🔥 justbrand_create — новый участник рейтинга НеТОП на AffPapa!
🏆 Своё место в топе честно купил justbrand_create: https://affpapa.org/netop/go/28?src=broadcast
💰 Ставка: $111 · сейчас #1 в рейтинге
Весь рейтинг → https://affpapa.org/netop
🏆 Своё место в топе честно купил justbrand_create: https://affpapa.org/netop/go/28?src=broadcast
💰 Ставка: $111 · сейчас #1 в рейтинге
Весь рейтинг → https://affpapa.org/netop
How to diagnose whether bid shading is helping or hurting you — step by step
Bid shading (the algorithm that lowers your first-price bid toward the predicted clearing price) is a black box on most DSPs. Here's how to interrogate it.
1. Export bid-level logs with three fields per auction: your submitted bid, the shaded bid actually sent, and the clearing price when you won.
2. Compute the shave: (submitted − shaded) / submitted. Bucket auctions into deciles by this value.
3. In each decile measure two things — win rate and surplus, where surplus = (shaded bid − clearing price). Surplus is the money the shader left on the table that you didn't need to spend.
4. Look for the inversion point: the decile where pushing the shave deeper starts dropping win rate faster than it grows surplus. That's the shader's efficient frontier.
5. If high-shave deciles show near-zero surplus AND collapsing win rate, the model is over-shading you out of inventory. If low-shave deciles show large surplus, it's under-shading and overpaying.
Most shaders are tuned for the median auction and mishandle your tails — branded direct deals get over-shaded, remnant open exchange gets under-shaded.
Why it matters: You cannot fix what you cannot see. Logging the submitted-vs-shaded delta turns an opaque margin into a controllable lever, and the inversion-point analysis is the only honest way to know if your DSP's shading is working for you or for the exchange.
Bid shading (the algorithm that lowers your first-price bid toward the predicted clearing price) is a black box on most DSPs. Here's how to interrogate it.
1. Export bid-level logs with three fields per auction: your submitted bid, the shaded bid actually sent, and the clearing price when you won.
2. Compute the shave: (submitted − shaded) / submitted. Bucket auctions into deciles by this value.
3. In each decile measure two things — win rate and surplus, where surplus = (shaded bid − clearing price). Surplus is the money the shader left on the table that you didn't need to spend.
4. Look for the inversion point: the decile where pushing the shave deeper starts dropping win rate faster than it grows surplus. That's the shader's efficient frontier.
5. If high-shave deciles show near-zero surplus AND collapsing win rate, the model is over-shading you out of inventory. If low-shave deciles show large surplus, it's under-shading and overpaying.
Most shaders are tuned for the median auction and mishandle your tails — branded direct deals get over-shaded, remnant open exchange gets under-shaded.
Why it matters: You cannot fix what you cannot see. Logging the submitted-vs-shaded delta turns an opaque margin into a controllable lever, and the inversion-point analysis is the only honest way to know if your DSP's shading is working for you or for the exchange.
A diagnostic checklist for a Deal ID that won't spend
A Deal ID (the negotiated handshake binding a buyer, an SSP, and specific inventory at agreed terms) failing to deliver is rarely one problem. Work the chain top to bottom.
1. Confirm the deal is active on both sides and the seat ID the SSP whitelisted matches the seat your DSP actually bids from — a seat mismatch silently drops every bid request.
2. Check the deal type. A Preferred Deal (non-guaranteed, fixed price, first look) behaves differently from a Private Auction (PMP with a floor). If your bid sits below the negotiated floor, the SSP filters it pre-auction and you see zero bid requests, not zero wins.
3. Inspect incoming bid requests for the deal object. No deals array in the request means the SSP isn't tagging the impression — a packaging error on their end, not yours.
4. If deals arrive but you don't bid, check your targeting: a deal does not override DSP-side brand safety, geo, or frequency filters layered on top.
5. If you bid and lose, the deal is competing in the auction. Confirm whether it's truly a guaranteed first-look or just a floor you're bidding into against open-market demand.
Why it matters: Each layer fails differently and the symptoms look identical from the dashboard. A structured top-to-bottom trace isolates whether the break is in packaging, seat mapping, floor logic, or your own targeting — saving days of blaming the wrong party.
