinants of customer assets, one of the approaches to

value customers in a general form is presented here:

(3) CFPi = f (Transaction behaviori, Marketing costi, Demographic/firmographic

variablesi, Economic and environmental factorsi)

where, CFPi refers to customer future profitability of customer i;

Simply put, future profitability of the customer depends on the past and current transaction

behaviors exhibited by the customers, the marketing efforts of the firm, the identity and profile

of the customers (i.e., demographic variables), and the environment these customers exist in

(i.e., economic and environmental factors). Further, when modeling CFP, statistical issues such

as heterogeneity, endogeneity, and simultaneity are factored in the estimation of such models.

Once the CFP is modeled, business intelligence software systems can be used to score, update

customer information, and rescore CFP on a periodic basis. Further, technology can also be

incorporated to target customers on a real-time basis through relevant messages in an effort to

 11

increase future cash flows. Based on this valuation approach, we advance the following

testable propositions.

Transaction behavior. Also known as exchange characteristics, the transaction

behavior broadly includes all the past and current transaction variables that affect and

influence the customer-firm relationship. The commitment-trust theory (Morgan and Hunt

1994) proposed that firms look to establish positive relationships with customers through

developing commitment and trust with the customers. While a customer’s positive affect

impacts his/her commitment to the firm, research has also uncovered other dimensions of

customer commitment. For instance, Allen and Meyer (1990) proposed a three-component

model (affective, calculative, and normative) of commitment. Recently, Keiningham et al.

(2015) proposed that customer commitment be a five-dimension construct (affective,

normative, economic, forced, and habitual). Additionally, beyond repeat purchases, purchase

habits have been found to play an important role in determining customer transaction

behavior (Ascarza et al. 2016; Duhigg 2013). Specifically, customer habits have also been

found to impact future volatility and vulnerability of cash flows (Shah et al. 2017) and firm

performance (Shah et al. 2014). Further, the importance of primary market research in

understanding customer behavior patterns has also been highlighted, especially in light of

the abundance of behavioral information made possible by big data (Knowledge@Wharton

2014). As a result, frequent customer-firm interactions should increase customer trust and

commitment in the firm at a faster rate, provided the interactions are satisfactory. Therefore,

P1: Customer transaction activities significantly influence customer future

profitability.

This proposition has been tested across various industries and markets (B2B and
 12

B2C) and has found that customer future profitability to be positively influenced by a host of

variables that includes (a) prior customer spending level (Reinartz and Kumar 2000), (b)

customer cross-buying behavior (Kumar et al. 2008a; Venkatesan and Kumar 2004), (c) the

intensity of customer purchases within a product category (Reinartz and Kumar 2003), and

(d) members of rewards programs with the firm (Kumar et al. 2006a). In addition, studies

have also found the average interpurchase time and the number of product returns to have a

significantly positive impact on customer future profitability, up to a certain threshold

(Reinartz and Kumar 2003).

Marketing cost. Marketing cost can include, among others, past, current, and future

promotional costs (towards customer acquisition, retention, and win-back), technology

upgrades, service improvements, employee management, and quality control. The

importance of effective management of customer assets to enhance firm profitability (Bolton

et al. 2004), directed the attention towards understanding the impact of marketing

expenditures on customer value; and actively using marketing communication actions (i.e.,

customer contact channels) to maximize customer value. Prior research has established that

well-timed communication efforts (Kumar et al. 2008b) and well-managed content (Kim and

Kumar Forthcoming) between firms and customers reduces the propensity of a customer to

quit a relationship (Morgan and Hunt 1994). However, too much communication has also

been found to be detrimental to the relationship (Fournier et al. 1998), thereby indicating the

presence of an optimal communication level.

P2: Marketing cost nonlinearly influences customer future profitability.

Studies have revealed an inverted U-shaped relationship between marketing contacts

(involving rich modes – e.g., sales personnel contact, and standardized modes – e.g.,
 13

telephone or direct mail contact) and customer behavior (Reinartz et al. 2005; Venkatesan et

al. 2007). Further, in a permission-based marketing context, a firm’s marketing contact

policy influences both the length of time a customer stays in an e-mail program and the

average amount a customer spends on a transaction while he/she has subscribed to the e-mail

program; and that too much marketing may not only make customers less likely to opt in, but

also make them opt out more quickly (Kumar et al. 2014b).

Demographic/firmographic variables. These variables refer to the distinguishing

characteristics of the customer (end user or business customer). In the case of a business

customer, the firmographic variables include among others, the type of industry, the age and size

of the firm, the level of annual revenue, and the location of the business. In the case of an end

user, the demographic variables include among other factors, age, gender, income, and the

physical location of the customers. The demographic/firmographic variables can aid firms in

characterizing attractive segments into identifiable and measurable groups of customers

(Zeithaml 2000). In the case of end users, the heterogeneity in profit contributions can be

better understood through a customer’s demographic and psychographic variables

(Chintagunta et al. 1991), and that the demographic variables affect customer store choice

(Craig et al. 1984), shopping channel choice (Inman et al. 2004), profitable lifetime duration

(Reinartz and Kumar 2003), and migration of shopping choice (Thomas and Sullivan 2005),

to name a few. As a result, classifying customers based on their distinguishing

characteristics can help firms in customer segmentation and CRM efforts. Therefore,

P3: Demographic/firmographic variables significantly influence customer future

profitability.

The testing of this proposition has revealed that demographic variables such as
 14

household income, population density of the neighborhood, gender, age of the head of the

household, marital status, education level, and so forth, to significantly impact future

customer profitability (Kumar et al. 2008a; Kumar et al. 2006a). Further, research has also

established that firmographic variables such as the size of the firm and the industry category

of the business customers significantly explained the variation in contribution margin for the

focal firm (Venkatesan and Kumar 2004). Specifically, the study found that the focal firm’s

business customers in the financial services, technology, consumer packaged goods, and

government industry categories provided, on average, a higher contribution margin than

firms in other industry categories.

Economic & environmental factors. Economic factors such as GDP per capita help

determine the consumption pattern of a country. It has been established that consumers’

response to macroeconomic factors is a function of not just their ability to buy (as measured

by current and expected future income), but also their willingness to buy (Katona 1975).

This underlying theory explains how various macroeconomic conditions impact price

changes (Gordon et al. 2013), changes in consumers’ frame of mind (Chhaochharia et al.

2011), and overall household utility (Kamakura and Du 2012), to name a few. Further,

research has also shown that changes in consumers’ economic constraints have varying

effects on their profit contributing potential. For instance, Sunder et al. (2016) demonstrated

that high CLV customers are least affected by changes in their budgetary constraints when

compared to low CLV customers. Therefore, if a country has a high GDP and high

purchasing power, its consumers will have more disposable income and spend more.

P4: The economic & environment factors significantly influence a customer’s future

profitability.
 15

Studies that have tested this proposition have found that GDP per capita, the

country’s economic wellbeing (how customers feel about and experience their daily lives),

cultural characteristics, and the employment rate significantly impact future customer

profitability (Kumar and Pansari 2016b; Kumar et al. 2014a; Umashankar et al. 2016).

Customer Valuation Theory

Based on the customer valuation approach discussed above, the CVT can be defined as “a

mechanism to measure the future value of each customer that is based on (1) their direct

economic value contribution, (2) the depth of the direct economic value contribution, and (3) the

breadth of the indirect economic value contribution; by accounting for volatility and

vulnerability of customer cash flows.” The key components of this definition include:

• Direct economic value contribution: This refers to the economic value of the customer
relationship to the firm, expressed as a contribution margin or net profit. A firm can both
measure and optimize its marketing efforts by incorporating customer value at the core of its
decision-making process. When implemented at firms, it aids in (a) computing future
profitability of a customer, (b) arriving at a good measure of customer value, (c) optimal
allocation marketing resources to maximize customer value, and (d) identifying ways to
maximize the return on marketing investments.
• Depth of direct economic value contribution: This refers to the intensity and inclusiveness of
customers’ direct value contributions to the firm through their own purchases that have
produced significant financial results for the implementing firms. Examples of such
instances include acquiring and retaining the profitable customers based on their future value
potential, customer purchase potential across multiple channels of buying, and the possibility
of customers to buy across multiple product categories, among others.
• Breadth of the indirect economic value contribution: This refers to customers’ indirect value
contributions to the firm through their referral behavior, online influence on prospects’ and
other customers’ purchases, and feedback on the firm offerings. These indirect measures also
contribute significantly to the cash flow contributions. This indirect contribution can also be
extended to accommodate other contexts such as salesperson productivity, donations (in the
 16

case of nonprofit firms), and business references.

