I’ve been digging into how third-party cookie deprecation is reshaping performance marketing, especially from a game UA perspective, and I think most of the discussion around it is still framed incorrectly.

A lot of teams treat this like a sudden loss of capability. In practice, most cookie-based tracking was already degraded long before full deprecation.

TL;DR (high level)

• Cookie-based attribution was already unreliable (inflated ROAS, duplicated conversions, fraud exposure, consent loss)
• The shift isn’t removing signal it’s changing where the signal comes from
• First-party data + server-to-server tracking are becoming the core measurement layer
• “More data” is being replaced by “cleaner data”
• The winners are systems built around direct user relationships, not browser-based inference

What was already broken before cookies disappeared

Even before deprecation, most performance stacks were operating on compromised data:

Attribution was heavily duplicated
Multiple platforms often claimed the same conversion, especially in multi-channel setups. This made ROAS look stronger than actual incremental performance.

Fraud and invalid traffic were underestimated
Browser-based tracking created room for manipulation (click injection, attribution spoofing, etc.), which distorted channel quality.

Consent reduced usable datasets
GDPR/CCPA-style opt-outs already removed a large portion of users from tracking pools, meaning a lot of “tracked” audiences were never truly representative.

What actually changes after cookies

The shift is less about “loss” and more about restructuring:

1. First-party data becomes the baseline

Owned audiences (logged-in users, CRM lists, app users) become significantly more important because they are not dependent on browser behavior.

2. Server-to-server (S2S) tracking replaces browser pixels

Instead of relying on client-side scripts, conversion events are passed directly between systems.

This reduces:

• duplicate firing
• cross-device noise
• browser restrictions (Safari/Chrome privacy changes)

And improves:

• consistency of attribution
• fraud resistance
• data cleanliness

3. Measurement shifts from volume to quality

Instead of optimizing for:

• installs / clicks / raw ROAS

Teams move toward:

• retention-based cohorts
• LTV signals
• incremental lift testing

Who actually benefits from this shift

From what I’ve seen across campaigns and discussions, the advantage shifts toward:

1. Teams with first-party audiences
Apps, games, and products with direct user relationships have a structural advantage because they’re not dependent on external tracking systems.

2. Server-side tracking setups
Teams using MMPs (or similar infrastructure) with proper S2S integrations generally get cleaner attribution than browser-based setups.

3. Controlled traffic environments
Environments where the user relationship is explicit (opt-in audiences, logged-in ecosystems) tend to produce more stable conversion signals than open aggregated traffic sources.

What changes in practice for UA / performance teams

Most teams adapting well are doing some combination of:

• moving key tracking events server-side (S2S)
• reducing reliance on last-click reporting alone
• validating campaigns through incrementality tests
• separating “reported performance” from “true incremental performance”

The biggest shift isn’t technical it’s mental:
Stop trusting dashboards at face value and start validating signal quality.

Key takeaway

Cookie deprecation didn’t remove performance marketing tracking.

It exposed how noisy and inflated a lot of it already was.

What replaces it is not a single tool, but a stack built around:

• direct user relationships
• server-side event flow
• and measurement methods that prioritize real lift over reported attribution

Question for discussion

For those working on UA / growth:

• Are you still heavily relying on browser-based attribution models?
• Have you fully shifted parts of your stack to server-side tracking?
• What’s been the hardest part of trusting “cleaner but smaller” datasets?

Curious how others are adapting this in real production setups.

I’m working on a small mobile game (hyper-casual puzzle style), and user acquisition has felt noticeably harder over the past year.

Even when installs look okay, the actual player quality seems very inconsistent.

I’ve been experimenting with different acquisition approaches recently and started paying closer attention to how users behave after install rather than just install volume.

A few things I’ve noticed:

• Some campaigns that look “cheap” don’t bring engaged users
• Retention seems to tell a much clearer story than acquisition cost
• It’s getting harder to understand what’s actually driving quality users
• Tracking and attribution feel less reliable than before

At this point, I’m starting to question whether focusing on cost per install is even useful anymore without deeper engagement data.

Curious how others are approaching this right now:

Are you still optimizing mainly for install cost, or have you shifted focus more toward retention / long-term value?

What’s actually working for you in mobile UA lately?

Hey everyone 👋

I’ve been thinking a lot about how small businesses are approaching growth in 2026, and it feels like the landscape has shifted quite a bit over the last few years.

Paid ads are more expensive, organic reach is less predictable, and many traditional channels don’t seem as consistent as they used to be.

From what I’m seeing, a few things still seem to be working depending on the business:

• niche communities and partnerships
• SEO/content in very specific verticals
• email-based retention strategies
• referral-driven growth loops
• founder-led distribution on social platforms

I’ve also noticed more businesses revisiting affiliate and referral systems recently, especially with tools like FirstPromoter making it easier to manage recurring commissions and partner tracking for SaaS products.

But overall, it feels like there’s no single “standard playbook” anymore growth now seems more about combining multiple smaller channels together effectively.

Curious what others here are seeing:

• What’s been your most reliable growth channel recently?
• Are referrals/word-of-mouth still strong in your experience?
• Have you found any underrated channel surprisingly effective lately?

Would love to hear real-world examples from other founders and small business owners here.

I’ve been experimenting with multi-step AI workflows recently (especially ones involving research + structuring outputs), and I’ve noticed something interesting.

A lot of systems perform well at individual tasks like:

• summarizing text
• answering questions from context
• extracting key points

But when you chain these steps together into a pipeline (e.g. retrieve → filter → organize → format), the reliability drops quite a bit.

Common issues I’ve seen:

• early outputs look fine, but later steps drift in structure
• inconsistencies accumulate across steps
• final results often need manual cleanup even if each step “worked” individually

It made me think about how we evaluate ML systems.

We often test components in isolation, but real-world usage depends more on end-to-end stability than per-step accuracy.

I’ve been trying a few structured approaches (breaking tasks into explicit stages instead of single-pass generation) to see if it improves consistency, but it’s still very experimental.

Curious how others here think about this:

How do you usually evaluate multi-step ML or LLM pipelines per-step accuracy, or end-to-end output quality?