Stop optimizing to average CPM; model the clearing price distribution
Average CPM is a single number standing in for a distribution, and that abstraction hides the levers that actually move your win rate. The richer object is the clearing-price distribution: for a given segment, the full curve of what it took to win.
1. Pull every auction you bid on for one segment and record the minimum winning price where known (from win notices and loss reasons in log-level data).
2. Plot the distribution. You will usually see it is right-skewed — most impressions clear cheaply, a long tail clears expensive.
3. Your win rate is just the fraction of that curve sitting below your bid. Raising your bid sweeps you further up the curve, but with diminishing returns once you've cleared the dense low part.
This reframes bidding from 'pick a CPM' to 'pick a percentile.' Bidding to the 60th percentile of the clearing distribution might win 60% of available impressions; chasing the 90th costs far more per incremental win because the tail is expensive and sparse.
Why it matters: the distribution tells you where the cheap, winnable volume sits and where you're about to overpay for scarcity. Two segments with identical average CPMs can have completely different efficient bid points once you look at the shape.
Average CPM is a single number standing in for a distribution, and that abstraction hides the levers that actually move your win rate. The richer object is the clearing-price distribution: for a given segment, the full curve of what it took to win.
1. Pull every auction you bid on for one segment and record the minimum winning price where known (from win notices and loss reasons in log-level data).
2. Plot the distribution. You will usually see it is right-skewed — most impressions clear cheaply, a long tail clears expensive.
3. Your win rate is just the fraction of that curve sitting below your bid. Raising your bid sweeps you further up the curve, but with diminishing returns once you've cleared the dense low part.
This reframes bidding from 'pick a CPM' to 'pick a percentile.' Bidding to the 60th percentile of the clearing distribution might win 60% of available impressions; chasing the 90th costs far more per incremental win because the tail is expensive and sparse.
Why it matters: the distribution tells you where the cheap, winnable volume sits and where you're about to overpay for scarcity. Two segments with identical average CPMs can have completely different efficient bid points once you look at the shape.
Loss reason codes are the most underused field in the bidstream
When you lose an auction, many exchanges return a loss reason code: a small integer telling you why you lost. Most teams discard it. It is arguably the highest-signal field in log-level data.
1. Codes broadly split into 'lost on price' (you bid, someone bid more) and 'lost for non-price reasons' (creative rejected, below floor, bidder timeout, blocklisted advertiser, currency mismatch).
2. Aggregate the codes for a campaign that's underdelivering. If 70% of losses are non-price, raising bids does nothing — you are being filtered for a creative or eligibility reason.
3. 'Below floor' as a dominant code means the publisher's floor exceeds your bid; that's a targeting or PMP-negotiation problem, not a bidding problem.
The trap is that win-rate dashboards lump all losses together, so a campaign losing on creative policy looks identical to one losing on price. The reason codes are the only thing that separates them cleanly.
Why it matters: before you change a bid, pull the loss reason distribution. A surprising share of 'we can't win this inventory' problems are creative rejections or floor mismatches that no amount of money fixes. The field is already in your feed — wire it into your reporting.
When you lose an auction, many exchanges return a loss reason code: a small integer telling you why you lost. Most teams discard it. It is arguably the highest-signal field in log-level data.
1. Codes broadly split into 'lost on price' (you bid, someone bid more) and 'lost for non-price reasons' (creative rejected, below floor, bidder timeout, blocklisted advertiser, currency mismatch).
2. Aggregate the codes for a campaign that's underdelivering. If 70% of losses are non-price, raising bids does nothing — you are being filtered for a creative or eligibility reason.
3. 'Below floor' as a dominant code means the publisher's floor exceeds your bid; that's a targeting or PMP-negotiation problem, not a bidding problem.
The trap is that win-rate dashboards lump all losses together, so a campaign losing on creative policy looks identical to one losing on price. The reason codes are the only thing that separates them cleanly.
Why it matters: before you change a bid, pull the loss reason distribution. A surprising share of 'we can't win this inventory' problems are creative rejections or floor mismatches that no amount of money fixes. The field is already in your feed — wire it into your reporting.
