---
title: Retention by Channel: Campaign and Creative Cuts
description: Blended retention hides what each channel does. Learn to cut a cohort table by channel, campaign and creative, keep naming clean and spot a channel mix shift.
url: https://retentioncohorts.com/learn/retention-by-channel/
---

[Home](https://retentioncohorts.com/) / [Guides](https://retentioncohorts.com/learn/) / Retention by channel and campaign

# Retention by channel and campaign

Blended retention hides what each channel does. Learn to cut a cohort table by channel, campaign and creative, keep naming clean and spot a channel mix shift.

**Short answer**

Retention by channel splits the cohort table by install source, so you can see which channels, campaigns and creatives bring users who come back. Blended retention can fall even when every channel is flat, simply because the mix of installs moved towards a lower-retaining channel.

## What is retention by channel?

Retention by channel is the same cohort retention you already track, split by the install source credited to each user, so every channel, campaign or creative gets its own row of Day 1, Day 7 and later values.

**Install source:** the channel and campaign an attribution tool credits for an install, or organic when no campaign is credited.

**Blended retention:** retention for all installs together, with every channel pooled into one number.

## Why blended retention hides what is happening

A blended number averages channels that behave very differently. If organic users retain better than paid ones, the blended figure depends as much on how many installs came from each as on how well any channel retains.

That leads to two common misreadings:

- A blended drop is blamed on the product when it was caused by more paid spend.
- A blended rise is credited to a product change when organic installs simply grew.

## The channel mix trap

In the sample app on the home page, a made-up app, a paid campaign scales up partway through the period. Classic Day 1 retention looks like this:

|  | Earlier cohorts | Later cohorts |
|---|---|---|
| Organic | About 43% | About 42% |
| Paid | About 24% | About 23% |
| Blended | About 37% | About 30% |
| Paid share of installs | About a third | About two thirds |

Blended Day 1 falls by about 7 points, yet neither channel changed. The mix moved, not retention. This is a version of Simpson's paradox, and the [mix section of the reading guide](https://retentioncohorts.com/read/#mix) shows it in the full table. You can reproduce it in the [cohort explorer](https://retentioncohorts.com/) by switching the channel filter.

## How to cut the cohort table

Work from broad to narrow, and only go one level deeper when the level above shows something worth explaining.

1. **Channel first.** Organic against each paid network. This tells you whether the mix explains a blended move.
2. **Campaign next.** Within a channel, one campaign often drags the average down.
3. **Creative last.** Creatives with the same targeting can bring different users, especially if one promises something the app does not do.

At each level, keep the same retention definition, the same cohort dates and the same day boundary. The [channels section](https://retentioncohorts.com/read/#channels) of the reading guide lays out the table, and there are more ways to slice cohorts in this piece on [six ways to cut cohorts](https://linkrunner.io/blog/best-6-mobile-app-cohort-analysis-techniques-for-growth-teams?utm_source=retentioncohorts&utm_medium=referral&utm_campaign=learn-retention-by-channel&utm_content=inline).

## How small is too small?

Small cohorts swing a lot, because one or two users returning can move the rate by several points. Read small cohorts as directional, not as results.

- Pool several days or a week of cohorts before comparing campaigns.
- Look for a pattern that repeats across cohorts rather than a single high or low cell.
- As a rule of thumb, if adding or removing one returning user would change your decision, the cohort is too small to act on.

The [sample size notes](https://retentioncohorts.com/read/#sample-size) explain why this matters more at creative level, where cohorts are smallest.

## Keep campaign names consistent

Retention by campaign only works if campaigns roll up cleanly. If the same campaign appears as three slightly different names, its users are split across three small rows that all look unreliable.

- Agree one naming pattern for channel, campaign and creative, and stick to it.
- Use the same case and separators everywhere.
- Fix names at the source rather than cleaning them in a spreadsheet each week.

This guide to [inconsistent UTM naming](https://deeplinkdoctor.com/problems/utm-naming-inconsistent/) covers the usual problems and fixes.