A Deal ID (the negotiated handshake binding a buyer, an SSP, and specific inventory at agreed terms) failing to deliver is rarely one problem. Work the chain top to bottom.
1. Confirm the deal is active on both sides and the seat ID the SSP whitelisted matches the seat your DSP actually bids from — a seat mismatch silently drops every bid request.
2. Check the deal type. A Preferred Deal (non-guaranteed, fixed price, first look) behaves differently from a Private Auction (PMP with a floor). If your bid sits below the negotiated floor, the SSP filters it pre-auction and you see zero bid requests, not zero wins.
3. Inspect incoming bid requests for the deal object. No deals array in the request means the SSP isn't tagging the impression — a packaging error on their end, not yours.
4. If deals arrive but you don't bid, check your targeting: a deal does not override DSP-side brand safety, geo, or frequency filters layered on top.
5. If you bid and lose, the deal is competing in the auction. Confirm whether it's truly a guaranteed first-look or just a floor you're bidding into against open-market demand.
Why it matters: Each layer fails differently and the symptoms look identical from the dashboard. A structured top-to-bottom trace isolates whether the break is in packaging, seat mapping, floor logic, or your own targeting — saving days of blaming the wrong party.
Forwarded from AFF.TOP - про арбитраж трафика и CPA рынок!
This media is not supported in your browser
VIEW IN TELEGRAM
Google отменил ручную пессимизацию в Еврозоне
Google перестал пессимизировать крупные новостники за паразитные страницы с казино и другими партнёрскими офферами в ЕЭЗ. Для арбитража вывод простой: в Европе схема с «пирогами» больше не даёт преимущества от траста основного домена, а Google впервые применяет разные правила по GEO под давлением регулятора.
➡️ Читайте на сайте: https://aff.top/blog/google-otmenil-ruchnuiu-pessimizaciiu-v-evrozone
🧠 Ещё больше инсайтов → в канале AFF.top
Google перестал пессимизировать крупные новостники за паразитные страницы с казино и другими партнёрскими офферами в ЕЭЗ. Для арбитража вывод простой: в Европе схема с «пирогами» больше не даёт преимущества от траста основного домена, а Google впервые применяет разные правила по GEO под давлением регулятора.
➡️ Читайте на сайте: https://aff.top/blog/google-otmenil-ruchnuiu-pessimizaciiu-v-evrozone
🧠 Ещё больше инсайтов → в канале AFF.top
Forwarded from AFF.TOP - про арбитраж трафика и CPA рынок!
This media is not supported in your browser
VIEW IN TELEGRAM
Вышел OpenClaw 2.0
OpenClaw вышел на новый уровень: совместная работа, нормальный веб-интерфейс и более простая настройка. Разбираем, зачем это обновление важно и как оно меняет работу с ИИ-агентом.
➡️ Читайте на сайте: https://aff.top/blog/vyshel-openclaw-2-0
🧠 Ещё больше инсайтов → в канале AFF.top
OpenClaw вышел на новый уровень: совместная работа, нормальный веб-интерфейс и более простая настройка. Разбираем, зачем это обновление важно и как оно меняет работу с ИИ-агентом.
➡️ Читайте на сайте: https://aff.top/blog/vyshel-openclaw-2-0
🧠 Ещё больше инсайтов → в канале AFF.top
How to decompose a falling win rate into its real causes
Win rate (the share of auctions you enter that you win) dropping is a symptom, not a diagnosis. Decompose it in this order before touching bids.
1. Split win rate into its two factors: bid rate (auctions you choose to bid on) × clear rate (bids that win). A falling number could be either; they have opposite fixes.
2. If bid rate fell, the cause is upstream of the auction — pacing throttled you, budget capped, or a targeting filter tightened. Check your DSP's bid-eligibility logs, not the auction.
3. If clear rate fell, segment by floor exposure. Pull the share of lost auctions where your bid was below the published floor versus lost to a higher competing bid.
4. Floor losses mean a publisher raised floors or moved to first-price; competitive losses mean demand intensified. The first is solved by floor-aware bidding, the second by valuation.
5. Finally, segment competitive losses by margin. Losing by 2% across thousands of auctions is a tractable shading problem; losing by 50% means you've simply mispriced the audience.
Why it matters: Buyers reflexively raise bids when win rate drops, but if the cause was a pacing throttle or a floor change, raising bids just burns margin on auctions you'd have won anyway. The decomposition tells you which of four distinct problems you actually have.