Since CVT enables managers to actively manage customer relationships based on future

customer contributions through specialized customer strategies, it creates a positive impact on

firm performance. Specifically, the implementation of the CVT can help firms improve their

marketing productivity and realize higher firm value through (a) valuing customers as assets, (b)

managing portfolio of customers, and (c) nurturing profitable customers. Therefore, it is critical

to understand the nature of the linkage between CVT and firm value. Figure 2 provides an

overview of how the components of CVT function in driving firm value.

[Insert Figure 2 here]

In summary, the proposed CVT is relevant for the following reasons:

• Unlike prior marketing applications of the financial portfolio theory, this theory focuses on
customer management at the individual customer level (based on his/her profitability)
instead of the customer segment level. Though customers are grouped based on profitability
(i.e., high, medium, and low profit segments), the subsequent strategies that are developed
are always deployed at an individual customer level due to a higher level of effectiveness.
Such an approach is different from how investors handle financial assets.
The CVT implicitly accounts for customer risks (i.e., volatility and vulnerability in cash
flows) when modeling future customer profitability. In doing so, the model focuses on how
the risks ultimately impact customer profitability (and thereby, overall firm profitability),
and treats it accordingly. For this reason, the CVT does not explicitly provide a beta value or
any indicator for the risk-free return equivalent in the case of investments. Further, the CVT
enables managers to identify and manage the diversifiable risks through tailored offerings by
focusing on the drivers of customer value.
• Since the CVT focuses on customer profitability, the variation in associated customer costs
at the individual level are accounted for.
• The CVT is highly active in terms of generating actionable firm strategies. By dynamically
managing the volatility and vulnerability in cash flows, this theory enables managers to
actively monitor customer relationships over time, and undertake necessary remedial
 17

measures. In comparison, the financial theories largely suggest investors to invest (or divest)
at any point in time as determined by the discounted cash flow analyses. As a result, the
financial theories remain passive by not providing adequate directions to investors on
influencing any future changes.
• Since future costs drive future margins, the CVT-based strategies advise managers to better
manage their acquisition and retention of profitable customers, which can then be used to
actively refine and manage the customer valuation approach. However, no such option is
available with investing in stocks as stocks yield fixed return, and that the investor cannot
influence or manage the return on a stock.
• The CVT recognizes that the investment-to-earnings relationship in the case of customers to
be nonlinear (unlike in valuing stocks), and uses an appropriate customer valuation
approach. This is subsequently reflected in customer strategies that can be developed.
Valuing Customers as Assets (Concepts)

This component of the CVT delves into the concepts behind customer valuation, and the related

financial benefits they hold for firms. To contextualize the practice of customer valuation to the

proposed theory, it is essential to review prior literature in this topic. In recent years, the idea of

treating customers as assets of a firm has emerged as the most popular and efficient way of

doing business (Hunt and Morgan 1995). This entails identifying future customer profitability

and designing marketing guidelines that will advise managers on profitable customer

management. Traditionally, firms have used several metrics to value customers (Kumar and

Reinartz 2012; Petersen et al. 2009). The guidance from these metrics has driven decisions

pertaining to the allocation of marketing resources.

When considering a customer’s value contribution to the firm, a crucial part is his/her

contribution in the future periods. It is this future component that is of immense interest to

academicians and practitioners. The concept of future value contribution has been

conceptualized in the form of the CLV metric that refers to the present value of future profits
 18

generated from a customer over his or her lifetime with the firm (Gupta and Lehmann 2005;

Venkatesan and Kumar 2004).

The conceptualization of CLV was strongly influenced by the corporate finance body of

knowledge, and specifically to the contribution of the present value concept by Irving Fisher

(Fisher 1965) and Eugene Fama’s asset pricing theory (Fama and Miller 1972). Applying this

knowledge to customer asset management, we can see that customers pose risks in terms of

generating returns for the firm in the future. However, the impact of these risks on customer

profitability is not uniform across all customers. In this regard, the CLV approach identifies

opportunities to contain the variation in returns, also known as cash flow volatility, and thereby

the total risk of changes in the value of the firm (measured by future returns) (Shah et al. 2017).

This is possible by understanding the drivers of customer profitability and their impact on CLV.

While the inspiration from other sciences such as economics and finance is apparent, the

adaptation and inclusion into the customer asset valuation would not have been possible without

the development of new methodologies specific to the marketing milieu. Specifically, the

conceptualization of the CLV metric and the development of substantive methodologies served

as a stepping stone for managing profitability by selecting the right customers for targeting and

determining the allocation of resources for customer acquisition, retention, and growth. Further,

this important contribution also demonstrated that the CLV framework can help firms manage

risk through appropriate actions directed at the individual customers.

Direct Economic Value Contribution

While the valuation of assets/projects is typically done at the aggregate level, the CLV metric

enables firms to capture the direct value contribution of customers at both the aggregate and the

individual level. At the aggregate level, the average lifetime value of a customer is derived from
 19

the lifetime value of a cohort or, segment or even a firm. Here, the estimation of CLV can be

accomplished by identifying and measuring the factors that drive CLV. At the individual level,

the CLV is calculated as the sum of cumulated cash flows – discounted using the weighted

average cost of capital (WACC) – of a customer over his or her entire lifetime with the company

(Kumar 2008). It is a function of the predicted contribution margin, the propensity for a customer

to continue in the relationship, and the marketing resources allocated to the customer. It is

important to note that WACC is one of the measures that represent the discount factor used to

compute CLV, among other options (e.g., T-bills rate). In its general form, CLV can be

expressed as (Venkatesan and Kumar 2004):

(Future Contribution Margin it -Future Cost it )

(4) CLVi = ∑Tt=1 (1+d)t

where, i is the customer index, t is the time index, T refers to the number of time periods

considered for estimating CLV, and d is the discount rate.6 The CVT proposed in this study is

based on the individual level CLV, rather than the aggregate level.

Although a “true” CLV measure implies measuring the customer’s value over his or her

lifetime, for most applications it is a 3-year window.7 The reasons for this time frame are – (a)

since future cash flows are heavily discounted, a significant portion of profit can be accounted

for in the first three years and the contribution in the following years are very small, (b) the

predictive accuracy of the models decline over a longer timeframe, (c) changes in customer

needs and life cycle are likely to change significantly beyond a 3-year window, (d) product

offerings change in response to technological advancements and customer needs, and (e) CLV

6
While the prediction of future contribution margin and future costs does generate risk in the CLV calculation, a
higher discount rate can be used to account for the uncertainties in the prediction.
7
In the B2B markets, some categories may not confirm to the 3-year window (e.g., capital goods such as plant and
machinery).
 20

predictions are anyhow updated based on a rolling-time horizon to accommodate changes in

other environmental factors. However, specific industry trends do lead to some exceptions for

this 3-year window. For instance, automakers can expect the customers to make a purchase

every 4 to 6 years (we suggest using a longer window to accommodate at least a couple of

purchases’ or use purchase intention measures to forecast future value; computer manufacturers

can expect customers to make a purchase every 1 to 2 years (we suggest a 4 to 5-year window to

account for at least 2-3 purchases); and insurance companies can take up to 7 years to recover

the acquisition costs. The exceptions aside, the above-listed reasons do advocate the use of a 3-

year timeframe in computing the CLV.

The extant CLV literature has covered a wide range of business conditions through the

measurement approaches. This coverage has expanded the scope and application of CLV-based

models for a multitude of industries and markets. Some of the later developments in modeling

CLV have accommodated several modeling challenges that has led to sophistication and precise

estimation of customer value. Kumar and Reinartz (2016) provide a detailed review of select

approaches that have made significant contributions in modeling CLV. Table 1 provides a

summary of the model form, merits, and shortcomings for the popular modeling approaches.

