Lifetime Value: Empirical Generalizations and Some Conceptual Questions
Robert C. Blattberg
Edward C. Malthouse
Scott A. Neslin
Robert C. Blattberg1
Edward C. Malthouse2
Scott Neslin3
August 2007
1
Polk Bros. Professor of Retailing; Professor of Marketing; Director of the Center for
Retail Management Center, Kellogg School of Management, rblattberg@northwestern.edu
2
Theordore and Annie Sills Associate Professor of Integrated Marketing
Communications, Medill School of Journalism, Northwestern University,
ecm@northwestern.edu
3
Albert Wesley Frey Professor of Marketing, Tuck School of Business at Dartmouth,
scott.a.neslin@tuck.dartmouth.edu
Lifetime Value: Empirical Generalizations and Some Conceptual Questions
Abstract: From the extant literature on LTV we identify four empirical
generalizations (well-defined, consistent effects found by at least three different
authors): (1) customer satisfaction increases LTV; (2) marketing efforts are
associated with higher LTV; (3) cross-buying is associated with higher LTV; and
(4) multichannel purchasing is associated with higher LTV. The frequency and
monetary value of previous purchases generally have a positive effect on LTV,
although there are contradictory findings. We identify additional issues that have
received limited attention in the literature, but require further empirical study: the
effects of pricing and promotions on LTV, managing a sequence of contacts to
maximize response rates and LTV, and whether LTV can be forecasted
sufficiently accurately. Eight conceptual or strategic issues are identified and
discussed.
Many firms are now focusing on identifying their most profitable customers and
nurturing long-term relations, which represents a different way of making and evaluating
marketing decisions and the product-centric approach. At the center of the approach is
Lifetime Value of a customer (LTV). It is a pivotal concept in the customer-centric
approach to marketing that pervades the many customer relationship management
approaches that are frequently discussed and practiced by firms such as one-to-one,
loyalty, and database marketing. Lifetime value (LTV) is the present value of all the
future cash flows attributed to a customer relationship (Pfeifer et al. 2005, p 17). Due to
uncertainty in future customer, firm, and competitor behavior, LTV is in reality a random
variable and most applications calculate expected LTV, which can be written as:
 
~
E Vt
LTV  
t 1
t 1 (1  d )

(1)
~
where Vt is the customer’s net contribution in period t, and d is the discount rate. Net
contribution is driven by: (1) customer duration; that is, whether the customer is still
active in period t; (2) revenues generated by the customer in period t, given he or she
survives to that period; and (3) costs of serving the customer in period t. There are thus
four components of LTV: (1) duration, (2) revenues, (3) costs, and (4) discount rate.
LTV can be used to guide the firm’s acquisition and retention activities, and is
sometimes aggregated over customers as a measure of firm or segment value. Research
on LTV and its components is an active area and there are many research articles that
propose methods of estimating LTV or its components under various conditions, study
their antecedents, attempt to maximize LTV over some space of marketing actions, or
discuss its applications. Given this widespread interest in customer-centric marketing
and LTV, it is important to take stock of what is known and needs to be known about
LTV.
There are excellent surveys of this subject (e.g., see Jain and Singh 2002 or
Blattberg, Kim and Neslin, 2008). As an indication of breadth and interest on this
subject, the survey in the latter reference spans multiple chapters. We shall not attempt to
duplicate this effort at summarizing the field here. In particular, space will not permit us
to review the models used to estimate LTV. The purpose of this article is to (1) discuss
empirical generalizations that can be drawn from extant academic research literature, (2)
discuss empirical findings that do not yet reach the threshold for generalization but are
suggestive and interesting, and (3) discuss conceptual and strategic issues relating to
LTV. This is a subjective list of generalizations and conceptual issues based on our
review of the literature and what we think is most important.
Blattberg, Briesch and Fox (1995, p G123) defined empirical generalizations as
“(1) the topic being analyzed is well defined; (2) there are at least three articles by at least
three different authors in which empirical research has been conducted in the specific
area; and (3) the empirical evidence is consistent, i.e., the sign of the effect is the same in
each of the articles.” It is important to study empirical generalizations for several
reasons. From a research viewpoint, multiple studies reporting a similar empirical
conclusion can verify theory or identify areas where theoretical work is needed. From a
practical viewpoint, empirical generalizations can provide managers with guidance in
making marketing decisions.