Win rate (the share of auctions you enter that you win) dropping is a symptom, not a diagnosis. Decompose it in this order before touching bids.
1. Split win rate into its two factors: bid rate (auctions you choose to bid on) × clear rate (bids that win). A falling number could be either; they have opposite fixes.
2. If bid rate fell, the cause is upstream of the auction — pacing throttled you, budget capped, or a targeting filter tightened. Check your DSP's bid-eligibility logs, not the auction.
3. If clear rate fell, segment by floor exposure. Pull the share of lost auctions where your bid was below the published floor versus lost to a higher competing bid.
4. Floor losses mean a publisher raised floors or moved to first-price; competitive losses mean demand intensified. The first is solved by floor-aware bidding, the second by valuation.
5. Finally, segment competitive losses by margin. Losing by 2% across thousands of auctions is a tractable shading problem; losing by 50% means you've simply mispriced the audience.
Why it matters: Buyers reflexively raise bids when win rate drops, but if the cause was a pacing throttle or a floor change, raising bids just burns margin on auctions you'd have won anyway. The decomposition tells you which of four distinct problems you actually have.
Forwarded from AFF.TOP - про арбитраж трафика и CPA рынок!
This media is not supported in your browser
VIEW IN TELEGRAM
Павел Дуров анонсировал Gram Wallet
Дуров анонсировал Gram Wallet — нативный некастодиальный криптокошелёк внутри Telegram. Он обещает мгновенные переводы с нулевой комиссией между пользователями и более простые обновления за счёт архитектуры с валидаторами. Запуск уже идёт, а полный релиз ждут в ближайшие недели.
➡️ Читайте на сайте: https://aff.top/blog/pavel-durov-anonsiroval-gram-wallet
🧠 Ещё больше инсайтов → в канале AFF.top
Дуров анонсировал Gram Wallet — нативный некастодиальный криптокошелёк внутри Telegram. Он обещает мгновенные переводы с нулевой комиссией между пользователями и более простые обновления за счёт архитектуры с валидаторами. Запуск уже идёт, а полный релиз ждут в ближайшие недели.
➡️ Читайте на сайте: https://aff.top/blog/pavel-durov-anonsiroval-gram-wallet
🧠 Ещё больше инсайтов → в канале AFF.top
Forwarded from AFF.TOP - про арбитраж трафика и CPA рынок!
This media is not supported in your browser
VIEW IN TELEGRAM
Новые ограничение в Instagram для ИИ-профилей
Instagram ужесточает условия для УБТ: аккаунты помечают как созданные ИИ, а без такой маркировки можно словить теневой бан. Если нейросеть лишь улучшает контент, санкций нет. Для арбитражников это значит, что привычные схемы в FB и Инсте будут работать хуже, а обход антифрода станет сложнее.
➡️ Читайте на сайте: https://aff.top/blog/novye-ogranichenie-v-instagram-dlia-ii-profilei
🧠 Ещё больше инсайтов → в канале AFF.top
Instagram ужесточает условия для УБТ: аккаунты помечают как созданные ИИ, а без такой маркировки можно словить теневой бан. Если нейросеть лишь улучшает контент, санкций нет. Для арбитражников это значит, что привычные схемы в FB и Инсте будут работать хуже, а обход антифрода станет сложнее.
➡️ Читайте на сайте: https://aff.top/blog/novye-ogranichenie-v-instagram-dlia-ii-profilei
🧠 Ещё больше инсайтов → в канале AFF.top
Forwarded from AFF.TOP - про арбитраж трафика и CPA рынок!
This media is not supported in your browser
VIEW IN TELEGRAM
Оборот ChatGPT Ads достиг $1 миллиарда
OpenAI вывела ChatGPT Ads в self-service для Индии, Европы, Ближнего Востока и Северной Африки, а оборот платформы уже достиг $1 млрд. Для арбитража это сигнал присмотреться к новому источнику: трафик из нейронок выглядит горячим, но вход дорогой — CPC в tier-1 GEO около $5, поэтому тестировать стоит точечно и с небольшим бюджетом.
