Should You Use Google, Meta, or TikTok Ads to Promote Your App?

Three differences I kept noticing

The same app can look like three different businesses when you compare platform dashboards. In my work, three patterns kept bringing me back to the question of platform fit.

Cheap installs can hide expensive customers

On TikTok, an attractive CPI or registration cost did not always translate into stable performance further down the funnel. Once I looked at applications, payments, or subscriptions, the picture could change substantially—especially after increasing the budget.

I generally found deeper conversion performance more stable on Google and Meta in the kinds of campaigns I worked on. That is a description of my experience, not a reason to assume that a TikTok user is inherently less valuable. The event we optimize for, the message, and the feedback reaching the platform all matter.

A winning creative is not a permanent asset

I’ve seen Google creatives continue to generate conversions for months. In my TikTok campaigns, some creatives started weakening within three or four days; a week of steady performance could already feel like a good run. Meta often sat somewhere in between.

Those time spans are observations, not expiry dates. They changed the way I thought about capacity: an independent developer needs to ask whether they can keep making videos; a larger team needs a repeatable process for developing and testing new ideas.

Creative review can become a production bottleneck

In my experience, TikTok review was often the strictest and least predictable of the three, particularly around sensitive categories. Captions, voiceovers, visuals, and promises could all become points of friction.

That affects the practical ability to scale. A winning campaign does not solve the problem if its replacements cannot get approved. I plan around the supply of usable creative, not just the number of files produced.

Three ways of understanding a user

My shorthand is simple: Google understands intent. Meta understands identity. TikTok understands attention. It is a lens for planning, not a technical description of everything inside each advertising system.

Google: what problem is the user trying to solve?

A person searching for a document scanner, a translation tool, or help with a specific problem has already expressed a need. That gives the advertiser a useful starting point: present a relevant answer.

Google App campaigns also reach beyond Search, including Google Play, YouTube, and other placements. I do not treat every Google impression as an active search. The broader point is that Google’s ecosystem gives me a way to think about existing demand, rather than assuming the creative has to introduce the need from scratch.

Meta: who is likely to become a customer?

I think about Meta through patterns of interests, engagement, and behavior. Someone may not be searching for an app today, but their behavior can still make them a promising prospect. The creative has to connect a recognizable situation with a product that fits.

This is why a strong message and meaningful conversion feedback belong together. Getting attention is useful; helping the system learn which of those people become valuable customers is the next task.

TikTok: what will make someone stop scrolling?

On TikTok, the content itself is central to the encounter. What someone watches, finishes, replays, or responds to gives a very different starting point from a direct search query.

For visually driven products, the connection between content interest and purchase interest can be close. Someone watching running-shoe reviews may be a good prospect for running shoes. For a financial or productivity app, that relationship can be less obvious.

Two people might watch the same football clips and comedy videos while having very different needs, payment intentions, or suitability for a product. My question is therefore: how closely is the customer value we care about connected to the content this person consumes?

The creative has to attract attention and qualify the user at the same time.

When that connection is weak, I put more emphasis on a message that makes the relevant need explicit—and on feedback from the deeper event the business cares about.

Why conversion feedback matters

One way to understand ad delivery is through two predictions: how likely someone is to click, and how likely that click is to lead to the desired outcome. Consider a deliberately simplified example.

A platform can observe a click on its own surface. Learning what happens later inside an app requires a conversion-feedback path. A registration, application, or payment is much more useful when its meaning is consistent and the relevant feedback arrives reliably.

The boxing-referee analogy

I think of Google, Meta, and TikTok as fighters in a ring, with an MMP such as AppsFlyer or Adjust acting as a referee for cross-channel measurement.

A person might see an ad on TikTok, click another on Meta, and later install through Google. Several platforms may have a reason to claim a contribution. The MMP applies its attribution rules to produce a cross-channel view.

But deciding who receives credit and deciding which events are shared are separate questions. The feedback a partner receives depends on the integration, sharing settings, consent, and measurement path. The referee analogy is useful as long as it does not imply that every event is sent only to the final winner.

Self-attribution adds another view of the journey

In the same analogy, self-attributing networks have their own “game footage”: their records of ad interactions. With the relevant event sharing enabled, they can match conversion feedback against those records. The MMP and the platform may then report different views of the same journey.

My working hypothesis is that differences in the depth and maturity of conversion feedback help explain some of the instability I have seen on TikTok. It is a hypothesis I use to guide investigation—not something I can establish just by looking at CPI and downstream conversion rates.

