LET’S GET MAD (Meme Analysis Database + Proposer Pipeline Analytics)

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“Is Nouns activity growing?” “Who is helping increase engagement and spreading the meme?” “Which proposals are driving growth?” “What’s our spend rate?” “How many builders are contributing to the Nouns ecosystem?” “Are the amount of proposal candidates increasing?”

TL;DR

There are a lot of questions that the Nouns community needs data support on. We want to create a database that helps anyone answer these questions, and an example website on top to support a first analysis: What is the proposer pipeline and how can Nouns best support builders?

The database will integrate activity across social channels and highlight which Nouns, advocates, and proposals have best supported the Noun’s meme propagation. Our data wrangling creative motto is: Track Memes, Not (just) People.

As a starting point, this work will gather the following data: [1] onchain activity (supported by the Nouns subgraph) and [2] two sources of social media (Farcaster and Instagram). This data will be available for others to query and use to investigate additional questions.

Within the scope of the work, we will also create a basic website including [3] a limited set of general metrics for context, and [4] the first analysis website focusing on the specific challenge of understanding the proposal conversion process. In this way, we will be the first “app analysis” built on the dataset.

Background

As of the moment, the Nouns’ community and audience members produce a wide variety of data. Over time, more data is likely to migrate onchain (e.g. similar to vote comments today) and data will be produced across even more digital localities. The United World of Nouns will have a wide-ranging data footprint. Combining data across these localities can become a powerful tool for proposal development and decision-making.

However, as @Krel mentioned in their Request for Proposers:

“Nouns DAO does not have a reliable way to measure ‘growth’ or ‘success’. Its not clear what should be measured, or what growth in the context of Nouns even means.”

This project will create an initial repository of activity growth combining onchain and offchain data. We are long overdue to experiment on this front!

Scope Overview

The Noun Pulse has two components:

• [A] the open database and
• [B] a single-purpose website with general metrics and a first analysis

In addition, we will include an analysis report explaining current trends, issues, and opportunities in the proposer pipeline.

To begin, we will include onchain data available in the Nouns Subgraph and two social media channels: Farcaster and Instagram. We will then publish a selection of core metrics that provide clarity to Noun holders and the wider community.

Most importantly, the underlying data pool (Meme Activity Database) will serve a step towards a data-driven Nouns culture, which community members and potential builders can use to find opportunities for proposal submissions. This should enable builders to bypass building a new database. Instead, we can amend it, expand it, or fork it. As a result, we can focus our energy on answering new business questions.

Success of this project will be measured based on data pool connections (e.g. external usage of the Meme Activity Database), web usage (unique visitors), and discussions or proposals that successfully reference the data.

This work will enable future analysis to include key derivative NFT projects (Gnars, LilNouns, cc0-lib) and other dominant social spaces (Discord, Discourse, X, Lens, etc).

Product Components

Meme Activity Database (a.k.a. The MAD)

We’ve named the database the “Meme Activity Database.” The reason is that we don’t want to just track people; we want to track the content. Our ambition is that this database is an MVP aggregation of all-things Nouns, reliable meme-data-as-a-service, such that we can service a variety of new data-powered projects.

As mentioned, this database will be open source and free access: anyone should be able to contribute to it (e.g. add a new source or table) and build on top of that.

At the end of the day, we want to facilitate the use of data across the Nouns ecosystem. As the cc0 library enables media usage, the activity database enables tracking and impact analysis.

The Web API interface will be available publicly distributed per specific sources (onchain Nouns, Farcaster, and others) as well as in aggregated form to enable the development of tools such as alerts, bots, and analytics dashboards.

Data will be synchronized in real-time directly from the Nouns’ Subgraph, Farcaster, Instagram to an SQL database. The sync mechanism will be architected in a uniform adapter interface for easier future integration with other onchain data and offchain sources, such as Twitter, Discourse, Discord, Telegram, DeSo, and Lens.

The Noun Pulse Website: General Metrics

We believe that many Noun community members have a variety of needs. We will supply an overview of general metrics to serve as a source of truth for conversations.

These include:

Example Onchain Metrics, Supporting Clarity on Arbitrage

• Treasury
• Treasury Usage Rate (Monthly Spend)
• Burn Value
• Estimated Burn Value at Next Fork
• Average Auction Price
• Current Arbitrage Opportunity (3-day average auction price minus value at next fork)
• Count of Unique Holders
• Count of Active Holders (Unique Holders with Vote w/ Reason in past 30 days)

Example Proposer Pipeline Metrics, Supporting Clarity in Builder Ecosystem

• Proposer Pipeline (with addresses and available contact information, details in next section)
• Conversion Rates from Stage to Stage
• Excerpts of Builder Stories
• Count of Prop House Submissions
• Count and % of Prop House Submissions from New Builders
• Count and % of Passes Prop House Submissions
• Count of Candidates
• Count and % of Candidates from New Builders
• Count and % of Candidates converting to Onchain Proposals
• Count of Proposals
• Count and % of Proposals from New Builders
• Count and % of Passed Proposals
• Count of Unique Proposers
• Count of Unique Funded Builders
• Growth in Onchain Comments
  • Growth in Candidate Feedback
  • Growth in Prop Vote with Reason
  • Growth in Prop Updates

Example Offchain Metrics, Supporting Clarity in Nouns Reach

• Number of Posts
• Total Engagement (Likes+Comments+Shares)
• Engagement Rate (Ratio of Engagement to Posts)
• Most Engaged Content
• Total Followers
• Total Followers with Onchain Identity
• Follower Growth Rate (Updated based on API opportunity)

Representative Image of General Metrics

The design for the website will use assets from the cc0 library and default Noun design parameters from the main website noun.wtf

Alpha Analysis Report: The Proposer Pipeline

The first iteration of the database will be focused to help address a key opportunity in the Nouns DAO: attracting, shepherding, and retaining quality builders. The data we select and analyze will be guided by this problem statement.

