Playbook: Detect In-App Creative Fatigue Before CPI Balloons

Fatigue shows up before the CPI chart panics. Catch it early:
— Track creative-level CTR and install-rate daily, not just weekly spend
— Watch frequency per user — in-app fatigue hits fast on capped audiences
— Flag the early signal: CTR holding but install-rate falling = users clicking, not converting
— Compare each creative's age to its performance curve; most rewarded/interstitial creatives decay in 1-2 weeks
— Segment by source — a creative fatigued on one network may still be fresh on another
— Set an auto-pause rule when install-rate drops a set % below the creative's peak
— Rotate in variants before the winner dies, not after

✓ Buys you lead time to refresh before CPI spikes
✓ Install-rate decay catches fatigue earlier than CPI does
✗ Short test windows can mislabel normal variance as fatigue
✗ Needs per-creative granularity some networks hide

Verdict: Use install-rate decay as your early-warning metric; refresh creatives on a 1-2 week cadence for high-frequency in-app placements.
Best for: UA managers running rewarded/interstitial creative at scale.

Claude Cowork, Claude Design объединили в один Claude

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Microsoft планирует вставлять рекламу в игры

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Playbook: Configure Attribution Windows So You Don't Over- or Under-Credit

Default windows rarely fit your funnel. Set them deliberately:
— Map your real conversion lag — how long from click to install to first purchase, by source
— Set the click-through window to cover that lag without inviting click-flood fraud (7d is common, not gospel)
— Set view-through separately and shorter; view-through over-claims if left wide
— Align the window with your SKAN measurement window so iOS and Android tell consistent stories
— Keep the same window across networks for fair cross-channel comparison
— Document every window value; an undocumented change silently shifts all your CPAs
— Re-test windows when you launch a new vertical with different purchase timing

✓ Stops over-crediting from windows that are too wide
✓ Consistent windows make cross-network math honest
✗ Too tight a window undercounts genuine slow converters
✗ View-through windows are a fraud magnet if generous

Verdict: Use a click window that matches your measured conversion lag and a short view-through window; never leave MMP defaults unexamined.
Best for: Anyone whose CPA comparisons feel inconsistent across in-app channels.

CTV-to-app attribution: IP-match window tuning moved measured installs +19%
A brand ran CTV to drive app installs but attribution was near-zero — CTV has no click, only an impression, matched by household IP within a window.

Working with Kochava, they widened the view-through IP-match window from 1h to 24h (matching real household behavior — people install later that evening) and added co-viewing dedup.

Measured CTV-driven installs rose 19% and finally cleared the noise floor for budgeting.

✓ CTV needs longer VT windows than mobile click campaigns
✓ Household-IP matching is the only signal — tune it deliberately
✗ IP match is fuzzy; co-viewing inflates without dedup
✗ Privacy shifts (IP masking) threaten this method long-term

Verdict: CTV attribution lives or dies on the IP-match window — don't default it.
Best for: brands testing CTV as a performance channel.

Playbook: Run a Supply Path Audit on Your In-App Programmatic Buys

Every hop in the chain skims margin and adds fraud surface. Trace it:
— Pull the sellers.json and app-ads.txt for the apps you're buying
— Verify each reseller is authorized — unauthorized lines in app-ads.txt = spoofable inventory
— Count the hops from your DSP to the publisher; more intermediaries = more take rate and risk
— Identify direct vs resold paths to the same app and prefer the shortest authorized one
— Check the bundle ID and app name in bid requests against the real store listing for spoofing
— Measure win rate and CPM per path; redundant long paths usually cost more for the same impression
— Consolidate to the cleanest direct paths and block the rest

✓ Cuts hidden tech-tax and spoofed-inventory exposure
✓ Uses public app-ads.txt / sellers.json — no special tooling
✗ Path consolidation can shrink reach short-term
✗ Smaller apps have messy or missing app-ads.txt

Verdict: Use direct authorized paths for in-app programmatic; skip long resold chains unless they prove unique, cheaper reach.
Best for: Programmatic buyers tightening in-app supply quality and margin.

