Executives wrestle with how the budget is divided among customer acquisition, customer retention, customer reacquisition, upselling and cross-selling. Often, allocation of money in these activities has 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 year’s sales or percentage of last year’s budget. With the advent of computer modeling, however, quantitative marketing and research on customer lifetime value lets executives confidently make budgetary decisions that optimize long-term profitability.
In a survey by Centurion Consulting Group at the DM Expo in Los Angeles in June, more than 60 percent of respondents indicated they did not use computer modeling, yet all indicated that they wanted computer modeling tools to assist them with calculations and decisions.
One of the basic calculations is customer lifetime value – a customer’s worth 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 revenue generated by each new client, subtract the expenses, including marketing, and calculate how much money each new client contributes to or deducts from your profit. A certain amount of those new customers will become retained customers, hopefully through your marketing. The revenue of the retained customer minus the expenses gives you a contribution to profits.
These contributions occur one marketing period in the future. Like any investment, those cash flows need to be discounted. The sum of current and future discounted contributions divided by the current period customers yields a customer lifetime value. One basic business goal is to increase the customer lifetime value. Yet only 20 percent of survey respondents calculate CLV.
The customer lifetime value calculation increasingly is used as a metric for management. Though 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: Are we spending too much or too little on acquisition? Should we operate at a loss on acquisition to be more profitable on future retention? Should we push more to reacquire a lost customer? Should we focus on cross-selling or upselling the current customer base?
In the DM Expo survey, 50 percent of respondents know their acquisition spending per customer and retention rate. Slightly more than 40 percent calculate the margin that each customer contributes, but no one calculates the margin per retained customer. Yet each of these areas can be easily calculated with a computer model.
By using a computer model, executives can optimize the budget allocation. The data include the past budget across activities, number of new customers, lost customers, reacquired customers, expansion of current product/service (upsell) and additional products/services sold (cross-sell).
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. Program the model to perform real-time recalculation and presentation of results. This can help the audience see the effects of modified assumptions and inputs, and aid in comprehension of the logic behind the cells.
Industries rife with data such as financial, catalog and publishing are ideal candidates to optimize customer resources. Companies with little data will find resource optimization painful 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 means to increase revenue and profit.