In the following section, we present issues that satisfy the above definition of an
empirical generalization. Next, we discuss issues for which there is some research
evidence, but not enough (in our judgment) to reach the status of an empirical
generalization. We then discuss conceptual issues that are not empirical per se but
influence our understanding of how to apply LTV. We close with a brief summary.
Empirical Generalizations
We have identified four findings that we believe qualify as empirical
generalizations regarding LTV: customer satisfaction, marketing, cross-buying, and
multichannel purchasing all have positive relationships with LTV.1 Table 1 summarizes
research addressing each generalization. Much of the evidence is based on using one of
the earlier described components of LTV as the dependent variable. Because of equation
(1), if there is a relationship with a component, there will be a relationship with LTV.2
1.
Customer satisfaction has a positive effect on LTV. There is a large literature
relating customer satisfaction with loyalty and measures of firm performance. The
consensus is that satisfaction has a positive relationship with loyalty, retention and
profitability,3 although Yeung et al. (2001) find that the strength of the relationship and
the magnitude of the impact appear to vary with the choice of performance measures and
industry sector. For example, the relationship is strong in the financial sector, but much
weaker in the technology and communications sectors. This suggests that there are sector
characteristics that moderate the relationship between satisfaction and profitability and
points out the need for a richer theory. Yeung’s articles also find that the amount of
variation in performance measures explained by satisfaction is generally small,
1
We omit demographics from this discussion because their effects are likely to be product-specific. For
example, age might be associated positively with LTV for financial services, but negatively for music
downloads.
2
Note however that ideally, the empirical work would use LTV as the dependent variable. We
acknowledge the caveat that it is possible some factor, for example cross-buying, might relate positively to
customer duration, but also increase costs, so that LTV might indeed relate negatively to cross-buying.
3
Some authors, e.g., Heskett et al. (1994), theorize that loyalty is a consequence of satisfaction and an
antecedent of profitability, i.e., satisfaction → loyalty → profitability
suggesting other causal factors should be included. Most of the empirical work has used
measures of firm profitability as dependent variables and more work is needed to
investigate this relationship at the customer level.
2.
Marketing efforts are associated with higher LTV: The evidence summarized in
Table 1 suggests a strong association between marketing efforts and customer duration.
One study looked at customer “profitability” (Reinartz, Thomas, and Kumar 2005) and
found a positive relationship. That marketing influence LTV is a requirement for LTV to
be a useful marketing metric. However, the finding of a positive association is nontrivial.
Firms might spend more money trying to rescue customers who have shorter duration;
they might spend too much on marketing to customers who would have purchased
anyway, reducing the profitability of these efforts.
The ramifications of this generalization are crucial because they suggest firms can
formulate marketing activities to manage LTV over time. Note however that most of the
evidence is based on associations. The problem is that firms might choose to expend
higher marketing efforts on more valuable customers, and thus LTV caused increased
marketing rather than the reverse. A formal selectivity model (Woodridge 2002) would
be needed to sort out the causality issue, where there would be two equations: one for
customer profitability and the second for firm marketing efforts.
3.
Cross-buying is associated with higher LTV. Reinartz and Kumar (2003, pp. 81-
82) give a literature review and develop a theoretical rational for cross-buying having a
positive effect on duration. Table 1 shows that there is generally a positive effect,
although some authors do not find a significant effect. There is some question as to
whether the relationship between cross-buying and LTV is spurious. It could be that
customers who highly prefer a company buy often from it and also buy from several
departments. For example, if customers are loyal to a given electronics retailer because
of its service, they are likely to make multiple purchases from that retailer such as TV’s,
DVD’s, and computer equipment. Reinartz, Thomas, and Bascoul (2006) used Granger
causality tests to assess the direction of causality, and found that profitability caused
cross-buying rather than the reverse. So while we clearly have a positive association
between cross-buying and LTV, we still need to determine causality.
4.
