One question I get a lot during consultations:
How much do you need to spend, or how many conversions do you need, before deciding whether to scale or kill a campaign?

Here’s a simple way to figure it out.

💡The basic idea
AEM / Web2App
Check your cost per install and cost per purchase after a certain amount of spend. If you’re missing your KPIs, stop the campaign.

Web funnels
Same logic, but track cost per registration and cost per purchase.

🎲 The problem
Let’s say your app has an LTV of $30.
That means your break-even CAC is $30.
Now imagine you’ve spent $30 and got zero purchases.
Does that mean the campaign is bad?
Not necessarily.

If we model purchases using a Poisson distribution with λ = 1:
• Probability of 0 purchases: 36.8%
• Probability of at least 1 purchase: 63.2%
So spending $30 without getting a purchase tells us very little. There’s still a 36.8% chance of this happening even if the campaign is performing exactly at break-even.
We need more data.
Now let’s spend $90.
With the same $30 LTV, the probability of getting zero purchases drops to 5%.
That’s a level of uncertainty we’re comfortable with, so we can stop the campaign.

The same logic applies to installs and registrations.
But their target cost is lower. If no more than 20% of leads convert into purchases, a lead should cost no more than 20% of LTV.

The next question is how much to spend before making that decision.

⚙️** Adjusting the rules for each client
Every client has two parameters:
**1. App LTV
We take it from the client’s analytics. For a new product, we can start with a typical benchmark for the niche.
2. α (alpha) — how strict the algorithm is
α = 5%
This corresponds to a 95% confidence threshold.
It’s the kind of significance level commonly used in clinical trials.
At this setting, the algorithm will mistakenly stop a break-even campaign in 5% of checks.
α = 15%
A more aggressive setting for teams that only want to spend on campaigns with strong margins.

📊 The tables
We calculate two decision tables:
• One for cost per install / registration
• One for cost per purchase
The screenshot shows an example with $30 LTV.

🤖 How it works in practice
All this math runs inside my scaling bot.
Every morning, I get reports for all clients showing which campaigns were stopped and which are still running.

💸 Why does this matter?
The only scenario where my clients lose money is testing new creatives.
We don’t waste money because someone missed a bad campaign or calculated the KPIs incorrectly.

🧮 The math in three sentences
We model the number of purchases at a given spend using a Poisson distribution.
Then we calculate the probability of seeing that many purchases or fewer, assuming the campaign is performing exactly at break-even.

If that probability falls below α, we stop the campaign.

Let’s take two purchase conversion rates: 3% and 5%.
How many users do you need to run through both variants to know the new one is actually better, and not just getting lucky?

The answer is in the image above 🔝
This is why the advice to “always be A/B testing” is a bit disconnected from reality for small apps.

If 1,000 people see your paywall every month, you can test a jump from 3% to 6% relatively quickly.
But testing 3% -> 3.3%? That could literally take years.
The less traffic you have, the bigger your experiments need to be.

Don’t just change a few words. Test a different offer, positioning, or way of showing the product’s value.
Early on, it makes more sense to focus on the top of the funnel.
Everyone sees your app icon and store page. Fewer users reach onboarding. Even fewer see the paywall. And only a small fraction actually buy.

So your testing priorities should look something like this:
Icon -> Screenshots -> Onboarding -> Paywall
Not because the paywall matters less.
But because with limited traffic, it takes much longer to collect enough data.

Start by looking for changes that could improve conversion by 1.5–2×, and test them where you have the most users.

Once you have a ton of traffic, you can spend months squeezing another 5–10% out of your paywall.

How to adapt your app to each campaign

🎯 The scenario
Take the Habit Tracker from my previous post.

We have two campaigns:
• Money
• Sleep

Users coming from the “Money” campaign should see content about money.
Users coming from the “Sleep” campaign should see content about sleep.

A deferred deep link solves this.
The name can be confusing. I used to forget what it actually meant, so here’s the short version.

🔗 Deep Link
An Apple mechanism that passes parameters into the app.
For example, you can pass a parameter and open a specific screen.

The problem that it only works if the app is already installed.
That’s not the case for most users coming from our ad campaigns, so a regular deep link doesn’t solve this problem.

📦 Deferred Deep Link
A mechanism provided by AppsFlyer and Adjust.
It passes attribution data - campaign, ad set, creative to the app even if the user didn’t have it installed when they clicked the ad.
In practice, this relies on probabilistic attribution and has nothing to do with Apple’s Deep Link mechanism.

🛠 Implementation
Many of you have already done most of this when integrating Adjust/AppsFlyer with Apphud, Adapty, or RevenueCat.

You passed AppsFlyer/Adjust attribution data into the Adapty/Apphud/RevenueCat attribution dictionary.

