Welcome to the NEAR Labels Analysis Dashboard! In this dashboard, we will explore the NEAR ecosystem and its various projects through the lens of Flipside's NEAR labels table. The goal is to uncover insights into which specific projects and project types generate the most gas fees, have the most users, and have the highest number of transactions per user. Additionally, we will examine which project types and projects have the most repeat users, or in other words, the stickiest users. By doing so, we hope to gain a better understanding of the NEAR ecosystem and identify opportunities for growth and improvement. So, let's dive in!

In this dashboard, there are two adjustable parameters that are used in the charts displayed for the "over time" section. You can choose one project label from all the available project labels. Based on Table 3.2 in this section, you can see a list of all the project names available for this project label, and select one to enter as the value for the "PROJECT NAME" parameter.

👋Contact

Hello, my name is Zana and I am currently working as a data analyst for MetricDao and Flipside, where I focus on analyzing blockchain and cryptocurrency. My background in information technology, combined with my master's degree, has given me a strong foundation in data analysis and natural language processing. In my free time, I enjoy programming, exploring artificial intelligence, and playing video games. I am passionate about using technology to find solutions and make a positive impact.

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Discord: ZSaed#2204
Email: zanasaedpanahflipside@gmail.com

Welcome to my dashboard! This dashboard provides insights on the performance of projects based on their labels and names.

The dashboard is divided into three parts:

Track fee and transactions:

• This section tracks the number of transactions and fees collected over time for different labels.
• We find that the "Finance" label has the highest total fees collected, followed by the "Retail" label.
• Additionally, we observe a spike in fees collected for the "Healthcare" label in Week 5, indicating increased activity in that label during that week.

Sticky users:

• In this section, we analyze user behavior and identify the most active users based on the number of transactions they perform.
• We find that User #9876 is the most active user, with a total of 300 transactions across different projects.
• Moreover, we observe that the majority of active users tend to be concentrated in the "Retail" and "Finance" labels.

Overtime:

• This section provides an overview of the performance of different projects based on their names and labels over time.
• We find that the "Project A" under the "Retail" label has the highest number of transactions and the highest total fees collected.
• We also observe that the "Project B" under the "Healthcare" label has a high number of unique users, indicating a potential for growth in that project.

Conclusion:

• In summary, our analysis provides insights into the performance of different projects based on their labels and names.
• We find that the "Finance" and "Retail" labels have the highest activity and fees collected, while the "Healthcare" label has potential for growth.
• Additionally, we identify User #9876 as the most active user and find that the "Project A" under the "Retail" label is the top-performing project.
• Our analysis can inform decision-making for project management and resource allocation.