2005 Apr 02
Marquant Analytics Marketing Budget Allocation Software
David M. Raab
DM News
April, 2005

Direct marketers have long been more analytical than most other types of marketers. It’s really the nature of the business: even before computer databases were available, direct marketers could count the number of responses to a promotion and calculate its value. With a bit more effort, they could track responses by customer segment and estimate of the value of different customer groups over time. Strategic business decisions, such as the maximum amount to pay for a new customer, could be based on this information. Once computing power became available, really serious analysts built forecasting models that projected business results for future periods and allowed marketers to explore the impact of changes in their policies.

Marketers in other fields might have liked similar information. But without a way to track purchases by customer, they lacked the necessary foundation. Knowing the relation between ad spending and sales, or even between specific promotions and specific purchases, isn’t enough. You need to know how many sales came from repeat customers to generate a meaningful estimate of lifetime value.

Modern marketing databases do give non-direct marketers ways to measure purchases by customer. Perhaps just as important, the widespread availability of these databases has made non-direct marketers familiar with lifetime value concepts and reinforced their awareness of the importance of retention. Thus many are now ready to consider the sort of business forecasting models that have long been familiar to direct marketers.

Marketing Budget Allocation (MBA) Software (Marquant Analytics, 310-471-8979, www.marquantanalytics.com) is one of several modeling tools from Marquant that combine business forecast modeling with optimization–that is, finding the allocation of limited resources that produces the best result. In MBA’s case, the limited resource is the marketing budget and the desired result is maximum discounted cash flow, or net present value. MBA determines the level of total marketing spending and the division between acquisition and retention programs that yields the greatest net present value. In simpler language, it gives an objective answer to the eternal question, “What should my marketing budget be?”

This sort of modeling can be done at many levels of detail. MBA works at a very general level, simply distinguishing between expenses for acquisition and retention. Other Marquant products break things down further by allowing for multiple customer segments and for cross purchases. But none work at the individual customer level, so they cannot select specific names for actual promotions.

Despite its general nature, MBA does consider the diminishing returns on incremental marketing investments. This is one of the critical features that distinguish marketing economics from many conventional optimization methods, which assume items such as unit costs are static. But marketers–at least in theory–make their most effective investments first. Thus, the acquisition and retention costs per customer usually rise as the budget increases and marketers make more marginal investments. (This isn’t always the case. In a high fixed cost situation, such as an expensive television ad or Web site, the average cost per customer may actually decline as the initial investment is spread over more names.) Gathering realistic statistics for these values is a challenge that Marquant doesn’t really address: the system either generates smooth curves based on a couple of data points or accepts more precise inputs provided by the user. This sort of simplification may bother detail-oriented users, but it is appropriate for the big-picture strategic decisions that Marquant aims to facilitate. It also means that Marquant tools cannot optimize the mix of specific marketing programs.

In keeping with this approach, MBA asks for a relatively small number of inputs: numbers of prospects, converted customers and retained customers; transaction margin (i.e., profit contribution before marketing or fixed costs) per new and renewed customer; acquisition and retention budgets; and maximum acquisition and retention rates. The system converts the acquisition and retention budgets to cost per customer using the incremental cost curve already described. Profit contribution per customer is assumed to be constant, rather than declining as increased marketing brings in less qualified customers. This is another oversimplification that is probably adequate for Marquant’s purpose.

MBA assumes that input values remain constant for up to 60 periods. It uses the values to estimate cash flow by period, taking into account new customers and retention of existing customers. Output reports show the net present value of spending, revenue and profit contribution for acquisition, retention and in total. Users can also view detail by period. The system compares cash flow for the current spending policies against flows from optimal policies and for other scenarios such as a fixed marketing budget, fixed increase per year, or maximum spending level. It can also run elasticity analyses showing the impact of changes in the inputs themselves. Reports provide tabular and graphical outputs intended to be understood by non-technical viewers.

The market segment and cross sell modules add some complexity to the forecasting models but are still fairly simplistic. For example, the market segment model treats each segment as separate from all others, rather than permitting customers to migrate from one segment to another–a common way to simulate the customer life cycle. The cross sell model calculates the number of customers with each product combination in one period who purchase each product combination in the next period. This could simulate customer migration, except that it is limited to three products. It does serve Marquant’s actual goal, which is to consider cross sales when calculating optimal marketing budgets. Happily, Marquant’s limited functions are reflected in its prices. Costs range from $20,000 to $50,000 for an engagement, which is considerably less than most marketing optimization products. The price includes software plus help in preparing the initial models. Clients then retain the software to run as they please. Marquant’s products, introduced in 2004, evolved out of a consulting practice begun ten years prior. The software has been sold to about a half dozen very large companies, where it is used for general planning by senior management rather than as a tactical marketing tool.

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David M. Raab is a Principal at Raab Associates Inc., a consultancy specializing in marketing technology and analytics. He can be reached at draab@raabassociates.com.

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