➡️ Читайте на сайте: https://aff.top/blog/oborot-chatgpt-ads-dostig-1-milliarda
🧠 Ещё больше инсайтов → в канале AFF.top
OpenAI вывела ChatGPT Ads в self-service для Индии, Европы, Ближнего Востока и Северной Африки, а оборот платформы уже достиг $1 млрд. Для арбитража это сигнал присмотреться к новому источнику: трафик из нейронок выглядит горячим, но вход дорогой — CPC в tier-1 GEO около $5, поэтому тестировать стоит точечно и с небольшим бюджетом.
➡️ Читайте на сайте: https://aff.top/blog/oborot-chatgpt-ads-dostig-1-milliarda
🧠 Ещё больше инсайтов → в канале AFF.top
Forwarded from AFF.TOP - про арбитраж трафика и CPA рынок!
This media is not supported in your browser
VIEW IN TELEGRAM
Автоматизация в арбитраже трафика: зачем и для кого?
В статье объясняется, какие сервисы автоматизации реально помогают в арбитраже трафика: автозалив, сценарии в антидетект-браузерах и low-code/no-code решения. Главный вывод — автоматизация экономит время и снижает рутину, но не заменяет команду, а ошибки в настройке могут повысить риск бана и лишних затрат.
➡️ Читайте на сайте: https://aff.top/blog/avtomatizaciia-v-arbitrazhe-trafika-zachem-i-dlia-kogo
🧠 Ещё больше инсайтов → в канале AFF.top
В статье объясняется, какие сервисы автоматизации реально помогают в арбитраже трафика: автозалив, сценарии в антидетект-браузерах и low-code/no-code решения. Главный вывод — автоматизация экономит время и снижает рутину, но не заменяет команду, а ошибки в настройке могут повысить риск бана и лишних затрат.
➡️ Читайте на сайте: https://aff.top/blog/avtomatizaciia-v-arbitrazhe-trafika-zachem-i-dlia-kogo
🧠 Ещё больше инсайтов → в канале AFF.top
A migration checklist for re-bidding after an exchange flips to first-price
When an exchange moves from second-price (you pay just above the runner-up) to first-price (you pay exactly what you bid), your old bids overpay instantly. Re-tune in sequence.
1. Confirm the flip with evidence, not the announcement: in second-price your win price clusters below your bid; in first-price win price equals bid. Plot the win-price-to-bid ratio over time and watch it jump to 1.0.
2. Freeze bid changes for 48 hours and log a clean baseline of clearing prices under the new regime.
3. Estimate the clearing-price distribution per segment — the prices at which you actually won. Your new bid should target a percentile of that distribution, not your old second-price bid.
4. Introduce or re-tune bid shading specifically for this exchange; a shader calibrated on second-price history will systematically overpay until it relearns.
5. Validate by watching surplus (bid − clearing price). If surplus is large and stable, lower the target percentile until win rate just begins to soften.
Why it matters: The single most expensive moment in programmatic is the days after a silent first-price flip, when bids tuned for second-price auctions pay their full face value. A disciplined re-baseline-and-shade sequence stops the bleed before it compounds across a month of spend.
When an exchange moves from second-price (you pay just above the runner-up) to first-price (you pay exactly what you bid), your old bids overpay instantly. Re-tune in sequence.
1. Confirm the flip with evidence, not the announcement: in second-price your win price clusters below your bid; in first-price win price equals bid. Plot the win-price-to-bid ratio over time and watch it jump to 1.0.
2. Freeze bid changes for 48 hours and log a clean baseline of clearing prices under the new regime.
3. Estimate the clearing-price distribution per segment — the prices at which you actually won. Your new bid should target a percentile of that distribution, not your old second-price bid.
4. Introduce or re-tune bid shading specifically for this exchange; a shader calibrated on second-price history will systematically overpay until it relearns.
5. Validate by watching surplus (bid − clearing price). If surplus is large and stable, lower the target percentile until win rate just begins to soften.
Why it matters: The single most expensive moment in programmatic is the days after a silent first-price flip, when bids tuned for second-price auctions pay their full face value. A disciplined re-baseline-and-shade sequence stops the bleed before it compounds across a month of spend.
Forwarded from AFF.TOP - про арбитраж трафика и CPA рынок!