Soft floors, hard floors, and why first-price killed the trick
A price floor is the minimum a publisher will accept. There were historically two kinds, and the distinction used to be exploitable.
1. A hard floor rejects any bid below it. Simple.
2. A soft floor (under second-price rules) was a threshold below which you paid your own bid, and above which you paid second price. Clever buyers bid just over the soft floor to win cheaply.
3. With the move to first-price, the soft-floor game collapsed: you now pay your bid regardless, so there's no second-price discount to engineer toward.
What remains is a strategic interaction between your shading engine and the publisher's floor. If the publisher sets a dynamic floor that learns from your bids, and your shading learns from clearing prices, you have two adaptive systems pushing against each other. Floors can ratchet up to capture the surplus your shading was trying to keep.
Why it matters: dynamic floors are why a publisher's effective CPM can rise even as the open market softens — the floor is harvesting the gap shading leaves. When a specific publisher's clearing price climbs against the market trend, suspect a learning floor and test how your DSP responds to floor signals in the bid request.
A price floor is the minimum a publisher will accept. There were historically two kinds, and the distinction used to be exploitable.
1. A hard floor rejects any bid below it. Simple.
2. A soft floor (under second-price rules) was a threshold below which you paid your own bid, and above which you paid second price. Clever buyers bid just over the soft floor to win cheaply.
3. With the move to first-price, the soft-floor game collapsed: you now pay your bid regardless, so there's no second-price discount to engineer toward.
What remains is a strategic interaction between your shading engine and the publisher's floor. If the publisher sets a dynamic floor that learns from your bids, and your shading learns from clearing prices, you have two adaptive systems pushing against each other. Floors can ratchet up to capture the surplus your shading was trying to keep.
Why it matters: dynamic floors are why a publisher's effective CPM can rise even as the open market softens — the floor is harvesting the gap shading leaves. When a specific publisher's clearing price climbs against the market trend, suspect a learning floor and test how your DSP responds to floor signals in the bid request.
Duplicate bid requests: the same impression asks you twice
In a header-bidding world, one ad slot can generate multiple bid requests that reach your DSP through different exchanges. You are being asked to bid on the same physical impression more than once, and how your DSP handles it changes your economics.
1. Identify duplication by hashing stable fields — user ID, slot/placement ID, timestamp window, page URL — across exchanges in your log-level data.
2. If your DSP bids independently on each copy, you can win the same impression's auction on two paths and effectively bid against yourself, or you win one and waste compute on the rest.
3. Worse, duplication inflates your apparent available supply and corrupts reach and frequency math, because two requests look like two opportunities when they're one.
The mitigation is request deduplication before bidding and choosing the single best path per impression — which is SPO operating at the request level, not just the seller level.
Why it matters: if your DSP doesn't dedupe, your supply path optimization is incomplete and your frequency capping is leaking, because the unit of analysis (a unique impression opportunity) is being double-counted upstream. Measure your duplication rate per publisher; double-digit percentages are common and quietly distort everything downstream.
In a header-bidding world, one ad slot can generate multiple bid requests that reach your DSP through different exchanges. You are being asked to bid on the same physical impression more than once, and how your DSP handles it changes your economics.
1. Identify duplication by hashing stable fields — user ID, slot/placement ID, timestamp window, page URL — across exchanges in your log-level data.
2. If your DSP bids independently on each copy, you can win the same impression's auction on two paths and effectively bid against yourself, or you win one and waste compute on the rest.
3. Worse, duplication inflates your apparent available supply and corrupts reach and frequency math, because two requests look like two opportunities when they're one.
The mitigation is request deduplication before bidding and choosing the single best path per impression — which is SPO operating at the request level, not just the seller level.
Why it matters: if your DSP doesn't dedupe, your supply path optimization is incomplete and your frequency capping is leaking, because the unit of analysis (a unique impression opportunity) is being double-counted upstream. Measure your duplication rate per publisher; double-digit percentages are common and quietly distort everything downstream.