## Read paid and organic on the same definition

Before comparing channels, check that both use the same retention definition. Rolling is always at least as high as classic and keeps rising as late returns arrive, so a rolling figure for one channel against a classic figure for another is not a comparison. Classic is the safer basis. The guide to [why paid D1 is lower than organic](https://retentioncohorts.com/learn/paid-d1-lower-than-organic/) goes deeper on that gap.

## Where do you get retention by channel?

Attribution tools (MMPs) and product analytics tools can split retention by install source; examples to check include Linkrunner, AppsFlyer, Adjust, Singular and Branch. Linkrunner [reports classic, rolling and cumulative retention as separate columns](https://docs.linkrunner.io/features/retention?utm_source=retentioncohorts&utm_medium=referral&utm_campaign=learn-retention-by-channel&utm_content=inline). Whichever tool you use, check its documentation for which definition its retention column uses before you compare numbers across tools or against a benchmark.

**Note:** Example numbers come from a made-up app to show the method, not a benchmark.

See this on your own app

Linkrunner reports classic, rolling and cumulative retention per campaign, so these cuts start from attributed installs rather than a spreadsheet.

- [How to Measure App Retention by Channel](https://linkrunner.io/blog/measure-app-retention-by-channel?utm_source=retentioncohorts&utm_medium=referral&utm_campaign=learn-retention-by-channel&utm_content=panel)
- [Create a free account (25k installs free)](https://dashboard.linkrunner.io/sign-in?utm_source=retentioncohorts&utm_medium=referral&utm_campaign=learn-retention-by-channel&utm_content=panel)

## Questions

**Why did my blended retention drop when no channel changed?**

The share of installs from each channel probably moved. If a lower-retaining channel grows, the blended number falls even when every channel holds steady. Split the table by channel before blaming the product.

**Should I cut retention by creative?**

Yes, once channel and campaign cuts show something worth explaining. Creative cohorts are the smallest, so pool several days and read them as directional.

**Which retention definition is best for comparing channels?**

Classic, because it is fixed once the day passes and does not favour older cohorts. Whatever you choose, use the same definition for every channel in the comparison.

**How do I stop campaigns splitting into many small rows?**

Agree one naming pattern and apply it at the source. Inconsistent case, spelling or separators turn one campaign into several rows that are each too small to read.

**Is organic retention a fair baseline for paid?**

It is a useful reference but not a target, because organic users usually arrive with intent. Track the gap over time and by campaign rather than expecting paid to match organic.

**What is the difference between classic, rolling and cumulative retention?**

Classic counts users active on exactly Day N. Rolling counts users active on Day N or any later day, and cumulative counts users active at least once from Day 1 to Day N. They use the same raw activity but answer different questions.

## More guides

- [01 **Classic vs rolling vs cumulative retention** Classic, rolling and cumulative retention count different users on the same day. See the definitions, a ten-user worked example and when to use each one.](https://retentioncohorts.com/learn/classic-vs-rolling-vs-cumulative/)
- [02 **How to read a retention curve** A retention curve plots one cohort's retention against Day N. Read the early drop, the flattening point and smile curves, and why shape depends on definition.](https://retentioncohorts.com/learn/read-a-retention-curve/)
- [03 **Why paid D1 retention is lower than organic** Paid D1 retention below organic is partly normal, as organic users arrive with intent. See the causes, from targeting to measurement errors, and how to check.](https://retentioncohorts.com/learn/paid-d1-lower-than-organic/)
- [04 **Choosing the cohort start event: install or first purchase** How to define cohorts by choosing a start event: install, first open, sign-up or first purchase, what each answers, survivorship bias, day boundaries, channels.](https://retentioncohorts.com/learn/cohort-start-event/)
- [05 **Retention for weekly-use vs daily-use apps** Why classic Day N retention undercounts apps people use weekly, and how cumulative windows, weekly cohorts and DAU/MAU give a fairer read of weekly-use apps.](https://retentioncohorts.com/learn/weekly-vs-daily-use-apps/)
- [06 **Turning retention into decisions: LTV, payback and budget** How to turn app retention into decisions: which definition feeds LTV and forecasts, a simple LTV from a retention curve, cost per retained user and payback.](https://retentioncohorts.com/learn/retention-to-decisions/)