--- title: Cannibalisation Lab: When Paid Steals Organic Installs description: See how a paid app campaign can be credited with installs that would have come organically, with a sample timeline of organic, claimed and caused installs. url: https://incrementalitycalculator.com/labs/cannibalisation/ --- [Start](https://incrementalitycalculator.com/) / [Labs](https://incrementalitycalculator.com/labs/) / Cannibalisation lab # Cannibalisation lab How to spot a UA campaign that is cannibalising organic installs, on a sample timeline. **Short answer** A campaign cannibalises organic installs when it is credited with users who would have installed anyway. The tell-tale pattern: attributed installs rise, organic installs fall by a similar amount, and total installs barely move. In the sample, a push credited with 1,500 installs a day only lifts total installs by 600. > Interactive tool (cannibalisation lab): runs in the browser at https://incrementalitycalculator.com/labs/cannibalisation/#cannibal-lab. Its text content follows. **Cannibalisation lab** Runs in your browser. Sample data. Weekends run about 15% higher in this sample. What the ad platform or attribution tool reports. In real life this is what an incrementality test measures. Sample data: a made-up app to show the method, not a benchmark. Organic, shown as organic Organic, credited to the push (like Ana) Caused by the push (like Leo) Organic baseline **Credited to the push per day** 1,500 **Truly caused per day** 600 **Credited but would have come anyway** 900 **Organic the dashboard shows (weekday)** 1,100 **Apparent organic drop** 45% **Real rise in total installs** 30% ## How to spot it in your own data - **Watch total installs, not just paid.** If paid installs climb but total installs stay flat, the extra paid installs came out of organic. - **Look for a mirror image.** Organic dropping in the same week paid starts, by a similar amount, is the classic shape in the chart above. - **Check brand and retargeting first.** Campaigns aimed at people who already know the app carry the highest risk. - **Confirm with a test.** The pattern is a clue, not proof. Seasonality or a store feature can produce the same shape, so pause the suspect campaign in some regions and compare. The [cannibalisation guide](https://incrementalitycalculator.com/learn/cannibalisation-brand-retargeting/) covers brand search and retargeting in detail. For the same idea at the scale of one person, the [three users walkthrough](https://incrementalitycalculator.com/#three-users) follows Ana, who was going to install anyway but tapped a brand search ad on the way. ## The maths caused = credited × incremental share claimed = credited − caused organic shown = organic baseline − claimed total = organic baseline + caused In real data you only see credited installs and organic shown. The baseline and the incremental share are what a holdout or geo test estimates. ## Read next - [Which UA campaigns are cannibalising organic installs?](https://incrementalitycalculator.com/learn/cannibalisation-brand-retargeting/) - [What is incrementality in app marketing?](https://incrementalitycalculator.com/learn/what-is-incrementality/) Run this on your own data Cannibalisation shows up when organic and attributed installs sit on one daily chart. Linkrunner reports both side by side, and the attribution window you set decides how long after a tap an install can still be credited to an ad. 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=lab-cannibalisation&utm_content=panel) - [How attribution windows work (docs)](https://docs.linkrunner.io/features/attribution-windows?utm_source=incrementalitycalculator&utm_medium=referral&utm_campaign=lab-cannibalisation&utm_content=panel) - [Create a free account](https://dashboard.linkrunner.io/sign-in?utm_source=incrementalitycalculator&utm_medium=referral&utm_campaign=lab-cannibalisation&utm_content=panel) ## Questions **How do I identify which UA campaigns are cannibalising organic installs?** Compare each campaign's start and stop dates with the organic line and with total installs. Campaigns where organic falls as paid rises, and total installs barely change, are the suspects; confirm with a holdout or regional pause. **Is all cannibalisation bad?** Not always. Bidding on your own brand can stop a competitor taking the top slot, and some retargeting brings back users who would otherwise lapse. The question is whether the price you pay matches the installs you would actually lose without it. **Why does organic drop when paid starts?** Usually because some people who were going to install anyway tapped an ad on the way, so attribution moves them from organic to paid. The installs did not disappear; their label changed. **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. ## Other labs - [01 **Holdout test simulator** Pick a holdout size, an audience and a true lift, then watch a simulated test day by day until the confidence interval clears zero.](https://incrementalitycalculator.com/labs/holdout-simulator/) - [02 **Lift test reader** Enter test and control results and get the lift, a 95% confidence interval, a plain-English verdict and the usual misreadings.](https://incrementalitycalculator.com/labs/lift-reader/) - [03 **Attributed vs incremental CPI and ROAS** Two sample channels side by side: how ranking them on attributed CPI and on incremental CPI can point budget in opposite directions.](https://incrementalitycalculator.com/labs/incremental-roas/)