Win notices bias your training data, and the bias is systematic
A win notice is the callback an exchange fires to tell you that you won and at what price. DSPs learn clearing prices largely from these notices. The problem is that win notices are a biased sample of all auctions, and the bias is predictable.
1. You only get a clean price signal on auctions you win. Auctions you lose return, at best, a loss code — rarely the actual clearing price.
2. This is censored data: the expensive auctions you lost are systematically missing from your observed price distribution.
3. A model trained naively on wins alone will underestimate true clearing prices, because it never sees the high-priced auctions it couldn't afford.
This is the same censoring problem that haunts bid shading, viewed from the data-science side. The fix is survival-analysis-style methods that account for the missing losses, or deliberate high exploration bids to occasionally observe the expensive tail.
Why it matters: any pricing model that learns only from won impressions will drift optimistic and gradually price you out of competitive inventory without anyone noticing. When evaluating a DSP's bidding intelligence, ask specifically how it corrects for censored losses — it's the difference between a model that holds calibration and one that slowly hallucinates a cheaper market than exists.
A win notice is the callback an exchange fires to tell you that you won and at what price. DSPs learn clearing prices largely from these notices. The problem is that win notices are a biased sample of all auctions, and the bias is predictable.
1. You only get a clean price signal on auctions you win. Auctions you lose return, at best, a loss code — rarely the actual clearing price.
2. This is censored data: the expensive auctions you lost are systematically missing from your observed price distribution.
3. A model trained naively on wins alone will underestimate true clearing prices, because it never sees the high-priced auctions it couldn't afford.
This is the same censoring problem that haunts bid shading, viewed from the data-science side. The fix is survival-analysis-style methods that account for the missing losses, or deliberate high exploration bids to occasionally observe the expensive tail.
Why it matters: any pricing model that learns only from won impressions will drift optimistic and gradually price you out of competitive inventory without anyone noticing. When evaluating a DSP's bidding intelligence, ask specifically how it corrects for censored losses — it's the difference between a model that holds calibration and one that slowly hallucinates a cheaper market than exists.
Take-rate stacking: where your CPM actually goes
The price you pay and the revenue the publisher receives are separated by a stack of take rates — the cut each intermediary keeps. Supply path optimization is, concretely, take-rate reduction. To optimize it you have to be able to see it.
1. Each hop (SSP, reseller, sometimes a second SSP) takes a percentage. These compound: a 15% SSP cut on top of a 10% reseller cut leaves the publisher roughly 76% of your spend, not 75%.
2. Some intermediaries take a fixed margin; others take a dynamic, undisclosed margin that varies by auction. The undisclosed ones are where money disappears.
3. You can estimate take rate per path by comparing your spend on a publisher through path A versus path B for matched inventory — the path that delivers the same audience for less is the lower-take path.
Log-level data from both sides (your spend, the publisher's payout, where you can get it) lets you measure the stack directly. Without it you are inferring.
Why it matters: SPO's real prize is not 'fewer paths' for tidiness — it's routing spend through the lowest-take path that preserves the publisher relationship, often recovering 10–20% of media cost. You can't manage a take rate you've never measured, so make path-level effective cost a standing report.
The price you pay and the revenue the publisher receives are separated by a stack of take rates — the cut each intermediary keeps. Supply path optimization is, concretely, take-rate reduction. To optimize it you have to be able to see it.
1. Each hop (SSP, reseller, sometimes a second SSP) takes a percentage. These compound: a 15% SSP cut on top of a 10% reseller cut leaves the publisher roughly 76% of your spend, not 75%.
2. Some intermediaries take a fixed margin; others take a dynamic, undisclosed margin that varies by auction. The undisclosed ones are where money disappears.
3. You can estimate take rate per path by comparing your spend on a publisher through path A versus path B for matched inventory — the path that delivers the same audience for less is the lower-take path.
Log-level data from both sides (your spend, the publisher's payout, where you can get it) lets you measure the stack directly. Without it you are inferring.
