1. Publisher
    1. Single Lifetime Profile in Marketplace = single Block in the Blockchain = #Address WEBSITE: brandvertisor.com/website/CNN.com = CNN-pub:website-#Address APP: brandverisor.com/app/gameX.app = gameX-pub:app-#Address Influencer: brandvertisor.com/influencer/Gary-Vee = GaryVee-pub:Influencer-#Address Network: brandvertisor.com/network/GDN = GND-pub:Network-#Address AR/VR: brandvertisor.com/AR/GlassesY = GlassesY-Pub:AR-#Address IoT: brandvertisor.com/IoT/RefrigeratorX = RefrigeratorX-Pub:IoT-#Adress Service/Solutions/AdTech Providers: Brandvertisor.com/Service/Solution/AdTech/AppNexus = * AppNexus-service:programmatic-#Address * AppNexus-solution:RTB-#Adress * AppNexus-AdTech:Header-Bidding-#Address
      1. Block content storage :
        1. Merkle tree of Publisher name & publisher type ? * CNN-Publisher:Website-#Address
          1. STATIC AGGREGATED/IN-HOUSE DATA:
          2. I. Merkle of Public Gathered/Aggregated Data: 1. Rankings & Traffic Statistics: Alexa, Quantcast, SimilarWeb, SemRush, Majestic 2. Competition analysis: WhatRunsWhere, SpyAds, iSpionage, Compare Ads, AdBeat
          3. II. Merkle of 3rd Party Data: 1. Publisher 1st Party synchronyzed Data: * Google Analytics, Piwik etc., * existence RTB infrastructure : SSP, Header Bidding Data 2. API access to: * Major DMPs, SaaS Tools & Traffic Analytics Data providers access * Programmatic networks 3. ADS.TXT Data: Publisher IDs Validator & Aggregator 4. Post-cookie advertising: by IP audience & interests targeting DMPs
          4. III. Merkle of Global API Standartization Data of Ad Delivery infrastructures/ecosystems: (PUBLIC API STORED/MANAGED ACCESS & PARTICIPANTS VERIFICATION) ADTECH INTEGRATION WITH BLOCKCHAIN INFRASTRUCTURES: 1. Global API Standartization: AdTech crossing Blockchain infrastructures: * Major AdTech high frequency ad delivery providers: AppNexus, GDN, OpenX etc. * AppNexus-AdTech:HighFrequency-#Address * Major Blockchain AdTech high frequency ad delivery/click fraud providers: Papyrus.global, Adex, AdToken, Xchng.io, AEthernity, Hashgraph
          5. IV. 1st Party Brandvertisor Marketplace Data: 1. Campaigns Data: A.) DSP White Label Provider Campaign Data: * Impressions, Clicks, Conversions * Rates: CPM, CPC, CPA, CPS * Campaign analytics: CTR, ROI B.) Open Source Header Bidding in-house Campaign Data: * Impressions, Clicks, Conversions * Rates: CPM, CPC, CPA, CPS * Campaign analytics: CTR, ROI 2. Blockchain Transaction Data: A.) DSP Providers Transaction Data: * Payment * Transaction details: when, how much, each middlemen party accepted answers/got paid etc DSP > Programmatic Network > SSP > Publisher B.) Open Source Header Bidding in-house infrastructure: * Payment * Transaction details: 2 sides accepted Advertiser > HB in-house 7 % > Publisher 3. Open channels & Oracles Data: * Accepted Answers: accepted transaction, enough ad inventory, accepted CPM rates etc * Unaccepted Answers: higher bidding by else participant, not enough inventory, wrong audience, different contextual interests etc.
          6. V. Merkle of Transactions based Feedback / Reviews: 1. After finished transaction : * Advertiser feedback for publisher traffic performance with > 4 ratings based on: Support, traffic quality, speed of delivery, audience targeting report, future cooperation interest? etc > Text written review *Publisher feedback for advertiser performance: > ratings based on: communication, advertiser creatives targeting, audience matching, future cooperation interest? > Text written review
          7. DYNAMIC ACTIONABLE DATA:
          8. I. Merkle of in-house Cross-matched & Machine Learning = Executable Data 1. Cross-Matched synchronized data from multiple sources for same Publisher: * Categories Cross-Matching: Alexa*iAB advertising categories*SimilarWeb*SemRush * Alexa Ranking <> Internal Brandvertisor Users/Moderator Ranking <> SimilarWeb Ranking * DMP data for PublisherX Audiene <> Alexa/SimilarWeb PublisherX Audience * Multiple 3rd Party PublisherX Audience*Conversion*ROI <> Internal campaigns for PublisherX Vertical*ROI 2. Simplify decision making /advanced search/ process: * By Vertical: Cross-matching and algorithms organized >> clarity the ecosystem by industry * By Audience: Best monetization for that Audience/ Best Verticals for that Audience * By Ad Delivery: Quality of Traffic & Price comparison 3. Actionable Data processing: *** Cross matching Vertical*Audience*Vertical*Creatives formulas + constant machine learning algorhitms > constant ecosystem clarity and growth of the value delivery players.