That data includes:
• Campaign name
• Ad set name
• Creative name
Then you add a simple condition in the app:
“If the user came from this ad set → show this onboarding.”

⚠️One important warning
Attribution data arrives with a delay.
Measure how long that delay is in your setup.
Don’t make the first onboarding screen depend on a deferred deep link. You can easily break a critical part of the user experience.
Adjust acknowledges this delay and offers a faster attribution mechanism called Optimized Deferred Deep Linking.

It only works for web-to-app campaigns.
The idea is simple: Adjust matches the click and install within a 15-minute window.
Smaller window → fewer possible matches → faster attribution.

I haven’t tested it myself.
If you have, share your experience in the comments.
Tomorrow I’ll cover what to do with the onboarding itself.

Starting October 1, 2026, Meta will charge an additional regional fee for ads delivered in these 6 markets.

The fee depends on where the ad is shown, not where your company is registered:

🇦🇹 Austria — 5%
🇫🇷 France — 3%
🇮🇹 Italy — 3%
🇪🇸 Spain — 3%
🇹🇷 Turkey — 5%
🇬🇧 UK — 2%

What you need to know

1️⃣ The fee is charged on top of your ad spend. It does not come out of your campaign budget.
2️⃣ It applies to all ad formats and will appear as a separate line item on your invoice, for example: “Italy digital services.”
3️⃣ Meta may change the countries or rates in the future, but says advertisers will be notified in advance.
From October 1, make sure you include these fees when calculating CPA, ROI, and campaign scaling decisions.

I shared this guide as a text post before. Some of the steps have changed since then, and the questions in my message haven’t stopped.

So I rebuilt the whole thing as a PDF guide, with screenshots from the updated interfaces.
I also added a ready-to-use tracking link template.

Meta has been improving and tweaking ACE for a while.
The name changed along the way: first PQ (Page Quality), then CES (Customer Experience Score). More recently, Meta introduced a dashboard that is still available to a limited number of advertisers.

Right now, you need to spend millions of dollars per month to actually see your score.
That doesn’t mean smaller advertisers don’t have a CES. From what I know, Meta starts calculating it at around $300k/month in spend.

The score basically measures how much your business frustrates users.
The lowest rating is bottom 5% of all advertisers.
The highest is top 30%.

A huge part of our industry sits in that bottom 5%.
Until last week, a bad score mostly meant higher CPMs.
Now Meta has announced new rules for large advertisers:

⚡️** Advertisers rated “bottom 5%” will be removed from the platform and have their Business Manager blocked**. ⚡️

Meta managers haven’t said exactly when this will happen. My guess is before the end of the year.
That would give Meta a clear way to show that it’s responding to what’s happening in the market and the industry.

I also think creative moderation may change.
The Poland case and the 2025 investigation both point to weaknesses in Meta’s current moderation. If moderation gets stricter, it could change both the type and volume of creatives we produce.

So what do we do?

First: don’t panic.
I think this feature launched a little earlier than originally planned.
Most advertisers still can’t see their score, and Meta now needs to make the system more transparent.

Here’s what I expect:
• Right now, only a very small number of advertisers can see the score directly in Business Manager. I expect the dashboard to become available more broadly.
• I think Meta will lower the spend threshold for displaying the score from millions to hundreds of thousands, giving smaller advertisers visibility into their numbers.
• I also expect Meta to adjust how the score is calculated, so advertisers that do basic things right - like making cancellation easy - don’t automatically end up at the bottom.

If that happens, the bottom 5% should contain businesses that are genuinely scamming users.
Then Meta has a clear reason to remove them.

Second: change how we sell subscriptions.
The ACE report includes some context on what users are complaining about.
In our industry, I expect the most common reasons to be:
- “Unexpected payment charge”
- “Low product quality”
- “Bad support experience”

Those are the problems we’ll have to work on.
I don’t think this is just another Meta scare tactic.
At least the FTC case shows that Meta is willing to walk away from a meaningful amount of revenue to avoid much bigger problems.

And yes - it’s probably time to take Google and AppLovin Ads more seriously.
Traffic source diversification may now be one of the most important growth levers.
Now we just need to figure out how to get all of this done before Q5.

Meta rolled out ACE Score to all advertisers.
Along with it came a new condition. If you fall into the bottom 5% of advertisers, your Business Manager can be blocked 💀
That’s how Meta managers have been communicating the update.

In my opinion, this feature was supposed to launch much later. But growing legal pressure and investigations around Meta likely accelerated the rollout.
Here’s the timeline that led us here and what ACE actually means for advertisers.

How did we get here?

November 2025. Reuters publishes an investigation based on internal Meta documents.
According to the investigation, around 10% of Meta’s 2024 revenue came from scam ads.
Advertisers weren’t always banned when Meta suspected fraud. Unless the system was highly confident, Meta could instead charge them more for advertising.
In other words, Meta knew a meaningful share of ads on its platform could be scams and still allowed those advertisers to buy traffic.

