Personalization has been a driving force behind the success of Web2 platforms, shaping user experiences and setting new standards for digital interaction. Whether it's the curated playlists on Spotify, product recommendations on Amazon, or the tailored content feeds on social media, personalization drives engagement to the extent that we almost take it for granted. However, such personalization is fundamentally absent in the web3 ecosystem, despite the wealth of available on-chain data.
This blog explores the current state of personalization, comparing the established practices in Web2 with the emerging possibilities in Web3. We’ll delve into the strengths and limitations of each, and explore how Web3 can evolve to offer the same level of tailored experiences that Web2 has perfected.
Data Centralization
Personalization in Web2 is powered by centralized platforms that aggregate vast amounts of user data. Data collected from every interaction—searches, clicks, purchases, likes, etc. is analyzed using sophisticated algorithms to predict user preferences and behaviors.
Algorithmic Recommendations
The algorithms that power Web2 personalization are often based on collaborative filtering, content-based filtering, or hybrid models. These algorithms analyze patterns in user data to offer recommendations that are likely to resonate with individual users. The result is a highly engaging experience where users are continually presented with content or products they are likely to enjoy or need.
The Privacy Trade-Off
However, this level of personalization comes with a significant trade-off: privacy. Users in Web2 are often required to give up substantial amounts of personal data in exchange for personalized experiences. This data is stored in centralized servers, making it vulnerable to breaches and misuse. While some platforms have introduced measures to enhance data security, the underlying model remains inherently privacy-invasive.
Decentralization
Web3 promises a decentralized ecosystem where users have greater control over their data, given increasing advances in decentralized data storage, computing, and physical infrastructures. Data isn’t stored on centralized servers but distributed across networks of nodes, which is envisioned to allow users to retain ownership of their personal information. This shift presents both opportunities and challenges for personalization.
Fragmented Data
Such decentralization comes with an inherent challenge of data fragmentation. Unlike Web2, where data is aggregated by a few entities, Web3 data (and hence information) is scattered across different chains, storage solutions, and platforms. This makes it difficult to collect, analyze, and act on user data to deliver personalized experiences.
Data Exhaustiveness
In addition to the on-chain data being fragmented across chains and platforms, Web3 teams often have limited to no exposure to off-chain data. This makes it difficult to build holistic profiles of on-chain entities. While the decentralized identity (DID) technologies have been evolving, we are still far from being able to build holistic and defensible profiling models.
Privacy by Design
On the flip side, blockchain architecture naturally aligns with the principle of privacy by design. Evolving zero-knowledge-proof infrastructures should enable the creation of robust privacy-preserving recommendation systems. That being said, these infrastructures are also in their early stages and require significant development to match the sophistication of Web2 personalization.
The above factors pose both a tremendous opportunity and a challenge. So far, most on-chain interactions have been generic and one-size-fits-all. Consider the following examples of ‘what-is” and “what-could-be” with respect to on-chain personalization.
What is: same automation and risk parameters, regardless of a user’s risk tolerance.
What could be: Tailored risk adjustments and automation strategies based on a user’s historic activity and risk profile. This can benefit both the users (in terms of better returns) and protocols (in terms of building a more loyal user base).
What is: NFT platforms typically present users with a broad and generic array of options, irrespective of individual tastes or collecting preferences.
What could be: Targeted collection recommendations based on a user’s current/historic holdings, his participation in communities, and risk preferences. This can benefit both the collectors and marketplaces / NFT issuers (in terms of efficient distribution and building an aligned community).
What is: Web3 projects currently distribute airdrops and rewards through a series of quests, which often attract mercenary activity. This also hurts genuine user’s interests.
What could be: Evaluate users’ engagement history, holdings, governance participation, etc. to reward the most relevant users. This can also enable protocols to weed out mercenary activity and attract loyal holders who can contribute to the ecosystem’s long-term growth.
We have a lot of the primitives and foundational technologies built out already. If we were to onboard the “next billion users” on-chain, the interactions need to be a lot more intuitive and tailor-made than what exists now. So, the potential for personalization algorithms in web3 is immense.
Personalization efforts in a decentralized setup would look very different from their Web2 counterparts, with the key advantages being transparency and ownership. Building such privacy-preserving recommendation systems from the ground up is an exciting avenue that will define how on-chain citizens will interact and engage over the coming years.
This is where solutions like Lucidity will come into play.
Stay tuned to know what’s cooking!