---
title: How to Act on Incrementality Results: Budget Moves
description: 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.
url: https://incrementalitycalculator.com/learn/act-on-incrementality-results/
---

[Start](https://incrementalitycalculator.com/) / [Learn](https://incrementalitycalculator.com/learn/) / Acting on results

# 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.

**Short answer**

Convert each tested channel's attributed numbers into incremental CPI and incremental ROAS, then move budget in steps from the channel with the worst incremental return to the best, planning on the cautious end of each interval. Retest after each big move, because incrementality usually falls as spend rises.

## From test result to budget decision

Acting on incrementality means ranking channels by what they cause, not what they are credited with, and moving budget accordingly.

**Incremental CPI:** spend divided by incremental installs.

**Incremental ROAS (iROAS):** revenue from incremental conversions divided by spend. This glossary entry on [return on ad spend](https://linkrunner.io/glossary/what-is-return-on-ad-spend?utm_source=incrementalitycalculator&utm_medium=referral&utm_campaign=learn-act-on-incrementality-results&utm_content=inline) covers the attributed version it is adjusting.

Both are always equal to or worse than their attributed versions, because incremental conversions are a subset of attributed ones.

## Step 1: put every tested channel on the same footing

For each channel you have tested, take its attributed installs and revenue for a normal month and multiply by its measured incrementality.

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

| Made-up month | Channel A (brand search) | Channel B (social prospecting) |
|---|---|---|
| Spend | $20k | $20k |
| Attributed installs | 20k | 8k |
| Incrementality (from a test) | 20% | 70% |
| Attributed CPI | $1 | $2.50 |
| Incremental CPI | $5 | about $3.60 |
| Attributed ROAS ($4 revenue per install) | 4.0x | 1.6x |
| iROAS | 0.8x | 1.1x |

On the dashboard, Channel A is two and a half times cheaper. On what it causes, Channel B is cheaper and is the only one returning more than it costs. The [ROAS lab](https://incrementalitycalculator.com/labs/incremental-roas/) lets you change each input and see when the order flips back.

## Step 2: use the cautious end

Each incrementality figure comes from a test with an interval. Plan with the low end for channels you are scaling up, and the high end for channels you are cutting, so the decision survives the uncertainty. If the decision changes depending on which end you use, the test needs more volume before you move much budget. The [results guide](https://incrementalitycalculator.com/learn/read-lift-test-results/) covers reading intervals.

## Step 3: move budget in steps, not all at once

**Diminishing returns:** each extra dollar in a channel tends to buy fewer incremental installs than the one before, as the channel runs out of the most responsive people.

That means a test result describes incrementality at the spend level you tested. Doubling a channel's budget will not double its incremental installs. So:

1. Move a modest share of budget (for example a fifth) from the worst incremental performer to the best.
2. Run for a few weeks and watch total installs and revenue, not just attributed numbers.
3. Retest the channel you grew once its spend has settled at the new level.

This [budget reallocation framework](https://linkrunner.io/blog/the-marketing-budget-reallocation-framework-using-attribution-data?utm_source=incrementalitycalculator&utm_medium=referral&utm_campaign=learn-act-on-incrementality-results&utm_content=inline) describes a weekly rhythm for moving spend on attributed data; incrementality results tell you how much to discount each channel inside that rhythm.

## Step 4: decide what to do with low-incrementality channels

A low result rarely means "switch it off" straight away.

- **Brand search:** if competitors bid on your name, some spend may protect installs you would otherwise lose. Cap it and judge it on incremental installs.
- **Retargeting:** narrow the audience to users who have lapsed longer, where the nudge is more likely to matter, and retest.
- **Prospecting with a low result:** check creative and targeting before cutting. A test on a weak campaign measures that campaign, not the channel's potential.

## Channels attribution cannot see well

Some spend, such as connected TV, outdoor or influencer bursts, shows weakly in [click-based attribution](https://linkrunner.io/glossary/what-is-click-through-attribution?utm_source=incrementalitycalculator&utm_medium=referral&utm_campaign=learn-act-on-incrementality-results&utm_content=inline), so attributed ROAS understates or misstates it. The way to measure the true return of a CTV campaign for app installs is the same as any other channel: a geo test. Run CTV in some regions and not in matched ones, read total installs, and compute incremental CPI and iROAS from the difference. The [test types guide](https://incrementalitycalculator.com/learn/incrementality-test-types/) has the geo set-up.

## Include the full cost

Incremental CPI is only as honest as its spend figure. Include agency fees, creative production and measurement tools when comparing channels with very different cost structures. MMP Price Watch has a rundown of [MMP costs that do not show on the price page](https://mmppricewatch.com/blog/mmp-hidden-costs/) if measurement is a meaningful line in your budget.

## When to retest

- After moving more than about a quarter of a channel's budget in either direction.
- When the season changes sharply (festival periods, year-end).
- When the product, pricing or store listing changes in a way that shifts organic demand.
- At least once a year for every channel that takes a large share of spend.

Run this on your own data

Moving budget on incremental numbers means discounting each channel's attributed results every week, which starts with clean channel-level data from your MMP. Linkrunner's free plan covers 25k attributed installs, with no credit card and every feature included.

- [A budget allocation framework for multi-channel app growth](https://linkrunner.io/blog/budget-allocation-framework-for-multi-channel-app-growth-meta-google-tiktok-and-beyond?utm_source=incrementalitycalculator&utm_medium=referral&utm_campaign=learn-act-on-incrementality-results&utm_content=panel)
- [Incrementality testing, defined](https://linkrunner.io/glossary/what-is-incrementality-testing?utm_source=incrementalitycalculator&utm_medium=referral&utm_campaign=learn-act-on-incrementality-results&utm_content=panel)
- [Create a free account](https://dashboard.linkrunner.io/sign-in?utm_source=incrementalitycalculator&utm_medium=referral&utm_campaign=learn-act-on-incrementality-results&utm_content=panel)

## Questions

**Should I cut a channel with an iROAS under 1x?**

Not straight away. Check the interval, whether the test ran in a typical period and whether a smaller budget would be more incremental. Reduce in steps and retest, rather than switching off a channel on one result.

**How do I measure the true ROI of CTV campaigns for app installs?**

Run a geo test: CTV on in some regions, off in matched regions, for at least a few weeks. The difference in total installs and revenue, divided by CTV spend, gives incremental CPI and iROAS without relying on view-through attribution.

**Can I apply one channel's incrementality to another?**

No. Incrementality depends on the channel, audience, creative and spend level. Use each channel's own test, and label any untested channel's figures as an assumption.

**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 **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.](https://incrementalitycalculator.com/learn/cannibalisation-brand-retargeting/)
- [07 **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/)