How can onchain data be used to find customer insight?

Blockchain technology has revolutionized transacting and storing data, creating a new ecosystem of decentralized applications, platforms, and marketplaces. One of the key benefits of blockchain is its inherent transparency, which provides a wealth of data that can be analyzed to gain insights into user behavior, demographics, transaction history, and feedback. By leveraging this data, businesses can better understand their users and make informed decisions about improving their products and services. Companies need to have the necessary tools to analyze and understand on-chain data. By doing so, they can better serve their users, improve user engagement, and stay ahead of the competition in the rapidly evolving blockchain ecosystem.

 

Blockchain Transaction history analysis

Transaction history analysis involves examining the data generated by transactions on a blockchain to understand patterns in customer behavior. By analyzing transaction data, businesses can gain insights into the types of products and services that customers are most interested in, the times when transactions are most likely to occur, and other valuable information.

For example, let’s say that a business is operating a decentralized application (dApp) that allows users to purchase virtual real estate using cryptocurrency. By analyzing the transaction history of this dApp, the business might discover that certain types of virtual real estate are more popular than others. They might also notice that transactions occur more frequently during certain times of the day or week, indicating when users are most likely to be active on the platform.

This information can inform decisions about product development, pricing, and marketing. For example, the business might focus more resources on developing virtual real estate in the most popular categories or adjust pricing to reflect when transactions are most likely. Another example could be a business that operates a decentralized exchange (DEX) for trading cryptocurrencies. By analyzing the transaction history of the DEX, the business might discover that specific pairs of cryptocurrencies are more commonly traded than others. They might also notice that the volume of trades tends to increase during certain market conditions, such as when Bitcoin’s price is rising rapidly.

This information can inform decisions about adding new cryptocurrency pairs to the exchange or adjusting trading fees to encourage more trading during periods of high volume.

User behavior analysis

User behavior analysis involves examining users’ actions within a blockchain ecosystem, such as interacting with a decentralized application (dApp) or participating in a decentralized autonomous organization (DAO). By analyzing user behavior, businesses can gain insights into how their customers use their products and services and identify areas where improvements can be made.

For example, let’s say that a business operates a dApp that allows users to create and sell digital art as non-fungible tokens (NFTs). By analyzing user behavior, the business might discover that users are having difficulty navigating the platform or that certain features are not being used as much as expected. They might also notice that users tend to create and sell more NFTs during certain times of the day or week. This information can be used to improve the platform’s user experience by redesigning the user interface or adding new features to meet users’ needs better. The business might also offer incentives, such as lower fees or rewards, during periods of high activity to encourage more users to create and sell NFTs.

Another example could be a DAO that allows users to participate in decision-making by voting on cryptocurrency proposals. By analyzing user behavior, the DAO might discover that specific proposals are more likely to be approved or that certain users are more active in voting than others. This information can be used to improve the governance of the DAO by better understanding what types of proposals are most likely to be successful and identifying areas where user engagement could be improved. The DAO might also incentivize more users to participate in voting by offering rewards or recognition for active participation.

Demographic analysis

User demographic analysis involves examining the characteristics of a blockchain ecosystem’s user base, such as age, gender, location, and other demographic factors. By analyzing user demographics, businesses can gain insights into who their customers are and how they can better serve them.

For example, let’s say that a business operates a decentralized finance (DeFi) platform that allows users to earn interest on their cryptocurrency holdings. By analyzing user demographics, the business might discover that most of its users are male, aged 25-34, and located in the United States. This information can be used to tailor the platform’s marketing efforts and user experience to better appeal to this demographic. For example, the business might choose to create targeted advertising campaigns aimed at young male investors in the US or design the platform’s user interface with this demographic in mind.

Another example could be a decentralized social media platform that allows users to earn cryptocurrency rewards for posting and engaging with content. By analyzing user demographics, the platform might discover that a significant portion of its user base is located in Asia and primarily uses mobile devices to access the platform. This information can be used to optimize the platform’s mobile user experience and to tailor content and rewards to better appeal to users in Asia. The platform might also offer localized customer support in languages commonly spoken in the region.

Feedback analysis

User feedback analysis involves collecting and analyzing user feedback on a blockchain ecosystem to gain insights into how users are experiencing the platform, what features they like, and what improvements they would like to see. By analyzing user feedback, businesses can identify areas for improvement and prioritize development efforts to meet user needs better.

For example, let’s say that a decentralized exchange (DEX) receives feedback from users that the platform’s user interface is challenging to navigate and lacks essential features. By analyzing this feedback, the DEX might identify specific confusing areas of the user interface and prioritize developing new user-requested features. Another example could be a decentralized social media platform that receives feedback from users that they are concerned about the platform’s privacy and security. By analyzing this feedback, the platform might identify specific security and privacy features that users request and prioritize developing these features to improve user trust and engagement.

User feedback analysis can also help businesses to better understand the needs and preferences of their user base. For example, a decentralized finance (DeFi) platform might receive feedback from users that they are interested in earning higher yields on their cryptocurrency holdings. By analyzing this feedback, the platform might identify specific investment opportunities that would appeal to users and prioritize developing these opportunities to attract and retain users.

Conclusion

Blockchain ecosystems are highly transparent, providing much data that can be analyzed to gain valuable insights into user behavior, demographics, transaction history, and feedback. By leveraging this data, businesses can better understand their users’ needs and preferences and make informed decisions about improving their products and services. By utilizing on-chain data and analysis, businesses in the blockchain ecosystem can gain a competitive edge by understanding their users and providing the products and services that meet their needs.