Multichannel purchasing is associated with higher LTV: This is a significant
finding in the multichannel literature (see Neslin and Shankar (2008) in this issue of
Journal of Interactive Marketing, as well as Neslin et al. (2006)). Multichannel
purchasing could increase LTV because it creates switching costs or increases customer
satisfaction. For example, customers who use a bank’s ATM, branch office, and online
service, must extricate themselves from several contact points in order to switch to
another bank. Hence switching costs are high. In a more positive vein, customers may
be more satisfied because dealing with the bank is convenient and they therefore give it
more business.
The evidence in favor of positive association is fairly strong, especially, although
not exclusively, in the retail industry. One interesting exception can be found in
Campbell and Frei (2006), who find that while adopters of online banking increase usage
frequency, total revenue goes down, possibly due to customers managing their assets
more effectively.
The question of causality rears its head again in the case of multichannel usage,
similarly to the case of cross-buying. Conceptually, the phenomena are very similar –
multichannel purchasing is “cross-buying” across the firm’s channels rather than the
firm’s departments. Again, the multichannel shopper may become more satisfied with
the company, and hence becomes more loyal and more valuable. The shopper may also
receive more marketing simply by visiting various channels. On the other hand, highvalue customers may self-select into using all the firm’s channels. This explanation is
refuted by Ansari et al. (2007), but that is only one study. Further work is needed in this
area. If indeed the relationship is multichannel increases LTV, the implication is clear –
firms should encourage multichannel usage.
An Issue with Conflicting Empirical Evaluations
5.
How does RFM affect LTV? Previous purchase behavior is often summarized by
the time since the most recent purchase (R=recency), the number of previous purchases
(F=frequency), and the total amount spent (M=monetary value) – RFM. Since these
variables are widely known for existing customers, they are often included in LTV
models and used to make customer-level estimates. Frequency and monetary are often
highly correlated and it could be argued that, at least in some situations, they are different
measures of the same underlying construct, previous buying intensity. In our discussions
with practitioners, most believe that recency has a negative relationship with LTV, and
frequency and monetary to have a positive relationship with customer value,4 but this has
not been found consistently in the academic literature. Nagar and Rajan (2005) find a
positive relationship loan volume and firm profitability. Reinartz and Kumar (2003) find
positive relationships between previous spending levels and lifetime duration. The
models in Malthouse and Blattberg (2005) were optimized for predictive accuracy and
4
Because of the correlation between monetary and frequency, one must be careful about sign-flips in
models involving both variables.
some had substantial multicollinearity, inhibiting the interpretation of effect signs, but
across over 100 catalog companies, a software company, a nonprofit organization, and an
educational service provider they consistently found frequency and monetary variables
had positive relationships with individual-level long-term value and recency had a
negative effect (the longer a customer has been inactive the lower LTV is). Li (1995),
however, finds a negative effect on duration at the customer level and Niraj et al. (2001)
find a negative relationship with customer profit, but it is unclear how Li defines his
“Usage” variable and Niraj et al. define their “Number of Orders” variable. Clearly this
area needs more research, but our view is that frequency and monetary are likely to have
a positive effect and recency a negative effect on LTV.
Issues with Few Empirical Evaluations
6.
How does pricing affect LTV? There has been very little research on this
question. Thomas et al. (2004) study the effects of reacquisition pricing strategies for
newspaper subscriptions on the likelihood of reacquiring a customer and on the duration
after reacquisition. In a longitudinal study, they examine the price offered at the time of
reacquisition as well as prices charged after reacquisition. They conclude that an optimal
pricing strategy would be to offer a low reacquisition price and higher prices after a
customer has been reacquired. Reinartz and Kumar (2000) study the converse question
of how a customer’s length of tenure with a firm affects LTV. They find that the
shortest-life segment of customers have a significantly higher average price paid for a
single product item for a catalog company but warn that this finding could be confounded
with other variables. Further empirical work is necessary to understanding pricing
effects. The direction of causality is also not clear.
7.
