---
title: Revenue impact methodology
description: How Dozenfold compares affected and clean sessions, pools the conversion gap with a 95% interval, and labels an issue's revenue impact as measured or directional.
slug: revenue-impact
---

# Understand revenue impact

Dozenfold estimates how many purchases an issue cost in the selected window, and what those purchases
were worth. It compares sessions that hit the issue with comparable sessions that did not, and shows
how certain that comparison is. The estimate helps you decide which fix comes first.
It is not booked revenue loss, and a useful technical finding does not need a price tag.

## Compare like journeys

Every session that reaches a journey stage (product page, cart, checkout steps) is an **entry** at that
stage. Entries are compared within the same **stratum**: the same journey stage, device, environment
and sampling rate, inside the selected window.

- **Affected entries** hit the issue within 30 minutes of entering the stage.
- **Clean entries** come from sessions with no occurrence of this issue at all.
- Sessions that had the issue but not in that window are left out of both groups.

The calculation requires a verified link between the browser SDK and the Shopify Pixel session,
verified event timing and a known, uniform sampling rate. Entries without them are excluded and stay
visible as coverage: the issue detail shows how many affected sessions were compared.

## Give both groups the same follow-up

A purchase counts if it happens within 30 minutes of entering the stage, for affected and clean entries
alike. An error that happened after the purchase still counts as exposure. Requiring the error to come
first would quietly favour the affected group, because it would only keep shoppers who stayed long
enough to see it. Counting both sides the same way can only make the gap smaller, never larger.

Recent entries wait until their 30-minute window is complete. Session evidence is recomputed about
once a minute, so the newest activity can be a few minutes behind.

## Pool the gap and show its certainty

Within each stratum, Dozenfold compares the clean and affected purchase rates. It then pools those
differences into one **conversion gap** (a Mantel–Haenszel risk difference). Strata where affected
sessions converted *better* pull the gap down; they are not ignored. Each pooled gap comes with a
**95% interval**.

| Label | Meaning |
| --- | --- |
| **Measured** | The whole 95% interval is above zero. Affected sessions convert less. |
| **Directional** | The gap is positive, but the interval still includes zero. |
| **No conversion gap** | Affected sessions converted at least as often as clean sessions. |
| **Collecting** | Fewer than 30 affected or 100 clean entries can be compared so far. |

## Put a value on it

Missed purchases are the gap times the compared affected entries. Their value uses the **clean average
order value**: complete order values from clean sessions, in the store's most common order currency.
At least 20 such orders are required; otherwise the gap stays visible without an amount.

For example, 480 compared affected entries purchased 10% of the time, while comparable clean entries
purchased 34% of the time. The pooled gap is 24.0 points with a 95% interval of 21.2–26.8 points, so the
loss is **measured**. That is about 115.2 missed purchases. At a clean average order value of $159.90,
the estimate is **$18,420** for the selected 30 days.

Amounts describe observed sessions. They are not expanded to traffic that was not captured.

## No monthly or annual forecast

Dozenfold does not multiply a short window into a monthly or annual loss. Changing from 7 to 30 days
changes the compared sessions, so the estimate can rise or fall. Separate issues can affect the same
sessions, so their amounts must not be added into a store total.

## Know the limits

- Association is not causation. This is a comparison, not a controlled experiment: traffic mix,
  campaigns, releases and shopper intent can still contribute to a gap.
- A directional result is a reason to keep watching, not a confirmed loss.
- No conversion gap does not mean an issue is harmless; it can still hurt experience or other metrics.
- Occurrences, affected sessions, regressions and experience findings stay useful when money is unavailable.

Read [Revenue impact is unavailable](/docs/impact-unavailable) for the states shown while evidence builds up.