Why it matters: SPO's real prize is not 'fewer paths' for tidiness — it's routing spend through the lowest-take path that preserves the publisher relationship, often recovering 10–20% of media cost. You can't manage a take rate you've never measured, so make path-level effective cost a standing report.
Neighbor spotlight: @SmartlinkOps. They go deep on Smartlinks — the kind of channel you actually keep notifications on for.
Forwarded from Потрачено! Клуб спящих бизнесменов!
This media is not supported in your browser
VIEW IN TELEGRAM
🚀 aff.top — вся индустрия арбитража в одном месте
🧠 Блог про арбитраж и ИИ — как нейросети меняют залив и антифрод
🚨 База спамеров — ежедневно собираем спамеров и ведём рейтинг
🛠 70+ инструментов — от клоаки до антифрод-чека
🎬 1000+ видео — весь YouTube про трафик в одной ленте
👤 2400+ персон — байеры и фаундеры с контактами напрямую
Без регистрации, без платных «премиумов».
👇 Подписывайся на канал
🧠 Блог про арбитраж и ИИ — как нейросети меняют залив и антифрод
🚨 База спамеров — ежедневно собираем спамеров и ведём рейтинг
🛠 70+ инструментов — от клоаки до антифрод-чека
🎬 1000+ видео — весь YouTube про трафик в одной ленте
👤 2400+ персон — байеры и фаундеры с контактами напрямую
Без регистрации, без платных «премиумов».
👇 Подписывайся на канал
This media is not supported in your browser
VIEW IN TELEGRAM
Алиса AI будет конкурировать с Google AI Studio
Яндекс разворачивает экосистему AI-агентов на базе Алисы с доступом сначала для компаний, затем для всех. Агенты уже работают в Яндекс Такси и Лавке, скоро появятся в браузере и студии разработки. Платформа интегрирует стандартные функции — заказ такси, покупки, анализ данных. Алиса AI показывает неплохие результаты: менее известна, чем конкуренты, поэтому предлагает щедрые лимиты на видеогенерацию и работу с контентом. Яндекс планирует внедрить…
➡️ Читайте на сайте: https://aff.top/blog/alisa-ai-budet-konkurirovat-s-google-ai-studio
🧠 Ещё больше инсайтов → в канале AFF.top
Яндекс разворачивает экосистему AI-агентов на базе Алисы с доступом сначала для компаний, затем для всех. Агенты уже работают в Яндекс Такси и Лавке, скоро появятся в браузере и студии разработки. Платформа интегрирует стандартные функции — заказ такси, покупки, анализ данных. Алиса AI показывает неплохие результаты: менее известна, чем конкуренты, поэтому предлагает щедрые лимиты на видеогенерацию и работу с контентом. Яндекс планирует внедрить…
➡️ Читайте на сайте: https://aff.top/blog/alisa-ai-budet-konkurirovat-s-google-ai-studio
🧠 Ещё больше инсайтов → в канале AFF.top
This media is not supported in your browser
VIEW IN TELEGRAM
В Zennoposter добавили ИИ-помощник
Zennolab добавил в Zennoposter встроенный ИИ-кубик с доступом к четырём моделям (Gemini, DeepSeek, Claude, ChatGPT) — 50 бесплатных запросов в сутки. Есть режимы Assistant (чтение) и Agent (автоматическое создание скриптов), плюс новый GET-запрос по API. Нейросети хорошо справляются с регистрацией, постингом, фармингом аккаунтов и простым кодированием, но требуют проверки при парсинге динамических сайтов и диагностике ошибок. В связке с Zennoobr…
➡️ Читайте на сайте: https://aff.top/blog/v-zennoposter-dobavili-ii-pomoschnik
🧠 Ещё больше инсайтов → в канале AFF.top
Zennolab добавил в Zennoposter встроенный ИИ-кубик с доступом к четырём моделям (Gemini, DeepSeek, Claude, ChatGPT) — 50 бесплатных запросов в сутки. Есть режимы Assistant (чтение) и Agent (автоматическое создание скриптов), плюс новый GET-запрос по API. Нейросети хорошо справляются с регистрацией, постингом, фармингом аккаунтов и простым кодированием, но требуют проверки при парсинге динамических сайтов и диагностике ошибок. В связке с Zennoobr…
➡️ Читайте на сайте: https://aff.top/blog/v-zennoposter-dobavili-ii-pomoschnik
🧠 Ещё больше инсайтов → в канале AFF.top
Bid request enrichment: the fields that decide before you do
A bid request is not a neutral fact sheet; it's a partly self-reported document, and several fields shape your bid before your logic runs. Knowing which fields are trustworthy is core craft.