[Insert Table 1 here]

Whereas the models discussed here are the most popular ones, there will always be

continual improvements to the CLV model because of the nature of the availability of customer

data and the business situation. In addition, the knowledge of how to implement the models is

also important in determining how CLV can be managed at different firms.

Depth of Direct Economic Value Contribution

The depth of direct economic value contributions, as measured by CLV, have focused on the
 21

impact of customers’ direct profit contributions to the firm through their own purchases.

Tracking and valuing these contributions have produced significant financial results for the

implementing firms. In this regard, recent research has demonstrated that when firms try to

understand and leverage the true power of measuring and maximizing CLV, it ultimately results

in enhanced firm value (Berger et al. 2002; Kumar et al. 2000; Kumar and Shah 2009). Further,

Kumar (2008; 2013) explored the implications and generated a Portfolio of Strategies that has

enabled firms to address marketing issues with greater confidence and ensure better decision

making. This set of strategies answers the following questions:

(i) Customer selection – How do firms identify the ‘right’ customers to manage? Reinartz and
Kumar (2000) found that long-life customers are not necessarily profitable customers, and
call for the use of a forward-looking metric like CLV to identify the “right” customers to
manage.
(ii) Managing repeat purchases and profitability simultaneously – How can firms ensure
profitability while improving customer repeat purchases? When customized actions were
implemented at a B2C catalog retailer based on segmenting customers on repeat purchases
and profitability, Reinartz and Kumar (2002) found that loyal customers are aware of their
value to the company and demand premium service, believe they deserve lower prices and,
spread positive word-of-mouth only if they feel and act loyal.
(iii) Optimal allocation of resources – Which customers should the firms interact through
inexpensive channels (e.g., internet or phone), and which customers to let go of? When
resources were reallocated based on the optimal mix and frequency of communication
channels, a business-to-business (B2B) company realized 100% more revenue and 83%
more profits across all their four customer segments (Venkatesan and Kumar 2004).
(iv) Cross-buy – How to increase customers purchases across multiple product categories to
improve customer profitability? By encouraging customers to buy across more product
categories through profitable customer management strategies, Kumar, et al. (2008a) found
metrics such as revenue per order, margin per order, revenue per month, margin per month,
and orders per month increased as customers shopped across multiple product categories.
 22

However, not all cross-buying is good. Shah, et al. (2012) found that across 5 B2B and
business-to-customer (B2C) firms, 10%–35% of the firms’ customers who cross-buy are
unprofitable and account for a significant proportion (39%–88%) of the firms’ total loss
from its customers. Therefore, discerning profitable cross-buying from unprofitable cross-
buying behavior is essential.
(v) Next logical product – How do firms decide the timing of an offering to a customer? When
done right, results have showed that firms increased their profits by an average of $1,600
per customer, representing an increase in ROI of 160% (Kumar et al. 2006b).
(vi) Preventing customer attrition – How do firms decide which prospect will make a better
customer in the future, and is therefore worthwhile to acquire? Using test and control
groups, Reinartz, et al. (2005) showed that acquiring and retaining the ‘right’ customers
garnered a B2C firm an incremental profit of $345,800 with an ROI close to 860%.
(vii) Product returns – Should the firm encourage or discourage product return behavior, and
how should they manage this process? Petersen and Kumar (2009) found that the ideal
level of product returns should be one that maximizes firm profits. For a B2C catalog
retailer, they found that the optimal percentage of product returns that maximized
profitability was 13%. Further, Petersen and Kumar (2015) addressed the aspects of
perceived risk and optimal resource allocation into the product returns process for a B2C
catalog retailer and found that the firm was able to generate approximately $300,000 more
in profits compared to the next best available resource allocation strategy.
(viii) Managing multi-channel shoppers – Having convinced to transact with the firm, what kind
of sales and service resources should the firm allocate, to conduct future business with that
customer? Kumar and Venkatesan (2005) identified the drivers of profitable multi-channel
shopping behavior and found that adding one more channel resulted in an average net gain
of about 80% in profits.
(ix) Branding and customer profitability – Should firms invest in building brands or customers?
Kumar, et al. (2016) have shown that by understanding the link between investments in
branding and CLV, firms can efficiently allocate their resources to improving customer
brand value to generate maximum lifetime value. It was found that a 5% increase in the
investments in branding causes the CLV to go up by over 25%.
(x) Acquiring profitable customers – How should firms monitor customer activity, in order to
 23

readjust the form and intensity of their marketing initiatives? In managing firm actions
regarding customer acquisition and retention efforts, Reinartz, et al. (2005) has found that
it is not sufficient to consider how much to spend on acquisition or retention alone, but
instead on how firms must balance acquisition and retention spending together to
maximize profitability and doubling the ROI.
(xi) Interaction orientation – Should firms realign themselves to realize augmented CLV, and if
so, is interaction orientation the answer? (Ramani and Kumar 2008).
(xii) Referral marketing strategy – How can firms enhance their value through the customers’
referral behavior? By implementing customized campaigns for each customer value
segment, B2B and B2C firms have realized large profit gains, representing a higher ROI
(Kumar et al. 2013b; Kumar et al. 2010b; Kumar et al. 2007).
(xiii) Linking CLV to shareholder value – How do firms leverage the CLV metric to drive their
stock price and provide more value to their stakeholders? By linking CLV-based actions to
a firm’s stock price, B2B and B2C firms have reported significant increases of about 35%
and 57% in their stock prices, better prediction of stock price movements, and superior
performance with respect to the stock market index and rival firms (Krasnikov et al. 2009;
Kumar and Shah 2009).
Breadth of Indirect Economic Value Contribution

Apart from customers’ own contributions, the CLV metric also applies to customers’ indirect

profit contributions to the firm such as their referral behavior, online influence on prospects’ and

other customers’ purchases, and review/feedback on the products and services they consume.

When firms pursue opportunities to draw out indirect profit contributions from customers, it

implies engaging with customers by identifying the various sources of profit. Such a focus

would result in maximizing customer engagement value (CEV). Conceptually, CEV is the total

value provided by customers who value the brand such that they engage with the firms (a)

through their purchase transactions (or CLV), (b) through their ability to refer other customers to

the firm using the firm’s referral program (or CRV), (c) through their power to positively

influence other customers about the firm’s offerings in the social media (or CIV), and (d) by
 24

providing feedback to the firm for product and service ideas (or CKV) (Kumar et al. 2010a).

Customer referral value. With respect to indirect customer contributions to profit,

promoting customer referrals is a popular practice adopted by firms. The CRV metric captures

the net present value (NPV) of the future profits of new customers who purchased the firm

offerings as a result of the referral behavior of the current customer (Kumar et al. 2010b). When

targeted referral behavior campaigns were offered to select customers of a telecommunications

firm, it resulted in overall value gains of $486,090 that represented an ROI of 15.4 (Kumar et al.

2007). This study established that customers who score highly on CLV are not the same as those

who are successful at referring new customers, and made the case for measuring both CLV and

CRV when evaluating marketing campaigns. To compute CRV, the first step is for firms to

integrate their customer transaction database with the referral database. In the absence of referral

behavior information, firms can collect it from new customers by asking questions such as:

Were you referred, and if so, by whom? Or, to what degree did the referral impact your decision

to transact with us? The CRV metric is, however, not relevant for all business situations. For

instance, customers may not make referrals if they are not attached to the product (e.g., fast

moving consumer goods). Further, customer recommendation for one product does not

necessarily apply to the firm’s portfolio of products.

The concept of referral behavior has also been extended to apply to the business-to-

business (B2B) relationship setting through the business reference value (BRV) metric. This

metric computes the amount of profit the existing client firm can help generate from the prospect

firms who purchase the firm offerings as a result of the client reference. Significant differences

were found between high BRV and low BRV clients of a telecommunications and financial

services firm that indicated high BRV clients (a) contribute more value, (b) stay longer with the
 25

firm, (c) are more likely to provide a video reference than a “call me” reference, and (d) are

larger in size and annual revenue, than low BRV clients (Kumar et al. 2013b). By linking CRV

and BRV to CLV, firms can begin to identify the value of customers and enhance it through

optimally- designed marketing campaigns.