Before blaming the platform’s model, I want to know what event we selected, whether it is being recorded and shared correctly, and whether we are comparing users at the same age. Attribution, reporting, and learning are connected, but they are not interchangeable.

Creative output is part of media buying

The operational difference that mattered most to me was the need for a steady supply of usable creatives. On TikTok, an ad enters a feed full of entertainment. A feature list that makes sense beside an explicit search may not earn two seconds of attention there.

My explanation for the faster wear-out I observed is partly about that environment: viewers expect new situations, new presentation styles, and something worth watching. This is a way of interpreting my campaign experience, not proof that the organic recommendation system determines ad lifespan.

I watch declining response together with delivery and downstream conversion. Rising CPM, weaker CTR, or an ad group that stops spending can prompt a fatigue investigation; any one of them can also have other causes. Here is the related guide to distinguishing creative fatigue from a different problem.

Make the need more specific

Consider a hypothetical financial-app creative that says, “High limits. Fast approval. Download now.” It may attract broad interest in an easy application. That does not tell us much about whether those people fit the product.

A different approach starts with a specific need: “If you travel often, how do you compare cards for overseas spending and travel benefits?” That narrows the conversation. Whether it improves results still needs testing, but the creative now expresses a clearer hypothesis about whom we want to reach.

For me, a useful creative does three jobs: it makes someone stop, helps them recognize a relevant need, and gives the right person a reason to continue.

Build a process around ideas, not just variations

Teams that can keep supplying new hooks, situations, demonstrations, and explanations have more opportunities to learn. A winning ad should become an input to the next brief: what need did it make clear, what objection did it answer, and what should remain constant in the next test?

Changing a caption color is different from testing a new reason to use the app. I want a production process that records that difference. Otherwise, we can produce more files without learning more about our customers.

There may be opportunities when the right message helps a platform find valuable users at an attractive cost. I would treat that as something to test, rather than assume that a less mature prediction system guarantees cheap, high-quality traffic.

How to compare the same app across channels

The practical value of the platform framework is knowing what to investigate. “TikTok understands attention” is context. It does not explain why a particular campaign changed last week.

If one channel has the lowest CPI but fewer valuable users, I work through four questions before deciding where the problem sits.

1. Are we comparing the same outcome? Define the App, dates, timezone, currency, and valuable event. Keep install cohorts and observation windows comparable. A new user observed for one day cannot be evaluated as though they have had a week to pay.
2. Where does the difference appear? Compare spend and delivery first, then installs and the deeper event. Use the definitions and metrics returned by the connected sources. Identify whether the gap starts before the install or later in the App.
3. What else changed? Put budget and creative changes beside App releases, onboarding changes, event tracking, and business rules. Use available records and the context supplied by the team; a missing change log is a gap to resolve.
4. What would distinguish the explanations? Look for evidence supporting a channel-specific change, a problem affecting the App more broadly, a measurement difference, or a mixture. Report what argues against the leading explanation as well as what supports it.

What I took away from working on Akulaku is a habit: follow acquisition beyond the first cheap event. The value of those installs is a separate question. Platform knowledge helps us ask better questions; the account evidence determines how far we can answer them.

Which platform should you try first?

My starting point would be the match between the app’s value, the creative the team can produce, and the conversion feedback we can measure. Here is how I would turn the observations above into a first test.

• Consider Google first when the app solves a clear, recognizable need. Make that need explicit in the message, and measure whether installs turn into the action the business values. Google’s intent signals make it worth testing in this situation, but App campaigns span Search, Google Play, YouTube, and other placements; an install is not necessarily someone actively searching for your solution.
• Consider Meta first when you can clearly explain who the app helps and why they should care. Test different benefits, use cases, and objections. In the campaigns discussed here, deeper conversion performance often felt more stable on Meta and Google; I would use that experience to frame a test, then judge it against the app’s own downstream results.
• Consider TikTok first when the app’s value is easy to demonstrate in a short video and the team can keep producing fresh ideas. A compelling hook needs a relevant reason to install. If creative production is already a bottleneck, I would resolve that before making TikTok the main acquisition channel.

If the budget is limited, I would start with the channel where those conditions are strongest and give the test enough time to observe the valuable conversion. Expand to another channel when there is a clear question to answer. Choose the next budget allocation using cost and quality at the same conversion stage—not the cheapest install alone.