This media is not supported in your browser
VIEW IN TELEGRAM
В публичный релиз вышел Fable 5.1
➡️ Читайте на сайте: https://aff.top/blog/v-publichnyi-reliz-vyshel-fable-5-1
🧠 Ещё больше инсайтов → в канале AFF.top
➡️ Читайте на сайте: https://aff.top/blog/v-publichnyi-reliz-vyshel-fable-5-1
🧠 Ещё больше инсайтов → в канале AFF.top
Forwarded from AFF.TOP - про арбитраж трафика и CPA рынок!
This media is not supported in your browser
VIEW IN TELEGRAM
1xBet перестал спонсировать эмоции
История о том, как казахстанцы зарегистрировали рекламный слоган 1xBet, а сам бренд оказался в юридической ловушке: после сделки с TonyBet права на товарный знак так и не выкупили. На фоне ареста активов Романа Семиохина и уголовного дела по азартным играм вывод простой: с 1xBet сейчас лучше не строить рекламные связки на рынке Казахстана.
➡️ Читайте на сайте: https://aff.top/blog/1xbet-perestal-sponsirovat-emocii
🧠 Ещё больше инсайтов → в канале AFF.top
История о том, как казахстанцы зарегистрировали рекламный слоган 1xBet, а сам бренд оказался в юридической ловушке: после сделки с TonyBet права на товарный знак так и не выкупили. На фоне ареста активов Романа Семиохина и уголовного дела по азартным играм вывод простой: с 1xBet сейчас лучше не строить рекламные связки на рынке Казахстана.
➡️ Читайте на сайте: https://aff.top/blog/1xbet-perestal-sponsirovat-emocii
🧠 Ещё больше инсайтов → в канале AFF.top
Forwarded from AFF.TOP - про арбитраж трафика и CPA рынок!
This media is not supported in your browser
VIEW IN TELEGRAM
За продажу аккаунтов в мессенджере теперь грозит статья
С 1 сентября 2026 года продажа аккаунтов соцсетей и мессенджеров в России стала уголовно и административно рискованной: штраф до 700 тысяч рублей, принудительные работы или лишение свободы до 2–3 лет. Если через аккаунт украдут деньги, продавца могут записать в соучастники мошенничества по ст. 159 УК РФ с риском до 10 лет.
➡️ Читайте на сайте: https://aff.top/blog/za-prodazhu-akkauntov-v-messendzhere-teper-grozit-statia
🧠 Ещё больше инсайтов → в канале AFF.top
С 1 сентября 2026 года продажа аккаунтов соцсетей и мессенджеров в России стала уголовно и административно рискованной: штраф до 700 тысяч рублей, принудительные работы или лишение свободы до 2–3 лет. Если через аккаунт украдут деньги, продавца могут записать в соучастники мошенничества по ст. 159 УК РФ с риском до 10 лет.
➡️ Читайте на сайте: https://aff.top/blog/za-prodazhu-akkauntov-v-messendzhere-teper-grozit-statia
🧠 Ещё больше инсайтов → в канале AFF.top
How to stand up log-level analysis from zero — a 6-step setup
Log-level data (one row per bid opportunity, not aggregated reports) is the only ground truth in programmatic. Here's the order to build the pipeline.
1. Request the feed from your DSP and confirm the schema includes, at minimum: auction ID, timestamp, bid, win price, win flag, supply chain object, and deal ID.
2. Land raw files in object storage partitioned by date and hour — never overwrite, you'll need replays when a metric looks wrong.
3. Build one canonical join key. The auction ID ties bid requests to wins; without it you cannot connect a loss to its clearing price.
4. Reconcile against the dashboard first. Sum spend and impressions from logs and confirm they land within 1-2% of the UI. If they diverge wildly, you're missing a partition or double-counting multi-seat bids.
5. Materialize three core daily tables: bids, wins, and losses-with-clearing-price. Most analysis is a query against these.
6. Only now compute derived metrics (win rate, surplus, shave). Derived numbers built on an unreconciled feed are confidently wrong.
Why it matters: Teams rush to dashboards on top of log data before reconciling the raw feed, and then make six-figure decisions on numbers that silently disagree with billing. The reconciliation step is unglamorous and non-negotiable — it's what separates analysis from guessing.
Log-level data (one row per bid opportunity, not aggregated reports) is the only ground truth in programmatic. Here's the order to build the pipeline.
1. Request the feed from your DSP and confirm the schema includes, at minimum: auction ID, timestamp, bid, win price, win flag, supply chain object, and deal ID.
