MADNetwork Mad Price USD

The MAD Network is a set of decentralized applications designed to seamlessly connect every stakeholder within the complex adtech value chain. The MAD Network is not operated by one central party. Decentralized miners ensure that advertisements are constantly being served, averting the possibility of a single point of failure. Today’s ad tech supply chain is opaque - neither the advertiser nor the publisher has insight into agreed-upon prices. The MAD Network is completely open and accessible by all parties. Blockchain technology gives each entity confidence in the veracity of the data in the network. The MAD Network puts an end to today’s convoluted programmatic advertising system in which intermediaries capture 60 percent of every ad dollar spent. By establishing trust within the MAD ecosystem, network participants can transact without the need for a middleman. MADnet Books The first solution built into the network will be MADnet Books, a decentralized payment rail that allows advertisers to stake insertion orders into the MAD Network. This payment rail will open a tokenized marketplace for exchanging the value of an ad impression. MADnet Core The next layer built within the MAD Network will be MADnet Core, a decentralized ad server and exchange. MADnet Core will store, match and serve ads propagated through the network. MADnet Core is powered by a proof-of-real-work algorithm that combs through large amounts of ad requests and campaigns to find the best possible match. MADnet Data MADnet Data is a decentralized data management platform (DMP) that will capture information from the use of the MAD Network and allow participants to exchange data in a peer-to-peer architecture. HOW MAD WORKS Connecting Advertisers and Publishers Advertiser creates insertion order on MADnet Books by staking MADtoken Ad request information is propagated to MADnet Core Ad serving miners compete to find the best possible match Smart contracts within MADnet Books pay publishers and miners as the advertisement plays in near-real-time.