---
title: Find UA Campaigns Cannibalising Organic Installs
description: How to identify which UA campaigns are cannibalising organic installs: brand search and retargeting patterns, a four-step data check, and how to test it.
url: https://incrementalitycalculator.com/learn/cannibalisation-brand-retargeting/
---

[Start](https://incrementalitycalculator.com/) / [Learn](https://incrementalitycalculator.com/learn/) / Cannibalisation

# Which UA campaigns are cannibalising organic installs?

How to identify which UA campaigns are cannibalising organic installs: brand search and retargeting patterns, a four-step data check, and how to test it.

**Short answer**

A campaign is cannibalising organic installs when it is credited with users who would have installed anyway. Brand search and retargeting are the usual suspects. The sign is attributed installs rising while organic installs fall by a similar amount and total installs barely move; a regional pause confirms it.

## What cannibalisation means

Cannibalisation is when a paid campaign takes credit for installs that would have happened organically, so attributed installs go up while organic installs go down by about the same amount.

**Cannibalisation:** paid attribution capturing users who were already going to install. The installs are real; the campaign just did not cause them.

It is not fraud and it is not an attribution bug. [Last-touch attribution](https://linkrunner.io/glossary/what-is-last-touch-attribution?utm_source=incrementalitycalculator&utm_medium=referral&utm_campaign=learn-cannibalisation-brand-retargeting&utm_content=inline) credits the last ad touched, and people who are already looking for your app touch a lot of ads on the way. This article on [when paid installs cannibalise organic growth](https://linkrunner.io/blog/paid-installs-cannibalise-organic-growth?utm_source=incrementalitycalculator&utm_medium=referral&utm_campaign=learn-cannibalisation-brand-retargeting&utm_content=inline) describes the pattern from the attribution side.

## The usual suspects

### Brand search

Someone searches the App Store or Google for your app by name. Your paid result sits above your organic listing; they tap it. The ad gets the install, but the search itself shows the person already wanted the app. The [three users walkthrough](https://incrementalitycalculator.com/#three-users) on the start page follows one such person, Ana, through her week.

- High attributed conversion rates and very low CPIs are typical, which makes brand campaigns look like the best channel on the dashboard.
- The open question is what a competitor bidding on your name would take if you stopped. That is worth testing rather than assuming.

### Retargeting

**Retargeting:** showing ads to people who have already visited, installed or used the app. This glossary entry on [retargeting](https://linkrunner.io/glossary/what-is-retargeting?utm_source=incrementalitycalculator&utm_medium=referral&utm_campaign=learn-cannibalisation-brand-retargeting&utm_content=inline) covers the basics.

Retargeting audiences are, by design, people most likely to act anyway. Re-engagement and reinstall campaigns often report the highest ROAS of any channel for that reason. Some of it is real (lapsed users nudged back); some is credit for users who would have returned.

### Broad campaigns during busy weeks

During festivals, sales or a press moment, organic interest rises. Prospecting campaigns running at the same time collect credit for part of that rise, and they look unusually efficient.

## How do you identify which campaigns are cannibalising organic?

A four-step check you can run on your own data, before any formal test:

1. **Plot total, organic and attributed installs by day** for the last three to six months, with campaign start and stop dates marked.
2. **Look for mirror images.** Organic dipping in the same week a campaign starts, by a similar amount to the campaign's attributed installs, is the classic sign. The [cannibalisation lab](https://incrementalitycalculator.com/labs/cannibalisation/) draws exactly this shape on sample data.
3. **Check total installs.** If paid rose by 1,000 a day and total installs rose by 200, roughly 800 of the paid installs were relabelled organics. If total rose by 900, the campaign is mostly incremental.
4. **Rank the suspects.** Brand terms first, then retargeting, then anything that started at a seasonal peak.

This check needs one data source that sees organic and paid installs on the same basis. An MMP such as Linkrunner, AppsFlyer or Adjust shows organic and attributed installs side by side by day, which is the view to start from.

Sample data: a made-up app to show the method, not a benchmark.

A made-up example: a weekday organic baseline of 2,000 installs. A push is credited with 1,500 installs a day, but only 40% of those are caused by it. The dashboard shows organic falling to 1,100 a day (a 45% drop) while total installs rise by only 600 a day (30%).

## Confirm with a test

The patterns above are clues. Seasonality, store featuring or a competitor's move can produce the same shapes, so confirm before cutting spend.

- **Brand search:** pause brand terms in some countries or regions for two to four weeks; keep them running elsewhere. Compare total installs from search, not paid installs.
- **Retargeting:** use a holdout on the retargeting audience if the platform supports it; otherwise pause in matched regions.
- **Read the test on total installs.** The [lift test reader](https://incrementalitycalculator.com/labs/lift-reader/) handles holdout results; the [test types guide](https://incrementalitycalculator.com/learn/incrementality-test-types/) covers geo set-up.

