The leader in Web3 growth marketing, Addressable, reported today that its second investment round of the year closed successfully, raising a total of $13.5 million to date. With the additional funding, Addressable will be able to develop its Web3 Growth Suite further and extend its offering to other blockchains and ad networks, further establishing its leadership position in the Web3 growth marketing space.
This noteworthy accomplishment is the result of Addressable’s relentless progress in resolving the Web3 Growth issue for companies. In addition to current investors Viola Ventures, Fabric Ventures, Mensch Capital Partners, North Island Ventures, and numerous additional strategic investors, BITKRAFT Ventures led the investment round. Strategic investor Karatage also participated.
Thanks to Addressable’s greatly enhanced capabilities, the attribution of cryptocurrency wallet owners is now possible on 400,000 websites, including Bloomberg, Yahoo Finance, and The New York Times. In addition to initially covering the Twitter ad network, Addressable now covers three additional ad networks: Unity, Pubmatic, and Magnite. With this growth, Addressable will be able to offer more extensive and focused paid advertising campaigns on websites, mobile apps, and social media platforms.
Additionally, marketers can easily measure website conversions, wallet connects, and blockchain conversions with Addressable’s 1-Click SDK Installation, approved by Google and accessible through Google Tag Manager. Conversion event tracking is further streamlined by integrating Mixpanel, Google Analytics, DSP pixels, and Twitter Pixel.
With over 50 clients and 750 campaigns completed, Addressable is pleased to present improvements, including customized playbooks for gaming, deFi, blockchain infrastructure, services, and exchanges.
Addressable, founded in June 2022 by Tomer Sharoni, Tomer Shlomo, and Dr Asaf Nadler, is evidence of their extensive knowledge of AI and data analytics. They have coauthored over 20 works on big data, blockchain, and machine learning.