Customer influence value. The power of the online medium has influenced customers to

(a) persuade and convert others into customers, (b) continually use the firm’s offerings, and (c)

change/modify their own purchase pattern. In an effort to conceptualize and metricize the

customer influence on others, research has contributed two key metrics – customer influence

effect (CIE) and customer influence value (CIV). While the CIE measures the net spread and

influence of a message from a particular individual; the CIV calculates the monetary gain or loss

realized by a firm that is attributable to a customer, through their spread of positive or negative

influence. Tracking these two metrics for a company’s social media campaign, Kumar et al.

(2013a) was able to demonstrate a 49 percent increase in brand awareness, 83 per increase in

ROI, and 40 percent increase in sales revenue growth rate. While this study described the

applicability of the CIE and CIV metrics in the case of an offline retailer, it can be extended to

online retailers also. Companies such as Starbucks and Staples already have established CRM

practices alongside a vibrant social media presence.

Customer knowledge value. Since customer input is a valuable resource in the product

development process, the value of this contribution needs to be captured and included as part of

a customer’s value to the firm. This was captured in the conceptualization of the CKV metric,

which refers to the monetary value attributed to a customer by a firm due to the profit generated

by implementing an idea/suggestion/feedback from that customer. This customer feedback not

only identifies the areas that are in need of improvement, but also helps provide suggestions and
 26

solutions for future upgrades and modifications to the offerings. This feedback has the potential

to make the entire offering more attractive to existing and potential customers, apart from

improving process efficiencies. As a result, the customers have to be attributed to the

corresponding feedback to get credit towards their asset value (Kumar and Bhagwat 2010).

While the value of customer feedback can be substantial to any firm, juxtaposing CLV

with CKV can yield even greater insights to firms. Normally, customers with low CLVs have

little experience with the product and/or they are likely to be unenthusiastic about the firm and,

therefore are likely to provide very little feedback to the firm. Consequently, the higher a

customer’s CLV, the more positive that customer will perceive the company and its products,

and the more are the opportunities for the company to receive input. However, at very high

levels of CLV (an indication of a close fit between the company’s products and a customer’s

needs), the customers are likely to be highly satisfied, and thereby have little incentive to

provide feedback. These customers can, however, be encouraged to assist less experienced and

less knowledgeable customers when firms implement a communication medium to do so.

Customer brand value. The three tangible value metrics – CRV, CIV, and CKV –

collectively capture customer indirect profit contributions. In addition to these metrics, an

attitudinal metric (intangible value) – Customer Brand Value (CBV) – measures the value that

the customer attaches to the brand as a result of all the marketing and communication messages

delivered via different media. Conceptually, CBV refers to the total value a customer attaches to

a brand through his or her experiences with the brand over time (Kumar et al. 2016). The CBV is

a multi-dimensional metric that measures the customer’s brand knowledge, brand attitude, brand

purchase intention, and brand behavior; and enables companies to devise appropriate strategies

depending on where the problem exists – awareness, trust, or repeat purchase.

 27

The monitoring all the components of CBV becomes important from a branding

standpoint. That is, brand building efforts is directed towards inducing favorable behavior

outcomes towards the brand such as longer duration, higher purchase frequency, higher

contribution margin, and positive customer referrals. Such behavioral outcomes determine the

CLV scores. With this understanding, managers can work on optimizing the components of

CBV in order to improve an individual’s CLV score. In this regard, Kumar (2013) shows that

CBV is the foundation for managing CLV, CRV, CIV, and CKV.

Managing Customer Portfolios (Metrics)

Having developed and discussed the concepts of customer valuation, the next component of the

CVT relates to identifying the metrics to ascertain the value of customers. Firms constantly

struggle with managing customers while ensuring profitability. In many cases, the cost of

serving a customer may far exceed the returns from that customer. In such a scenario, firms are

caught between retaining their customer portfolio/base, and ensuring a healthy bottom line. The

CLV metric will help firms in managing healthy customer portfolios.

Direct Economic Value Contribution

As mentioned earlier, the CLV stream of research has uncovered several resource reallocation

insights that have helped firms maximize their value. Specifically, studies have focused on the

magnitude of cash flows (the expected returns as measured by CLV), and on the volatility and

variability of the cash flows (the risk element associated with customer revenue contributions).

This dual focus has enabled firms to better understand customer portfolio decisions, and manage

customers. Through this process, it is possible to select and invest in customers who will

optimize the overall customer cash flow, while balancing the risk in cash flow variations.

While the concept of managing customer portfolios is similar to the financial portfolio
 28

theory, a couple of key differences have to be noted. First, financial assets are typically

classified based on their historic risk/return characteristics. However, the same approach does

not apply when treating customers as assets. Research has shown that past customer

contributions are not highly correlated with future profit potential when using traditional or

backward-looking metrics (Venkatesan and Kumar 2004). The CLV metric, on the other hand,

has been found to be a good predictor of future customer profitability. Second, finance managers

typically invest in individual assets, and try to maximize the portfolio’s return. However,

marketing managers largely allocate resources at the customer segment level and increase

customer profit. This is due to the nonlinear relationship between customer investments and

returns. In other words, customer responses operate within certain thresholds of the level of

marketing investments, and too little or too much of marketing investments may not elicit the

desired customer response. While early studies have contributed to managing customer

portfolios by optimizing segment level risk/return (Buhl and Heinrich 2008; Reinartz and Kumar

2003; Tarasi et al. 2011), more recent studies are focusing on optimizing resource allocation at

the individual customer level to maximize overall profitability (Kumar et al. 2014b; Luo and

Kumar 2013; Petersen and Kumar 2015).

Depth of Direct Economic Value Contribution

To realize the full potential of customer contributions, firms must focus on the augmented

customer value while managing customer portfolios. This is possible through optimal allocation

of resources by prioritizing customers on their augmented profitability and their receptiveness to

marketing efforts. A decile analysis of the predicted CLV has to be performed to achieve this.

Here, the baseline CLV (i.e., NPV of future profits that considers only the focal product

category that the customer purchases) and the incremental CLV (i.e., NPV of future profits that
 29

considers customer purchases only from new product categories) are separately tracked.

Conceptually, the augmented CLV is the sum of baseline CLV and the monetary impact of the

Portfolio of Strategies discussed earlier. Further, the medium value customers have the highest

gain from the Portfolio of Strategies than even the high value customers. This indicates the

receptiveness of customers to firm-initiated marketing actions. Following this, firms can

prioritize marketing resources and actions on the impact on future profits and on firm value.

A good way to understand the impact of optimal allocation of resources is through the

firm’s usage of marketing communication channels (Venkatesan et al. 2007). In order to

optimally allocate their resources, firms must first identify their most profitable customers and

those who are the most responsive to marketing efforts. Performed at the individual customer

level, the selection of the best mix of communication channels is determined based on the

responsiveness of each customer. The cost-effectiveness of these channels is also considered to

measure the customers’ potential revenue contribution based on the contacts made. The

frequency of contacts through these channels, and at what interval, is then decided by the firm in

order to develop a customer contact strategy. Analyzing customer behavior in relation to these

factors provides firms with valuable information about the preferences and attitudes of their

customers. By carefully monitoring the purchase frequency of customers, the time elapsed

between purchases, and the contribution margin, managers can determine the frequency of firm

initiatives to maximize CLV through an optimal contact strategy.

The benefits of optimal resource allocation using CLV as the metric can be observed

even in a complex business setting. When IBM adopted the CLV metric to measure customer

profitability and allocate resources for customers, they determined the level of contact and

outreach efforts through telesales, e-mail, direct mail, and catalogs on an individual customer
 30

basis. At the conclusion of this program (based on about 35,000 customers), IBM was able to

effectively re-allocate resources for about 14 percent of customers as well as increase revenues

by about USD 20 million. This was done without increasing the investment, and was

accomplished through abandoning ineffective techniques based on the spending history in favor

of the CLV metric (Kumar et al. 2008c).

Breadth of Indirect Economic Value Contribution

In managing customer portfolios, firms must realize that customers have multiple ways to

contribute value, and all avenues have to be explored in order to maximize customer

contributions. In this regard, the CLV metric has resulted in the creation of other metrics that can

used to manage and maximize customer value. These metrics have to be managed independently

since a customer could contribute in one or more of these ways. Further, firms must identify the

ideal combination of direct & indirect value contribution from the metrics that provide the

highest profits to the firm. Figure 3 illustrates the metrics that embed the principles of CLV.

