The decentralized web — also known as Web3 — gives everyday people secure ownership of their digital lives through the blockchain. Forward-thinking brands are shifting to this new digital frontier, positioning themselves in a consumer market expected to reach nearly $3 billion in 2023.
To successfully reach this emerging high-tech breed of consumer, marketers must leverage tools like machine learning to help them keep pace in a changing landscape and increase revenue while operating at scale. Read on to discover how these five machine learning NFT marketing strategies can enhance your next Web3 project.
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Machine Learning NFT Marketing Strategies
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One of the most important advancements in performance marketing came with the advent of personalization. Instead of blasting out the same advertisement to millions of people in the hopes a handful of viewers click on your ad, now you can target specific audiences based on their age, gender, browsing habits, and personal tastes. With personalization, you can advertise your product — in this case, NFTs — to a smaller, more dedicated audience that is far more likely to purchase, thus ensuring your marketing dollars reach their maximum potential.
But what do you do if you don’t know specifics about your potential customer base? Or what if you know your current audience but want to expand to other demographics but don’t have any user data to target? That’s where machine learning can help.
Strategies like collaborative filtering can help you make inferences about similar user profiles, enabling you to make suggestions to other users based on things they like or interact with. For example, if you’re trying to advertise NFTs for an electronic music group, you can target potential buyers who are fans of other acts within that genre. Machine learning can help you make those connections more quickly and on a much larger scale than if you manually analyze user profiles for preferences.
Want to learn more about how machine learning NFT marketing can enhance your next Web3 campaign? Dibbs can help. Get in touch today and find out how our tokenization-as-a-service (TaaS) platform can maximize the potential of your next drop.
Where personalization can help you meet people where they are, predictive analytics helps you determine where they’re going.
Predictive analytics is its own discipline, separate from machine learning, which uses statistical modeling to analyze past and current data sets to predict future outcomes. However, machine learning can enhance these statistical models by automating the data-gathering process.
By training these algorithms on the correct data (while also ensuring they are discarding irrelevant or outdated information), marketers can better understand where consumer trends are heading and where potential pitfalls may lie. This enables them to be more proactive, optimizing NFT marketing campaigns to reach their maximum potential or reducing churn among their current customer base.
Social media is a powerful tool for staying on top of conversations about your product and the overall industry. Tracking everything — and converting that information into actionable data, like what Blaze AI is doing — is a different story. With billions of users across multiple platforms sharing text, videos, photos, and audio, compiling posts and acting on them in an effective way can be overwhelming.
Feeding this content into machine learning algorithms isn’t just about sorting a multitude of content into different buckets. It’s about sifting through this content to determine the emotions your customers are sharing and analyzing the context behind those emotions. Then, you can act on that context, enabling you to better respond to customer needs in marketing campaigns and improve the quality of services your organization offers. Picture aligning your OKRs with your customers' emoji and meme usage as it relates to your product. This is the future of marketing.
Blockchain technology — like the NFTs themselves — all operate under the idea that each transaction is unique and secure, inherently building security into Web3 projects. Machine learning can further enhance fraud detection through a series of user access rules that can be trained and updated over time to recognize patterns in fraudulent behavior.
For example, AWS uses machine learning algorithms to prevent unauthorized credit card transactions. Through a series of AWS services, its system can store data sets of valid transactions and compare those against fraudulent transactions to build its machine learning algorithm. This is particularly handy for organizations that process thousands upon thousands of transactions daily — the resources required to confirm each one by hand would be astronomical.
These tools can be similarly used for Web3 projects to prevent unauthorized users from accessing paying customers’ NFT wallets or redeeming special rewards without the necessary token.
To keep blockchain transactions speedy and transparent, they will often employ smart contracts — effectively a series of commands written in code programmed to execute when specific conditions are met. For example, if an offer on an NFT reaches a certain amount of money, then the smart contract will kick in and sell that NFT to the user and receive the funds.
This example is a relatively simplistic one, but smart contracts can be far more complex, encompassing multiple products, actions, real-world conditions, or even entire infrastructure. Project owners can implement machine learning to enhance and automate the creation of these smart contracts, ensuring that requested events go off without a hitch. When end users can trust these outcomes, they’re more likely to trust your organization in the future and give it repeat business.
We at Dibbs have years of expertise in blockchain technologies and the collectibles market. Our tokenization-as-a-service platform is designed to combine the digital and physical worlds, generating additional revenue streams for your brand in a secure, state-of-the-art environment. Ready to learn more? Get in touch today.