Most direct mail acquisition programs underperform for one simple reason …

The targeting just isn’t that good.

We see this all the time – teams try to scale by adding more volume, more lists, more spend. But if the model behind it isn’t strong, you’re just scaling inefficiency.

And it usually starts with the customer file. If your data isn’t clean, representative and built off your best customers, the model is flawed before it even starts. Quality in, quality out.

From there, good modeling goes deeper than basic demos or generic lookalikes.
It’s about identifying real signals, like transactional data, channel behavior and modeled propensity to respond to direct mail, and then ranking all correctly.

If your top deciles aren’t clearly outperforming the rest of the file, that’s a red flag.

From what we consistently see:
• The top 30% of the audience drives the majority of performance.
• The bottom half of the file often adds more cost than it adds return.

However, we have a successfull strategy that lowers postal costs by 30 – 40% in some cases while keeping incremental reach in play.

The shift is simple but not easy: Stop asking, “How do we reach more people?” and start asking, “Are we reaching the right people?”

A strong customer file → better model → smarter scaling.

If your program feels stale or performance is flattening, it’s probably time to take a hard look at both your inputs and how you’re deploying the file. Inputs are certainly the first place to look, but there are subtleties in designing a mail file that can dramatically approve productivity and results. More to come in a future post.

👉 Happy to chat if you’re thinking about refreshing your acquisition strategy.

Contributing Author:
Justin Bronce
Director of Client Strategy
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