付费渠道止损框架：7步自查清单与决策逻辑

I’ve been running app campaigns for long enough that I’ve seen one problem arise time and time again — and nobody seems to have a solid answer for it. When should you give up on a channel?

We’ve all had it happen: an app is running paid user acquisition campaigns on one specific ad network and getting really bad results. There’s multiple reasons that could explain the performance, but after weeks (or even months) trying to address these issues, the performance hasn’t improved. Eventually, the Founder or CEO asks the questions:

• When should we give up on this channel?
• How far should we go with different testing and experimentation?
• Should we explore cheaper channels?

In my UA playbook for one-person teams, I said to pick one channel and make it a success before even thinking about a second one. But a few months ago I also said on LinkedIn that you should explore cheaper channels if you’ve been trying one channel for three weeks with no results.

So which one is it? Should you persist, or should you move on?

The honest answer is: both, depending on the situation. And that’s exactly the problem. I’ve seen founders killing a channel after five days because “TikTok doesn’t work for us”, and others continuing to burn money on a channel for months because the modeled conversions in the dashboard still look nice.

This decision is only getting harder with new privacy limitations, AI deleting barriers, CPMs increasing, and a whole new generation of app builders shipping apps at an unimaginable speed.

But there is a clear way to decide when to hold onto or let go of a channel — keep reading for my fix, pause, kill framework.

Quitting too early vs. holding on too long

When this challenges comes up, you typically see the same two profiles again and again:

The tourists: They launch Meta, TikTok, and Apple Ads at the same time with $100–200/day budgets while they check hourly delivery. After five days, they discard most of them and only stick with one channel, concluding that the other channels “don’t work for our app”.

When you look at the channels, you’ll see that none of them got a proper setup (campaign optimization goal or creative strategy are often the main mistake) or none of them got enough signals. As Lucas Moscon explained in his article on ad channel diversification, launching more channels doesn’t mean lower risk if your budget, audiences, or team capacity can’t afford multiple learning phases at once.

The loyalists: This is the opposite case — and in my experience, the most expensive one. These are marketers that used to have a working channel bringing consistent growth. For some reason, it stopped working and yet, they keep burning money on it, even when customer acquisition cost (CAC) has been going up for weeks. Sometimes it’s an emotional tie (“it worked last year”), sometimes it’s down to knowledge gaps (the UA responsible “owns” that channel) and very often it’s a data problem: they’re either cannibalizing organic or trusting modeled conversions too much.

To support these people and guide your decisions, you need a clear framework for when a channel deserves more time, and when it doesn’t.

7 Checks to see if it’s you: the pre-kill channel checklist

Let’s be super honest here: most of the time when someone says “this channel doesn’t work for us”, the problem is not the channel, it’s something on the company’s side that is being exposed by the channel. So before blaming the channel or cutting it off, go through this checklist. If you answer ‘no’ to any of these questions, the problem might be on your end.

1. Does your product convert within benchmarks?

First off, is your product converting as it should be. Use the RevenueCat App Health calculator or the State of Subscription Apps report to find the relevant benchmarks for your vertical, then examine:

• Install rate
• Install-to-trial rate or install-to-paid rate (if you don’t have a free trial)
• Trial-to-paid rate

Obviously, if your conversion rates (CVR) are far below your category, no channel will save you. You will just see the same numbers but with different CPMs.

I personally lived this scenario with one of my clients: we tested Google, TikTok, Meta, Apple Ads, even influencers, and we struggled for months, testing thousands of creative angles. Ultimately, our install-to-paid rate was simply too low for the CAC that we needed to achieve in order to sustain business margins. Until we stopped trying to fix that with an introductory offer, and focused on creating a solid activation journey, we couldn’t run paid UA consistently.

2. Does your optimization event have enough volume?

The theory is that you need around 10 events per day (or 50 per week) to exit learning phases. According to my experience, the reality is that after 30 per week, you can judge the quality you get on that channel. However, if you’re running campaigns with $50/day optimizing towards subscriptions, you should expect your performance to fluctuate constantly — because you’re not feeding the algorithm enough signals.

Thomas Petit has said this multiple times: focus on actions that happen within the first 24 hours. 55% of 3-day trial cancellations happen on Day 0, so first-24 hours actions are critical. On top of that, I recommend moving to a proxy event if you can’t afford optimizing directly towards your subscription event.

3. Did you test enough creatives?

We all know that targeting is now done through creatives, not audience settings (with some exceptions) so having a proper creative mix with enough volume is key to really see if your CTRs, hook rates, and hold rates are according to the benchmarks for every user persona you think you can target.

If you tested a channel with three creatives and never rotated them, you didn’t test the channel. You tested three creatives. As a reference, in my $0–500 framework, I recommend starting with 8–10 creatives and rotating the ones that don’t spend every 48 hours in Meta and every 24 hours on TikTok (creative fatigue is much faster there).

4. Is your budget enough for that specific channel?

There are some specific channels where small budgets won’t work. For example in Moloco, you will never see the reality of the channel if you spend $100 a day because the algorithm needs a much longer time and a greater amount of signals to exit the learning phase (minimum is $500/day).

Another example is Apple Ads, where you can’t have a $50/day budget if you want to test 15 generic keywords and 20 competitor keywords. You would need weeks to really figure out the performance. So before cutting a channel off, consider whether your budget is feeding it enough to see any real impact.

5. Are you measuring correctly?

This one is especially critical on iOS. Probabilistic attribution estimates which ad led to an install by modeling aggregated signals (like timing, IP address, and device type) rather than matching a confirmed user ID — so the numbers are educated guesses, not exact counts. This technique and modeled attribution (AEM, ICM, ADC, etc.) can under-report or over-report what a channel is really bringing. I've seen channels that looked unprofitable in the dashboard but were bringing dozens of unreported trials per week when checking the baseline uplift. And I've seen the opposite with DSPs and 24 hour view-through windows. If you haven't checked the baseline before vs. after launching the channel, you don't really know how it performs.

6. Is the delivery going to the right audience?

A cheap CPA can hide a terrible channel, and an expensive CPA can hide a great one. Check the age and placement distribution (when you can) and try to ask basic demographic questions on your onboarding so you can use first-party data to analyze before and after launching a channel.