How do promotions affect LTV? Anderson and Simester (2004) studied the long-
term impact of promotion depth for a catalog company using three randomized controlled
experiments. All customers in the study received a catalog including merchandise
indicated as being “on sale,” but the prices for the sale items were lower for the
“promotion” group. In all tests promotions increased short-term sales, but those in the
promotion group purchased less in the 12 months after the promotion than during the
subsequent year, suggesting acceleration. They also found that deeper price discounts
increased future purchases by first-time customers, but reduced future purchases by
established customers. This conclusion should be tested for other firms and in other
industries. Li (1995) also finds that the hazard of cancelling long-distance telephone
service decreases when a discount plan is offered. Overall, there is some evidence,
mostly through Anderson and Simester’s study, that database marketing communications
can act like promotions and induce the same long-term effects observed in the
promotions literature (e.g., see Neslin 2002 or Blattberg et al. 1995), but more empirical
work is needed to measure these effects in other settings. Optimal contact models should
consider these issues. For example, a communication may accelerate a purchase, so it
would not make sense to communicate again until sufficient time has elapsed for the
customer to become ready to buy again.
8.
What is the relationship between the number of contacts and response rates? Can
too many contacts actually decrease response rates? These questions are important
because they provide a non-cost reason to limit the number of contacts – fewer contacts
could potentially produce more sales. The answer to the second question seems to be
yes, but further work is necessary on the first to know the points at which customers
“wear out” and understand the theoretical reasons they do so. Ansari et al. (2007) find a
negative interaction between purchase frequency and like communications as well as
different communications. For example, they find that two catalogs delivered at nearly
the same time have a smaller impact on purchase frequency than if the catalogs are
delivered at more highly separated times. They find the same negative interactions
between successive emails and between successive emails and catalogs. In evaluating a
new way of deciding which customer should receive a catalog, Gönül, Kim and Shi
(2000) conclude that “it is better to send (not too many) a few catalogs with (not too long
but) a moderate amount of time between them to encourage purchase (pp. 2-3),” also
supporting wear out. Using self-reported data, Eastlick, Feinberg and Trappey (1993)
conclude that the relationship between the number of catalogs and purchase history (e.g.,
frequency and monetary) is fit better by a quadratic inverted-U function than a linear one.
Ganzach and Ben-Or (1996) critique their conclusions and rationale, and Feinberg,
Eastlick, and Trappey (1996) defend them. Campbell et al. (2001) suggest the
relationship is concave with diminishing returns, but do not report that the returns
become negative at some point.
In summary, it seems clear that wear-out is a real phenomenon, in that there are
decreasing returns to marketing within a given time period, and that the spacing between
contacts can counteract wear-out. While this makes it plausible that a company could
produce higher LTV with fewer communications, even if the communications are
costless, this specific finding needs to be demonstrated. Further if the shape of the curve
is an inverted-U, it is critical to determine the wear-out point. Also, theories need to be
developed which explain why the shape is an inverted-U.
9.
Do optimal contact models, which select a combination of contacts to maximize
LTV, work? The positive associations between marketing and LTV, and between
multichannel usage and LTV, suggest that firms may be able to actively manage each
customer’s LTV over time, through an “optimal contact model.” For example, Sun and
Li (2005) present a dynamic programming model where customers are “right-channeled”
to the specific calling center that optimally trades off their costs of using that channel
versus their satisfaction. The first issue of the Journal of Interactive Marketing published
an article highlighting longitudinal contact strategies as an important research topic
(Kestnbaum et al. 1998). Many models have been proposed for this task (Bitran and
Mondschein 1996, Gönül and Shi 1998, Gönül et al. 2000, Campbell et al. 2001, Elsner
et al. 2003, Ching et al. 2004, Rust and Verhoef 2005, Simester et al. 2006, Neslin et al.
2007). Blattberg et al. (2008, Chapter 28) summarize and contrast the different
approaches. They note that while some of these methods have been field tested with
positive results, more work is needed, especially to compare dynamically optimized
contact models and simpler myopic approaches.
10.
