Iconista
Hyperconnecting people within and outside ICON
Project Category: Development
Project Duration: 3 months
Funding Amount Requested: $30,000
Public Address: hx219426d578e27f3d9ad8b66e66a33ae4e92c9e3b
Project Description
Iconista’s vision is to hyperconnect ICONists and enable exponential growth for the ICON community.
We aim to make this vision a reality by:
- strengthening existing network effects in the community by solving retention.
- building new social flywheels that lead to the possibility for exponential growth in adoption.
Supporting ICON’s vision to “Hyperconnect the world”, we aim to build a social experience that makes the voting process more engaging and rewarding.
A crucial element of this vision is to create a frictionless onboarding experience for newcomers to the ICON community.
This proposal documents the first phase of Iconista.
We were driven to solve this problem after observing similar problems being echoed within the ICON community:
Observations from ICON community discussions:
DApp ideas for mainstream adoption - DApp ideas for mainstream adoption - ICON Community forum
Ideas to Encourage Educated Voting - Ideas to Encourage Educated Voting - ICON Community forum
Ideas to Encourage Educated Voting - Ideas to Encourage Educated Voting - ICON Community forum
Ideas to Encourage Educated Voting - Ideas to Encourage Educated Voting - ICON Community forum
Our ideation process
Strengthening existing network effects in the community by solving retention.
Currently retention takes a hit since users need to understand and analyze multiple different parameters like p-rep projects, monthly rewards, votes, share, voters, etc. to select a handful of trusted and active p-reps, who contribute to the community, out of a list of more than 100 and growing, to vote on to minimize risk.
To reduce the above friction and make the voting process “simple and fun”, we aim to create:
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A P-Rep recommendation system: that will map new voter’s interests to relevant p-reps and projects.
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Iconista influencer profiles - new and existing users can claim, modify and share their profiles.
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Profiles will visualize voter’s participatory network activity by visualizing relationships developing between p-reps and voters using a “network view”.
- The above profiles will be similar to validator profiles on Polkadot network as demonstrated here.
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Profiles will serve as social proof:
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Users can share links to their profiles to existing social networks creating network effects to promote growth.
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Imagine Iconista influencers tweeting “I am proudly voting for these p-reps + earning rewards on my capital? What are you doing?” to get more people involved.
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A Voter Leaderboard: for most active voters to incentivize healthy competition and mass participation.
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Review System: to increase accountability in the network - p-reps are reviewed and rated by voters to incentivize good behavior and more contribution.
In the future, Iconistas can form groups or “Alliances” to pool their ICX and follow the same voting strategy - similar to what happens in the real world where you have voter blocks.
Building new social flywheels that lead to the possibility for exponential growth in adoption.
To onboard new users into the ICON ecosystem, we’ll be creating a Twitter content generator bot that will attempt to leverage network effects of existing social networks and drive a portion of it to the ICON community.
- Social Validation for top voters: the bot will post profile links of top voters on leaderboard (with voters mentioned by their twitter handle) on Twitter daily to give top voters a platform to show-off their efforts.
- Recognizing top P-reps: bot will post profile links of P-reps who are being voted on for their work for the community.
- Incentivizing mimetic effects: newcomers will become curious about what’s happening in the ICON community as the bot’s efforts starts compounding over time. This will enable a a top funnel of curious newcomers straight to ICON.
To demonstrate how impactful this approach for marketing can be, attached below is a screenshot of analytics of a twitter bot we created to post updates about the Polkadot Network. Notice that it received over 34,000+ organic impressions in 1 Month.
NOTE: The bot went live in late December, 2019. As with any social media effort, it takes time to build compounding returns:
Additional Information
Are there any similar projects? If yes, how is Iconista different?
Currently, to make voting decisions, ICONists resort to tools like:
These tools are very useful for experienced ICONists who prefer a “DYOR” approach for voting.
However, it can be time-consuming to analyze multiple parameters like votes, projects, number of voters, etc. to select a handful of p-reps to vote for.
For new users it can prove to be a barrier of entry.
Iconista will serve this niche of new or existing users who prefer a ready to use recommendation system for voting and increase participation by creating network effects. Iconista also takes a social approach to create network effects to increase the rate of growth of the ICON community by enabling human to human interaction within the community.
A glimpse of Iconista
Tentative designs for the platform have been added below
(NOTE: The design is indicative and the actual result may vary to some extent).
