Relevance in direct mail starts with the list: four ways to improve yours

Next Tuesday I'm appearing on our partner Lob's State of Direct Mail panel with USPS and SimpliSafe. Lob surveyed 2,000 consumers for their annual report, which we'll be discussing, and one finding has stuck with me: when it comes to direct mail, relevance is beating discounts.

And when we think about relevance in DM, I'm sure most of us go straight to creative and personalization — which makes sense, creative is what we see when we get the postcard.

But here's the thing about relevance in mail. It starts long before anyone writes the copy or sends a file to the printer. By those points you've already made many of the decisions that determine how well the campaign actually does: whether these are the right people, whether you're pointing them at the right location, whether the person they live with already got the same mailer last month and converted.

Sure, bad creative can kill your conversion rate. But a great offer addressed to some stranger named Sandra who moved out two years ago doesn't help either.

Creating the list is where we've been putting our attention at Faraday. Here are four things we shipped this quarter to help you get yours right, plus some strategic recommendations at the end.

New mover data: target the right people at the right time

Most names on most mail lists have no particular reason to act this week. They aren't bad prospects. They're just being contacted at an arbitrary moment.

A recent move is the opposite of arbitrary. Someone who just moved is replacing providers, buying for a new space, and setting habits they'll keep for years. It's one of the few times a piece of mail arrives already relevant.

New mover data lets you build audiences around that window — people who have recently moved into your service area, combined with whatever else you already filter on.

It cuts the other way too, and this is the part that gets skipped. The households that used to live in your area are still on your list. Every piece addressed to them is postage spent on someone who has moved on. So the same signal does two jobs: it adds the people whose timing is right, and it surfaces the ones who quietly stopped being reachable.

This one is new enough that there's no docs page yet — if you want it on your next drop, your account team can get you set up.

Nearest Location: put the right store on every row

If you have physical locations, something has to decide which one each piece points to. For most teams that's a ZIP code or a regional map, which means a household three blocks from one store might get mailed about another one across town.

That proxy also distorts what you'll pay for a lead. Distance is a real constraint on conversion for anything that requires showing up — glasses get tried on, flooring gets seen. A lead 40 miles from your nearest showroom is worth less than one at four miles, and if you can't tell them apart you're paying the same for both.

Nearest Location measures the actual distance from every person to every location you have, and returns their nearest location, your own ID for it, and the distance. It's a real number rather than yes/no flags at thresholds someone set in advance, so "within 12 miles" is a filter you apply, not a rebuild you request. It runs backwards too: draw a radius around your locations from an Atlas and build an audience of every household inside it. There's a full walkthrough in the docs.

Every row on your list ends up carrying the store that's actually closest, and you can build the list itself by real distance instead of by map.

Address-level exclusion: don't send the same offer to two people in one house

Suppression has always worked at the individual level. You mailed Jane Smith, so Jane Smith comes off the next drop. That might be the right logic for email, but it's insufficient logic for mail.

Mail arrives at addresses, not directly to the individual. So the household you already converted, already hit twice, or already heard "please stop" from can come back in the next campaign under a different resident's name.

To solve this problem, Pipelines now have an Exclude at address level control. Add a cohort and Faraday suppresses everyone who shares an address with anyone in it. The old option is unchanged and now labeled Exclude at individual level, so you pick which one a campaign needs — both sit under the suppression settings in your pipeline's population settings, and there's an API field for it if you build programmatically.

Household analysis: find out how many households are on your list

Here's a number that only matters in direct mail: how many households are in your audience.

In most channels you set a threshold and let the volume land where it lands. In mail you buy a quantity — you tell the printer 40,000 pieces. And 50,000 individuals is not 50,000 pieces. It might be 31,000 households, and that's the difference between two very different quotes.

Every Pipeline now has an Analysis tab that answers this before you build a deployment, with no usage required. You get your population counted both ways, individuals and residences, broken out by state and ZIP code — which is also what postal sortation and regional drop planning run on. And for every prediction in the payload, you can see how many people fall into each score range, with running totals as you go further down — so your list length comes from where the counts actually add up, not from a round number somebody picked.

Where I'd start

Four things at once is a lot. If I were picking up one of them first, this is the order I'd go in.

1. Focus on households, not individuals. Use Faraday to ensure you de-dupe your list and send only one mailer per household. Get the household count before you get your next print quote. If you are already using Faraday, these two steps take less than 5 minutes.

2. Identify the nearest location to send them to. Direct mail can feel more personalized if you add subtle, intentional touches like details about the closest store. This step requires adding your locations to Faraday, but it's worth it to drive a little more personalized experience.

3. Use new mover data. You can check out how many people are moving in (or out) of your service areas with our handy tool. Knowing who just moved in lets you time your drop — and your offer — to the moment they're actually shopping. For existing Faraday customers, there is an additional cost for this data set.

It's ironic: the list is the part you have the most control over, and it's usually the part that gets the least attention. These four features are designed to make it faster and easier to build a sharp audience — so your time goes to the creative and the offer.

Ready to optimize your list?

If you want to talk through where your direct mail program is leaking, talk to a Context Consultant. Already a Faraday customer? Your account team can turn any of these on.

And if you'd like to hear the rest of Lob's findings, join us on the panel next Tuesday.