SKAN 4 vs Apple's Aggregated Advanced Measurement: which postback to design for

SKAN 4 added tiers and crowd-anonymity gates. The right setup depends on your campaign size.

SKAN 4 multi-postback + fine value
— ✓ Up to three postbacks reveal early/mid/late funnel windows
— ✓ Fine-grained values when you clear the crowd-anonymity threshold
— ✗ Small campaigns get downgraded to coarse or null, the tiers don't trigger

AAM (web-to-app aggregate)
— ✓ Covers Safari-driven app installs SKAN misses
— ✗ Aggregate-only, no per-campaign granularity for bidding

If your campaigns push real daily volume, design for SKAN 4's three-postback windows and map values to your D0/D3/D7 funnel. If you're sub-scale, don't over-engineer fine values you'll never unlock, plan for coarse.

Verdict: SKAN 4 multi-postback for high-volume UA, coarse-value design for small campaigns, AAM only for web-sourced installs.
Best for: UA managers rebuilding conversion schemas for iOS scale.

Programmatic DSPs vs self-attributing networks: who controls the attribution truth

The difference isn't reach, it's who marks the install as theirs.

Self-attributing networks (Meta, Google, TikTok, AppLovin)
— ✓ Rich in-platform optimization, they see their own funnel
— ✗ They claim attribution and you trust their math, prone to over-claiming overlap

Programmatic DSPs (Moloco, Liftoff, Bigabid)
— ✓ MMP is the neutral referee, cleaner cross-channel dedupe
— ✓ Transparent bid-level logs for fraud and supply-path audits
— ✗ Smaller scale than the walled gardens, more hands-on management

Let SANs run for scale but distrust their self-reported attribution, validate against MMP and incrementality. Use DSPs where you want auditable, MMP-refereed numbers and supply transparency.

Verdict: SANs for scale with skepticism, DSPs when you need a neutral attribution referee.
Best for: buyers reconciling inflated walled-garden claims against reality.

Myth: a null SKAdNetwork conversion value means the install was wasted

The advice you keep hearing: filter out null-value postbacks, they're junk. Wrong, and it costs you signal.

A null (or 0) conversion value usually means the user installed but didn't trip your updateConversionValue call before the timer locked — slow first session, app killed early, or your SDK fired the lock prematurely. The install is real; SKAdNetwork still attributes it.

✓ Null postbacks still count toward your true CPI denominator
✓ A rising null rate is a diagnostic — your value schema fires too late
✗ Treating null as fraud inflates your 'clean' CPI and hides a measurement bug
✗ Networks love it when you discard nulls — your reported CVR looks worse, theirs looks fine

Fix the lock window before you blame the source. Re-time your conversion value to fire inside the first 6 seconds for app-open events.

Verdict: keep nulls, fix your timer — they're a broken-instrumentation alarm, not waste.
Best for: UA buyers running SKAN campaigns who prune postbacks by value.

На Anthropic, OpenAI, Google и SpaceXAI подали в суд из-за ИИ

В Калифорнии против ИИ-компаний подали антимонопольный иск: регулятору показалось подозрительным, что игроки синхронно призывают ограничить развитие нейросетей ради безопасности. Смысл спора в том, что инвестиции в ИИ уже обгоняют реальный прогресс, а бизнесу выгодны правила, которые защитят капитал. Вывод: быстрых прорывов ждать не стоит, лучше выжимать максимум из текущих инструментов.

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Google ads начал показывать расходы конкурентов

Google Ads запустил Peer Spending — инструмент, который сравнивает расходы аккаунта с рекламодателями из той же ниши без раскрытия чужих данных. Он показывает, тратите вы больше, меньше или примерно на уровне конкурентов на уровне кампаний и групп объявлений. Для арбитража это скорее ориентир по бенчмаркам, чем инструмент прямого усиления залива.

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Минфин РФ планирует выпустить собственный стейблкоин

Власти РФ обсуждают запуск рублёвого стейблкоина: сейчас решают, как его обеспечить, какие операции разрешить и будет ли на него спрос. Основной кейс — международные переводы, а не использование физлицами. Если проект доведут до запуска, он может стать частью новой криптоинфраструктуры и альтернативой токенам, привязанным к дружественным валютам.

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