Magical Thinking

JUSTIFICATION IS THE name of the game when it comes to determining a direct marketing budget. It’s no longer good enough to make generalizations about how much revenue you’ll earn or your margin contribution to the bottom line. Management wants to know what the return on investment is going to be before releasing funds, and it wants to see detailed pro forma metrics (figures provided in advance) regardless of a program’s size.

But many DMers harm themselves by engaging in magical thinking — that is, they come up with unrealistic supporting numbers.

They may start a campaign with a clear goal in mind: better response rates, increased awareness or higher sales. But they don’t have a budget. So they must come up with one that can deliver the success criteria in question, and to do so with an acceptable ROI.

ROI has become the yardstick that business uses to judge performance. To be effective as an analytical tool, however, the data that goes into the calculation must be reliable.

In particular, response and/or conversion rates are sometimes not available or suspect. Perhaps there’s no response history because the product or service is new. Maybe the marketing conditions have changed. Or perhaps the response history is questionable because of incomplete or inaccurate data. Whatever the reason, response and/or conversion rates need to be developed so that a realistic revenue estimate can be established and then compared with costs associated with the program.

An answer that works reasonably well if one doesn’t have enough sound data is to create a range of data points (high, middle, low) and apply a weighted average using criteria tied to at least one of the following:

• Industry norms.

• Earlier campaigns for similar products or services.

• Previous tests done on the product or service in question.

This process of setting a weighted average also can be applied to the revenue side of the ROI ratio. The chart on page 53 recaps an ROI calculation using weighted averages for a financial services company offering insurance products to businesses. In this example, the firm knows that smaller accounts (those with fewer than 50 employees) are each worth $5,000 annually. Middle-market accounts (51 to 150 employees) are worth $40,000 and large accounts (151 or more employees) are each worth $80,000.

Let’s assume this is a cross-sell program where we target all existing customers. We’ve decided that the expected DM response rate will be between 0.45% and 0.6% and the projected conversion rate will be 50%. Assuming a universe of 2,500 customers, we will generate between five and eight new sales.

Using the revenue amounts previously discussed, we then calculate a total first-year revenue range tied to the high and low response-rate range for each account grouping. Once this range is set, we assign probability weights to each group of accounts and calculate the total expected revenue estimate tied to the initial response rate range. These weight-averaged revenue estimates are then divided into the budget to create the ROI ratios.

Many times a marketer will start the process with a predetermined budget and a sales goal. Thus, the ROI already has been set and what’s left is developing the strategy to achieve both sides of the ratio. But the budget and/or the sales goal may be unrealistic in light of expected response and conversion rates. The client is justified in challenging its agency to work with limited dollars or aggressive sales goals because the client must map the program’s ROI into its business model.

The problem some DMers fall into is allowing unrealistic response or conversion metrics to be used to make the ratio work. Sometimes a “breakthrough” idea will emerge that boosts results in a dramatic way. But that’s usually the exception, not the rule. The more likely scenario is that response falls short and the program is judged a failure when in fact it was doomed from the start because of an unrealistic ROI.

A DMer can avoid this trap by building a case for using a longer time horizon that shows the break-even point between revenue and cost. This way at least everyone can see how long it will take before a customer becomes profitable.

BRUCE WAGONER is managing director of Wilde Direct, a Holliston, MA direct marketing agency.

Cross-Sell Campaign ROI