2. Land raw files in object storage partitioned by date and hour — never overwrite, you'll need replays when a metric looks wrong.
3. Build one canonical join key. The auction ID ties bid requests to wins; without it you cannot connect a loss to its clearing price.
4. Reconcile against the dashboard first. Sum spend and impressions from logs and confirm they land within 1-2% of the UI. If they diverge wildly, you're missing a partition or double-counting multi-seat bids.
5. Materialize three core daily tables: bids, wins, and losses-with-clearing-price. Most analysis is a query against these.
6. Only now compute derived metrics (win rate, surplus, shave). Derived numbers built on an unreconciled feed are confidently wrong.
Why it matters: Teams rush to dashboards on top of log data before reconciling the raw feed, and then make six-figure decisions on numbers that silently disagree with billing. The reconciliation step is unglamorous and non-negotiable — it's what separates analysis from guessing.
Forwarded from AFF.TOP - про арбитраж трафика и CPA рынок!
This media is not supported in your browser
VIEW IN TELEGRAM
Google выпустил в релиз Gemini 3.8 flash
Google выпустил Gemini 3.8 Flash спустя две недели после 3.7: модель обещает сильный кодинг и быстрый отклик, а цена остаётся низкой — $0,75 за млн входящих токенов и $3,75 за млн исходящих. Вывод простой: пока Google демпингует, это выгодный вариант для тех, кому нужны дешёвые и быстрые нейросетевые запросы.
➡️ Читайте на сайте: https://aff.top/blog/google-vypustil-v-reliz-gemini-3-8-flash
🧠 Ещё больше инсайтов → в канале AFF.top
Google выпустил Gemini 3.8 Flash спустя две недели после 3.7: модель обещает сильный кодинг и быстрый отклик, а цена остаётся низкой — $0,75 за млн входящих токенов и $3,75 за млн исходящих. Вывод простой: пока Google демпингует, это выгодный вариант для тех, кому нужны дешёвые и быстрые нейросетевые запросы.
➡️ Читайте на сайте: https://aff.top/blog/google-vypustil-v-reliz-gemini-3-8-flash
🧠 Ещё больше инсайтов → в канале AFF.top
Forwarded from AFF.TOP - про арбитраж трафика и CPA рынок!
This media is not supported in your browser
VIEW IN TELEGRAM
Яндекс запустил сервис ПроБлогер
Яндекс запустил ПроБлогер — платформу для монетизации небольших каналов и групп во ВКонтакте, Дзене, Максе, Telegram, YouTube и Rutube. Для модерации нужны от 1000 подписчиков, свежие публикации, статус самозанятого, ИП или юрлица и соблюдение закона. Доход доступен через автопостинг с оплатой за просмотры и партнёрские ссылки; CPM можно задать самому или отдать аукциону.
➡️ Читайте на сайте: https://aff.top/blog/iandeks-zapustil-servis-probloger
🧠 Ещё больше инсайтов → в канале AFF.top
Яндекс запустил ПроБлогер — платформу для монетизации небольших каналов и групп во ВКонтакте, Дзене, Максе, Telegram, YouTube и Rutube. Для модерации нужны от 1000 подписчиков, свежие публикации, статус самозанятого, ИП или юрлица и соблюдение закона. Доход доступен через автопостинг с оплатой за просмотры и партнёрские ссылки; CPM можно задать самому или отдать аукциону.
➡️ Читайте на сайте: https://aff.top/blog/iandeks-zapustil-servis-probloger
🧠 Ещё больше инсайтов → в канале AFF.top
Forwarded from AFF.TOP - про арбитраж трафика и CPA рынок!
This media is not supported in your browser
VIEW IN TELEGRAM
Google ads упростил перенос креативов из Asset Studio
➡️ Читайте на сайте: https://aff.top/blog/google-ads-uprostil-perenos-kreativov-iz-asset-studio
🧠 Ещё больше инсайтов → в канале AFF.top
➡️ Читайте на сайте: https://aff.top/blog/google-ads-uprostil-perenos-kreativov-iz-asset-studio
🧠 Ещё больше инсайтов → в канале AFF.top
How to reverse-engineer a publisher's hidden floor price
Publishers rarely disclose floors (the minimum bid an SSP will accept), but you can infer them from your own loss data. Here's the procedure.
