Cannibalisation lab
How to spot a UA campaign that is cannibalising organic installs, on a sample timeline.
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.
Sample data: a made-up app to show the method, not a benchmark.
- 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 covers brand search and retargeting in detail. For the same idea at the scale of one person, the three users walkthrough follows Ana, who was going to install anyway but tapped a brand search ad on the way.
The maths
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.
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
- 01Holdout test simulatorPick a holdout size, an audience and a true lift, then watch a simulated test day by day until the confidence interval clears zero.
- 02Lift test readerEnter test and control results and get the lift, a 95% confidence interval, a plain-English verdict and the usual misreadings.
- 03Attributed vs incremental CPI and ROASTwo sample channels side by side: how ranking them on attributed CPI and on incremental CPI can point budget in opposite directions.