Can LTV be predicted with sufficient accuracy at the individual level to allocate
resources? If firms allocate marketing resources on the basis of LTV, and LTV is
estimated without certainty, then it follows that there will be a chance the resources will
be misallocated because customers are misclassified. For example, a LTV estimate could
indicate that a customer should not receive some contact, when, in fact, the customer
deserves it. Malthouse and Blattberg (2005) propose the 80-15 and 20-55 rules – of the
actual top 20% of customers, approximately 55% will be misclassified and of the actual
bottom 80%, approximately 15% will be misclassified. They conclude that the
probabilities and costs of misclassification should be considered when deciding whether
to allocate resources based on LTV. An alternative, which is widely used in many
industries, is to allocate perks on the basis of actual future behavior through earned
rewards (see issue 16 below).
Conceptual and Strategic Issues Concerning LTV
11.
Absolute or incremental LTV? Should firms allocate marketing resources based
on how much a customer is worth (absolute LTV), or the potential change in LTV
(incremental) that could result from the investment? For example, many loyalty
programs assign customers to tiers based on absolute LTV; e.g, airlines offer frequent
flyers with the most miles top-tier status and thus more marketing resources. (The
appropriate objective could be that if an airline did not give perks to those with many
miles the frequent fliers would switch to another carrier, thus also affecting incremental
LTV, but tier assignments are made based on absolute LTV.) Kumar, et al. (2004)
recommend as a best practice “prioritizing and selecting customers [to target with
appropriate marketing actions] on the basis of [absolute] LTV (p 65).” Rust and Verhoef
(2005), on the other hand, use one-year incremental profit as the dependent variable and
determine how much marketing to allocate to each customer to maximize incremental
profit. Gönül, Kim, and Shi (2000) suggest using the incremental profit derived from a
contact when deciding which customers to mail. This is a complex issue because firms
are fearful of not offering high absolute LTV customers the best services and benefits.
Economic theory would imply using incremental LTV.
12.
Best customers or brand concept? These are different philosophies of marketing.
The best-customer approach recommends identifying historically profitable customers (or
estimates of future LTV, which are usually highly correlated with historical profitability)
and making marketing decisions based on the needs of this customer group. It also
suggests segmenting customers by value and developing marketing programs for
different value-based segments (e.g., Zeithaml, Rust, and Lemon 2001). In brandfocused marketing the business unit (or brand manager) articulates a brand concept in a
positioning statement that guides marketing decisions. Firms that sell consumer package
goods such as Kraft and P&G tend to be very good at this kind of marketing. The two
approaches could lead to different decisions. Should firms modify their marketing
activities to cater to their best customers? Can brand-focused and best-customer
marketing be integrated? If so, how? Under what circumstances is one approach
preferred over the other? Malthouse and Calder (2005) attempt to reconcile the two
approaches.
This issue is related to the customer-centric versus product-centric debate
(Peppers and Rogers 1993). Note both of these approaches are marketing strategies; the
question is whether one should start with the customer and personalize marketing
accordingly, or start with a product line developed based on the needs of a definable
target group?
This issue therefore relates to targeting and segmentation, which are central
concepts in modern marketing. Under the best-customer approach, segments are defined
based on historical profitability and all segments could potentially receive marketing,
albeit at different levels. In brand-centric marketing segments are defined based on other
criteria such as attitudes, beliefs, needs, psychographics, or demographics, and a single
segment is targeted. This creates confusion with the term segment, e.g., business units
that have well-articulated brand concepts such as Eddie Bauer or Courtyard by Marriott
have targeted some market segment, which is further partitioned into value-based
segments. It also creates the possibility that the marketing efforts designed to
communicate the brand concept are inconsistent with those offered to the value-based
segments, especially when these marketing activities are managed in different
departments within an organization. To what extent should the marketing programs
developed for the value-based segments be consistent with the branding efforts? Are
there adverse effects when the two are uncoordinated? Of course, not all businesses have
well-articulated brand concepts, and in such cases should firms focus on identifying,
satisfying and retaining their best customers, or on identifying a brand concept and
making marketing decisions to develop the brand (the two may not be incompatible)?
13.