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Voter profile (standard view):
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Description: A familiar “profile” from web2.0 will ease the onboarding for new users. The profile of a voter will contain on-chain information about their activities, their delegations, stake amount, etc. and off-chain information about the voter like their name, bio, interests, etc.
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Design:
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Voter profile (network view):
- Description: A network view for voters will allow users to visualize the relations between the selected voter and p-reps they delegate to, making it easy for users to understand how ICON ecosystem functions
- Design:
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Notification system UX:
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Description: Voters will be notified about their achievements (like being in the top 5 voters leaderboard, unlocking a new rank like staking duke), activities of p-reps and iconistas that they follow and voting recommendations based on their interests
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Design:
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P-rep recommendation system UX:
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Description: Voters will be recommended high rated p-reps based on the intersection of the interests of the Iconista and the activity of the p-reps, reducing the cognitive load for users and making staking fun, easy and a shareable experience
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Design:
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P-rep page (standard view):
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Description: P-rep pages will contain on-chain information like votes, I-rep, I-score, transaction activities, etc. and off-chain info like reviews from Iconistas, ratings and bio. Such consolidated easy to digest information will make it easy for new users to make staking decisions and encourage contribution within the community.
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Design:
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P-rep page (network view):
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Description: A network view for p-reps will allow users to visualize the relations between the selected p-reps and voters who delegate to them, making it easy for users to understand how ICON ecosystem functions
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Design:
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Voter leaderboard:
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Description: A staking leaderboard for voters incentivizes healthy competition and mass participation
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Design:
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Project Milestones
Milestone 1 — Building voter specific features of Iconista - 1 month
Deliverables:
- Voter profiles: implement UI/UX and backend for the social voting profiles and network view as indicated by the mockups above
- P-Rep recommendation system.
Milestone 2 — Building P-rep specific features of Iconista — 1 month
Deliverables:
- P-rep profiles
- p-rep review system
Milestone 3 — Executing marketing plan to leverage network effects of existing social networks — 1 month
Deliverables:
- Voter leaderboard
- Twitter Bot - that scaffolds over all the elements above to make a bridge between Twitter and ICONISTA.
This milestone also accounts for buffers for unforseen delays + final touches.
Team
Contact & additional information
Team Name: BUIDL Labs
Email: karan@buidllabs.io
Telegram: @saumyakaran
Twitter: https://twitter.com/BuidlLabs
Website: http://buidllabs.io/
Code Repos: https://github.com/buidl-labs/
Team members
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Saumya Karan
- Currently spending mindspace in solving UX problems in onboarding people to decentralized economic networks at BUIDL Labs.
- Led project management and design for PolkAnalytics and Polkabot which has received over 34K organic impressions (without any paid marketing) in Feb, 2020.
- Previously built and shelved a social network for developers. Led design and product management for it.
- GitHub
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Abhinav Thukral
- Currently spending mindspace in solving UX problems in onboarding people to decentralized economic networks at BUIDL Labs.
- Selected for HCI program at Georgia Tech University. Has 2 patents in the blockchain space.
- Web-links: Portfolio, LinkedIn, GitHub, Dribbble
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Prastut Kumar
- Currently spending time in researching bridge chains between existing blockchains at BUIDL Labs. Hence became interested in ICON.
- Has 5+ years of product-dev experience across multiple startups, research labs and companies
- His startup career involves 3 startups:
- NEETPrep - an ed-tech startup - does $3 million in annual revenue. Built the product that gained 4.5k daily active users, 28k monthly active users across all users (free/paid) within 3 months.
- Centify - a social live streaming consumer app - the startup got selected in YC startup school under the mentor track.
- Cambuzz - a decentralised youth networking social platform - the startup got 5k college students across three colleges actively using the platform at it’s peak.
- His research career involves work in the field of HCI. He has done research in async learning at CHILI Lab @ EPFL, The Berkman Klein Center for Internet & Society at Harvard University, and Stanford HCI Crowd research collective. Published research in International Journal of Computer Graphics & Animation.
- He has also been contracted by companies to work on their design problems. Previous clients involve Google, Paytm (India’s largest payment company). Amongst the top 10 people who were selected as finalists for World UX Championship.
- His startup career involves 3 startups:
- Education: Dropped out of Carnegie Mellon University’s Human Computer Interaction Program.
- Web-links: LinkedIn, GitHub
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Jay Prakash