1. Declared fields like viewability prediction, audibility, and content category come from the publisher or SSP and can be optimistic — they describe expected, not realized, outcomes.
2. Some fields are enriched mid-stream by intermediaries (added segments, inferred demographics). Enrichment can add value or add noise you're effectively paying for.
3. Signals like the supply chain object and sellers.json relationship are verifiable; signals like 'predicted viewability 80%' are claims.
The discipline is to separate verifiable fields from claimed ones and to calibrate claimed fields against your own measured outcomes. If a seller's declared viewability is 80% but your post-bid measurement says 55%, you should discount that seller's declared field by the observed gap.
Why it matters: you are bidding on a mix of facts and marketing. Building a per-seller calibration layer — comparing each declared field to what actually happened — turns the bidstream from a document you trust into one you score. That calibration is where sophisticated buyers extract edge that pure price competition can't.
A bid request is not a neutral fact sheet; it's a partly self-reported document, and several fields shape your bid before your logic runs. Knowing which fields are trustworthy is core craft.
1. Declared fields like viewability prediction, audibility, and content category come from the publisher or SSP and can be optimistic — they describe expected, not realized, outcomes.
2. Some fields are enriched mid-stream by intermediaries (added segments, inferred demographics). Enrichment can add value or add noise you're effectively paying for.
3. Signals like the supply chain object and sellers.json relationship are verifiable; signals like 'predicted viewability 80%' are claims.
The discipline is to separate verifiable fields from claimed ones and to calibrate claimed fields against your own measured outcomes. If a seller's declared viewability is 80% but your post-bid measurement says 55%, you should discount that seller's declared field by the observed gap.
Why it matters: you are bidding on a mix of facts and marketing. Building a per-seller calibration layer — comparing each declared field to what actually happened — turns the bidstream from a document you trust into one you score. That calibration is where sophisticated buyers extract edge that pure price competition can't.
This media is not supported in your browser
VIEW IN TELEGRAM
Новую Google reCapcha прошли статичной картинкой
Google выпустил обновленную reCAPTCHA, требующую движений рук для прохождения, но система оказалась уязвима к обходу. Достаточно транслировать статичное изображение с нужным жестом через виртуальную камеру с помощью простого Python-скрипта, чтобы нейросеть пропустила пользователя. Это создает серьёзный риск для сайтов: защита от ботов, позиционировавшаяся как прорыв, на деле не работает. Баг остается актуальным и позволяет спамерам легко автомат…
➡️ Читайте на сайте: https://aff.top/blog/novuiu-google-recapcha-proshli-statichnoi-kartinkoi
🧠 Ещё больше инсайтов → в канале AFF.top
Google выпустил обновленную reCAPTCHA, требующую движений рук для прохождения, но система оказалась уязвима к обходу. Достаточно транслировать статичное изображение с нужным жестом через виртуальную камеру с помощью простого Python-скрипта, чтобы нейросеть пропустила пользователя. Это создает серьёзный риск для сайтов: защита от ботов, позиционировавшаяся как прорыв, на деле не работает. Баг остается актуальным и позволяет спамерам легко автомат…
➡️ Читайте на сайте: https://aff.top/blog/novuiu-google-recapcha-proshli-statichnoi-kartinkoi
🧠 Ещё больше инсайтов → в канале AFF.top
Forwarded from AFF.TOP
This media is not supported in your browser
VIEW IN TELEGRAM
DeepSeek представит последнюю версию v4
