ON RETAIL BANKING
Divvying Up
the Marketing Pie
BY DOMINIQUE M. HANSSENS AND BARBARA LEWIS
Modeling
can help optimize marketing mix resource
allocation to maximize customer equity.
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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.
Questions
or comments about this article? Post
them at the Banking
Strategies blog.
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