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Pain Point

Presently, the Web3 ecosystem does not have a widely adopted affiliate marketing model similar to the Web2 CPA model as seen in platforms like ClickBank or Amazon Associates. The decentralized nature of Web3, powered by blockchain technology, introduces unique challenges and opportunities that differ from the centralized Web2 landscape.
One of the primary reasons for the absence of a well-established Web3 affiliate marketing model is the lack of standardized infrastructure and protocols dedicated to affiliate marketing in a systematic manner. While Web3 has witnessed significant advancements in decentralized finance (DeFi), non-fungible tokens (NFTs), and decentralized applications (dApps), the development of a comprehensive affiliate marketing framework has not gained the same level of attention.
The decentralized nature of Web3 also presents challenges in terms of tracking and attribution. Web3's focus on privacy and user ownership of data makes it more difficult to implement traditional tracking mechanisms commonly used in Web2 CPA models; for instance, the accessibility of cookies and data storage on websites is not trusted and therefore inapplicable to most dApps. The transparent and immutable nature of blockchain technology can potentially provide solutions to these challenges, but its practical implementation in the context of affiliate marketing is still in its early stages.
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As of 2023, Bloomberg's data reveals that there are more than 50 million influencers globally, and the creative economy, encompassing these influencers, is poised to surpass a staggering $1 trillion this year. Numerous platforms have emerged over time, facilitating the search for influencers and offering insights into their performance, while also enabling the posting of technical tasks with payments for influencers. Nonetheless, the absence of assured outcomes remains a challenge, even with the utilization of such platforms.
The disconnect between a crypto company's founder and a crypto blogger is evident, even if they share the same goal or objective. The primary issue lies in companies struggling to identify and actively manage suitable influencers with the right audience and high ROI, leading to negative experiences. Despite paying for reviews and going through consultancies, companies often face losses due to a lack of understanding in influencer selection, collaboration, and result-oriented relationships. On the other side, influencers encounter problems with multiple offers from anonymous or dubious companies, resulting in wasted time and rejections. Even when they agree to collaborate, companies seek guarantees and precise outcome figures from the reviews, while many crypto companies lack digitization, leaving both parties without crucial data to assess success. This communication barrier and data gap cause mutual losses for companies and influencers alike.
A notable challenge arises when traditional Web2 clients seek collaborations with crypto KOLs, necessitating time-consuming screening on socials like Twitter. For instance, AI companies aiming to expand beyond their tech-oriented communities to target gaming or anime audiences in Web3 face difficulty finding concentrated platforms to connect with suitable influencers. Additionally, the preference for on-chain payouts among content creators, leveraging cryptocurrencies for personal and financial reasons, presents a barrier to seamless integration between the two worlds. Pirat3 addresses this issue by offering a comprehensive marketplace equipped with advanced filters, empowering influencers to proactively select compatible companies and products that best resonate with their unique audiences. Our platform caters to diverse demands and requests from a wide spectrum of companies, providing a compelling solution for affiliate marketing.
All in all, Web3 ecosystem currently lacks a simple system that serves advertisers and affiliates to connect and collaborate across the worlds of Web 2 and 3, Pirat3 aims to link them seamlessly while, comprehends users’ behavior and activities to make personalised recommendations for further exploration of interests and direct product conversion demands using Artificial Intelligence (i.g. “read more” and “buy now”).
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