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MindMap Gallery BRANDVERTISOR AND BRAND TOKEN
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BRANDVERTISOR AND BRAND TOKEN
This is a mind map that contains information about the decentralized advertising marketplace.
Edited at 2020-10-08 03:56:39
BRANDVERTISOR AND BRAND TOKEN
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BRANDVERTISOR: DECENTRALIZED ADVERTISING MARKETPLACE BRAND TOKEN: ETHEREUM SMART CONTRACT
PUBLISHER
Single Lifetime Profile in Marketplace = single Block in the Blockchain = #Address WEBSITE: brandvertisor.com/website/CNN.com = CNN-pub:website-#AddressAPP: brandverisor.com/app/gameX.app = gameX-pub:app-#AddressInfluencer: brandvertisor.com/influencer/Gary-Vee = GaryVee-pub:Influencer-#AddressNetwork: brandvertisor.com/network/GDN = GND-pub:Network-#AddressAR/VR: brandvertisor.com/AR/GlassesY = GlassesY-Pub:AR-#AddressIoT: 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
Block content storage :
Merkle tree of Publisher name & publisher type ? * CNN-Publisher:Website-#Address
STATIC AGGREGATED/IN-HOUSE DATA:
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
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
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
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 etcDSP > Programmatic Network > SSP > Publisher B.) Open Source Header Bidding in-house infrastructure:* Payment* Transaction details: 2 sides acceptedAdvertiser > 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.
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
DYNAMIC ACTIONABLE DATA:
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.
TRANSACTION
Transaction Marketplace Steps:
I. ADVERTISER Browse Context Categories with PublishersContextual Search Engine with Publishers Tags ("startup magazines, beauty blogs, crypto influencers")
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
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
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
V. Brandvertisor cross-advertising-data campaigns data: 1. Campaign process:Campaign details + DMP > Programmatic Networks/SSP > Clickfraud > Brandvertisor Dashboard campaign CTR, ROI storage
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
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
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
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-#AddressCNN-website-#Address <> Small_Brand1-Brand-#AddressCNN-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 & Token Ratings & Rankings publicity & constant status updates gamification: * More ratings > more #Address awareness > more clients > more ratings
Merkle of Ad Delivery Processing DataOracles answers by DSP/Header Bidding ad delivery processes:
Ad Delivery Infrastructure & Pricing
Brandvertisor DSP = 7 %
Programmatic Exchange = 10-30 % (AppNexus, OpenX, GDN)
Header Bidding Infrastructure/SSP = 10-30 %
Publisher
Brandvertisor HB Pricing = 7 %(Open Source Infrastructure)
Publisher
ADVERTISER
Single Profile in Marketplace = single Block in the Blockchain = #Address Marketer-Media Buyer: brandvertisor.com/marketer/Neil Patel = Neil-Patel-#AddressAgency: brandvertisor.com/agency/Publicis = Publicis-#AddressBrand: brandvertisor.com/brand/Unilever = Unilever-#AddressInfluencer as advertiser: brandvertisor.com/adv/influencer/Gary-Vee = GaryVee-adv-#Address ?!?AdTech Partnerships: brandvertisor.com/adtech/AppNexus = AppNexus-Adtch-#AddressAd Networks: brandvertisor.com/networks/GDN = GDN-adv-#Address
Block Content Storage:
Merkle tree of Advertiser name & publisher type ? * McDonalds-Advertiser:Brand-#Address
STATIC AGGREGATED/IN-HOUSE DATA:
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.
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
III. Merkle of Global Brand/Industry API Standartization Data 1. Global Brands standards 2. Industry b2b infrastructures API stardards *iAB advertising Categories & creative formatting
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 etcDSP > Programmatic Network > SSP > Publisher B.) Open Source Header Bidding in-house infrastructure:* Payment* Transaction details: 2 sides acceptedAdvertiser > 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.
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
DYNAMIC ACTIONABLE DATA:
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