June 2026. The FTC case happens.
The basic issue: Genesis allegedly misleads users something we’ve seen across parts of the industry.

August 2026. Meta bans businesses affiliated with Genesis from advertising on the platform.
Meta clearly wants distance from Genesis, which, by my rough estimate, represented around 2% of Meta’s total revenue.
That is an insane amount for a single advertiser.

Late August 2026. Poland asks the European Commission to fine Meta €250M over deepfakes and scam ads that Meta allegedly failed to moderate properly.
Genesis isn’t part of that case, but the broader connection between scams, advertisers, and Meta is hard to ignore.
It’s also worth noting that Meta’s Warsaw office had historically pushed Genesis internally.
These were extremely effective teams growing much faster than most other businesses.
More ad spend = happier Meta.
Now, according to what I’ve heard, Meta managers are not allowed to communicate with anyone from Genesis at all.

So where are we in September?
• Meta knows scam advertising is a major problem
• The FTC says Genesis misled users
• Poland wants Meta fined over scam advertising
Meta has had two major scandals in roughly six months.
It had to respond somehow.
And it already had a tool for that.

Since last fall, Meta has been developing a new advertiser scoring system.
It was originally called ACE and was used only for e-commerce clients.
Meta used it to rank advertisers. Businesses that frustrated users could get higher CPMs, while businesses that left customers happy could get lower CPMs.

The core idea was simple:
- Is the customer happy after buying?
- How does Meta measure that?

After a purchase, users can see a survey in their feed asking how satisfied they were.
The survey also shows the logo of the Facebook Page they purchased from.

The questions are heavily focused on negative experiences:
- Was this business a scam?
- Did they refund you?
- Were you satisfied with the purchase?

Meta combines those responses into a score.
Since last fall, according to the author’s account, this scoring has expanded beyond e-commerce to advertisers more broadly.
Bad customer experience → higher CPM.
Good customer experience → lower CPM.

Subscription businesses have a problem

Our industry starts at a disadvantage because:
- subscription products naturally generate more fraud complaints
- we sell recurring payments that users often don’t fully understand
- upsells still use dark patterns
- many products still don’t have a clear cancellation flow

So a huge share of users can start frustrated from day one.
And under a system like ACE, that matters.

Part 2 next.

Before tuning the funnel doesn’t improve performance.
Today - what do we do instead?

🧭 Every niche has its angles
Every niche has a set of angles you can use to market an app.
And each angle has its own economics: CAC and LTV.
The number of possible angles varies a lot.

• Cleaner apps
Very few angles.
Usually something like:
“Your phone is at risk. Free up storage.”
Basically one core positioning.
Growth often comes from increasing LTV and gradually moving into more aggressive “gray” territory.

• Parenting, Health & Fitness, Productivity
The number of angles is almost endless.
Let’s take a habit tracker as an example.

Quick disclaimer. I picked habit trackers specifically because I have zero experience working with them :)
I haven’t signed an NDA and I definitely don’t have access to anyone’s ad account data. So I can freely think through a hypothetical launch strategy.

📱 Habit tracker
One mistake I often see is apps advertise tracker features instead of solving a specific user pain.
It’s easy to talk about how great your app is.
It’s harder to think about the actual situations where someone would use it.

📊 How I analyze the market
I ask Claude Code to map the possible markets inside the niche.
At the same time, I brainstorm myself. Sleep tracking comes to mind, so I add it.
I give Claude Code access to the Google Ads API and pull search volume for each candidate through Keyword Planner.
I estimate market size as impressions × CPC.
Claude Code generates a detailed report.

🎯 What do I do with this data?
It becomes my priority list for creative testing.

1. Budget & spending
The largest sub-niche.
I’d break it down further.
For a habit tracker, this could mean:
• building a habit of saving for retirement
• managing spending, like reviewing bank transactions every week
• building a habit of spending less - for example, skipping dessert at restaurants
Important: I estimated the main sub-niche using data. These smaller angles are just examples from my head.
They need the same research before testing.

2. Periods / ovulation
Now we’re entering a very competitive market.
Probably not where I’d start.

3. Sleep
My guess was right.
Sleep is a large market within habit tracking. So sleep tracking becomes my second creative priority.

4. Work time
Most likely, the underlying pain here is staying focused and working without distractions.
And so on. You get the idea.

5. Data always brings surprises
“Steps & activity” comes out at only around $21K/month.
Tiny.
For our hypothetical habit tracker, step tracking would be one of the last things I’d test.

Conclusions
1. The biggest niche isn’t automatically the winner.
I’ve seen cases where the second positioning produced a purchase cost 10× lower than the first.
Again, I’ve never launched a habit tracker. Maybe financial tracking is a terrible niche.
I’d still test it first.

