Balancing the Direct Mail Triangle

As seen in Chain Store Age

There are many factors that go into the overall performance of a direct mail program, but there are really three key ROI drivers:

  1. Targeting: Are we are talking to the customers/prospects most likely to become long-term, high-value customers?
  2. Messaging: Is our message personalized and relevant?
  3. Execution: Is the mail piece the most cost-effective format and is it being produced and mailed in a streamlined manner?

Most retail direct marketers we talk to know in their gut that their programs could be performing better—but are not sure what improvements might optimize ROI. Here’s a simple exercise that can help you figure out where to focus on improving your program.

: Assessing Your Program

Using an equilateral triangle where the corners represent the ROI drivers of Targeting, Messaging and Execution, draw three lines to the corners from the midpoint of the triangle and, with the midpoint being zero, mark each line with levels 1, 2 and 3—with 3 being in the corners. Start from the midpoint and rank each dimension of your program in terms of the level of sophistication on a scale from 1 to 3, with 3 being the highest rating. Once you have plotted each dimension, you can connect the points and create your program triangle inside of the large triangle.

“Since the implementation of this new targeting strategy, the client is reaching positive ROI within one month instead of six months.”

The program triangle will likely not be equilateral. The idea is to balance the triangle by improving the dimension(s) that lag behind the others. The long-term goal is to keep improving all three dimensions until you fill the large triangle.

Example: Improving the Targeting

In one of our client examples (acquisition of pharmacy customers), we found that execution was good but both targeting and messaging needed to be improved. The message itself was alright (good offer, decent copy and images) but it was only minimally personalized and there was no variation of content based on the make-up of the household.

There was also an opportunity to upgrade the targeting. They were using a geo-targeting approach which was focused mainly on zip codes within a certain radius of a retail location that had weak-to-moderate penetration. But there was no consideration being given to the long-term revenue potential of the individual households.

So both the messaging and targeting needed improvement. Based on our experience, we believed that improved targeting would yield the largest improvement in program ROI—so we started with that.

The idea was to improve upon the geo/market share targeting by finding a way to identify the prescription potential of households in the selected zips. We can’t share the details of the process, but using commercially available data we created models that predict the likelihood of a household to have certain medical conditions present. We then used those scores to refine the targeting so that those households with multiple high condition scores are targeted and those with low scores are not.

This strategy was deployed with the desired goal of bringing in higher value customers (not necessarily the most customers) so that a positive ROI would be achieved much faster (higher average number of prescriptions per new customer). Since the implementation of this new targeting strategy, the client is reaching positive ROI within one month instead of six months with geo-targeting only.

Example: Improving Messaging and Execution

Another client was experiencing a different problem—they were trying to stimulate sales of their private-label brand products, but their traditional direct mail coupon efforts were not performing very well.

After we used DMboost to evaluate the program, we found that the targeting was pretty good (active customer, bought private-label previously, bought in category previously) but the messaging was muddled. Everybody was getting the same offers—and too many offers at once. Execution was acceptable, but it was a simple program so that was to be expected.

We focused on creating an improved messaging strategy. We identified the top three products (with a private-label alternative) purchased by a customer and then showed those three products next to their private-label alternative and calculated the price savings for each item and across the three items. This higher level of relevancy and personalization certainly cost more to create, but it more than made up for that in higher response rates and ongoing private-label sales once the customers converted.

An interesting side note on this project: As we developed this variable-print messaging strategy we realized that the solution we had in mind would not be possible to execute in the existing environment. This happens frequently as you increase the level of targeting and messaging—it forces an increase in the level of execution as well.

“As you increase the level of targeting and messaging—it forces an increase in the level of execution as well.”

DMboost is a useful tool to use because it deals with the reality that it is often better to change one thing at a time with a program and it focuses you on the area that needs the most improvement. It also reminds you that you should have a mindset of continuous improvement.

And you thought that geometry you learned in high school would never come in handy.

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