[Insert Figure 3 here]

While most of the metrics illustrated in Figure 3 have been discussed in earlier sections

of this study, a brief description of salesperson lifetime value (SLV), donor lifetime value

(DLV), and employee engagement value (EEV) is provided here:

Salesperson future value. Despite numerous studies conducted on the sales function, the

future value of salesperson to the firm and how organizational factors affect that has received

little attention. Recognizing the importance of this knowledge to firms, Kumar et al. (2014c)

conceptualized the salesperson future value (SFV) metric and empirically demonstrated the short

run and long run effects of managing SFV. The SFV is defined as the NPV of future cash flows

from a salesperson’s customers (i.e., CLV) after accounting for the costs of developing,
 31

motivating, and retaining the salesperson. By measuring the value of the salespeople using the

SFV metric, and linking the performance with the types of training and incentives each

salesperson receives, it is possible to identify the best-performing salespeople and tailor the

training and incentives to maximize their performance. When this approach was implemented in

a Fortune 500 Business-to-business (B2B) firm, they were able to reallocate training and

incentive investments across salespeople that resulted in an 8% increase in SFV across the sales

force and a 4% increase in firm revenue (Kumar et al. 2015).

Donor lifetime value. Just like for-profit firms, nonprofit firms also need to build

relationships to be able to raise donations. In this case, the relationships will be created with

potential donors who can contribute. Further, the nonprofit firm will have to be selective about

which donors to build relationships with, so that they judiciously use their limited resources. To

make this possible, Kumar and Petersen (2016) conceptualized the donor lifetime value (DLV)

metric, which refers to the sum of donations in the future years discounted to the present value,

over a donor’s lifetime with the firm. Using the donor demographic information, past donation

behavior, and marketing information, the DLV is modeled by predicting (a) the likelihood of

donation, (b) the value of donation, given that they will donate, and (c) marketing costs

expended in seeking donations. By computing the DLV, it is possible for the nonprofit firms to

rank-order donors based on their future donation value to the firm. This metric can predict who

will be able to donate with 90 percent accuracy, the value of donations with 80 percent accuracy,

and the DLV with 75 percent accuracy.

Employee engagement value. Firms can leverage the power of an engaged customer base

better, if they have a workforce that interacts well with the customers. Interactions between

customers and employees contributes to creating perceptions about the firm, which affects repeat
 32

customer purchases (Sirianni et al. 2013). These perceptions lead to attitudinal and behavioral

outcomes through their impact on purchases, referrals, influence and knowledge the customers

provide to the firm. In this regard, a positive interaction between customers and the employees is

likely to motivate how customers talk about the brand, and recommend it to their friends and

relatives. Kumar and Pansari (2014) define employee engagement (EE) as “a multidimensional

construct which comprises of all the different facets of the attitudes and behaviors of employees

towards the organization”. The dimensions of EE comprise of employee satisfaction, employee

identification, employee commitment, employee loyalty, and employee performance. Kumar and

Pansari (2016a) have developed an approach to measure employee engagement value (EEV)

using a 5-point scale. When implemented in the airline, telecommunication and hotel industries,

the study found that the highest level of growth in profits (10% to 15%) occurs when a

company’s employees are highly engaged (i.e., have a high EEV); the lowest level of growth

(0% to 1%) occurs when the company’s employees are disengaged (i.e., have a low EEV).

Ongoing research continues to uncover several facets of the metrics presented in Figure

3, and the insights generated thus far have advised firms in valuing customers.

Nurturing Profitable Customers (Strategies)

Finally, the concepts and metrics discussed earlier lead to the establishment of strategies that will

aid in growing customer value. In developing strategies, firms are often misled by the belief that

loyal customers are profitable. Further, their understanding that creating customer reward

programs can result in increased repeat purchasing behavior, and thereby improve firm

profitability is misplaced. Research in this area has shown that loyal customers are not

necessarily profitable, and the relationship between repeat purchases and profitability is more

complex than is often perceived. In addition, firms regularly use traditional metrics to measure
 33

the value of their customers, and these lead managers to implement flawed marketing strategies

that drain the firm’s resources. In this regard, the CLV metric is ideal for firms aiming to grow

and nurture customer profitability. When firms adopt a CLV-based approach, they can make

consistent decisions over time, about: (a) which customers and prospects to acquire and retain;

(b) which customers and prospects not to acquire and retain; and (c) the level of resources to be

spent on the various customer segments.

Direct Economic Value Contribution

When aiming to maximize the direct contribution of customers to the firm, the CVT proposes

that the focus to be on establishing profitable customer relationships that are based on

customer transactions. In this regard, Reinartz and Kumar (2002) found that (a) loyal

customers do not cost less to serve, (b) loyal customers consistently paid lower prices, and (c)

customers who were attitudinally and behaviorally loyal were more likely to be active word-of-

mouth marketers than those who were only behaviorally loyal. In effect, the study found that

while there may be long- standing customers who are only marginally profitable, there also may

be short-term customers who are highly profitable. The identification of the distinct customer

types led to the development of specific strategies such as:

(i) High repeat purchases and high profitability. Referred to as “True Friends”, these customers
buy steadily and regularly over time. They are generally satisfied with the firm, and are
usually comfortable engaging with the firm’s processes. Firms should build relationships
with these customers, since they present the highest potential to bring long-term profitability.
(ii) Low repeat purchases and high profitability. Referred to as “Butterflies”, these customers do
not repeat purchase often, tend to buy a lot in a short time period and then move on to other
firms, and avoid building a long-term relationship with any single firm. Firms should enjoy
their profits until they turn to competition.
(iii)High repeat purchases and low profitability. Referred to as “Barnacles”, these customers, if
managed unwisely, could drain the company’s resources. Firms must evaluate their size and
 34

share-of-wallet. If the share-of-wallet is found to be low, firms can up-sell and cross-sell to
them to make them profitable. However, if the size of wallet is small, then, strict cost control
measures have to be taken in order to prevent losses for the firm.
(iv) Low repeat purchases and low profitability. Referred to as “Strangers”, not only are these
customers a poor fit to the company, but offer very little profit potential to the firm. Firms
should identify these customers early on, and avoid any investment towards building a
relationship with these customers.
Thus, a relationship focus would lead firms to identify those customers who provide the

most value to the company and prioritize the marketing efforts accordingly.

Depth of Direct Economic Value Contribution

Firms are expected to demonstrate the profitability of their marketing actions at the individual

customer level, on an ongoing basis. At the same time, customers expect firms to customize

products and services to meet their demands. A successful management of these two types of

expectations depends on the firms’ ability to better interact with customers and create a unique

positioning in the future. This calls for implementing an interaction strategy wherein,

interactions between firm and customer, and between customers constantly occur. In other

words, the CVT advocates for a firm-customer exchange environment that focuses on constant

interactions, as opposed to just profitable transactions.

Implementing the interaction strategy requires the firm to adopt an interaction-oriented

approach, which consists of four components – customer concept, interaction response capacity,

customer empowerment, and customer value management – that firms can utilize to increase its

impact on a firm’s profitability (Ramani and Kumar 2008). First, the customer concept proposes

that the unit of every marketing action or reaction is an individual customer. This firmly places

the customer at the top of the hierarchy in the customer-firm relationship. By doing so, firms are

able to observe customer behavior and respond appropriately. Second, the interaction response
 35

capacity (the degree to which a firm can provide successive products or services based on

previous feedback from customers) highlights the importance of firms being attentive to and

promptly addressing customer needs. Third, the customer empowerment component refers to the

extent to which a firm allows its customers to (a) connect with the firm and design the nature of

transaction, and (b) connect and collaborate with each other by sharing information, praise, and

criticism about a firm’s product and services. Finally, the customer value management

component refers to the extent to which a firm can quantify and calculate the individual

customer value and use it to reallocate resources to customers. Firms such as IBM and American

Express, through their endorsement of practices that are consistent with the elements of

interaction orientation, have realized superior business performance, thereby demonstrating the

managerial significance of the interaction orientation approach.