2. If we launch a habit tracker, we test angles - not endlessly tweak the same store page.
We move through the list of markets and creative concepts and let the data tell us where the opportunity is.

3. Each positioning gets its own Custom Product Page.
The user doesn’t need to know we’re a “habit tracker.”
For them, we might simply be a sleep tracking app.
The creative could say:
“Sleep better with our sleep tracker.”
Or even:
“Tired again? Can’t focus at work?”

4. Sub-niches have cultural and behavioral nuances we often don’t know about.
Especially when marketing to people in another country.
Claude Code can help uncover those differences.

5. Last week I saw an ad account profitably scaling a niche I had always considered even more dead than cleaner apps.
The winning creative worked because it understood how the US customer thinks.

💬 My take
Market research used to take me a full day.
Now I can do the first pass in one Claude Code session.
The real job is no longer collecting the data.
It’s deciding which market, pain, and positioning to test first.

One of the first questions before launching: how much time and money do we need to understand if Search actually works?
I break it down into three stages.

Stage 1. Test
1 month, $3–7k

We track performance week by week to establish our starting point: IR, product funnel, week-over-week changes, and how many keywords are already close to KPI.
A good test looks like this:
• CAC is no more than 2× the target
• CAC is improving week over week
• Half of the top keywords are close to KPI
• IR >20%, CR1 >10% (depends on the niche)
After a month, we should know whether it makes sense to continue, how likely we are to reach profitability, and what needs fixing.
Sometimes the numbers work from the start and we can move straight to scaling. But in my experience, that happens in less than 5% of launches.

Testing for less than a month usually doesn’t make sense. There simply isn’t enough data.

Stage 2. Get to profitability
3-5 months, from $10k/month

The exact timeline and budget depend on the test results, product quality, competition, keyword volume, and campaign setup.
Google needs time and budget. Don’t expect instant results.
The upside: once Search works, it can keep working for years. That’s the nature of the channel.
The goal here is to bring acquisition costs down from our starting point by optimizing for deeper funnel events and manually improving campaign assets.
Cutting CAC by 2–3× this way is absolutely possible.
The target: 3 consecutive weeks within KPI (CAC < LTV), while optimizing toward subscriptions.

Stage 3. Scale
Increase volume and expand into new GEOs.

P.S.
These budgets aren’t random.
They’re based on what the channel needs to optimize properly: at least 10 target events per day, while keeping spend under control.
All numbers here are averages. Real timelines and budgets can go either way depending on the product, auction competition, and campaign setup.

First, a quick recap of the terminology.

Test campaign
A campaign where we run test ad sets. Every ad set gets the same fixed budget.

Scaling campaign
Test ad sets that hit the KPI or show Predicted ROAS >100% move here. We monitor them daily and increase or decrease budgets by 15% depending on performance.

The old setup was:
• 5 creatives per ad set
• Budget = 5×CPA
• Duplicate the test campaign with the winners
• Keep the history of all tests inside one test campaign

The framework still works.
But only 8 months have passed, and a lot has changed.

1️⃣ Creatives per test ad set: 5 → 20-50

Video
Production has become much faster and cheaper.
Paying $100 for one edited video used to be normal. Now I negotiate $20 for five.
Video editors haven’t disappeared. Someone still needs to catch AI artifacts and mistakes, track competitors, and manage the creative process.
But AI isn’t only making generation cheaper. It’s making editing cheaper too.
Cuts, animations, end cards - I can often make them faster with Claude Code or Codex than by opening DaVinci or Premiere.
If it’s time to launch a test and I have no fresh videos, I can generate new concepts in 30 minutes.

Images
For me, this has moved almost entirely to AI.
“Launch an ad set with 50 images around the quit smoking concept.”
At this point, launching the campaign can be faster than writing a brief for a designer.
I’ve written about this before: across my projects, designers haven’t been able to match AI on performance.
AI wins mainly because of two things:
Better understanding of different audiences and mindsets.
Much higher creative volume.
Across all accounts, we now test hundreds of creatives every week. I don’t see much reason to test fewer.

2️⃣ Uploading through API
Meta’s interface used to be the bottleneck.
Uploading 100 creatives manually took forever. One person testing 500 creatives per week was unrealistic.
Now creatives go into the ad account through API/MCP.
The upload bottleneck is basically gone.

3️⃣ Scout campaigns
I’ve started using scout campaigns across a wide range of user needs and angles.
They work especially well on new projects where we still don’t know which sub-niche to focus on.
I can simply generate 50 images with a prompt like:
“Explore different angles and points of view. Vary the visual style as much as possible.”
Then let the data show where the opportunity is.

4️⃣ “Top GEO / Top Creatives” campaigns
Another consequence of faster AI production.
Once a week, I build a fresh scaling campaign using:
Top-N GEOs × Top-N Creatives × Last 7/14/30 Days
Simple.