  2. Transaction
    1. Transaction Marketplace Steps:
      1. I. ADVERTISER Browse Context Categories with Publishers Contextual Search Engine with Publishers Tags ("startup magazines, beauty blogs, crypto influencers")
        1. II. Contextual listings with Publisher Profiles: 1. Sort by traffic rankings: Alexa, Quantcast, Brandvertisor moderator, Brandvertisor advertisers ranking 2. Sort by Audience: * Sex, Age, GEO, Language
          1. III. Browse Profile: 1. Traffic Statistics & Rankings 2. Competition Analysis 3. AdTech Infrastructures comparison: * DSP Pricing comparison * Header Bidding deals * DSP vs Header Bidding RTB comparison
          2. IV. Brandvertisor Ad Delivery Dashboard: 1.Infrastructure & Integrations: * White Label DSP vs Brandvertisor In-House DSP * Publisher Header Bidding/SSP Infrastructure * Brandvertisor In-House Header Bidding solution
          3. V. Brandvertisor cross-advertising-data campaigns data: 1. Campaign process: Campaign details + DMP > Programmatic Networks/SSP > Clickfraud > Brandvertisor Dashboard campaign CTR, ROI storage
          4. VI. Transaction/Campaign Feedback & Review made by Advertiser: 1. Advertiser feedback for publisher traffic performance with > 4 ratings based on: Support, traffic quality, speed of delivery, audience targeting report, future cooperation interest? etc > Text written review
          5. VI. Transaction/Campaign Feedback & Review made by Publisher: 1. Publisher feedback for advertiser performance: > ratings based on: communication, advertiser creatives targeting, audience matching, future cooperation interest? > Text written review
          6. VIII. Advertisers / Publishers ratings/rankings: 1. Public ratings from KNOWN Name-Pub/Adv Type-#Address Public Ratings will bring trust like in Facebook likes/shares by known friends/partners * Advertisers rate Publishers * Publishers rate Advertisers * Advertisers rate Service/Solutions Providers * Service/Solutions Providers rate Publishers * Publishers rate Solutions Providers
          7. IX. GAMIFICATION: 1. Transactions Marketing: Name-based-#Addresses will bring interest in both Advertisers and Publishers to process better quality traffic/ROI campaigns and to decentralize their contracts: CNN-website-#Address <> McDonalds-Brand-#Address CNN-website-#Address <> Small_Brand1-Brand-#Address CNN-website-#Address <> Small_Brand2-Brand-#Address 2. Long term & Loyalty partnerships discounts: * Clear & easy to visualise long term discount strategy: order 1 > order 2 (10 %) > order 3 (12 %) > order 3 (free service) etc. 3. Marketplace & AE Ratings & Rankings publicity & constant status updates gamification: * More ratings > more #Address awareness > more clients > more ratings
          8. Merkle of Ad Delivery Processing Data Oracles answers by DSP/Header Bidding ad delivery processes:
          9. Ad Delivery Infrastructure & Pricing
          10. Brandvertisor DSP = 3 %
          11. DSP = White Label Provider = 5-10 % (white label, in-house)
          12. Programmatic Networks = 10-30 % (AppNexus, OpenX, GDN)
          13. SSP = 5-10 %
          14. Publisher
          15. Brandvertisor HB Pricing = 7 %
          16. Header Bidding (Open Source Infrastructure)
          17. Publisher
  3. Advertiser
    1. Single Profile in Marketplace = single Block in the Blockchain = #Address Marketer-Media Buyer: brandvertisor.com/marketer/Neil Patel = Neil-Patel-#Address Agency: brandvertisor.com/agency/Publicis = Publicis-#Address Brand: brandvertisor.com/brand/Unilever = Unilever-#Address Influencer as advertiser: brandvertisor.com/adv/influencer/Gary-Vee = GaryVee-adv-#Address ?!? AdTech Partnerships: brandvertisor.com/adtech/AppNexus = AppNexus-Adtch-#Address Ad Networks: brandvertisor.com/networks/GDN = GDN-adv-#Address
      1. Block Content Storage:
        1. Merkle tree of Advertiser name & publisher type ? * McDonalds-Advertiser:Brand-#Address
