Why "just bid your true value" stopped being correct
Classic auction theory says a second-price (Vickrey) auction is truthful (your best strategy is to bid exactly what the impression is worth to you). Programmatic broke that guarantee, and buyers who still believe it overpay.
Why truthful bidding held in theory:
— In a sealed second-price auction, you pay the runner-up's bid, not your own. Raising your bid can only win more without changing what you pay; lowering it can only lose auctions you'd have profited from. So bidding your true value is optimal.
Why programmatic violated every assumption:
— The open exchange is first-price now: you pay your own bid, so over-bidding directly costs you. Truthful bidding is no longer optimal — shading is.
— Even where second-price survives, dynamic floors mean your bid influences the floor you'll face later, breaking the "bid can't affect price" assumption.
— Multiple parallel auctions for the same impression (header bidding plus the ad server) mean you're not in one sealed auction but a chain, where strategy in one stage affects another.
— Reserve prices and soft floors add price-setting levers the textbook auction doesn't have.
The practical stance: there is no single dominant strategy across programmatic. The correct bid depends on the path's pricing rule, its floor dynamics, and how censored your data is.
Why it matters: the elegant truthful-bidding result assumed a clean, single, static second-price auction. Real supply is first-price, multi-stage, and adaptive — so the only safe assumption is that no setting transfers unchanged from the textbook to the bidstream.
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В @B2BLabReport такого linkedin algorithm ещё много
Classic auction theory says a second-price (Vickrey) auction is truthful (your best strategy is to bid exactly what the impression is worth to you). Programmatic broke that guarantee, and buyers who still believe it overpay.
Why truthful bidding held in theory:
— In a sealed second-price auction, you pay the runner-up's bid, not your own. Raising your bid can only win more without changing what you pay; lowering it can only lose auctions you'd have profited from. So bidding your true value is optimal.
Why programmatic violated every assumption:
— The open exchange is first-price now: you pay your own bid, so over-bidding directly costs you. Truthful bidding is no longer optimal — shading is.
— Even where second-price survives, dynamic floors mean your bid influences the floor you'll face later, breaking the "bid can't affect price" assumption.
— Multiple parallel auctions for the same impression (header bidding plus the ad server) mean you're not in one sealed auction but a chain, where strategy in one stage affects another.
— Reserve prices and soft floors add price-setting levers the textbook auction doesn't have.
The practical stance: there is no single dominant strategy across programmatic. The correct bid depends on the path's pricing rule, its floor dynamics, and how censored your data is.
Why it matters: the elegant truthful-bidding result assumed a clean, single, static second-price auction. Real supply is first-price, multi-stage, and adaptive — so the only safe assumption is that no setting transfers unchanged from the textbook to the bidstream.
—
В @B2BLabReport такого linkedin algorithm ещё много
Bid shading is a feedback loop, not a discount
When the industry moved to first-price auctions (you pay exactly what you bid, not the runner-up's price), DSPs introduced bid shading: the DSP lowering your bid toward the expected clearing price so you don't massively overpay.
1. The shading model predicts a clearing price from recent win/loss data for similar inventory.
2. It submits a shaded bid below your stated value.
3. The outcome (win or loss, and at what price) feeds back into the model.
Here is the part most buyers miss. The model trains on its own shaded bids. If it shades too aggressively and loses, it never observes the true clearing price for that auction — it only sees a loss. That censored data biases the model toward thinking inventory clears lower than it does, which encourages more shading, which produces more losses on contested impressions.
The correction is exploration: a small fraction of bids submitted unshaded or lightly shaded specifically to observe real clearing prices and recalibrate. Vendors who skip exploration get a model that drifts confidently in the wrong direction.
Why it matters: when your win rate quietly erodes on premium supply but your CPMs look great, suspect a shading model that has stopped exploring. Ask your DSP what percentage of traffic it reserves for price discovery.
When the industry moved to first-price auctions (you pay exactly what you bid, not the runner-up's price), DSPs introduced bid shading: the DSP lowering your bid toward the expected clearing price so you don't massively overpay.
1. The shading model predicts a clearing price from recent win/loss data for similar inventory.
2. It submits a shaded bid below your stated value.
3. The outcome (win or loss, and at what price) feeds back into the model.
Here is the part most buyers miss. The model trains on its own shaded bids. If it shades too aggressively and loses, it never observes the true clearing price for that auction — it only sees a loss. That censored data biases the model toward thinking inventory clears lower than it does, which encourages more shading, which produces more losses on contested impressions.
The correction is exploration: a small fraction of bids submitted unshaded or lightly shaded specifically to observe real clearing prices and recalibrate. Vendors who skip exploration get a model that drifts confidently in the wrong direction.
Why it matters: when your win rate quietly erodes on premium supply but your CPMs look great, suspect a shading model that has stopped exploring. Ask your DSP what percentage of traffic it reserves for price discovery.
Measuring supply path quality with entropy
Supply path optimization (SPO) is choosing the fewest, cleanest routes from your DSP to a given publisher instead of buying the same impression through five resellers. Most teams audit SPO by counting paths. A sharper tool is borrowed from information theory: entropy.
1. For one publisher domain, list every seller path you won impressions through and the share of spend each carries.
2. Compute the spend distribution's concentration. One dominant path equals low entropy; spend evenly smeared across many resellers equals high entropy.
3. High entropy for a single domain is a red flag — it means you are buying the same audience through many intermediaries, each taking a cut and each a place where a bid request could be misrepresented.
Why entropy beats a raw path count: a publisher legitimately sold through two SSPs (an exchange that represents publisher inventory) is fine. The same publisher appearing across nine paths with spend spread thin is reseller sprawl, even though both are 'multiple paths.'
The action is to collapse toward the lowest-entropy route that still gives you scale — usually the direct or near-direct seller with the highest declared sellers.json relationship.
Why it matters: entropy turns a vague 'we should consolidate paths' into a ranked list of domains where consolidation recovers the most margin and removes the most spoofing surface.
Supply path optimization (SPO) is choosing the fewest, cleanest routes from your DSP to a given publisher instead of buying the same impression through five resellers. Most teams audit SPO by counting paths. A sharper tool is borrowed from information theory: entropy.
1. For one publisher domain, list every seller path you won impressions through and the share of spend each carries.
2. Compute the spend distribution's concentration. One dominant path equals low entropy; spend evenly smeared across many resellers equals high entropy.
3. High entropy for a single domain is a red flag — it means you are buying the same audience through many intermediaries, each taking a cut and each a place where a bid request could be misrepresented.
Why entropy beats a raw path count: a publisher legitimately sold through two SSPs (an exchange that represents publisher inventory) is fine. The same publisher appearing across nine paths with spend spread thin is reseller sprawl, even though both are 'multiple paths.'
The action is to collapse toward the lowest-entropy route that still gives you scale — usually the direct or near-direct seller with the highest declared sellers.json relationship.
Why it matters: entropy turns a vague 'we should consolidate paths' into a ranked list of domains where consolidation recovers the most margin and removes the most spoofing surface.