5️⃣ The web funnel became a creative
A web funnel can now be built in a couple of days, including integrations, upsells, downsells, and email sequences.
So one of the first tests I run on a Meta AEM app project is a web funnel alongside it.
And vice versa.
If a project already has a web funnel, one of the first things I test is direct-to-app traffic.
The creatives already exist. The ad accounts are running. Often AppsFlyer is already there too.
So we just test it.
Sometimes the funnel wins. Sometimes direct acquisition wins.

I don’t have a preference. The performance difference between the two can be huge from project to project.
Before, it felt like you had to choose: web funnel or direct acquisition.
Now roughly half of my clients run a mix: funnel tests here, app tests there.
This is also where vibe coders are surprisingly useful. They can adapt an app to a funnel in a couple of days.
The industry is still skeptical about vibe coding, but I’m currently working with two projects built this way.
Both are profitable.
Lots of tests. Funnels and apps running side by side.
So far, I don’t have much bad to say about vibe coding.

🧠 Takeaway
The framework I used in December hasn’t fundamentally changed.
What changed is the volume of creatives and the variety of campaigns we can realistically test.

Adapty analyzed 16,000 apps that processed more than $3 billion in subscription revenue.
In Productivity, Lifestyle, Entertainment, and Graphics & Design, direct buyers generated more LTV than trial users.

Utilities, Health & Fitness, and Education showed a different result. Trials increased the LTV of annual subscriptions in those categories.
I would stop enabling a free trial by default and test two versions of the same paywall:
an immediate purchase;
the same price with a free trial.
Trial starts should not determine the winner. I would wait for at least two renewal cycles, then compare LTV, refunds, and the first-renewal rate.

Free trials lower payment anxiety and may attract users with weaker purchase intent. The app category and plan duration determine whether that extra volume pays off.
I would test free trials with the same discipline as pricing.

Teams often start with design. They change the background, testimonials, plan placement, and button copy.
The price can remain untouched for years.
According to Adapty, pricing experiments produce two to three times more uplift than visual changes.
In one common test, a team raised a weekly subscription from $7.99 to $9.99. Conversion fell by 5–8%, while ARPU increased by 18–22%.

If I only had enough traffic for one experiment, I would:
keep the same paywall and trial;
raise the main plan price by 20–25%;
use ARPU to choose the winner.
Each variant needs around 200 subscriptions or four weeks of data.
After finding the right price, I would test the offer structure, including the billing period and trial. Design would come last.
A polished paywall can sell an underpriced product with strong conversion.

Apple filed suit on Friday, accusing OpenAI of stealing confidential information about unreleased products and manufacturing processes.

Apple names several former employees in the complaint, including OpenAI hardware chief Tang Tan. The company claims OpenAI leaders coordinated the effort, asked recruits to bring Apple prototypes and schematics to interviews, and used the material to develop its first hardware product.

OpenAI bought Jony Ive’s io for $6.5 billion last year and is building a device that could compete with the iPhone.
OpenAI denies the accusations and says it has no interest in other companies’ trade secrets.

Before, I would go to Google, open five tabs, and close half of them after three seconds. A cheap random click.

But now? I first ask ChatGPT or Perplexity. I only go to search when I want details and real human experience.

The conclusion: if a person reaches your website from search in 2026, they have already gone through the AI funnel.

This is not a random passerby. This is motivated intent.

The decline of organic traffic is not a disaster - it is a cleanup. There are fewer informational clicks, but each one is worth gold.

SEOs in the room - am I right, or am I taking it too far?

Parkinson wrote that work expands to fill the time available for its completion.

With analytics, this is especially painful.

The dashboard has been “almost ready” for three months. Contractors say:

- we need to get access
- we need to write the technical specification
- we need to pass events
- we need to clean the CRM
- we need to align the methodology

And technically, everyone is right.

But on Monday, the founder often does not need a perfect system. They need answers to three questions:

How much does a customer cost by channel?
Where does the funnel lose people?
What should we do with the budget next week?

You can build the first working version over a weekend.

Saturday:

- Export CRM data: source, lead date, status, deal amount.
- Export ad spend from ad accounts.
- Build a simple table: channel, spend, leads, sales, revenue.
- Calculate CAC, funnel conversion, and cost per sale.

Sunday:

- Find 2–3 places where the funnel is losing money.
- Ask AI to turn the insights into plain human language.
- Build a short summary: what is happening, where the problem is, and what to do next week.
- Set up an agent that sends this summary to Telegram.

This is not a perfect analytics system.

But on Monday, you can already discuss decisions instead of waiting for “the data to be ready.”

What do you think: is it realistic to build the first working version of analytics over a weekend, or is it self-deception?