Do customers “die,” or just become dormant with some reactivating later? Many
LTV models assume that customers die at some point, but there is little evidence whether
this is a reasonable assumption. This assumption seems to have been made out of
convenience rather than it being reasonable for a wide range of business situations. An
alternative is that at least some customers who appear to be dead have entered a
temporary dormant state and will return to an active state at a later time. This is the
migration or Markov chain model of LTV (e.g., see Pfeifer and Carraway 2000)
Gopinath et al. (2007) find that catalog customers and nonprofit donors who are
statistically “dead” do reactivate. For example, a customer could discontinue a
relationship with a telecom provider and switch to a competitor, but then return to the
first provider at a later time. If the segment of temporarily dormant customers is
sufficiently large and pervasive across industries, then more complicated models should
be developed to accommodate the possibility that customers migrate between states. It is
conceivable that there is also an intermediate “switcher” state.
14.
Lifetime value or long-term value? Many models for LTV attempt to estimate
cash flows in perpetuity, but is a finite horizon more managerially actionable? On a
practical level, managers in many industries change jobs frequently and may want to
show a more rapid return for their actions. Also, many firms can change the level of
investment in a particular customer over time taking into consideration future customer
actions. For examples, airlines and hotels adjust loyalty club tier assignments
periodically (e.g., annually). Catalog companies and nonprofit organizations make
decisions over time concerning which former customers/donors should receive another
mailing. If firms can adjust marketing investment levels over time, then over what time
period should projections be made? A related question is whether marketing decisions
would change if shorter horizons were used (how sensitive is the decision to the time
horizon?). Nagar and Rajan (2005) and Rust and Verhoef (2005) both use one-year
future windows. Malthouse and Blattberg (2005) vary the length of the future period and
find that predictive accuracy remains consistent.
15.
Does RFM accurately represent behavior? Is RFM an artifact due to
heterogeneity in preference or is there recidivism in buying behavior? What this means is
that those customers who prefer a given set of products or services offered by a
manufacturer, retailer or cataloguer will purchase more frequently. In a traditional RFM
matrix, it would appear that more frequent and more recent customers have a higher
probability of purchasing. While this is true, the issue is causation. Should a firm invest
in trying to increase a customer’s recency because that increases his or her probability of
buying in the future? If it is heterogeneity in preference and the preferences are
stationary, then the customer will not increase his or her probability of buying if the
customer purchased more recently. This issue is a critical issue to database marketers
because it has significant implications for targeting strategies.
16.
What is the role of LTV for firms that allocate marketing resources through
earned rewards? Many firms allocate a substantial fraction of their perks based on actual
future behavior. For example, airlines typically give free flights based on miles or the
frequency of flights. Hotels give free nights based on the frequency of stays. Tesco, a
British supermarket chain, returns a percentage of the amount spent during the previous
quarter to its loyalty club members. Credit cards give rewards (cash-back bonuses, miles,
etc.) proportional to the amount charged during a recent period. In each instance the perk
is earned through recent behavior. Those who do more of what the firm wants receive
greater rewards. How should LTV be used by such firms? How is incremental LTV
affected by this marketing allocation strategy?
17.
Marginal or full costing? Much of the research on LTV focuses on its revenue
and duration components. However, how one computes the costs that enter the LTV
calculation can have a dramatic impact. Perhaps the most important conceptual issue in
determining the appropriate costs is whether the calculation should only include the direct
variable costs of serving the customer (marginal costing) or also include an allocation of
overhead that does not change depending on whether the customer is served or not (full
costing).
The argument for marginal costing (summarized by Blattberg, Kim, and Neslin
2008, Chapter 6), is that full costing can lead to the rejection of customers (either by not
acquiring them or by “firing” them) who would clearly increase profits if they were
served. Consider the following situation: a company has two customers, one of whom
generates net revenues (net of variable costs) of $500; the other generates $150. The
company has a fixed cost (e.g., the salary of the CEO, the cost of the marketing analytics
group, the cost of a call center) that will not change if either or both of the customers are
served. Assume this fixed cost is $400, or $200 per customer. Under these assumptions,
we have the following LTV calculations:
Customer 1
Customer 2
Marginal Costing
LTV = $500
LTV = $150
Full Costing
LTV = $500-$200=$300
LTV = $150-$200=$-$50
The full costing calculation says that the LTV of Customer 2 is negative. One might
interpret this to mean that the customer has a negative long-term contribution and should
be terminated. In practice, this would mean that the customer would be allowed to churn
or would receive no efforts to have their subscription or contract renewed. However, the
company would then be left with the following profits:
Net Revenues
Fixed Costs
Total Profits
With Customer 1
$500
$400
$100
With Customers 1 and 2
$650
$400
$250
Clearly the firm is better off with keeping Customer 2, but Customer 2 has a negative
LTV under full costing!
