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Putting the publisher in the quarterback spot

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Publishers can finally take the offensive position in this new data-driven market. Justin Manes, COO of Alphabird, presents a no-nonsense play-by-play on the current RTB market, and will speak openly about the shift and repositioning of the publisher's role in it.

Presenter: Justin Manes, COO, Alphabird

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Putting the publisher in the quarterback spot

  1. 1. 2013 Publisher Solutions ContentConnect: Smart CSM and ETL We Help Publishers: YieldConnect: 1. Bring in more visitors to… AudianceConnect: PUBLISHER A SSP 1 eCPM (AudianceConnect) D AD Unit SSP 2C • Early, Look eCPMPC • Early, Filtration S SSP 3 2. Optimize content delivery • Early, Data ETRA > R V eCPM SSP 4 (ContentConnect)F Dsad asdasd asd asdadsdas E eCPMFI dasdasdasd asdas a asdas asd a asd adas asdas ad asd asd ada asfad dfdsfd sdfd sdf sdfds sd R SSP 5 3. Optimize AD revenueC eCPM (YieldConnect) We use data to drive the DataConnect: other three things. • Actionable Visitor Data. (DataConnect) • Reverse Re-Targeting.
  2. 2. 2013 Publisher Solutions One Visitor PUBLISHER P U Entry = Google AD B“Football Scores” • Web Unit A D • Mobile Phone S • Mobile Tablet E R • TV? V E R
  3. 3. 2013 Publisher Solutions YieldConnect STANDARD DATA PASS TO BIDDERS; GEO, IP, Browser, Referring URL, DMA, ETC… One Visitor PUBLISHER S P U Entry = Google AD U T P A B Direct DSP DSP SSP SSP AdNet“Football Scores” Unit E G A & 1 2 1 2 • Web R Floors RTB RTB HIST HIST FIXED D • Mobile Phone S • Mobile Tablet E R • TV? V E R
  4. 4. 2013 Publisher Solutions YieldConnect STANDARD DATA PASS TO BIDDERS; GEO, IP, Browser, Referring URL, DMA, ETC… One Visitor PUBLISHER S P U Entry = Google AD U T P A B Direct DSP DSP SSP SSP AdNet“Football Scores” Unit E G A & 1 2 1 2 • Web R Floors RTB RTB HIST HIST FIXED D • Mobile Phone S • Mobile Tablet E R LAST LOOK • TV? V E R CALC EST EST ?? $15 $1 ~$2 ~$3 ~$2 $8 NIKE.COM FORD.COM 2nd Price Auction. Floors, Winner, Data Pass. Auction / Choose Winner (Assume never seen before):  Determine ‘LAST LOOK’ bid entry based on data trading logic. (Other common terms; ‘Soft Floors’, ‘Dynamic Floors’, etc…) • Look for previous user data. If yes evaluate rules on direct sold. If no proceed with price optimization and reverse re-targeting. • If $14.99 = DSP 1 as winner, pays full $15, max rev, but has long term ‘burn’ risk to bidder relations. • If nothing bid = DSP1 as winner, pays $3.01, min rev • If $8 = DSP 1 as winner, pays $8.01, uplifted rev, bidder is remains under max and ‘burn’ risk is mitigated. SELECT AND EXECUTE AD.