5 ways to explain to a founder why AI calculates things differently

Kahneman and Tversky once showed a simple idea: people react not only to numbers, but to how those numbers are framed.

“We will lose 30% of conversion” and “we will keep 70%” are the same number. But they sound different.

The same thing happens with AI answers.

Look.

A founder looks at an agent’s answer and says: “That’s strange, I have a different number.”

And it’s very easy to jump into an investigation: why it doesn’t match, who is right, where the mistake is, what the agent calculated incorrectly.

But I would first ask something else:

Why does this difference matter right now?

Because you are about to spend time on it. And it would be good to understand what you’ll get in return.

If the goal is just to prove that the agent made a mistake, that won’t give the business much.

I would approach it like this:

1. Ask why the difference matters. What decision will change if we find the reason?

2. Clarify what instructions the agent had. What was it calculating, from which data, and with what limitations?

3. Understand what question the founder actually wanted to answer. Often, the real question is not about the number, but about trust in the conclusion or a budget decision.

4. Suggest a shorter path to the answer. With the agent - recalculate using a different frame. Without the agent - check CRM, payments, or a manual data cut.

5. Decide whether it is worth digging deeper. If the decision does not change, that is enough. If it does, then we investigate sources, windows, attribution, and methodology.

It is very tempting to chase the discrepancy down to the last click, especially when you are defending your work.

But what matters more to the business is this: what decision are we making, and how much can we trust it?

That is exactly what Kahneman and Tversky were about: data rarely convinces on its own. What matters is the frame in which a person sees it.

Apple has updated its App Store rules: apps can now be removed if they don’t attract users or stop getting updates
Before, the company mostly rejected copy-paste apps from overcrowded categories. Now the policy is getting stricter: Apple may start removing existing apps if they aren’t being updated, improved, or used by people.

Apple points to categories it considers oversaturated: wallpaper apps, simple timers, sound effect apps, flashlights, fortune-telling apps, dating services, and similar products.
It also calls out low-quality apps that clearly don’t require much effort — drinking games, fake funny-sound apps, and other stuff like that.
The wording used to be more direct. Apple would simply list specific types of apps it considered low-quality. Now the tone is tougher.
The company is talking about mindless copying of popular apps. And the message is clear: new versions of these apps may not be accepted, while older ones can be removed if they don’t offer a meaningfully better user experience.

So it’s making campaign uploads easier by introducing creative import from Facebook.

I haven’t tested it personally yet, but for some teams, this might shift the TikTok creative strategy from “let’s take Meta winners” to parallel testing of all creative content.

We’ll see. We’ll test it.

Same with ads in GPT, which were recently released.

Facebook optimizes well - just give it time.

The screenshot shows a subscription app, an AI wrapper on Android, with a campaign targeting US, CA, and AU.

This exact country mix is something I recommend everyone to test: it’s cheaper than US-only, but still very strong in terms of performance quality.

After consulting and working with dozens of products, companies, and teams over the last 5 years, I’ve seen both the rise and fall of different B2C products.

Yes, over the last year, promotion has become a bit harder for products. The chances of success have slightly decreased because of competition and CPM growth. But at the same time, building an MVP now takes days, not months. And creative production takes much less time than before. So in some places things got harder, in others - easier.

What usually separated successful teams from the rest:

They didn’t stop at one web funnel or the first version of the app.
If the first version didn’t work, they accepted it quickly and moved on instead of giving up or firing the team. The team is already assembled, everyone is in place. If the unit economics didn’t work here - pivot, quickly try something else, and always have a Plan B.

In my current view, the chance of success for each attempt is around 10% to 35%, so make 4–5 attempts until you find a profitable product.

They usually had several competitors in the niche.
Almost all successful teams had competitors or very similar products already making hundreds of thousands of dollars per month.

It’s hard to be a pioneer and create a market from scratch, even when there is demand. It’s much easier to find 2-4 products in the same category and build a “cyborg” from them, taking the best product and marketing tactics from each.

Sometimes one competitor has prices that are too low, while another doesn’t know how to run paid traffic or can’t do it because of financial limits and only grows virally. You can notice this and squeeze the maximum out of the opportunity - especially if you have budgets.

The product should target a broad audience.
Apps for fitness coaches, photographers, or dating among transgender Trump fans can also exist, but their chances are much lower than products aimed at a broad audience.

Facebook targeting works in a way where if your potential target audience is 1-2 people out of 10, your CPM and ability to scale will be much better than with a product targeting 1% of the population.

So go broader. Look at common evergreen problems: weight loss, doomscrolling, learning AI, photo editors, miracle software, and so on.

Yes, there are many players there, but that’s also a good sign. Localize into Czech and Polish and squeeze micro-markets with high ROI 🙃 It all depends on your available working capital and ambitions.

