Introduction:

Near.social is a social data protocol built on Near that allows users to own their data. Also users themselves can improve their user experience by creating small apps with accessible public data called widget. Almost all on Near.social site are widgets. Widget are open-source software components that can be combined, forked, and created by anyone with some coding knowledge. All existing widgets already offer users experience similar to social networks like Twitter.

The vision behind NEAR social is to build the foundation for an open web where people have real identities that go beyond just being a wallet. It has more than 6000 unique users now.

source: https://medium.com/nearprotocol/near-social-building-the-foundation-of-the-open-web-65672f4152dc

Dear reviewers I ask you to disregard any odd X Axis formatting of some charts due to a possible technical glitch on the app side. I'm looking into ways to make it look better.

Methodology:

Since october 2022 Near.social launched a mainnet.

In this dashboard using accessible data from flipside i tried to look at the main metrics for app's early development stage for over than 5 months: DAU, user churn rate, time that it took user to make first activity, total time of using the app. Some Near social features became available a little bit later, but as for the start of analysis daterange i chose october 4th when users regularly began to visit site.

First user interaction with contract recorded in fact_addkey_events table. All others interactions within the app are recorded in fact_decoded_actions. So in case user signed up the app but has no activities in fact_decoded_actions i'll mark him as invactive one. User interactions with profile are recorded in fact_profile_changes.

I'll use tables above to analyse further user behaviour on the Near.social, how they interact with app itself, other users and with their own profile.

Since start of 2023 number of active users per day is growing gradually. For last 2 months Near.social tripled their running total of new users. There are some peaks in new users. Perhaps this is due to some news related to Near.social, updates or marketing. So as for user churn i decided take percentage of users who didn't commit any transaction outside of addkey_events table. Average churn rate per month is 26%. Even though we have so many new users in March, quarter of them leave the project without any interaction. On the other side users who stick with us make their first transactions within the day they sign up. This means that interface is quite intuitive.

For user activity analysis i took the main typical actions for social network such as user follows, posts, likes, comments and also i choose mem posting as it was one of the first features. On the whole user activity increases. Since february it's clear that users leave more comments. A bunch of new users from start of march were also active in posting, commenting and likes. Also we can see periodicity in activity: saturday and sunday has the less activity than other days. As for interacting with widgets apart from peak in december the number of daily unique users interacting with widgets is increasing since the start of 2023 as well as daily number of transactions. Users following each other and unfollow rate is around 2%. Unfortunately most of users use Near.social for 1 day in total.

Conclusion:

1. Despite the fact that mainnet was launched more than 5 month ago, I suppose that it's still MVP and we can wait for major updates and marketing companies.
2. The project is growing, attracts new users. There is a huge peak of new users in start of March. It has also reflected on whole activity of Near.social
3. It is a very good indicator that the most of users commit their first actions within the day they sign up the Near social
4. Unfortunately the average churn rate per month is 26. It means that we lose every 4th user before he began interacting with app.
5. Users socially interact with each other and number of this interactions is also increasing. There is periodicity in users activity: saturday and sunday has less activity than other days. Also users interact with widgets more over the time.
6. The bad thing is that most of users don't interact with Near.Social more than 1 day. They still used to web2 apps.
7. Most of users interacted with name and image sections in profile. The most popular links they add in linktree are their twitter and telegram handle.
8. The most popular tags in the profiles are "learner" "web3" and "near"