DeepSeek выпустит v4 в середине июля с новой моделью ценообразования API: токены подорожают в 2 раза в часы пиковой нагрузки (09:00–12:00 и 14:00–18:00 по пекинскому времени). Компания планирует уведомлять пользователей по почте за 24 часа до изменения тарифов. Проблема с ошибками «server busy» останется, но обойдётся дороже — это может существенно повлиять на экономику проектов, которые активно используют API DeepSeek для автоматизации и масшта…
➡️ Читайте на сайте: https://aff.top/blog/deepseek-predstavit-posledniuiu-versiiu-v4
🧠 Ещё больше инсайтов → в канале AFF.top
DeepSeek выпустит v4 в середине июля с новой моделью ценообразования API: токены подорожают в 2 раза в часы пиковой нагрузки (09:00–12:00 и 14:00–18:00 по пекинскому времени). Компания планирует уведомлять пользователей по почте за 24 часа до изменения тарифов. Проблема с ошибками «server busy» останется, но обойдётся дороже — это может существенно повлиять на экономику проектов, которые активно используют API DeepSeek для автоматизации и масшта…
➡️ Читайте на сайте: https://aff.top/blog/deepseek-predstavit-posledniuiu-versiiu-v4
🧠 Ещё больше инсайтов → в канале AFF.top
Why frequency caps leak across the open programmatic graph
A frequency cap (limit how many times one user sees your ad) sounds like a counter. In open programmatic it's closer to an estimate, and the leak is structural, not a bug.
1. Capping requires stable identity. When match rate is partial and identifiers fragment across exchanges, the same person can appear as several distinct users to your DSP.
2. Each apparent identity gets its own cap allowance, so a user who resolves to three IDs can see your ad three times the intended limit.
3. Cross-device and in-app environments amplify this; the deterministic link between a phone and a desktop is often missing.
The consequence is overexposure on your heaviest users and wasted impressions that also depress performance through ad fatigue — the opposite of what the cap was meant to prevent.
Mitigation leans on a unified identity layer and, where deterministic IDs are absent, probabilistic stitching with known error rates. But you should treat your cap as a soft target with a leakage percentage, not a hard ceiling.
Why it matters: if you report a 3x cap but your true distribution has a tail of users seeing your ad 8–10 times, your reach and effectiveness numbers are both wrong. Measure realized frequency from log-level impression data against your set cap; the gap is your identity fragmentation, quantified.
A frequency cap (limit how many times one user sees your ad) sounds like a counter. In open programmatic it's closer to an estimate, and the leak is structural, not a bug.
1. Capping requires stable identity. When match rate is partial and identifiers fragment across exchanges, the same person can appear as several distinct users to your DSP.
2. Each apparent identity gets its own cap allowance, so a user who resolves to three IDs can see your ad three times the intended limit.
3. Cross-device and in-app environments amplify this; the deterministic link between a phone and a desktop is often missing.
The consequence is overexposure on your heaviest users and wasted impressions that also depress performance through ad fatigue — the opposite of what the cap was meant to prevent.
Mitigation leans on a unified identity layer and, where deterministic IDs are absent, probabilistic stitching with known error rates. But you should treat your cap as a soft target with a leakage percentage, not a hard ceiling.
Why it matters: if you report a 3x cap but your true distribution has a tail of users seeing your ad 8–10 times, your reach and effectiveness numbers are both wrong. Measure realized frequency from log-level impression data against your set cap; the gap is your identity fragmentation, quantified.