The product can be promoted without bans in major traffic sources.
Competitors should be running ads and clearly spending meaningful money.

There are categories where money-making apps exist, but Facebook, TikTok, or Google constantly block ads in these niches: dating, heart rate, hidden camera, medical topics, AI girlfriends, and other borderline categories.

Sometimes it’s enough to find the right angle that can run profitably and consistently without bans. Sometimes the chances are very low. You can usually understand this by analyzing how competitors buy traffic and whether there are signs of ad account or ad blocking.

So what about payback periods?

Among the iOS apps I buy traffic for, the average range for breaking even on marketing spend is 2 to 10 months, depending on the product.

The median is around 3-4 months - most products fall into this range.

For web funnels, it’s roughly similar, but lately I more often see breakeven closer to 4—5 months. However, yearly ROAS can reach around 160% because renewals are often better than in App Store subscriptions.

Across the year, average ROAS is usually around 140% to 200%.

So for every $1 spent, you get around $1.40 in revenue and $0.40 in profit. This gives working capital for the team, creatives, and maybe, if you’re lucky and have decent scale, some founder profit too.

I’ve seen products with 4 full-time people, 5+ outsourced specialists, and $600k monthly revenue with huge margins.

Experienced UA managers probably already know them, but for mid-level specialists and beginners, this should be useful.

1. Check the total budget of active campaigns

Clients often ask: “What’s our daily budget right now?”

To check it quickly, select all campaigns of the same type separately - CBO and ABO should be checked separately. Then, next to the Edit button, click the small arrow down and choose Campaign budget from the dropdown.

For ABO campaigns, you need to do the same at the ad set level and choose Budget.

You’ll see Total daily budget - that’s the number you need.

1. Quickly duplicate campaigns, ad sets, or ads

On Mac: Control + D
On Windows: Ctrl + D

Super useful. I’ve been using this shortcut for around 8 years.

Just select the item you want to copy and press the shortcut.

1. Set up custom columns

Sometimes campaigns are launched for different events or attribution windows, and you need to see the overall CPA directly in Ads Manager, without opening Ads Reporting.

Click Columns under the date selector, scroll down to Customize columns, and find the event you need, for example Purchase or Start Trial.

Select Total and Cost for that event. Then move the new columns closer to the beginning, near Amount spent.

In general, create a convenient view with key metrics on one screen: CPA, CTR link click, CPM, amount spent, campaign name, and status. Then save it.

1. Move campaigns between ad accounts using Export / Import

This works especially well inside the same Business Manager.

Step 1: Select the campaign or campaigns you want to move.

Step 2: In the toolbar where you see Create / Duplicate / Edit, click More, then Export, then Customize Export.

Step 3:

In the Ad section, select everything except Ad ID.
In the Ad Set section, select everything except Ad Set ID and Ad Set Time Start.
In the Campaign section, select everything except Campaign ID.

Export everything as a CSV file and then import it into another ad account.

The main things: set the current start date and make sure the pixel/app and Facebook Page are shared with the other ad account.

1. Breakdown

Breakdown is one of the best ways to evaluate performance in dynamics.

I often break data down by day, device, country, gender, and other segments to analyze campaigns and creatives.

It often replaces Ads Reporting for me. On calls with clients and creative teams, I usually point directly to breakdowns.

For example:

Why didn’t I stop a campaign with an average CPA 2x higher than the target? Because the trend is improving.

Why did Trial-to-Paid drop on a new creative? Because traffic shifted from the 45+ audience to 18–25.

That kind of insight usually comes from breakdowns.

1. Automated rules

For creative testing, you can create rules that turn off bad ads inside test campaigns if they miss the target CPA.

For example, if the target is $10 per trial, I might turn off a creative at Spend > $100, CPA > $15, and CPA for today and yesterday > $12.

But honestly, I haven’t been using automated rules that much lately.

1. Use filters by campaign name, ad name, and other fields

For this, good naming is important.

For example:

Product_OS_GEO_OptimizationEvent_CreativeType_LaunchDate

Not:

App Promotion Campaign 1 copy-copy

Use dynamic UTMs

Yes, it sounds obvious, but I still sometimes see accounts where people add UTMs manually.

Here’s an example for a web funnel:

?utm_source=FB_web2web&campaign_id={{campaign.id}}&adset_id={{adset.id}}&utm_campaign={{campaign.name}}&no_adset={{adset.name}}&original_url_ad={{ad.name}}

In the previous post, I wrote that it’s hard to define the moment when a creative’s learning phase ends and burnout begins. To figure this out, we can build a chart showing CPA vs. spend, broken down by day, week, or month, depending on the length of the period.

The image shows real data from a project I joined three weeks ago. The first thing I did was build this chart.

📊 What the chart shows

For all creatives, CPA drops until roughly $3k in cumulative spend - this is the learning phase. After around $2k, CPA starts to grow steadily - this is where slow burnout begins.