The above example makes a compelling case that marginal costing is preferred to
full costing. However, life is not always so simple. One complication is that some costs
are “semi-variable,” i.e., they are constant within a given range of customers, but then
jump according to a step function once a threshold is surpassed. For example, at some
point the next customer necessitates the institution of a second call center, and that cost
then becomes fixed for successive customers. It seems incorrect to allocate all the costs
of the second call center to the marginal customer who necessitates increasing the call
center. Also, it may be better to drop Customer 2 and instead acquire Customer 3, thus
avoiding having to add a new call center.
These are difficult issues. Blattberg, Kim, and Neslin (2008) emphasize that the
calculation of LTV needs to be linked to the decision at hand. For example, the firm may
be deciding between serving Customer 2, Customer 3, and Customers 2 and 3. Variable
costs may differ between the third and the first two options, due to the required addition
of the call center. But there is no need to allocate fixed costs – in fact it could lead to the
wrong decision, as in the example above – if these costs will not change within the range
of options being considered by the firm.
In summary, the argument for marginal costing requires that the firm consider the
full range of decisions it faces. This may require a longer-term orientation, e.g., thinking
about Customer 3 as well as Customer 2. Our point is to note the conceptual challenge of
this issue, and that one can find full costing (Searcy 2004) as well as marginal costing
(Mulhern 1999) applied in the literature. The field would be well-served to have this
issue resolved.
18.
What is the appropriate discount rate?5 Capital budgeting theory (e.g., see
Brealey, Myers, and Marcus 2004) would view customers as financial assets.
Accordingly, the appropriate discount rate is the opportunity cost of capital for the firm’s
investors, i.e., the rate of return the firm’s investors could achieve for an investment of
5
Note this issue is related to the lifetime versus long-term value issue raised previously, where the solution
to the discount rate problem essentially is to use a short time window where the level of discount, assuming
one is even used, will not make much of a difference.
similar risk. If the firm uses this discount rate, the financiers of the company will prefer
alternative investments to customers who have negative LTV’s.
Blattberg, Kim and Neslin (2008, Chapter 6) discuss how to calculate the
opportunity cost of capital following capital budgeting theory. This may seem
straightforward because this theory is so well-formulated. However, there are
complexities that emerge when we are talking about customer management. For
example, the key part of the formula is what “investors could achieve for an investment
of similar risk.” In practice, customers have different risks. Customer A may be a sure
bet to contribute $1000 per year ad infinitum, whereas Customer B may be contribute
anywhere between $500 and $3000; it is too early to tell. In capital budgeting
applications, companies handle this by introducing “project-specific” discount rates.
Does this mean that each customer should have a different discount rate according to his
or her risk? How would one handle this in practical terms?
Concluding Comments
In summary, we have endeavored to identify empirical generalizations regarding
LTV as well as suggestive findings that have not yet achieved the threshold for
generalizability. In addition, we have discussed conceptual/strategic issues involving
LTV that need better theory or practical solutions.
We found four findings that reached the requirements to be called empirical
generalizations: (1) Customer satisfaction increases LTV; (2) Marketing efforts are
associated with higher LTV; (3) Cross-buying is associated with higher LTV, and (4)
Multichannel purchasing is associated with higher LTV. While we cannot claim these
generalizations exhaust all the possibilities, they share a common theme in that they
suggest that LTV can be profitably managed. The one major caveat is that three of these
findings are associations; not enough evidence has been found to say for example that
cross-buying causes higher LTV rather than a third factor causing cross-buying to be
correlated with LTV. Future research needs to focus on distilling causality in these
relationships. Much of the extant research studies components of LTV rather than LTV
itself, and further research is needed with LTV as the outcome.
