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ON RETAIL BANKING
Divvying
Up the Marketing Pie
Saturday, September 24, 2005
BY DOMINIQUE M. HANSSENS AND
BARBARA LEWIS
Modeling
can help optimize marketing mix resource allocation to
maximize customer equity.
| SYNOPSIS | Financial institutions have
traditionally based their marketing budgets on a
percentage of last year’s revenues or budget, which
often results in suboptimal results and wasteful
spending. Better results can be achieved by using
econometric and optimization modeling to determine an
optimal marketing budget that allocates spending across
appropriate media to increase customer equity and
shareholder value. One bank found that a 100% increase
in direct mail spending would yield a 7% gain in
customer equity through better customer retention.
How should marketing dollars be
allocated? The question is one that financial services
and other industries have been wrestling with for more
than 100 years. Many organizations in 2005 find
themselves in the same situation as Sir William Hesketh
Lever, the soap-maker and eventual founder of Unilever,
who in the late 1800s commented: “Half the money I spend
on advertising is wasted, and the trouble is I don’t
know which half.”
Traditionally, marketing budgets are
based on a percentage of last year’s revenues or budget.
Such budgets are easy to create, but this is an approach
that falls short. It fails, for example, to address how
much should be invested in customer acquisition or
retention, which customer segments and products should
be targeted, and even the type of marketing media to be
used. Because it does not challenge marketing
investments to be productive, it can lead to suboptimal
results and wasteful spending.
To enhance marketing productivity,
many financial services organizations are turning to
econometric analysis and optimization modeling. The
former examines the relationships over time between
marketing mix variables that are controlled and
performance measures, such as sale or market share, that
represent the outcomes of marketing plans.
Optimization modeling is the ideal
allocation of resources to maximize objectives. The bank
that tracks its marketing spend across media and
customer acquisition/retention can gauge the
effectiveness of its spending and determine an optimal
amount of money to be allocated to various marketing
activities. The key is to find the point where the
expense of marketing in a channel creates the highest
shareholder value.
For example, one bank found that
increasing marketing dollars spent on direct mail by
100% increased “customer equity” by 7%, or $5.3 million.
This was accomplished by extending the average customer
lifetime — customers who stay longer with the bank can
yield more long-term value even if the annual
contribution they generate is unchanged.
CUSTOMER
LIFETIME VALUE
Underlying the marketing
productivity boost is the concept of “customer lifetime
value,” which is the net present value of a customer’s
current and future contributions to profit. The sum of
all customers’ lifetime values is customer equity. There
are four sources of customer equity:
- The attraction of new
customers, which are a source of revenue growth;
- Improvements in the retention of
customers, which makes customers stay longer;
- The cross-selling of current
customers to other business lines;
- The up-selling of current
customers to a higher consumption within a business
line.
Long used in the catalog industry,
customer lifetime value is becoming increasingly
prevalent in the financial services industry as
executives recognize that marketing success is not just
the number of customers but the value of those customers
over the course of their “lifetime” with the bank. Savvy
management aims to increase customer equity as a means
of enhancing the long-term value of the financial
institution.
Marketing instruments have a
differential impact on the four components of customer
equity. These impacts are classified into two areas:
- Marketing communications, whether
broad such as national TV, or very focused such as
direct marketing;
- Other controllable factors,
including branches per capita or service employees per
branch;
In addition, external economic
factors such as the consumer price index, stock market
index and housing starts, for example.
Econometric methods, which are used
to develop market response models, indicate how
marketing media such as TV, radio, print ads, direct
mail, etc. impact the various performance metrics,
specifically acquisition rate, retention rate and
revenues. For example, if television advertising
doubled, there would be a simultaneous impact on several
metrics in the short run — the effects of which can be
simulated. The long-term implications of these
short-term movements indicate how sound the strategy is
in the long run.
Development of these models requires
a bank’s IT department to gather prescribed data that
econometricians or statisticians can use to create the
response models. The response models are the basis of
optimization or simulation software developed for use by
the bank’s marketing department.
How “should” marketing dollars be
allocated? An optimization routine can find the “best”
combination of media expenses to give the institution
the highest customer equity value. By taking the major
elements of the marketing mix and performing a customer
equity optimization, the optimal allocation can be
calculated, both for the short- and long-run in concert
with the bank’s strategic goals. A plan to build market
share will favor a different marketing mix compared to
an objective of maximizing individual customer worth.
Short-term goals may focus on media
that increase acquisition, but these new clients may
stay only a few months, decreasing long-term metrics
such as customer equity. Most likely, the optimizations
and refocused marketing spend will have a beneficial
effect on short- and long-term objectives.
The critical question for customer
equity maximization value is: how does the short-term
revenue from new customers translate into long-term
revenue? For financial institutions, the answer depends
on two important metrics: the customer retention rate,
which is never 100%, and therefore gradually decreases
the size of the existing customer base, and the discount
factor, which ranges from around 6% to 12% annually. The
retention value of the institution’s customers
diminishes over time as the effects of attrition and
discount factors are compounded.