1. For a single publisher-SSP pair, pull every auction where you lost AND every auction where you won, with your bid on each.
2. Plot win rate as a function of your bid. Below the floor, win rate is flat at zero no matter how the bid varies within that band.
3. The bid value where win rate lifts off zero is the soft floor estimate. Below it, no bid clears regardless of competition.
4. Repeat by hour of day and by device. Many publishers run dynamic floors that flex with demand, so a single number hides the real structure.
5. Cross-check against unified pricing rules if the SSP runs them — a flat hard floor produces a sharp cliff; a dynamic floor produces a soft ramp.
6. Bid just above the inferred floor on price-sensitive segments and let valuation drive the rest.
Why it matters: Bidding blind into an unknown floor wastes auctions you can never win and overpays on ones you'd win cheaply. Reconstructing the floor curve from your own loss logs turns the publisher's private pricing into a map you can navigate — no SSP cooperation required.
Publishers rarely disclose floors (the minimum bid an SSP will accept), but you can infer them from your own loss data. Here's the procedure.
1. For a single publisher-SSP pair, pull every auction where you lost AND every auction where you won, with your bid on each.
2. Plot win rate as a function of your bid. Below the floor, win rate is flat at zero no matter how the bid varies within that band.
3. The bid value where win rate lifts off zero is the soft floor estimate. Below it, no bid clears regardless of competition.
4. Repeat by hour of day and by device. Many publishers run dynamic floors that flex with demand, so a single number hides the real structure.
5. Cross-check against unified pricing rules if the SSP runs them — a flat hard floor produces a sharp cliff; a dynamic floor produces a soft ramp.
6. Bid just above the inferred floor on price-sensitive segments and let valuation drive the rest.
Why it matters: Bidding blind into an unknown floor wastes auctions you can never win and overpays on ones you'd win cheaply. Reconstructing the floor curve from your own loss logs turns the publisher's private pricing into a map you can navigate — no SSP cooperation required.
A step-by-step audit of sellers.json and the supply chain object
sellers.json (the SSP's public list of who it's authorized to sell for) and the SChain object (the path an impression traveled) are your fraud and resell detectors. Audit them like this.
1. For each impression in your logs, parse the SChain object into its ordered list of nodes — each node is one intermediary that touched the impression.
2. Count nodes per path. A direct path has one node; every extra node is a reseller taking margin and adding latency.
3. Cross-reference each node's seller ID against that SSP's published sellers.json. A node not listed, or listed as CONFIDENTIAL with no domain, is a flag.
4. Match the final declared domain against ads.txt for that publisher. If the publisher's ads.txt doesn't authorize the SSP at the chain's head, the inventory is unauthorized resale.
5. Tally spend flowing through paths with 3+ nodes or any unverified seller ID. That's your at-risk budget.
6. Reroute that spend to the shortest verified path to the same domain, found in step 1.
Why it matters: Long, unverified supply chains are where margin leaks and made-for-advertising inventory hide. A mechanical SChain-vs-sellers.json-vs-ads.txt cross-check is the cheapest fraud control you own, and it runs entirely on data you already receive — no third-party verification vendor required to start.
sellers.json (the SSP's public list of who it's authorized to sell for) and the SChain object (the path an impression traveled) are your fraud and resell detectors. Audit them like this.
1. For each impression in your logs, parse the SChain object into its ordered list of nodes — each node is one intermediary that touched the impression.
2. Count nodes per path. A direct path has one node; every extra node is a reseller taking margin and adding latency.
3. Cross-reference each node's seller ID against that SSP's published sellers.json. A node not listed, or listed as CONFIDENTIAL with no domain, is a flag.
4. Match the final declared domain against ads.txt for that publisher. If the publisher's ads.txt doesn't authorize the SSP at the chain's head, the inventory is unauthorized resale.
5. Tally spend flowing through paths with 3+ nodes or any unverified seller ID. That's your at-risk budget.
6. Reroute that spend to the shortest verified path to the same domain, found in step 1.
Why it matters: Long, unverified supply chains are where margin leaks and made-for-advertising inventory hide. A mechanical SChain-vs-sellers.json-vs-ads.txt cross-check is the cheapest fraud control you own, and it runs entirely on data you already receive — no third-party verification vendor required to start.