## What to do with the answer

- **High cannibalisation, low competitor pressure:** cut or cap the campaign and watch organic recover.
- **High cannibalisation, competitors bidding on your name:** keep a defensive budget, but judge it on incremental installs, not attributed CPI.
- **Low cannibalisation:** the campaign is earning its credit. Check whether it can scale before the incremental share starts to fall.

For a reference point on how large the organic share typically is for apps of your size, AttributionBenchmarks has a short read on [what a good organic install share looks like](https://attributionbenchmarks.com/blog/good-organic-install-share/). The [acting on results guide](https://incrementalitycalculator.com/learn/act-on-incrementality-results/) turns the outcome into a budget move.

Run this on your own data

The four-step check needs organic and paid installs on the same daily basis. Linkrunner includes organic attribution, so both lines come from one source. Linkrunner's free plan covers 25k attributed installs, with no credit card and every feature included.

- [When paid installs cannibalise organic growth](https://linkrunner.io/blog/paid-installs-cannibalise-organic-growth?utm_source=incrementalitycalculator&utm_medium=referral&utm_campaign=learn-cannibalisation-brand-retargeting&utm_content=panel)
- [Incrementality testing, defined](https://linkrunner.io/glossary/what-is-incrementality-testing?utm_source=incrementalitycalculator&utm_medium=referral&utm_campaign=learn-cannibalisation-brand-retargeting&utm_content=panel)
- [Create a free account](https://dashboard.linkrunner.io/sign-in?utm_source=incrementalitycalculator&utm_medium=referral&utm_campaign=learn-cannibalisation-brand-retargeting&utm_content=panel)

## Questions

**How do I know if my brand search ads are cannibalising organic installs?**

Pause them in some regions for two to four weeks and compare total installs with regions where they kept running. If total installs barely change where brand ads were paused, most brand-ad installs were coming anyway.

**Is retargeting always cannibalisation?**

No. Some lapsed users only return because of a reminder, and that part is incremental. Test it with a holdout on the retargeting audience to find the split.

**Why does organic go down when we start paid campaigns?**

Usually because users who would have installed organically tap an ad along the way, so attribution relabels them as paid. If total installs rise by much less than paid installs, that relabelling is the likely cause.

**What is incrementality in app marketing?**

Incrementality is the share of conversions that would not have happened without the ad. It is measured by comparing people who could see the ads with a similar group who could not, so it captures what the ads changed rather than what they were credited with.

**Is attributed the same as incremental?**

No. Attribution credits a campaign when a user touched an ad before installing, even if that user would have installed anyway. Incremental installs are only the ones the ads caused, so they are always equal to or fewer than attributed installs.

**How big should my holdout group be?**

Big enough that the control group collects enough conversions to detect the lift you care about within the time you can wait. Lower conversion rates and smaller expected lifts need bigger holdouts or longer tests; the holdout simulator on this site shows the trade-off.

## More guides

- [01 **What is incrementality in app marketing?** Incrementality in app marketing explained: the share of installs and revenue your ads really caused, how it differs from attribution, and how to measure it.](https://incrementalitycalculator.com/learn/what-is-incrementality/)
- [02 **Holdout, geo lift, ghost ads, PSA and on/off tests** Compare incrementality test types for app marketers: user holdouts, geo lift, ghost ads, PSA and on/off tests, plus how to run a geo lift test for installs.](https://incrementalitycalculator.com/learn/incrementality-test-types/)
- [03 **How big should my holdout group be?** How to size an incrementality holdout for an app: control conversions needed, and how holdout share, install rate and expected lift set the test length.](https://incrementalitycalculator.com/learn/holdout-group-size/)
- [04 **How to read lift test results without fooling yourself** How to read an incrementality lift test: lift, confidence interval, p-value and incremental installs, the mistakes that turn noise into wins, what to report.](https://incrementalitycalculator.com/learn/read-lift-test-results/)
- [05 **Incrementality on Meta, Google and Apple Search Ads** How to test incrementality on Meta, Google App Campaigns and Apple Search Ads: what each platform's lift tools offer, their published rules and the fallbacks.](https://incrementalitycalculator.com/learn/incrementality-meta-google-apple/)
- [06 **MMM vs MTA vs incrementality testing** Media mix modelling, multi-touch attribution and incrementality tests compared for app marketers: what each measures, the data needed, how they fit together.](https://incrementalitycalculator.com/learn/mmm-mta-incrementality/)
- [07 **How to act on incrementality results** Turn incrementality test results into budget decisions: incremental CPI and iROAS by channel, moving spend in steps, when to retest, and reading CTV and brand.](https://incrementalitycalculator.com/learn/act-on-incrementality-results/)