Breadth of Indirect Economic Value Contribution

Firms can nurture customer profitability by implementing an engagement strategy that considers

the value generated by customers and employees, and results in superior firm performance. The

CVT proposes an engagement strategy that builds an engaged and committed customer and

employee base to enhance the firm’s overall profitability (Kumar and Pansari 2016a).

While the engagement approach is powerful by itself, its true potential is realized when it

is viewed from a long-term perspective. Engaged customers contribute to the long-term

reputation and recognition of the brand. Creating an environment where customers are more

engaged with the company may require an initial investment, but it has the potential to generate

higher profits in the long run through the creation of CE (Verhoef et al. 2010). Further, fostering

engagement within firms is effective even in a recessionary economy. In a recessionary period,

firms face budgetary challenges that significantly affect their marketing plans, which affect their
 36

levels of brand awareness and adoption. During this period, firms can mitigate the risks posed by

the dents in their marketing budgets if they have a highly engaged employee base, which

promotes the firm’s brand and its products / services to its customers. This would ensure

delivery of a superior customer experience; thereby increasing customer purchases, influence

and referrals – all without any additional marketing investments.

Benefits of the Customer Valuation Theory

The true measure of any marketing strategy or initiative is the improved financial result for the

firm implementing them. It is for this reason that studying customer reactions to firm actions is

critical. When firms can precisely link their actions to customer value and ultimately to

firm/shareholder value, they can begin to realize the potential of valuing customers as assets.

Using concepts from economics, finance, and marketing, this paper has created an interface that

connects the value of each customer (determined by evaluating the lifetime value of the

customer to the firm) with the performance of the firm, and therefore the valuation of the firm.

This connection has been established by (a) valuing customers as assets, (b) managing a

portfolio of customers, and (c) nurturing profitable customers. Each of these tactics plays a

unique role in the optimization of shareholder value, customer equity, and overall profitability,

and each tactic also works in combination with other tactics to increase the overall impact on the

firm value. Specifically, the CVT proposed here provides the following benefits:

(i) Benefit to the firms: When firms understand customer profitability and adopt CVT as the
desired approach, they will be able to (1) attract and retain the most valuable customers, (2)
nurture customers into a skilled resource base for the firm, (3) prevent their customers from
switching to competitors by instilling a heightened sense of ownership of the firm among its
customers, (4) consistently evolve their product/service offerings and match it to customer
needs and preferences, (5) develop the ability to accurately foresee customer responses, and
(6) exhibit superior aggregate business-level performance since the firm will be dynamically
 37

maximizing the profit function at every stage of business activity across all customers.
(ii) Benefit to the customers: The CVT essentially advocates that the unit of analysis of every
marketing action and reaction be the individual customer. This implies that the firms respond
to heterogeneous customers differently at different points in time by pooling information from
multiple sources and points in time. This approach provides customers avenues to (1) connect
with the firm and actively shape the nature of transactions, and (2) connect and collaborate
with each other by sharing of information and knowledge. Further, customers will be subjected
to only the product/service offerings that are appropriate to them, and not every offering that
the firm has as a part of its product portfolio. This keeps the customers focused, involved, and
connected to the firm, thereby increasing their lifetime with the firm.
(iii)Benefit to the environment: When firms base their marketing actions on the potential value
of the customers to them, it becomes easier to align and allocate the optimal amount of
resources towards each customer. For instance, firms can plan and optimize the printing and
mailing of marketing mailers only to those customers they are intended for, and not send
mass mailers. In addition to enhancing the effectiveness of the firm’s marketing efforts, this
would also prevent the wasteful usage of valuable environmental and infrastructural
resources, and help firms embrace sustainability as part of the core mission of the company
(Kumar and Christodoulopoulou 2014).
(iv) Benefit to the society: The benefits to the society from implementing the CVT is three-fold.
First, there is a clear line of communication from firms to customers in terms of what to
expect. The customer expectations relate to product/service offerings, and firm responses
through marketing actions. Second, the customer’s repeat purchases can now be consolidated
among firms. With firms displaying their respective needs and expectations, the repeat
purchases from customers gradually get aligned to matching firms. Over time, a self-
selection of repeat patronage by firms takes place that leads to all customers being taken care
of by the firms. Finally, customers become empowered wherein, they can exercise their
choice and free will, thereby having a definite say in the marketing transaction process. This
empowerment transpires through behavior ranging from customer advocacy (in case of
complete satisfaction of firm offerings) to customer boycott and negative word-of-mouth (in
case of extreme displeasure).
(v) Benefit to the employees: Employees are instrumental in providing a greater customer
 38

experience leading to customer engagement. It is possible for companies to engage their

customers only if the employees of the firm are committed to delivering the brand values and
perform to the best of their ability. Employees would be committed to the organization, only
if they understand the organization’s goals and their responsibilities towards these goals. The
employees have to be highly engaged with the firm to provide peak performance (Kumar and
Pansari 2014). Therefore, firms along with engaging customers also have to ensure that they
engage their employees.
Implications for Future Research

During the development of the CVT, the insights from the financial theories served as a good

starting point. Further, the modern portfolio theory provided insights into risk diversification and

maintaining a balanced portfolio. Overall, this study has showcased CVT as a forward-looking

approach to aid managers in the valuation and management of a customer’s future contributions.

Specifically, two key managerial implications emerge from this study. First, the

applicability of the CVT has been demonstrated in various scenarios spanning multiple markets

(e.g., B2B, B2C), business settings (e.g., contractual, non-contractual), regional contexts (e.g.,

domestic, global), and several industries. This should provide confidence to managers regarding

its relevance and applicability to the current marketplace. Further, experts opine that in the next

5-10 years, CLV will be the metric of prime importance to businesses (Fader 2016). Second,

given the evolution in the methodology to value customers, the availability of customer

information, and the 360-degree customer data, managers are enriched with improvements that

can result in better implementation. Despite the success of CLV, it has not tasted industry-wide

acceptance. For instance, a survey found that while 76 percent of the respondents agreed that

CLV was important to their organization, only 42 percent reported that they were able to

implement it (Charlton 2014).

In this light, we identify three areas that can benefit from future research. First, the
 39

identification of the relevant organizational structure required to facilitate and implement CVT-

based strategies is important. A customer-centric organization has been identified as a basis to

align an organization (Kumar 2013). Such an alignment can enable firms to view its customers

both as a source of business and as a potential business resource. However, more insights in this

regard is necessary to better understand the organizational requirements to have a successful

CVT implementation. Second, the role of multiple stakeholders will have to be explored. For

instance, studies have also explored the circumstances in which firm-stakeholder relationships

are forged that includes settings such as (a) the stakeholder is critical to the functioning of the

firm (e.g., institutional investors having an equity interest in a firm), (b) the stakeholder seeks to

gain from the relationship (e.g., customers seeking firm offerings), (c) the firm and the

stakeholder mutually gain from the association (e.g., the firm and the channel partners working

collectively to produce and sell offerings), and (d) the stakeholder has a moral rights attached to

the firm’s operations (e.g., customer rights and employee rights) (Freeman and Reed 1983).

However, precise recommendations have to be generated that can inform firms on better

stakeholder integration for a successful implementation of CVT. Finally, the time-varying

effects of certain drivers involved in the implementation of CVT needs to be better understood.

These elements include the valuation methodologies, data availability, the intensity of customer-

level data, and the emergence of smarter consumers. These elements are likely to have a

dynamic effect (over time) on CVT-based strategies.

In sum, by focusing on a CVT-based approach, firms can (a) acquire and retain profitable

customers, (b) employ resources productively, and (c) nurture profitable customers that would

ultimately result in higher firm value.