Analysis Context and Design

Every DAO has a funnel of builder activity. In the case of Nouns, we can roughly organize the participation process as:

1. Aware (Saw Nouns)
2. Curious (Engaged with Nouns, Offchain)
3. Audience (Engaged with Nouns, Onchain)
4. Voter (Vote with Reason)
5. Prop House Submitter (Submitted to the Garden)
6. Prop House Funded (Proposal Passed)
7. Candidate (Submitted Candidate Proposal)
8. Onchain Proposer (Submitted Onchain Proposal)
9. Funded Builder (Proposal Passed)
10. Committed Builder (Completed Proposal)
11. Multi-Proposal Builder (Multiple Proposals Passed)

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However, we currently don’t have clarity on the people involved throughout this proposal process, or where Nouns can focus on to coach builders and retain them.

As Noun40 describes in this proposal’s feedback, there are many open questions.

Some of ours include:

• How many of the candidate props are from net new ppl?
• Of the net new ppl, do their candidate props grow to be full props that pass (success cases)
• How did those prop builders find us? (number will be so small you could just ask them)
  • Note: We will document stories where data is not available
• What is the number of ppl that commented on the candidate proposal?
• Is that quantity and participant of candidate feedback growing?
• Are prop house builders in the garden round then submitting candidate proposals? If not, why?
• Are builders returning to submit new proposals? If not, why?

At the end of the day, “we want more net new quality prop builders and we want them to have success and a low drop off rate.” (Noun40)

Our team would add: we want those quality builders to come back.

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Using this framework as a starting point, we can analyze the data, create different visuals, and validate the journey hypothesis. We understand this may not be trivial and some data or identity mapping may not be available. Data will be complimented with builder stories.

Analysis Considerations

As part of the deliverable, we will also include documented business rules underlying the analysis. For example, we will include what means if a builder is net-new. This is not necessarily trivial since we want to avoid counting an ethereum address as “net-new” if it’s the same builder but submitting an application with a new ethereum address.

Each of these business rules will be discussed openly throughout the work to ensure the Noun community has opportunity to provide feedback.

Analysis Outputs: A Written Report

Creating graphs and metrics is not enough for action. As a result, the scope of the analysis will include a written report (In Notion or similar) with recommendations.

The intention is to test out the usefulness of the database and the website — to build for action. In this case, we will understand where the proposer pipeline issues are, support them with data, and provide tangible advice.

For example, insights may include ways to identify potential builders earlier, and help them find a Noun-sponsor prior to a candidate proposal submission; imagine if another group of Nouns community members (not just holders), offered to shepherd builders in the proposal process, similar to gardeners.

These insights will be framed as RFPs (Request for Proposals), such that the Nouns community has clearer direction on where to invest to strengthen the proposer pipeline.

Product Roadmap

The execution of this project will be done in an iterative fashion. By building in public and getting ongoing feedback, we can deliver what the community needs, and data that will support Nouns decisions.

Iterative Phases:

• Phase 1: Deliver alpha analysis plan outline and approach. Deliver mock-up UI for web application. Deliver database raw data components and MVP. Host open Q&A with Nouns (e.g. via Aburra) to gather community feedback.
• Phase 2: Deliver basic UI w/ metrics. Deliver initial list of insights from analysis. Provide update on database. Host open Q&A (e.g. via Aburra) to gather community feedback.
• Phase 3: Deliver UI updates. Deliver builder stories. Provide demo of database queries. Host open Q&A (e.g. via Aburra) to gather community feedback.
• Phase 4: Complete UI polishing and metrics calibration. Host open Q&A (e.g. via Aburra) to celebrate launch and discuss future analytics (where next?).

An Ambitious Vision

This project aims to uncover what “Nouns-Native” analytics could look like, and what data infrastructure is necessary to feed this analytics into the broader Nouns governance and participation process.

We’re certain there will be other innovations along the way. After all, we need to address challenges in crossing the web3-to-web2 data pipeline chasm. Is it possible? We think so.

Fortunately, the team is not alone. They have a wide array of connections with governance leaders across the DAO space: Optimism, Purple, Forefront, Radworks, SeedClub, FWB, Cabin, and others.

Nouns is a place for ambitious ideas, and we are an ambitious team.

Team

This project will be led and executed by Rafa Fernandez (Farcaster: @rafa) and Rafi Gutkowski (Farcaster: @rafi).

• Rafa Fernandez is a community architect with a focus on building Digital Public Utilities. He has decade of experience in corporate analytics and studied materials-science and engineering. He is the founder of Folklore (Folklore.Institute), a community that curates and commissions research on digital cultures. Most recently, he was part of the Summer of Protocols research program looking at online swarms. Previous to the program he worked at Mirror where he worked in creator partnerships and community development.
• Rafi Gutkowski is a software engineer and data scientist (libred.org, LinkedIn). He is building Farway, a Farcaster meta-client for social analytics. He started mining bitcoin in 2015 but only after exiting from his recent startup, did he double down on web3. Since then Rafi has been working on sementic analysis of decentralized social graphs, as well as creating web3-web2 bridges, such as Folklore’s web3-signature to feed [broadcast applet](https://www.no

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Read the rest of the prop at https://nouns.wtf/vote/427