Forwarded from AFF.TOP
This media is not supported in your browser
VIEW IN TELEGRAM
Anthropic выпустили Sonnet 5
30 июня вышла Claude Sonnet 5 — новая версия позиционируется как самая агентная в линейке и приближается к флагманской Opus 4.8. Модель лучше справляется со сложными многоуровневыми задачами, устойчива к вредоносным запросам и не генерирует эксплойты. Sonnet 5 доступна на Free-тарифе, но тестирование показало скромные улучшения: хотя работает лучше Sonnet 4.6, её обгоняют конкуренты, включая китайские модели, которые дешевле через API при лучшей…
➡️ Читайте на сайте: https://aff.top/blog/anthropic-vypustili-sonnet-5
🧠 Ещё больше инсайтов → в канале AFF.top
30 июня вышла Claude Sonnet 5 — новая версия позиционируется как самая агентная в линейке и приближается к флагманской Opus 4.8. Модель лучше справляется со сложными многоуровневыми задачами, устойчива к вредоносным запросам и не генерирует эксплойты. Sonnet 5 доступна на Free-тарифе, но тестирование показало скромные улучшения: хотя работает лучше Sonnet 4.6, её обгоняют конкуренты, включая китайские модели, которые дешевле через API при лучшей…
➡️ Читайте на сайте: https://aff.top/blog/anthropic-vypustili-sonnet-5
🧠 Ещё больше инсайтов → в канале AFF.top
Forwarded from AFF.TOP
This media is not supported in your browser
VIEW IN TELEGRAM
Clickstar прекращает работу
Clickstar закрывается. Легендарная пуш-сеть прекращает закуп трафика с 1 августа, полная остановка — 20 августа.
Сетка работала почти 8 лет и была одним из лучших источников качественного трафика на Россию и СНГ. Сейчас пуш-трафик стал слишком ботовым из-за гугловских банов на скрипты сбора.
Что это означает для арбитражников — разбираемся в ста…
➡️ Читайте на сайте: https://aff.top/blog/clickstar-prekraschaet-rabotu
🧠 Ещё больше инсайтов → в канале AFF.top
Clickstar закрывается. Легендарная пуш-сеть прекращает закуп трафика с 1 августа, полная остановка — 20 августа.
Сетка работала почти 8 лет и была одним из лучших источников качественного трафика на Россию и СНГ. Сейчас пуш-трафик стал слишком ботовым из-за гугловских банов на скрипты сбора.
Что это означает для арбитражников — разбираемся в ста…
➡️ Читайте на сайте: https://aff.top/blog/clickstar-prekraschaet-rabotu
🧠 Ещё больше инсайтов → в канале AFF.top
Auction density and why pacing is harder than it looks
Budget pacing spends evenly over a flight instead of front-loading. The naive model assumes auction opportunities arrive uniformly. They don't, and the non-uniformity breaks simple pacing.
1. Auction density — the rate of eligible auctions for your target — varies by hour, day, and audience, sometimes 5–10x between trough and peak.
2. A pacing system that targets constant spend-per-hour will either starve during low-density hours (no inventory to buy) or get forced to overpay to hit its hourly number.
3. Worse, raising bids to hit a pacing target during a low-density window pollutes your clearing-price data, teaching the shading model that inventory clears higher than it really does.
The better model paces against available opportunity, spending more when good inventory is abundant and cheap and less when it's scarce and expensive — which usually means accepting an uneven hourly spend curve to get an even-quality outcome.
Why it matters: pacing and bidding are coupled, not independent. A pacing rule that forces spend during scarce hours quietly inflates your CPMs and corrupts the very model that's supposed to keep prices honest. Evaluate pacing on cost-per-outcome across the flight, not on how smooth the hourly spend line looks.
Budget pacing spends evenly over a flight instead of front-loading. The naive model assumes auction opportunities arrive uniformly. They don't, and the non-uniformity breaks simple pacing.
1. Auction density — the rate of eligible auctions for your target — varies by hour, day, and audience, sometimes 5–10x between trough and peak.
2. A pacing system that targets constant spend-per-hour will either starve during low-density hours (no inventory to buy) or get forced to overpay to hit its hourly number.
3. Worse, raising bids to hit a pacing target during a low-density window pollutes your clearing-price data, teaching the shading model that inventory clears higher than it really does.
The better model paces against available opportunity, spending more when good inventory is abundant and cheap and less when it's scarce and expensive — which usually means accepting an uneven hourly spend curve to get an even-quality outcome.
Why it matters: pacing and bidding are coupled, not independent. A pacing rule that forces spend during scarce hours quietly inflates your CPMs and corrupts the very model that's supposed to keep prices honest. Evaluate pacing on cost-per-outcome across the flight, not on how smooth the hourly spend line looks.