ESTIMATING
CUSTOMER EQUITY
Financial institutions are in the
relationship business and thus have comprehensive
customer data that can be used to optimize marketing mix
resource allocation to maximize customer equity. This
data includes the marketing spending by channel, new and
lost customers, revenues segmented by products/services,
by branches, designated marketing areas, and customer
segments gathered on a weekly basis.
Econometric modeling uses changes in
marketing activities such as direct mail, TV, Web
activities, etc., so that the effects of those specific
spending changes on acquisition, retention and revenues
can be statistically isolated. The revenue from
acquisition and retention in the long term creates both
top-line performance (revenue) and bottom-line
performance (customer equity).
Banks can estimate customer equity
for 10 or more years out. After 10 years there is severe
discounting of the cash flows. For example, at 12%, a
$100.00 cash flow in the 11th year is worth only $28.75
in today’s value. The patterns of acquiring and losing
customers and generating revenues, along with the gross
margins on the various financial products, yield both
the short-term and the long-term estimated contributions
to profits. Then, by properly discounting these gross
profits back to the present, the institution can get an
estimate of customer equity. Naturally, these are
estimates or projections that are subject to revision
as, for example, economic and competitive conditions
change. Even so, the projections are strategically
useful as they provide a trajectory of future business
performance based on current and projected marketing
investments that management can evaluate and improve
upon.
For the optimization, the economic
factors are typically held at their most recent levels,
though it is possible to test the effects of different
scenarios such as a gradual improvement in the economic
environment.
The first step is to consider
marketing spending at its current level and allocation,
and to derive the implications of this policy for
customer-equity development. The second step is to
compare this trajectory with the marketing investment
strategy that is suggested by the optimization. The
optimization will indicate that the financial
institution should allocate its resources in proportion
to their effectiveness. As a result of allocating scarce
marketing dollars more productively, the financial
institution should enjoy an increase in customer equity.
Recommendations may increase
spending for certain marketing activities and cut
others. For example, cable TV spending may increase at
the expense of print advertising or vice versa. These
changes reflect different positions on the market
response curve, which is subject to diminishing returns
to scale. For example, the higher the lift in response
at the current level of spending, the more marketing
investment is justified, and vice versa.
The shape of the entire market
response curve is generated using the techniques of
econometrics, and results in estimates of response
elasticities for each marketing medium. For example, a
print advertising elasticity of 0.2 implies that, for
every 10% increase of print spending, revenue increases
by 2%.
In cases where the recommended
spending for a specific marketing activity is far
outside the current spending range, it is prudent to
engage in a marketing experiment to verify that the
response level is in line with that anticipated by the
optimization model. For example, a bank uses e-mail as a
direct marketing channel. The analysis indicates gross
under-spending in this channel and recommends a 500%
increase. Since a 500% increase has not been previously
tested, this would constitute a risk. So, a 100%
increase is implemented at first and simulated through
the model.
After the completion of the
marketing period, actual results are compared with the
predicted results. Assuming the actual results are close
to the predicted, the model is recalibrated, appending
the last period’s data. The institution now has more
confidence in the model’s recommendation for e-mail
spending.
OPTIMIZING
BUDGET ALLOCATIONS
As a result of the econometric and
optimization modeling, banks can see how different
customer segments and product categories will generate
revenues and gross margins. This is a very useful tool
strategically, because the financial institution can
analyze the consequences of top-line growth verses
bottom-line growth. For example, the bank may find that,
by spending more aggressively, it can expand its
customer base with customers whose acquisition costs are
nearly the same as their marginal revenue. In that case,
there will be top-line growth, but not necessarily
customer equity growth.
A realistic market response model
obeys the laws of diminishing returns to scale. If a
financial institution’s direct mail is currently
yielding a 3% response rate and if the direct mail
budget is doubled, the institution should expect a lower
response rate on the additional budget allocation. These
results will, of course, vary with the quality of
execution within each medium, which relies upon the
creative component in marketing communications.
Such qualitative changes may be
accounted for in an econometric model. However, the
easiest way to assess that is by running simple
marketing experiments rather than sophisticated
econometrics. Insofar as a financial institution is more
successful in increasing lift due to higher execution
quality, it follows that the institution should spend
more on that medium. The optimization generally assumes
that the quality of the financial institution’s
marketing execution remains the same, and thus its
marketing spending is subject to the laws of diminishing
returns. Different scenarios can be used to test the
sensitivity of customer equity to changes in the quality
of marketing execution.
If a financial institution
reallocates its marketing spending based on insights
from an optimization exercise, the impact will be
beneficial for both short-term and long-term
profitability. Pursuing a strategy of top-line growth —
expanding total customers and total revenues — can be
consistent with customer equity maximization, although
not always. The result depends on the customer equity
drivers and especially the drivers of customer
retention.
No longer should executives be in a
quandary about marketing budgets. The use of econometric
and optimization modeling can guide financial
institutions to determine their optimal marketing budget
and then optimally allocate that budget for heightened
customer equity and shareholder value.
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Dr.
Hanssens is executive director, Marketing Science Institute, Cambridge, Mass., and Bud Knapp Professor of Marketing at the UCLA Anderson School of Management in Los Angeles. Ms. Lewis is a partner with Los Angeles-based MarQuant Analytics, which consults with financial institutions on increasing marketing productivity. |