  5. 5. 2013 Publisher Solutions YieldConnect STANDARD DATA PASS TO BIDDERS; GEO, IP, Browser, Referring URL, DMA, ETC… Messaging and Data: Publisher Owned One Visitor PUBLISHER Data S P U Post trade messaging and data activity Entry = Google U T B • Send bidders auction results and logic string AD P A Direct DSP DSP SSP SSP AdNet“Football Scores” Unit E G A & 1 2 1 2 • Web R Floors RTB RTB HIST HIST FIXED • Cookie visitor and append/add data: D • Mobile Phone S • • (STANDARD PARAMS = TOP 300X250 Mobile Tablet E R LAST LOOK • TV? V E R DATE, TIME, GEO, ETC.) • (SEARCH RETARGET = ‘NFL SCHEDUEL’) CALC EST EST ?? $15 $1 ~$2 ~$3 ~$2 $8 NIKE.COM FORD.COM • (PAGE CONTEXT = ‘FOOTBALL’S WEEK TOP 2nd Price Auction. Floors, Winner, Data Pass. Auction / Choose Winner (Assume never seen before):  Determine ‘LAST LOOK’ bid entry based on data trading logic. (Other common terms; ‘Soft Floors’, ‘Dynamic Floors’, etc…) • Look for previous user data. If yes evaluate rules on direct sold. If no proceed with price optimization and reverse re-targeting. STORIES’) • If $14.99 = DSP 1 as winner, pays full $15, max rev, but has long term ‘burn’ risk to bidder relations. • If nothing bid = DSP1 as winner, pays $3.01, min rev • (AD = HIGH PROB BRAND • If $8 = DSP 1 as winner, pays $8.01, uplifted rev, bidder is remains under max and ‘burn’ risk is mitigated. SELECT AND EXECUTE AD. RETARGET, SportsWear, RTB BID=$15, PAID CALC 2ND=$8, RTB2(Auto)=$1, SSP1=$3, SSP2=$2, NET=$ Messaging and Data:  Post trade messaging and data activity Publisher Owned 3) • Send bidders auction results and logic string • Cookie visitor and append/add data: Data • IF OTHER ADS ON THE PAGE SIMILAR DATA • (STANDARD PARAMS = TOP 300X250 DATE, TIME, GEO, ETC.) • (SEARCH RETARGET = ‘NFL SCHEDUEL’) • (PAGE CONTEXT = ‘FOOTBALL’S WEEK TOP STORIES’) • And append that same data for each page of the user’s • (AD = HIGH PROB BRAND RETARGET, NIKE, RTB BID=$15, PAID CALC 2ND=$8, RTB2(Ford)=$1, SSP1=$3, SSP2=$2, NET=$3) • IF OTHER ADS ON THE PAGE SIMILAR DATA experience and for each return visit until the user clears • And append that same data for each page of the user’s experience and for each return visit until the user clears cache. cache. • Other 1st and 3rd party data may be appended to. • On winning transactions on or off network other data would be collected, including DR and CPA results. • In the case of DR and CPA results a true value score would be applied to the individual, and subsequently used to identify look-a- likes.
  6. 6. 2013 Publisher Solutions YieldConnect STANDARD DATA PASS TO BIDDERS; GEO, IP, Browser, Referring URL, DMA, ETC… Brand Sales AlphaBird and/or Publisher find an One Visitor PUBLISHER S P advertiser who is interested in U buying against the collected data. Entry = Google AD U T P A B Direct DSP DSP SSP SSP AdNet In an attribution model.“Football Scores” Unit E G A & 1 2 1 2 • Web R Floors RTB RTB HIST HIST FIXED D • Mobile Phone S • Mobile Tablet E R LAST LOOK • TV? V E R CALC EST EST DPM ?? $15 $1 ~$2 ~$3 ~$2 Attribution Modeling $8 NIKE.COM FORD.COM Data collection source is determined and other data is contributed: • AB/Pubs data 2nd Price Auction. Floors, Winner, Data Pass. Auction / Choose Winner (Assume never seen before):  Determine ‘LAST LOOK’ bid entry based on data trading logic. (Other common terms; ‘Soft Floors’, ‘Dynamic Floors’, etc…) • Look for previous user data. If yes evaluate rules on direct sold. If no proceed with price optimization and reverse re-targeting. • Buyers data • Neilson data • If $14.99 = DSP 1 as winner, pays full $15, max rev, but has long term ‘burn’ risk to bidder relations. • If nothing bid = DSP1 as winner, pays $3.01, min rev In most cases this is where attribution • If $8 = DSP 1 as winner, pays $8.01, uplifted rev, bidder is remains under max and ‘burn’ risk is mitigated. SELECT AND