OPTIMIZE
YOUR MARKETING BUDGET
By Barbara Lewis and Dan Otto
Historically,
marketing budgets have been rarely statistically calculated for
optimum productivity. Instead marketing budgets are usually based
on meaningless metrics such as percentage of last year's sales
or percentage of last year's budget. Once marketing managers receive
the budget, they divide the monies among marketing activities
such as new customer acquisition, current customer retention,
former customer reacquisition, up-selling and cross-selling. A
marketing budget can be segmented by customers, products, geographic
regions and media spend, for example. Oftentimes, the allocation
of monies across the various marketing activities and segments
is a guestimate with little statistical or financial data to determine
the specific portion of funds for each activity or segment.
However, with
the advent of computer modeling, marketing executives are able
to confidently make budgetary decisions that optimize long-term
profitability of the firm by properly allocating across the marketing
activities and segments. Underlying the marketing budget optimization
are three concepts: diminishing return market response curve,
customer lifetime value and customer equity.
The diminishing
return market response curve is based on a commonly accepted principle
that the next dollar spent will have less impact than the previous
dollar, and that no matter how much is invested, not every prospect
will be converted to a customer. Using historical data or marketing
experimentation, executives can calculate their products' market
response curves.
Customer lifetime
value (CLV) is the worth of a customer over a specific period
of time. (A more sophisticated and accurate definition of CLV
is the current value of future contributions of customers using
a discounted cash flow.) Customer equity (CE) is the net present
value of the contribution of all current and future customers.
Long used
by the catalog industry, CLV is sweeping through other industries
more recently, especially computer and technology companies that
are rich with data on their customers, which makes it easy to
calculate CLV. Gaining a foothold in the business vernacular,
CLV is finding its way into annual reports.
While certain
executives are still calculating return on investment (ROI) for
marketing spend, others are using CLV as a valuable metric. For
example, suppose you offer a promotion on a product that attracts
new customers and generates twice the amount spent. Such a promotion
would appear to have a very positive ROI. However, let's say that
not one of the new customers acquired through the promotion ever
buys again. In that case, these customers' lifetime values are
low, and the promotion contributed little to your customer equity
and the firm's value.
On the other
hand, suppose you offer a different promotion that manages only
to break even in the short run. However, every one of your newly
acquired customers orders again and again, for years to come.
The result: high retention rates that increase your customers'
lifetime values. The second promotion yields a higher CE, despite
its apparent low ROI in the short run.
Which is the
better scenario? Most executives probably would opt for the high
ROI; however, that metric may not be the most desirable. The better
metric is to maximize customer equity, which will increase long-term
profits. The short-term view to focus on immediate ROI may result
in a reduction of the long-term value of the firm.
Prior to computer
modeling, it was nearly impossible to calculate optimal marketing
budgets and their allocation. However, using the three basic principles
of diminishing return market response curves, customer lifetime
value and customer equity as the foundation for calculations,
the marketing budget with its allocation across marketing activities
such as acquisition and retention, customer segments, products,
regions and media can be optimized.
Using a computer
model, executives can take the data, constrained with minimums
and maximums, and optimize the budget allocation. The data includes
the past budget across activities and segments, number of new
customers, lost customers, re-acquired customers, expansion of
current product/service (up-sell) and additional products/services
sold (cross-sell).
With as few
as 11 inputs, executives can calculate the optimum marketing budget
for acquisition versus retention, for example. These inputs include
the number of prospects/customers in a period, the number of prospects
converted to customers/customers retained, the profit margin on
transactions, the acquisition/retention costs, the discount rate
(cost of capital) and the ceiling acquisition/retention rate.
Armed with this data, executives can begin to make intelligent
decisions about their marketing spend.
No longer
are marketing budgets left in the realm of guesswork or percentages
of dollar figures or target amounts. Marketing budgets can and
should be optimized to increase profits, as well as customer equity.
For executives focused on long-term value, optimized marketing
budget allocations are a must.
Industries
that are rife with data are ideal candidates to optimize their
marketing budget. Optimization through computer modeling can be
a competitive advantage, as well as a means to increased profits.
Barbara Lewis MBA and Dan Otto MBA are founders of MarQuant Analytics,
which has four analytical tools to optimize the marketing budget
across acquisition and retention, segments, business lines for
cross-selling and media for optimum media mix. Their clients include
ad agencies as well as end-users. They can be reached at 310.471.8979
or through their web site at www.MarQuantAnalytics.com