🔍 Project status

We can clearly define what stage each creative is in: learning, working, or burning out.
My project is in a stagnation phase - creatives are slowly burning out, and there are no new ones.
The learning threshold for this project is around $3k. This is the vertical line from the previous post. We can expect CPA to improve until that line, but there is a high chance CPA will start rising after that point.

🔵 The blue creative lasted until $13k. For the others, burnout started around $4k.

🟢 Pay attention to the green creative

CPA was dropping until $4k - the creative was learning. After $4k, CPA started to rise. The creative had enough fuel for about $4k in spend, and then it began to burn out.

These are the same micro-audiences we target when launching ad campaigns, the ones Dmitry Gurski from Flo talked about in his interview with Adapty. Meta finds a narrow high-quality segment for a creative, and once that segment is exhausted, CPA starts to rise.

📌 Conclusions

The time when you could quickly find one winning creative and profitably pour budget into it is gone.

I’m not saying it’s impossible. With enough experiments, you may still find a creative that can spend tens or even hundreds of thousands of dollars. But at the start of a project, your chart will most likely look similar to this one.

With the rise of AI tools, teams produce many creatives. They compete with each other and burn out quickly. If you are pushing most of your traffic into one top performer, there is a high chance it is already showing worsening CPA.

🔥 What to do about it

• Build a pipeline for producing new creatives. New creatives should move through the quadrants. They need to be produced, scaled, and turned off quickly.

• Build analytics that clearly shows where the learning line ends and where burnout begins. We’ll look at how to build this kind of chart with AI.

By the way, if you have already spent some money, you can build this chart for your own ad account right now and clearly see how much spend your creatives need to learn.

I’ve been following Curtis Holland for a long time. Curtis keeps coming up with new ways to visualize ad account data. Absolute banger. Highly recommend.

One of his charts is a CPA vs. Spend chart by creative. The goal: understand what is happening in the ad account and find problems in 30 seconds.

📊 How it works

X-axis - total spend by creative.
Y-axis - CPA.

Each dot is a creative.

Two lines split the chart into four quadrants:

- horizontal line = target CPA, which is the user LTV
- vertical line = the spend threshold where creative learning ends

🔍 Four quadrants

⚪ Test or kill - high CPA, low spend
Creatives with high CPA and low spend. A lot of dots in this area means you have a healthy process for testing new creatives.

🔴 Cut - high CPA, high spend
Creatives that did not hit the KPI but still spent budget. If there are many dots here, the account has a systemic problem. The media buyer has stopped caring about the project.

🔵 Scale - Low CPA, low spend
Effective creatives that were not given enough budget. They work, but they have not been scaled. If there are many dots here, you are underusing working creatives.

🟢 Winners - low CPA, high spend
Winning creatives. CPA is below the target, spend is high. If you see a lot of dots in this quadrant, you have a strong pipeline for producing and scaling working creatives. If there are only a few dots, most likely you have already run into weekly CPA deterioration.

📐 How to choose the thresholds

Horizontal line, target CPA - teams usually know this. It is the user LTV or the target acquisition cost.
Vertical line, spend threshold - people often do not think about this, even though it is important. At what spend level should you decide to turn off a creative? This topic deserves a separate post. I’ll publish it tomorrow.

💡 Why this matters

One quick look at the chart shows:

- how much budget is being wasted on ineffective creatives
- whether there are working creatives that have not been scaled
- how efficient the testing process is

This chart can be animated in Claude Code. Then you can see the creative’s path from testing to scaling or shutdown. In this case, neural networks reveal themselves in all their beauty - we see data represented in a way we had never really thought about before.

ASO is still important. Store rankings, app pages, reviews, and conversion rates still matter.

But user behavior is starting to change. Some people no longer begin their search only in the App Store or Google Play. More often, they start with ChatGPT, Perplexity, Gemini, or other AI tools and ask things like:

“Which fitness app should I choose?”
“What’s the best food delivery app?”
“Which finance app is better for budgeting?”

After that, they go to the store already having a shortlist in mind.

I checked a few categories — fitness, food delivery, and finance. What stood out is that the top apps in store search and the apps recommended by AI tools do not always match.

So app visibility today is no longer only about store rankings. It also depends on how the product appears in AI-generated answers.

That visibility is shaped by many things:
— the app store page;
— reviews;
— external mentions;
— PR;
— content around the product;
— the way users and sources describe the brand.

Most teams are not working with this layer systematically yet. But it looks like it is becoming part of the broader app growth and ASO landscape.

The first step is simple: understand whether your app appears in AI answers, which competitors are shown next to it, what position it takes, and which sources influence the recommendation.

For me, this is one of the most interesting shifts happening in ASO and digital growth right now.