Executives wrestle with how the budget is divided among new customer
acquisition, current customer retention, former customer reacquisition, up-selling and
cross-selling. Oftentimes, the allocation of monies in the five market activities is
by hit and miss with little statistical or financial data to determine the specific
portion of funds for each activity.
Marketing budgets may be based on meaningless metrics such as
percentage of last years sales or percentage of last years budget. These
types of budgeting decisions may have a short-term focus. However, with the advent
of computer modeling, quantitative marketing, coupled with research on customer lifetime
value, enables executives to confidently make current budgetary decisions that optimize
long-term profitability by proper resource allocation for their direct marketing efforts.
One of the basic calculations is customer lifetime value (CLV) -- 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.)
For example, you can total the amount of revenues generated by each
new client, subtract the expenses, including marketing, and calculate how much money each
new client contributes to your profit (or detracts from your profits). A certain
amount of those new customers will become retained customers, hopefully through your
marketing efforts. The revenues of the retained customer minus the expenses give you a
contribution to profits. These contributions occur one marketing period in the future.
Like any other investment, those cash flows need to be discounted. The sum of all the
current and future discounted contributions divided by the current period customers yields
a customer lifetime value. One of the basic business goals is to increase the customer
lifetime value. Yet only 20 percent of survey respondents calculate CLV.
The customer lifetime value calculation is increasingly used as a
metric for management. While it is useful to know the value of a customer, the more
important question is: How does one maximize that value?
- CEOs, CFOs and Marketing Managers want to know:
we spending too much or too little on acquisition?
- Should we operate at a loss on acquisition in order to be more profitable on
- Should we push more to reacquire a lost customer?
- How many periods should we attempt to reacquire a lost customer?
- Should we focus on cross-selling or up-selling the current customer base?
Yet each of these areas can be easily calculated with a computer
model, which assists executives in making macro budgeting decisions across the
activities. By 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, number of new customers, lost customers, re-acquired
customers, expansion of current product/service (up-sell) and additional products/services
A computer model, like any communication device, should provide
analytical results and conclusions on which to base decisions. The model should tell a
story in a graphic and easy to understand manner. It should be user friendly and
flexible. Spreadsheet programs capable of background programming, such as Excel and Visual
Basic for Applications, extend the analytical and interface capabilities. Program the
model to perform real-time recalculation and re-presentation of the results. This
instantaneous What If method can help the audience see the effects of modified
assumptions and inputs, and aid in the comprehension of the logic behind the cells.
Design the model to segregate input variables from results in a
well-defined area. Ensure the user knows what cells to manipulate by highlighting. Provide
key results on the same page as the inputs or mirror the inputs on the results page.
Summarize and chart relevant results and provide for scenario comparisons and
sensitivities of inputs.
Industries, such as financial, catalogue and publishing, that are
rife with data, are ideal candidates to optimize their customer resources. On the
other hand, companies with little data will find resource optimization a painful process
since mechanisms for data capture need to be implemented. This is an expensive and
lengthy route. Yet for the data-rich company, resource optimization through computer
modeling can be a competitive advantage, as well as a means to increase revenues and