---
title: "Direct mail attribution, what works and what lies"
description: "Last-touch and influenced-revenue counting lie. Scans, matchback, and promo codes give partial evidence. Only a holdout answers whether the mail caused anything."
canonical: https://trysincerely.com/guides/direct-mail-attribution
last_updated: 2026-08-18
---
# Direct mail attribution, what works and what lies

> Last-touch and influenced-revenue counting lie. Scans, matchback, and promo codes give partial evidence. Only a holdout answers whether the mail caused anything.

Source: https://trysincerely.com/guides/direct-mail-attribution

Most direct mail attribution lies to you. Last-touch models and influenced-revenue counting inflate the channel's credit, sometimes wildly. QR scans, PURLs, promo codes, and CRM matchback are honest but partial: they prove a specific person responded, not that the mail caused the deal. Only one method answers causation: hold out a random group of accounts, mail the rest, and compare outcomes. Everything else is evidence to weigh, not an answer.

## The two big lies

### Last touch

Last-touch attribution gives all credit to the final tracked interaction before the conversion. Mail loses this game by design. A postcard lands, the prospect thinks about it for a week, then Googles you and clicks an ad. The ad gets the credit. Or mail wins unfairly: a prospect who was already going to buy scans your QR code on the way, and mail claims a deal it did not create.

Either way, last touch measures the order of clicks, not causes. It punishes channels that work early and offline, and rewards whatever sits closest to the form fill.

### Influenced-window counting

The other lie is more flattering, which makes it more dangerous. "Influenced revenue" counts every deal where the account received mail inside some [attribution window](https://trysincerely.com/glossary/attribution-window), often 90 days. Mail 500 target accounts, and any deal from those 500 in the next quarter counts as influenced.

The problem: you mailed your best accounts on purpose. Many of them were going to buy anyway. Influenced counting takes credit for all of it. It cannot distinguish "the mail worked" from "we mailed people who were already converting". The bigger and better-targeted your list, the more this number lies.

## What partially works

These methods are worth using. Just call them what they are: response evidence, not proof of causation.

- **QR codes and PURLs.** A scan or a personalized-URL visit ties one response to one piece. That is real, direct evidence for that person. But scan rates undercount: plenty of recipients read the mail, then reply to your rep or visit your site untracked. See [QR codes on direct mail](https://trysincerely.com/guides/qr-codes-on-direct-mail).
- **Promo codes.** Same logic in transactional businesses. A redeemed code proves the mail reached a buyer. It says nothing about the buyers who converted without typing the code.
- **Matchback.** [Matchback](https://trysincerely.com/glossary/matchback) joins your mailed list against your CRM: which mailed accounts later booked a meeting or opened an opportunity? It catches the untracked responders that scans miss. But raw matchback has the influenced-window problem. Without a comparison group, "mailed accounts that converted" includes accounts that would have converted anyway.

A useful frame: scans, codes, and PURLs are direct evidence. Matchback alone is inference. Report them under those labels and nobody gets fooled.

## What answers causation: holdouts

A [holdout](https://trysincerely.com/glossary/holdout) is the only method on this page that answers "did the mail cause this?". Randomly split your target accounts before launch. Mail one group, hold the other back, treat everything else the same. After the measurement window, compare outcomes. The difference is [lift](https://trysincerely.com/glossary/lift), and because the split was random, lift is causal. The would-have-bought-anyway accounts appear equally in both groups and cancel out.

Holdouts have honest costs:

1. You give up revenue from the held-out group during the test.
2. Small programs may lack [statistical power](https://trysincerely.com/glossary/statistical-power). A 200-account campaign cannot detect a 1-point lift.
3. You need discipline: decide the primary outcome, the window, and the holdout size before launch, not after you see the data.

The full mechanics, including sizing, live in [the holdout guide](https://trysincerely.com/holdout-testing). The [holdout-size calculator](https://trysincerely.com/tools) tells you in a minute whether your campaign is big enough to measure.

## A layered setup that does not lie

You do not have to pick one method. Layer them by what each one proves:

| Layer           | Method                         | What it proves                                                   |
| --------------- | ------------------------------ | ---------------------------------------------------------------- |
| Causation       | Holdout with random assignment | Whether the campaign moved the metric at all                     |
| Direct evidence | QR, PURL, promo code           | That a specific person responded to a specific piece             |
| Inference       | CRM matchback                  | Which mailed accounts progressed, causal only versus the holdout |

This is how Sincerely reports by default. Campaigns lock the experiment design at launch: primary outcome, window, holdout percentage. Readouts show lift against account-level holdouts with confidence intervals, and say plainly when a result is not statistically ready. Scans and matchback appear too, labeled as evidence or inference. Details are on [the measurement page](https://trysincerely.com/measurement).

If your program is too small for a holdout, say so and fall back honestly: track scans and matchback, and treat the numbers as directional. That is a limitation stated plainly, which beats a precise-looking lie.

Attribution tells you who responded. Only a holdout tells you what you caused.

---

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