          1. STATIC AGGREGATED/IN-HOUSE DATA:
          2. I. Merkle of Public Gathered/Aggregated Data: 1. Public Research listings of marketing/advertising Services & Solutions Providers: * Yearly prognosis & rankings providers, Luma Partners, Forrester, Nielsen, iAB rankings etc. 2. Brands Research Data: * Brand competitive analysis, yearly reports, selling countries coverage, local competition etc. 3. Brand Social influencing: * B2C: Twitter , Facebook, blogs content analysis /curated content trend/ * B2B: Linkedin employees analysis 4. Brand Industry Analysis Public Data: * Industry leaders research yearly reports, country industry researches surveys and research reports * Industry Trends & Best Practices: * Follow & analyse industry experts CMO, CEO, COO, industry leaders interviews, industry leaders surveys 5. Matching by public suggested best marketig/advertising practices: * Brandsafe ads.txt Native Ads, curated content, programmatic influencers advertising, advertorials etc.
          3. II. Merkle of 3rd Party Data: 1. Advertiser 1st Party synchronyzed Data: * Google Analytics, * existence RTB infrastructure : Brand DSP/ Agency / Brand Advertising Standards (creative, content) 2. API access to: * Salesforce, CRM marketing automation, data management tools synchronized with GDPR * Programmatic networks * SaaS, Tools, Solutions providers 3. Ad /Programmatic/ Networks: * Advertising accounts synchronization
          4. III. Merkle of Global Brand/Industry API Standartization Data 1. Global Brands standards 2. Industry b2b infrastructures API stardards *iAB advertising Categories & creative formatting
          5. IV. 1st Party Brandvertisor Marketplace Data: 1. Campaigns Data: A.) DSP White Label Provider Campaign Data: * Impressions, Clicks, Conversions * Rates: CPM, CPC, CPA, CPS * Campaign analytics: CTR, ROI B.) Open Source Header Bidding in-house Campaign Data: * Impressions, Clicks, Conversions * Rates: CPM, CPC, CPA, CPS * Campaign analytics: CTR, ROI 2. Blockchain Transaction Data: A.) DSP Providers Transaction Data: * Payment * Transaction details: when, how much, each middlemen party accepted answers/got paid etc DSP > Programmatic Network > SSP > Publisher B.) Open Source Header Bidding in-house infrastructure: * Payment * Transaction details: 2 sides accepted Advertiser > HB in-house 7 % > Publisher 3. Open channels & Oracles Data: * Accepted Answers: accepted transaction, enough ad inventory, accepted CPM rates etc * Unaccepted Answers: higher bidding by else participant, not enough inventory, wrong audience, different contextual interests etc.
          6. V. Merkle of Transactions based Feedback / Reviews: 1. After finished transaction : * Advertiser feedback for publisher traffic performance with > 4 ratings based on: Support, traffic quality, speed of delivery, audience targeting report, future cooperation interest? etc > Text written review *Publisher feedback for advertiser performance: > ratings based on: communication, advertiser creatives targeting, audience matching, future cooperation interest? > Text written review
          7. DYNAMIC ACTIONABLE DATA:
          8. I. Merkle of in-house Cross-matched & Machine Learning = Executable Data 1. Cross-Matched synchronized Public & Tools/Solution Providers Data: * Category/Vertical Public Data*DMP Data*CRM Data 2. Simplify decision making /advanced search/ process: * Public Research Report*Salesforce*Programmatic Network Campaign Data 3. Actionable Data processing: *** Cross matching Vertical*Audience*Creatives formulas + constant machine learning algorhitms > constant ecosystem clarity and growth of the value delivery players. 4. Global Brand > Localization Solutions Providers & Agencies * Suggested partnerships per Vertical per country * Suggested partnerships per Trend providers * Suggested partnerships for Brand Localization