 40

Figure 1 – Differences in Valuing Stocks vs. Customers

Firm value

Stock Valuation Principles Customer Valuation Principles

• Investment-to-earnings ratio • Investment-to-earnings ratio

for stocks can be linear for customers is non-linear
• Information on the tenure of a • Information on the tenure of a
stock being traded is more customer is less accurate
accurate • Value and volume of
• Value and volume of investments in customers is not
investments in stocks is easily easily scalable
scalable • Rebalancing a customer
• Rebalancing a portfolio is portfolio is relatively difficult
Guide
relatively easier • Sentiments do not play an Guide Valuation Valuation of
Valuation of • Sentiments play an important important role in the valuation of non-customer
stocks role in the valuation • Speculation do not play an customers assets
• Speculation play an important important role in the valuation
role in the valuation • Identification of risks is
• Identification of risks is relatively difficult
relatively easier • Risk diversification is based
Investors • Risk diversification is based the risk’s impact on customer Firms
on the risk’s systematic or profitability
(Individuals unsystematic nature • Investments in customers can
and • Investments in stocks does impact stock value
Institutions) not impact customer value • Customer value prediction is
• Stock value prediction is more accurate in the short run
possible only for the long run than in the long run
• Passive approach to • Active approach to
managing future changes anticipating and managing
future changes
 41

Figure 2 – Understanding the Link between CVT and Firm Value

Figure 3 – Maximizing Indirect Value Contribution

 42

Table 1 – Summary of Select CLV Modeling Approaches

1. Estimating Models Independently (Venkatesan and Kumar 2004)

CLVit  
Ti
GCit n

 m
MCi ,m,l

(1  r ) fi l 1 (1  r )
t l
t 1

Model Form GCi,t = gross contribution from customer i in purchase occasion t

MCi,m,l = marketing cost, for customer i in communication channel m in time period l
fi (or frequency) = 12/expinti (where, expinti is the expected interpurchase time for customer i)
r is the discount rate; n = number of years to forecast; Ti = number of purchases made by customer i
• Accounts for customers to return to the firm after a temporary dormancy in a relationship
Merits
• Aids resource allocation decisions on marketing communication channels
Shortcomings • Does not account for competitive responses and consumers’ brand switching behavior.
2. Estimating Models Simultaneously (Venkatesan, Kumar, and Bohling (2007))
Likelihood function:
Ji cij (1-c )
* 2
L= ∏ni=1 ∏j=1 ∑K
k=1 ϕijk [fk (tij |αk ,λijk ,γk )*p (ΔQij |δi,k ,δk ,σk ) ] Sk (tij |αk ,λijk ,γk )
ij

f (tij|α, λi, γ) = the density function for the generalized gamma distribution
Model Form S (tij|α, λi, γ) = the survival function for the generalized gamma distribution
p (ΔQ|δi, δ*, σ2) = the density function for purchase quantity
cij = the censoring indicator, where cij = 1 if the jth interpurchase time for the ith customer is not
right censored and cij = 0 if the jth interpurchase time for the ith customer is right censored
• Accounts for endogeneity and heterogeneity
Merits
• More accurate results than independent estimation
Shortcomings • Model development and estimation is complex.
3. Brand Switching Approach (Rust et al. 2004)
ijT 1
CLV = ∑t=0 t Vijt *πijt *Bijt
(1+dj )fi
Tij = Number of purchases customer i makes during the specified time period
Model Form dj = Firm j’s discount rate
fi = Average number of purchases customer i makes in a unit time (e.g., per year)
Vijt = Customer i’s expected purchase volume of brand j in purchase t
πijt = Expected contribution margin per unit of brand j from customer i in purchase t
Bijt = Probability that customer i buys brand j in purchase t
• Can be used when the firm has cross-sectional and longitudinal database
Merits • Accounts for all types of marketing expenditures
• Can accommodate competition
• Sample selection can play an important role in the accuracy of the metric
Shortcomings • Often relies heavily on survey based data, thus leading to an increase in sampling cost and
survey biases.
4. Monte Carlo Simulation Algorithm (Rust, Kumar, and Venkatesan (2011))
p(Πit ,Purit ,Xit ) = p(Πit |Purit =1,Xit ) * p(Purit =1|Xit ) * p(Xit )
Model Form Purit is the indicator of purchase and is equal to 1, if customer i purchases from the firm in time t,
and zero otherwise
• Better predictive power over simpler competing models
Merits
• Better understanding of customer profitability and firm value
• Cannot be used in a lost-for-good setting
Shortcomings
• Heavy reliance long purchase histories
 43

5. Customer Migration Model (Dwyer 1997)

MMt Ct Pt
CE = ∑Tt=0
(1+d)t
MMt is a matrix that contains the probabilities of customers moving from one segment to another at
Model Form time t
Ct is a vector containing the number of customers in each segment at time t and,
Pt is the profit from each segment at time t
Merits • Considers probabilistic nature of customer purchases
Shortcomings • Can be used only in limited business settings
6. Deterministic Model (Jain and Singh 2002)
(R -Ci)
CLV = ∑ni=1 i i-0.5
(1+d)
i = the period of cash flow from customer transaction
Model Form Ri = revenue from the customer in period i
Ci = total cost of generating the revenue Ri in period i
N = the total number of periods of projected life of the customer under consideration
• Higher predictive accuracy
Merits
• Aids in firm-level strategy development
• Requires huge amounts of individual customer data
• Does not consider the relationship between model parameters
Shortcomings
• Descriptive, but not prescriptive and therefore less helpful in managerial decision making
• Does not account for competition
7. Probabilistic Model (Drèze and Bonfrer 2009)
(1+d)τ
CLVτ = (1+d)τ -p(τ)
A(τ)
Model Form τ is a fixed time interval between contacts
A(τ) is the expected surplus from communications following the interval and,
p(τ) is the probability of retention given that interval
• Can be used when the firm does not have longitudinal database
Merits
• Identification of sub-drivers aids in better resource allocation
• Assumes purchase volume and interpurchase time to be exogenous
Shortcomings • Calls for frequent updation of the model
• Heavy reliance on data and lesser reliance on managerial insight
8. Structural Model (Sunder, Kumar, and Zhao (2016))
𝑈𝑖𝑡 = ∑𝐽𝑗=1[𝜓𝑖𝑡 ln(1 + 𝑞𝑖𝑗𝑡 )] + 𝜆𝑖 ln(𝑦𝑖𝑡 − ∑𝐽𝑗=1[𝑃𝑗𝑡 𝑞𝑖𝑗𝑡 ])
𝑈𝑖𝑡= overall utility from consumption by consumer ‘i’ at time ‘t’
ψ𝑖𝑗𝑡= baseline utility
Model Form 𝑦𝑖𝑡= unobserved budget allocation within category by consumer ‘i’ at time ‘t’
𝑃𝑗𝑡= price of brand ‘j’ at time ‘t’
𝑞𝑖𝑗𝑡= quantity of brand ‘j’ consumed by consumer ‘i’ at time ‘t’
The 𝑞𝑖𝑗𝑡 can then be used in the assessment of CLV.
• Model based on theoretical underpinnings of consumer behavior
• Can account for various salient aspects of consumer behavior (e.g. multiple discreteness,
Merits
budgeting etc.) which cannot be addressed by other methods
• Aids in accurate out of sample prediction and managerial policy simulations
• Model development and estimation is very complex
Shortcomings
• Relies heavily on across and within variation in customer purchases
 44

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---- (2010b), "Driving profitability by encouraging customer referrals: who, when, and how," Journal of
Marketing, 74 (5), 1-17.

Kumar, V, and J. Andrew Petersen (2016), "Maximizing Donor Lifetime Value," Working Paper,
Georgia State University, Atlanta, GA.

Kumar, V, Sridhar N Ramaswami, and Rajendra K Srivastava (2000), "A model to explain shareholder
returns: marketing implications," Journal of Business Research, 50 (2), 157-67.
 48

Kumar, V, and Werner Reinartz (2016), "Creating enduring customer value," Journal of Marketing, 80
(6), 36-68.

Kumar, V, Denish Shah, and Rajkumar Venkatesan (2006a), "Managing retailer profitability—one
customer at a time!," Journal of Retailing, 82 (4), 277-94.

Kumar, V, Nita Umashankar, Kihyun Hannah Kim, and Yashoda Bhagwat (2014a), "Assessing the
influence of economic and customer experience factors on service purchase behaviors," Marketing
Science, 33 (5), 673-92.

Kumar, V, Rajkumar Venkatesan, and Werner Reinartz (2008b), "Performance implications of adopting a
customer-focused sales campaign," Journal of Marketing, 72 (5), 50-68.

