Introduction

Polygon, formerly known as the Matic Network, is a scaling solution that aims at providing an Ethereum alternative with a fast and low fee transactions.

User adoption is an important indicator of success of a project. User adoption can be measured by number of new addresses over time. However, user churn and activities are equally important. Projects strive to keep and increase users engagements.

This dashboard investigates how Polygon network is successful in keeping users active on the network.

Definition

Active address is defined as an address that has done at least one transaction or has received at least once a token in the past 3 months.

Results

The box below shows how many addresses on the MATIC network are currently active.

Note: all wallets who initiated at least one transaction or received a token in past three months have been calculated.

Note: For transactions flipside_prod_db.polygon.transactions and for transfers flipside_prod_db.polygon.udm_events (WHERE event_type = 'erc20_transfer') have been used.

The graph below shows the monthly evolution of active users.

We can see that in November 2021 Polygon experienced the highest amount of active users.

Note: The monthly number of active users are different with the monthly number of users who execute a transaction or receive a token in the month.

The graph below shows how the price of MATIC has been changed over time.

We can see that the price is lagging. For instance, we can see an increase in the number of active users in July 2021. The same increase can be seen in price in August 2021. In November and December 2021 also the same happened. First the number of active users increased then the price.

Conclusion

The data shows that the active users peaked in November 2021. The price is followed by the monthly number of active users.

I think the data shows that the price is influenced by the activities and engagements of users on the Network.

A note on data management

For the daily active users, the query got timout error. I decided to optimize the query by visualizing monthly active user.