Thank you for the question. An initial inspiration was measuring monthly active wallets (any wallet that has had at least one transaction during that period) to understand ICON’s on-chain activity. The idea expanded quickly after that, as we developed methods and metrics for diving deeper into the data. Once active wallets are identified, we can explore their behavior and analyze how ICON is used in the real world. We can ask questions such as:
- How active are individual (and aggregate) wallets?
- How is transaction activity distributed (1 wallet making 100 txns, or 100 wallets making 1 txn?)
- How is account and transaction volume distributed? (1 wallet/txn with 100 ICX, or 100 wallets/txns with 1 ICX?)
- Are the wallets weighted high to low volume?
- Which contracts are active wallets interacting with the most?
- Are there periodic trends in activity based on the time of day/week/month/year?
- (and much more)
Analyses like these enable us to to classify wallets and learn what types of wallet and transaction activities are driving the network, which will help us understand the needs of ICON’s high-value users. To do this we would build queries and visualizations around the on-chain data, connected to filters (by time, amounts, activity, etc), in order to explore historical and real-time ICON activity.
This is just one example of the types of analytics we would be setting ourselves up to perform but to be clear, our most important goal for this project is to set ourselves up to perform all the analytics over time with a professional stack that will be simple to expand on by the community or others. (For example, at Insight we have a large contributor pool of data scientists and engineers who would easily be able to continue expanding the metric list and analysis stack based on further community requests)
Hope that answers your question and let us know if you have any suggestions! We appreciate any input from the community on additional areas of investigation.