BRANDVERTISOR AND BRAND TOKEN

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-#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
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 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.
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 Publishers
Contextual 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-#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 & Token Ratings & Rankings publicity & constant status updates gamification:
* More ratings > more #Address awareness > more clients > more ratings
Merkle of Ad Delivery Processing Data
Oracles 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-#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
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 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.
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
35