Kumar, V, Xi Zhang, and Anita Luo (2014b), "Modeling Customer Opt-In and Opt-Out in a Permission-
Based Marketing Context," Journal of Marketing Research, 51 (4), 403-19.

Kumar, V., J Andrew Petersen, and Robert P Leone (2007), "How valuable is word of mouth?," Harvard
Business Review, 85 (10), 139-46.

Kumar, V., and Werner Reinartz (2012), Customer Relationship Management: Concept, Strategy, and
Tools. Heidelberg, Berlin: Springer-Verlag GmbH.

Kumar, V., and Denish Shah (2009), "Expanding the Role of Marketing: From Customer Equity to
Market Capitalization," Journal of Marketing, 73 (6), 119.

Kumar, V., Sarang Sunder, and Robert P Leone (2014c), "Measuring and Managing a Salesperson's
Future Value to the Firm," Journal of Marketing Research, 51 (5), 591-608.

Kumar, V., Sarang Sunder, and Robert P. Leone (2015), "Who’s Your Most Valuable Salesperson?,"
Harvard Business Review, 93 (4), 62-68.

Kumar, V., and Rajkumar Venkatesan (2005), "Who are the Multichannel Shoppers and How do they
Perform? : Correlates of Multichannel Shopping Behavior," Journal of Interactive Marketing, 19 (2), 44-
62.

Kumar, V., Rajkumar Venkatesan, Tim Bohling, and Denise Beckmann (2008c), "The Power of CLV:
Managing Customer Lifetime Value at IBM," Marketing Science, 27 (4), 585-99.

Kumar, V., Rajkumar Venkatesan, and Werner Reinartz (2006b), "Knowing What to Sell, When, and to
Whom," Harvard Business Review, 84 (3), 131-7, 50.

Kyle, Albert S. (1985), "Continuous Auctions and Insider Trading," Econometrica, 53 (6), 1315-35.

Lewis, Michael (2006), "Customer acquisition promotions and customer asset value," Journal of
Marketing Research, 43 (2), 195-203.

Luo, Anita, and V. Kumar (2013), "Recovering Hidden Buyer–Seller Relationship States to Measure the
Return on Marketing Investment in Business-to-Business Markets," Journal of Marketing Research, 50
(1), 143-60.

Mahoney, Joseph T, and J Rajendran Pandian (1992), "The resource-based view within the conversation
 49

of strategic management," Strategic Management Journal, 13 (5), 363-80.

Malkiel, Burton G. (1995), "Returns from Investing in Equity Mutual Funds 1971 to 1991," Journal of
Finance, 50 (2), 549-72.

Morgan, Robert M, and Shelby D Hunt (1994), "The commitment-trust theory of relationship marketing,"
Journal of Marketing, 58 (3), 20-38.

Odean, Terrance (1998), "Volume, Volatility, Price, and Profit When All Traders Are Above Average,"
The Journal of Finance, 53 (6), 1887-934.

Petersen, J Andrew, and V Kumar (2009), "Are product returns a necessary evil? Antecedents and
consequences," Journal of Marketing, 73 (3), 35-51.

Petersen, J Andrew, and V. Kumar (2015), "Perceived Risk, Product Returns, and Optimal Resource
Allocation: Evidence from a Field Experiment," Journal of Marketing Research, 52 (2), 268-85.

Petersen, J Andrew, Leigh McAlister, David J Reibstein, Russell S Winer, V Kumar, and Geoff Atkinson
(2009), "Choosing the right metrics to maximize profitability and shareholder value," Journal of
Retailing, 85 (1), 95-111.

Ramani, Girish, and V Kumar (2008), "Interaction orientation and firm performance," Journal of
Marketing, 72 (1), 27-45.

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

---- (2002), "The Mismanagement of Customer Loyalty," Harvard Business Review, 80 (7), 86-94.

---- (2000), "On the Profitability of Long-Life Customers in a Noncontractual Setting: An Empirical
Investigation and Implications for Marketing," Journal of Marketing, 64 (4), 17-35.

Reinartz, Werner, Jacquelyn S Thomas, and V Kumar (2005), "Balancing acquisition and retention
resources to maximize customer profitability," Journal of Marketing, 69 (1), 63-79.

Rust, Roland T, V Kumar, and Rajkumar Venkatesan (2011), "Will the frog change into a prince?
Predicting future customer profitability," International Journal of Research in Marketing, 28 (4), 281-94.

Rust, Roland T., Katherine N. Lemon, and Valarie A. Zeithaml (2004), "Return on Marketing: Using
Customer Equity to Focus Marketing Strategy," Journal of Marketing, 68 (1), 109-27.

Ryals, Lynette (2003), "Making customers pay: measuring and managing customer risk and returns,"
Journal of Strategic Marketing, 11 (3), 165-75.

Selnes, Fred (2011), "A Comment on 'Balancing Risk and Return in a Customer Portfolio'," Journal of
Marketing, 75 (3), 18-21.

Shah, Denish, V Kumar, Kihyun Hannah Kim, and JeeWon Brianna Choi (2017), "Linking Customer
Behaviors to Cash Flow Level & Volatility: Implications for Marketing Practices," Journal of Marketing
Research, 54 (1), 27-43.
 50

Shah, Denish, V Kumar, Yingge Qu, and Sylia Chen (2012), "Unprofitable cross-buying: evidence from
consumer and business markets," Journal of Marketing, 76 (3), 78-95.

Shah, Denish, V. Kumar, and Kihyun Hannah Kim (2014), "Managing Customer Profits: The Power of
Habits," Journal of Marketing Research, 51 (6), 726-41.

Shah, Denish, Roland T Rust, A Parasuraman, Richard Staelin, and George S Day (2006), "The path to
customer centricity," Journal of Service Research, 9 (2), 113-24.

Sirianni, Nancy J, Mary Jo Bitner, Stephen W Brown, and Naomi Mandel (2013), "Branded service
encounters: strategically aligning employee behavior with the brand positioning," Journal of Marketing,
77 (6), 108-23.

Srinivasan, Shuba, and Dominique M Hanssens (2009), "Marketing and firm value: Metrics, methods,
findings, and future directions," Journal of Marketing Research, 46 (3), 293-312.

Srivastava, Rajendra K, Tasadduq A Shervani, and Liam Fahey (1998), "Market-based assets and
shareholder value: A framework for analysis," Journal of Marketing, 62 (1), 2-18.

---- (1999), "Marketing, Business Processes, and Shareholder Value: An Organizationally Embedded
View of Marketing Activities and the Discipline of Marketing," Journal of Marketing, 63 (4).

Sunder, Sarang, V Kumar, and Yi Zhao (2016), "Measuring the Lifetime Value of a Customer in the
Consumer Packaged Goods (CPG) industry," Journal of Marketing Research, 53 (6), 901-21.

Tarasi, Crina O, Ruth N Bolton, Michael D Hutt, and Beth A Walker (2011), "Balancing risk and return
in a customer portfolio," Journal of Marketing, 75 (3), 1-17.

Thomas, Jacquelyn S, and Ursula Y Sullivan (2005), "Managing marketing communications with
multichannel customers," Journal of Marketing, 69 (4), 239-51.

Umashankar, Nita, Yashoda Bhagwat, and V Kumar (2016), "Do loyal customers really pay more for
services?," Journal of the Academy of Marketing Science, 1-20.

Venkatesan, R., and V. Kumar (2004), "A Customer Lifetime Value Framework for Customer Selection
and Resource Allocation Strategy," Journal of Marketing, 68 (4), 106.

Venkatesan, Rajkumar, V Kumar, and Timothy Bohling (2007), "Optimal customer relationship
management using Bayesian decision theory: An application for customer selection," Journal of
Marketing Research, 44 (4), 579-94.

Verhoef, Peter C, Werner J Reinartz, and Manfred Krafft (2010), "Customer engagement as a new
perspective in customer management," Journal of Service Research, 13 (3), 247-52.

Weber, Elke U, and Eric J Johnson (2009), "Mindful judgment and decision making," Annual Review of
Psychology, 60, 53-85.

Zeithaml, Valarie A (2000), "Service quality, profitability, and the economic worth of customers: what
we know and what we need to learn," Journal of the Academy of Marketing Science, 28 (1), 67-85.