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are likely to give. The SOW metric does reveal whether or not a customer is loyal but if
used as the sole metric for resource allocation, then SOW does not take into account the
size of the budget; the metric provides a SOW percentage but does not accompany a
dollar amount. Finally, the PCV metric does not look directly at profitability as a
variable, and makes the assumption that past spending behavior will indicate future
behavior (Kumar, 2008b). All of these metric-specific drawbacks, coupled with their
collective lack of predictive power, can lead a firm to misallocate precious resources
and to privileging the wrong customer or customer segment.
Because of the shortcomings of the strategic customer-based value metrics, there is
need for a metric that solves the issues that are inherent within them; a metric that can
accurately predict the future profitability of a customer and strengthen resource
allocation budgets. The CLV metric does just that (Kumar and Reinartz, 2006; Kumar
and Rajan, 2012). CLV is a forward-looking metric that does not prioritize loyalty
over profitability, meaning CLV makes certain that valuable (and not merely loyal)
customers are profitable (Kumar, 2008b). Unlike the previous three categories of popular
marketing metrics, the measurement of CLV includes the likelihood of a customer being
active in the future and the marketing dollars that need to be spent to retain the customer
and achieve a positive return on investment (ROI) (Kumar, 2008a). CLV also lets
managers “know when a customer buys, how much a customer buys and how much it
costs to make the sale (Kumar, 2008b)”. The above-mentioned aspects of CLV make it an
encompassing, revolutionary, and unique forward looking metric. Summing the CLV of
all customers leads to customer equity that forms the foundation for valuing firms
(Rust et al., 2004; Schulze et al., 2012). Additionally, competitive effects can be included
as the elements of customer equity to consider customers brand switching behavior
(Rust et al., 2004). Leading indicators of behavior such as what customers think about
the relationship with the firm and fit between customer needs and provided services can
also be used as sources of customer equity (Zeithaml et al., 2006). However, customer
equity and CLV do not provide every possible piece of information, and can only be used
to understand the profitability of the firm’s customers. Therefore, this paper argues for
the use of complementarity metrics. While customer equity is the most useful metric in
understanding the value of the customer base, it should be combined with other metrics
such as the expected churn rate, expected SOW, expected failure and service recovery
rates, human resource (HR) metrics, as well as operational metrics. Each of these metrics
can provide different pieces of valuable information that can inform managers about
the direction their business is going into. For instance:
(1) Customer engagement value (Kumar et al., 2010a, b) – this measure provides a
snapshot of customer health that encompasses CLV, customer referral value,
customer influencer value, and customer knowledge value.
(2) Customer engagement behaviors (Van Doorn et al., 2010) – beyond a
transactional basis; it is defined as a behavioral manifestation that are focused
around a firm or a brand which is a result of motivational drivers.
(3) Expected churn rate – necessary to understand:
. The degree to which retention is actually an issue.
. The potential financial losses associated with customer churn.
(4) Expected SOW – for firms in polygamous (simultaneous multi-brand
users) industries, it can be calculated using the wallet allocation rule

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