EXECUTE AD. modeling and predictive analytics would occur. As ads are displayed and actions recorded all systems receive Messaging and Data:  Post trade messaging and data activity Publisher Owned feedback loop data. Attributes grow in depth, breadth, and begin • Send bidders auction results and logic string • Cookie visitor and append/add data: Data to achieve value. Pricing and segments are modified • (STANDARD PARAMS = TOP 300X250 DATE, TIME, GEO, ETC.) according to results. • (SEARCH RETARGET = ‘NFL SCHEDUEL’) • (PAGE CONTEXT = ‘FOOTBALL’S WEEK TOP STORIES’) • (AD = HIGH PROB BRAND RETARGET, NIKE, RTB BID=$15, PAID CALC 2ND=$8, RTB2(Ford)=$1, SSP1=$3, SSP2=$2, NET=$3) • IF OTHER ADS ON THE PAGE SIMILAR DATA • And append that same data for each page of the user’s experience and for each return visit until the user clears cache. • Other 1st and 3rd party data may be appended to. • On winning transactions on or off network other data would be collected, including DR and CPA results. • In the case of DR and CPA results a true value score would be applied to the individual, and subsequently used to identify look-a- likes.
  7. 7. 2013 Publisher Solutions YieldConnect STANDARD DATA PASS TO BIDDERS; GEO, IP, Browser, Referring URL, DMA, ETC… Brand Sales AlphaBird and/or Publisher find an One Visitor PUBLISHER S P advertiser who is interested in U buying against the collected data. Entry = Google AD U T P A B Direct DSP DSP SSP SSP AdNet In an attribution model.“Football Scores” Unit E G A & 1 2 1 2 • Web R Floors RTB RTB HIST HIST FIXED D • Mobile Phone S • Mobile Tablet E R LAST LOOK • TV? V E R CALC EST EST DPM DSP / ?? $15 $1 ~$2 ~$3 ~$2 Attribution Modeling $8 NIKE.COM FORD.COM Data collection source is determined and other data is contributed: Bidder (machine and attribution functions • AB/Pubs data 2nd Auction / Choose Winner (Assume never seen before): Price Auction. Floors, Winner, Data Pass.  Determine ‘LAST LOOK’ bid entry based on data trading logic. (Other common terms; ‘Soft Floors’, ‘Dynamic Floors’, etc…) • Look for previous user data. If yes evaluate rules on direct sold. If no proceed with price optimization and reverse re-targeting. • Buyers data • Neilson data could exist here in some cases) Bidder sets starter criteria and • If $14.99 = DSP 1 as winner, pays full $15, max rev, but has long term ‘burn’ risk to bidder relations. pricing and bids across many • If nothing bid = DSP1 as winner, pays $3.01, min rev In most cases this is where attribution publishers looking for these same • If $8 = DSP 1 as winner, pays $8.01, uplifted rev, bidder is remains under max and ‘burn’ risk is mitigated. SELECT AND EXECUTE AD. modeling and predictive analytics users. And users that have these would occur. same qualities, (look-a-likes). As ads are displayed and actions recorded all systems receive Messaging and Data:  Post trade messaging and data activity Publisher Owned feedback loop data. Attributes grow in depth, breadth, and begin • Send bidders auction results and logic string • Cookie visitor and append/add data: Data to achieve value. Pricing and segments are modified • (STANDARD PARAMS = TOP 300X250 DATE, TIME, GEO, ETC.) according to results. • (SEARCH RETARGET = ‘NFL SCHEDUEL’) • (PAGE CONTEXT = ‘FOOTBALL’S WEEK TOP STORIES’) • (AD = HIGH PROB BRAND RETARGET, NIKE, RTB BID=$15, PAID CALC 2ND=$8, RTB2(Ford)=$1, SSP1=$3, SSP2=$2, NET=$3) • IF OTHER ADS ON THE PAGE SIMILAR DATA • And append that same data for each page of the user’s experience and for each return visit until the user clears cache. • Other 1st and 3rd party data may be appended to. • On winning transactions on or off network other data would be collected, including DR and CPA results. • In the case of DR and CPA results a true value score would be applied to the individual, and subsequently used to identify look-a- likes.