Forwarded from AFF.TOP
This media is not supported in your browser
VIEW IN TELEGRAM
Facebook запретил рекламу онлайн-казино Mr Vegas
Британский ASA запретил рекламу казино Mr Vegas из-за «слишком милых» мультяшных животных в креативах — регулятор счёл, что такой стиль привлекает детей, в том числе через Facebook. Рекламодатель запустил кампанию в феврале, бан вышел в июле. Логика регулятора вызывает вопросы: дети неплатёжеспособны, а таргетировать их на гемблинг бессмысленно.
➡️ Читайте на сайте: https://aff.top/blog/facebook-zapretil-reklamu-onlain-kazino-mr-vegas
🧠 Ещё больше инсайтов → в канале AFF.top
Британский ASA запретил рекламу казино Mr Vegas из-за «слишком милых» мультяшных животных в креативах — регулятор счёл, что такой стиль привлекает детей, в том числе через Facebook. Рекламодатель запустил кампанию в феврале, бан вышел в июле. Логика регулятора вызывает вопросы: дети неплатёжеспособны, а таргетировать их на гемблинг бессмысленно.
➡️ Читайте на сайте: https://aff.top/blog/facebook-zapretil-reklamu-onlain-kazino-mr-vegas
🧠 Ещё больше инсайтов → в канале AFF.top
Forwarded from AFF.TOP
This media is not supported in your browser
VIEW IN TELEGRAM
В Whatsapp скамят пользователей с помощью поддельных никнеймов
WhatsApp запустил никнеймы — и почти сразу начался скам. Мошенники регистрируют имена, похожие на бренды, звёзд и политиков, с минимальными опечатками.
Индия, где 500 млн пользователей WhatsApp, потребовала от Meta объяснений за 3 дня. Meta говорит, что точные совпадения заблокированы — но одна буква в другом месте защиту не триггерит.
Похоже, п…
➡️ Читайте на сайте: https://aff.top/blog/v-whatsapp-skamiat-polzovatelei-s-pomoschiu-poddelnykh-nikneimov
🧠 Ещё больше инсайтов → в канале AFF.top
WhatsApp запустил никнеймы — и почти сразу начался скам. Мошенники регистрируют имена, похожие на бренды, звёзд и политиков, с минимальными опечатками.
Индия, где 500 млн пользователей WhatsApp, потребовала от Meta объяснений за 3 дня. Meta говорит, что точные совпадения заблокированы — но одна буква в другом месте защиту не триггерит.
Похоже, п…
➡️ Читайте на сайте: https://aff.top/blog/v-whatsapp-skamiat-polzovatelei-s-pomoschiu-poddelnykh-nikneimov
🧠 Ещё больше инсайтов → в канале AFF.top
Forwarded from AFF.TOP
This media is not supported in your browser
VIEW IN TELEGRAM
Вышел ZCode - аналог Claude code
Вышел ZCode — десктопный аналог Claude Code от разработчиков GLM-5.2. Работает с API от Anthropic, поддерживает SSH-деплой на сервер, в том числе Linux.
Вместо пошаговых скриптов — система целеполагания Goal: закидываешь сложный промт, агент сам разбивает задачу и выполняет. Плюс управление через Telegram-бота.
Но главная фича — мультиагентность…
➡️ Читайте на сайте: https://aff.top/blog/vyshel-zcode-analog-claude-code
🧠 Ещё больше инсайтов → в канале AFF.top
Вышел ZCode — десктопный аналог Claude Code от разработчиков GLM-5.2. Работает с API от Anthropic, поддерживает SSH-деплой на сервер, в том числе Linux.
Вместо пошаговых скриптов — система целеполагания Goal: закидываешь сложный промт, агент сам разбивает задачу и выполняет. Плюс управление через Telegram-бота.
Но главная фича — мультиагентность…
➡️ Читайте на сайте: https://aff.top/blog/vyshel-zcode-analog-claude-code
🧠 Ещё больше инсайтов → в канале AFF.top