MOAT vs IAS vs DoubleVerify for in-app viewability

All three measure viewability and fraud, but in-app SDK coverage separates them.

MOAT (Oracle)
— ✓ Strong on video and rich-media viewability detail
— ✗ In-app SDK rollout lagged web, check coverage per format

IAS
— ✓ Best pre-bid avoidance segments, blocks bad inventory before you buy
— ✓ Solid Open Measurement SDK adoption
— ✗ Post-bid reporting can disagree with the SSP's own numbers

DoubleVerify
— ✓ Tightest fraud + brand-safety bundle, granular app-level blocklists
— ✓ Good MRC-accredited in-app measurement breadth
— ✗ Premium pricing, overkill for pure performance buys

Brand campaigns needing pre-bid control lean IAS. Fraud-and-safety-first buyers pick DoubleVerify. MOAT still shines for rich video specifics.

Verdict: IAS for pre-bid avoidance, DoubleVerify for fraud+safety, MOAT for video detail.
Best for: buyers running brand-sensitive in-app video at scale.

Open Measurement SDK vs proprietary viewability SDKs

IAB's OM SDK promised one integration for all verifiers. It mostly delivered, with caveats.

Open Measurement SDK
— ✓ One SDK, multiple verification vendors, no per-vendor bloat
— ✓ Publisher-friendly, lighter app footprint
— ✗ Vendor still controls signal interpretation, parity isn't guaranteed across measurers

Proprietary SDK (vendor-direct)
— ✓ Earliest access to that vendor's newest signals and beta metrics
— ✗ Each added vendor bloats the app and slows integration

For publishers, OM SDK is the clear default, one integration keeps your binary lean and buyers happy. Only go proprietary if a single demand partner contractually requires their own SDK or you need a metric OM hasn't standardized yet.

Verdict: OM SDK as default for app size and flexibility, proprietary only on contractual or bleeding-edge metric needs.
Best for: publishers managing SDK weight while satisfying verification demands.

ЕЮ Иванов продолжает кошмарить АффПапу, конторку, которая накинула говна на вентилятор этим летом. Тогда в AffPapa не знали, с каким говном идут бодаться, поэтому заслуженно проиграли. 😏

На этот раз ЕЮ зарегал товарный знак AffPapa — совсем скоро имя компании будет официально принадлежать ему. Чтобы убедиться в трушности мува, переходим по ссыл-Очке и вводим серийный номер: 2026793242. Там видим, что заявка на регистрацию подана лично Евгением Юрьичем.

Всё это выглядит забавно, но давайте не забывать, в какой сфере мы работаем и что реально может произойти с жирным троллем за воровство нейминга. Впрочем, толстому не привыкать отхватывать пиздов за проделки в интернете, поэтому ждем очередную фотку разбитого ебала и длинный пост с извинениями. 😏😏😏

В арбитраже денег нет 💵

Incrementality testing vs MMP last-touch ROAS: stop trusting the dashboard alone

Your MMP shows ROAS per source. It can't tell you which installs would've happened anyway.

MMP last-touch ROAS
— ✓ Granular, per-campaign, per-creative, real-time
— ✓ Great for tactical bid and creative decisions
— ✗ Over-credits retargeting and brand search, hides organic cannibalization

Incrementality (geo-holdout / PSA / ghost ads)
— ✓ Measures true lift over a control, exposes wasted spend
— ✓ Settles "is retargeting actually working" arguments
— ✗ Slow, needs scale for significance, hard on small budgets

Use last-touch ROAS daily for tactics. Run a quarterly geo-holdout on your biggest line items to recalibrate which channels actually drive incremental installs, then reallocate.

Verdict: MMP ROAS for daily ops, periodic incrementality tests to catch the spend that's lying to you.
Best for: teams whose retargeting ROAS looks suspiciously perfect.

Branch vs AppsFlyer OneLink for deep linking

Deep linking and attribution overlap, but the best tool depends on which job is primary.

Branch
— ✓ Deep-linking-first, best deferred deep link reliability across edge cases
— ✓ Strong web-to-app and journeys/banners tooling
— ✗ Attribution is solid but second to dedicated MMPs on SKAN tuning

AppsFlyer OneLink
— ✓ Tight loop with AppsFlyer attribution, one vendor for both jobs
— ✓ Good enough deep linking for most install-driven flows
— ✗ Edge-case deferred deep links and complex web journeys feel thinner than Branch

If deep linking is your core growth lever, owned-media journeys, referrals, smart banners, Branch leads. If attribution is the priority and deep linking is a supporting feature, OneLink keeps you on one stack.

Verdict: Branch when deep linking drives growth, OneLink when attribution is primary and you want one vendor.
Best for: growth teams deciding whether to split or consolidate vendors.

CTV-to-app vs in-app mobile attribution: different beasts, different tools

Buyers expanding into connected TV assume their mobile MMP just covers it. It half does.

In-app mobile attribution
— ✓ Mature SKAN/device-ID pipeline, deterministic where consent exists
— ✗ Built around click and view on the same device

CTV-to-app attribution
— ✓ Captures the big-screen-to-phone install journey via IP-graph and household matching
— ✓ MMPs (AppsFlyer, Kochava) now offer CTV modules
— ✗ Cross-device IP matching is probabilistic, accuracy drops in shared/NATed households

Don't judge CTV on mobile-grade attribution precision, it's structurally fuzzier. Use it for incremental reach measured via lift, not last-click. Validate with a holdout before scaling CTV spend.

Verdict: trust mobile attribution for last-touch, treat CTV-to-app as lift-measured reach, never last-click.
Best for: performance teams testing CTV without abandoning attribution discipline.

Kochava vs Singular: data pipeline depth vs UA aggregation

Two strong MMPs that win on different jobs. Pick by where your pain is.

Kochava
— ✓ Deepest raw-log and free data-marketplace access, great for in-house data teams
— ✓ Flexible identity and CTV coverage
— ✗ Steeper learning curve, dashboard less polished

Singular
— ✓ Best-in-class cost/ROI aggregation, pulls spend from every network into one ROAS view
— ✓ Cleanest creative-level reporting across channels
— ✗ Attribution is solid but the standout value is the marketing-data unification

If your bottleneck is reconciling spend and revenue across 20 networks, Singular's aggregation saves a full-time analyst. If you need raw logs piped into your own modeling, Kochava's pipeline wins.

Verdict: Singular for cross-network cost/ROI clarity, Kochava for raw-data engineering control.
Best for: UA teams choosing between reporting clarity and pipeline flexibility.

Predicted LTV models vs SKAN conversion windows: two ways to value an install early

You can't wait 90 days to bid. Both approaches estimate value fast, differently.

pLTV models (in-house or vendor)
— ✓ Use rich first-party behavior, predict D90 from D1-D3 signals
— ✓ Channel-agnostic, works across all your reporting
— ✗ Needs data-science investment and constant retraining as cohorts drift

SKAN conversion-value mapping
— ✓ No model needed, encode revenue or event tiers directly into 6 bits
— ✓ Network-native, networks optimize toward your value
— ✗ Crude buckets, locked timers, crowd-anonymity can null your signal

Use SKAN value mapping as the floor everyone must have on iOS. Layer a pLTV model on top once you have enough first-party data to beat the 6-bit ceiling, especially for high-LTV apps where bucket coarseness costs real money.

Verdict: SKAN value mapping as baseline, pLTV models when 6 bits can't capture your revenue spread.
Best for: subscription and IAP-heavy apps with wide LTV distributions.

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Each runs its own angle. Worth a scroll.