  8. 8. 2013 Publisher Solutions $ $ $ $$ YieldConnect Brand Sales $ $ $ $ $ $$ STANDARD DATA PASS TO BIDDERS; GEO, IP, Browser, Referring URL, DMA, ETC… $$ $ $ One Visitor PUBLISHER S P U AlphaBird and/or Publisher find an advertiser who is interested in buying against the collected data. $ $$$ $ $$ $ $ $ Entry = Google U T $ $ $ $$ AD P A B Direct DSP DSP SSP SSP AdNet In an attribution model.“Football Scores” Unit E G A & 1 2 1 2 • Web $ $$$ $ R D Floors RTB RTB HIST HIST FIXED $ • Mobile Phone $ S • Mobile Tablet E $ R LAST LOOK • TV? V E R CALC EST EST DPM DSP / Approved ?? $15 $1 ~$2 ~$3 ~$2 Attribution Modeling $8 NIKE.COM FORD.COM Data collection source is determined and other data is Bidder Off Network contributed: 2nd Auction / Choose Winner (Assume never seen before): Price Auction. Floors, Winner, Data Pass.  Determine ‘LAST LOOK’ bid entry based on data trading logic. (Other common terms; ‘Soft Floors’, ‘Dynamic Floors’, etc…) • AB/Pubs data • Buyers data (machine and attribution functions could exist here in some cases) Publishers: • Look for previous user data. If yes evaluate rules on direct sold. If no proceed with price optimization and reverse re-targeting. • Neilson data • If $14.99 = DSP 1 as winner, pays full $15, max rev, but has long term ‘burn’ risk to bidder relations. • If nothing bid = DSP1 as winner, pays $3.01, min rev In most cases this is where attribution Bidder sets starter criteria and pricing and bids across many (Many) publishers looking for these same • If $8 = DSP 1 as winner, pays $8.01, uplifted rev, bidder is remains under max and ‘burn’ risk is mitigated. SELECT AND EXECUTE AD. modeling and predictive analytics users. And users that have these would occur. Each ad delivered would return data same qualities, (look-a-likes). similar what AB is capturing. As ads are displayed and actions Would be missing auction results recorded all systems receive unless AB was also the SSP for that Messaging and Data:  Post trade messaging and data activity Publisher Owned feedback loop data. Attributes grow in depth, breadth, and begin pub. • Send bidders auction results and logic string • Cookie visitor and append/add data: Data to achieve value. Pricing and segments are modified Where an ad was served there • (STANDARD PARAMS = TOP 300X250 DATE, TIME, GEO, ETC.) would also be returned to all according to results. systems a record of • (SEARCH RETARGET = ‘NFL SCHEDUEL’) • (PAGE CONTEXT = ‘FOOTBALL’S WEEK TOP STORIES’) CLICK, DR, CPA type data. This • (AD = HIGH PROB BRAND RETARGET, NIKE, RTB BID=$15, PAID CALC 2ND=$8, RTB2(Ford)=$1, SSP1=$3, SSP2=$2, type of data would then result in NET=$3) true value scoring on the individual • IF OTHER ADS ON THE PAGE SIMILAR DATA user. And would inform look-a-like • And append that same data for each page of the user’s experience and for each return visit until the user clears cache. methods. Justin Manes • Other 1st and 3rd party data may be appended to. • COO On winning transactions on or off network other data would be collected, including DR and CPA results. • In the case of DR and CPA results a true value score would be applied to the individual, and subsequently used to identify look-a- AlphaBird likes. “Football Scores”

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