The suggestive findings are more aptly stated as questions, and include: (1) Does
previous purchasing increases LTV? (2) What is the impact of pricing on LTV? (3) What
is the impact of promotions on LTV? (4) Does “wear-out” imply that customers can be
over-contacted, even when the cost per contact is costless? (5) Do optimal contact models
work better than simpler alternatives? and (6) Can LTV be adequately predicted? These
are all empirical questions. They all have received some attention in the literature, but
more studies are needed. Again, the common theme is that once these issues are
generalized, companies will be better able to proactively managed customer lifetime
value.
We identified eight conceptual/strategic issues and we will not repeat all of them
here. These issues eventually could be researched empirically but most of them require
conceptual as well as strategic questions to be settled first. For example, the question of
full versus marginal costing is clearly a conceptual issue. The issue of whether customers
“die” gets at the core of whether the standard “simple retention model” is appropriate,
and under what conditions. The issue of best customer versus brand marketing borders
on the strategic question of whether firms should be customer-centric or product-centric.
We started with a simple definition – LTV is the net present value of the future
cash flows generated by a customer relationship – and have pointed out the complexities
as well as the progress that has been made in understanding this concept. While we were
able to identify four generalizations and other issues that have received substantial
empirical investigation, we believe it would be accurate to say the glass is half-empty
rather than half-full at the moment. This is meant optimistically, that the most exciting
findings and breakthroughs await us in the field of LTV. We hope that this paper
contributes to this ultimate outcome.
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TABLE 1
Summary of empirical generalization articles
Authors
Anderson et al. 94
Hallowell 96
Anderson et al. 97
Bolton 98
Bernhardt et al. 00
Yeung et al. 00
Yeung and Ennew
01
Yeung et al. 02
Guo et al. 04
Reinartz and Kumar
‘03
Reinartz and Kumar
Brusco et al ‘03
Brusco et al ‘03
Li ‘95
Reinartz, Thomas,
and Kumar ‘05
Li 95
Hallowell 96
Niraj et al. 01
Reinartz and Kumar
03
Garland 04
Nagar and Rajan 05
Reinartz, Thomas,
and Bascoul ‘06
Direction of Effect
Dependent Variable(s)
Customer Satisfaction
+
Firm profitability
+
Individual Duration
+
Firm profitability
+
Individual Duration
+ in time series, NS
Store profit
for cross-sectional
+, but R-squared low Firm profitability
Mostly +, but varies Firm and market generated
across industries
measures of firm performance
+ in 5/6 years
Net and Operating firm income
+ lagged effect
Firm profitability
Marketing
+ (expenditure level) Individual duration
+ (loyalty program)
+ (service)
+ (product quality)
+ (discount
programs)
+ (retention dollars,
marketing contacts)
+
+
NS
+
+
NS
+
Thomas and
Sullivan ’05
DoubleClick ’04
Kumar and
Venkatesan ‘05
Kushwaha and
Shankar ‘07
Campbell and Frei
‘06
+
Nagar and Rajan ‘05
Reinartz and Kumar
03
Malthouse and
Blattberg 05
+
+
+
+
+
-
+
Industry
Multiple industries
Retail banking
Multiple industries
Cell phones
Restaurant chain
Multiple industries
Multiple industries
Multiple industries
Multiple industries
Retail catalog
Individual duration
Individual duration
Individual duration
Individual duration
Retail catalog
Telecom
Telecom
Telecom
Individual profitability
B2B Hi Tech
Cross-Buying
Individual duration
Division profitability
Customer profitability
Individual duration
Customer profitability
Firm performance measures
Individual revenues and
purchase frequency
Multichannel Purchasing
Individual revenues and
purchase frequency
Individual revenues
Individual revenues and duration
Individual purchase frequency
and volume/purchase occasion
Revenues
Past Purchase Behavior
Firm performance
Individual duration
Individual-level profit
Telecommunications
Retail banking
Grocery distributor
Retail catalog
Retail banking
Retail banking
Direct mail book
seller; Catalog retailer
Retail
Retail
B2B Hardware and
Software
Retail
Retail banking
Retail banking
Retail catalog
Retail catalog, non
profit, education,
Li 95
Niraj et al. 01
-
Individual duration
Customer profitiabilty
software
Telecommunications
Grocery distributor