Essential quantitative and qualitative indicators for assessing the operational integrity, participation, and long-term sustainability of a decentralized autonomous organization.
LABS
DAO Governance Analytics and Metrics
Chainscore © 2025
Core Governance Health Metrics
Voter Participation Rate
Voter Turnout measures the percentage of eligible token holders who cast votes on proposals. A low rate can signal voter apathy or concentration.
- Calculated as (Voting Tokens / Total Eligible Tokens) * 100
- Example: A DAO with 1M eligible tokens where 150k vote has a 15% turnout
- High, consistent turnout indicates broad engagement and legitimacy for passed decisions.
Proposal State Duration
Time-in-State tracks the average time proposals spend in each phase (e.g., discussion, voting, execution).
- Monitors governance process efficiency and bottlenecks
- Example: Long voting periods with low activity may indicate poor proposal quality or voter fatigue
- Optimizing these durations improves DAO agility and operational throughput.
Voting Power Concentration
Gini Coefficient / Nakamoto Coefficient quantifies the distribution of voting power among token holders.
- A high Gini coefficient indicates power is concentrated with a few whales
- The Nakamoto Coefficient measures the minimum entities needed to collude for a majority
- Critical for assessing decentralization and resistance to malicious proposals.
Delegate Health & Activity
Delegate Metrics analyze the performance and engagement of those representing voter interests.
- Tracks delegate voting consistency, proposal submission rate, and communication
- Example: A delegate with 100% voting participation and detailed reasoning reports is highly active
- Healthy delegation is vital for scaling participation and informed decision-making.
Treasury Governance Flow
Treasury Runway & Burn Rate measures the organization's financial sustainability based on governance-controlled expenditures.
- Runway = Treasury Value / Monthly Average Spend
- Tracks proposals that allocate funds from the community treasury
- Essential for ensuring the DAO can fund its roadmap without excessive token dilution.
Proposal Success & Conflict Rate
Approval Ratio & Fork Risk evaluates governance outcomes and cohesion.
- Approval Ratio: Percentage of proposals that pass vs. fail
- High failure rates can indicate misaligned incentives or poor proposal standards
- Monitoring contentious votes (e.g., 51/49 splits) helps gauge community schism and fork risk.
Analytical Frameworks for Governance
Foundational Governance Metrics
Voter participation and proposal lifecycle are the primary lenses for initial analysis. These metrics reveal the basic health and engagement level of a DAO.
Key Metrics to Track
- Voter Turnout Rate: The percentage of eligible tokens used to vote on proposals. A consistently low rate, as seen in some early-stage DAOs, indicates voter apathy or concentration.
- Proposal State Duration: The average time proposals spend in discussion, voting, and execution phases. Long voting periods on Aave or Compound can signal inefficient processes.
- Proposal Success Rate: The ratio of passed to failed proposals. A very high rate may suggest a lack of contentious debate or high proposal quality thresholds.
- Unique Voter Count: Tracks the number of distinct addresses voting over time, highlighting decentralization of participation beyond token concentration.
Analytical Starting Point
Begin by plotting these metrics over time for a DAO like Uniswap. Correlate spikes in turnout with major protocol upgrade proposals or treasury management votes to understand what drives engagement.
Governance Metric Comparison
Comparison of key metrics across popular DAO governance platforms.
| Metric | Snapshot | Tally | Compound Governance |
|---|---|---|---|
Voting Gas Cost | Gasless (off-chain) | ~$15-50 (on-chain execution) | ~$80-200 (full on-chain) |
Proposal Threshold | Configurable (often token-based) | Set by governance | 100,000 COMP (~$500k) |
Voting Delay | Minimum 0 blocks | ~2 days (for on-chain execution) | 2 days |
Voting Period Duration | Configurable (typically 3-7 days) | Configurable (typically 3-7 days) | 3 days |
Quorum Requirement | Configurable (e.g., 4% of supply) | Configurable (e.g., 4% of supply) | 400,000 COMP (~2M votes) |
Execution Type | Off-chain signaling | On-chain via Governor contract | On-chain via Governor Alpha |
Delegation Support | No native delegation | Full token delegation | Full token delegation |
Conducting a Governance Analysis
Process overview
1
Define Scope and Data Sources
Establish the parameters and primary data for your analysis.
Detailed Instructions
Begin by defining the analysis scope, including the specific DAO, governance framework (e.g., Compound, Aave, Snapshot), and the time period under review. Identify and access the primary data sources. For on-chain voting, query the DAO's governance smart contract (e.g., 0xc0da01a04c3f3e0be433606045bb7017a7323e38 for Compound Governor Bravo). For off-chain signaling, use the Snapshot GraphQL API or subgraph. Aggregate proposal metadata, vote casts, delegate information, and token distribution data. Establish a baseline for the DAO's total voting power and quorum requirements to contextualize subsequent findings.
- Sub-step 1: Identify the DAO's core governance contracts and their addresses on a block explorer.
- Sub-step 2: Determine the relevant API endpoints for Snapshot or forum discussions.
- Sub-step 3: Use a tool like Dune Analytics or The Graph to verify data availability and structure.
graphql# Example Snapshot GraphQL query for proposals query Proposals { proposals( first: 20, skip: 0, where: { space: "ens.eth" } ) { id title start end scores } }
Tip: Bookmark the DAO's official documentation for contract addresses and governance parameters to ensure data accuracy.
2
Analyze Voter Participation and Turnout
Measure and evaluate the level and distribution of voter engagement.
Detailed Instructions
Calculate key partipation metrics to assess governance health. Compute the voter turnout for each proposal as (Total Votes Cast / Total Voting Power) * 100. Analyze the distribution of participation: identify if voting is concentrated among a few large token holders (whale dominance) or broadly distributed. Calculate the average voting power per participant and track its trend over time. Examine delegate behavior by analyzing the number of addresses delegating to top delegates and the proportion of total power they control. Use these metrics to identify risks of voter apathy or centralization.
- Sub-step 1: For each proposal, query the
getProposalVotesfunction on the governance contract to get voter addresses and weights. - Sub-step 2: Aggregate historical data to plot turnout and average voting power over a series of proposals.
- Sub-step 3: Calculate the Gini coefficient or Nakamoto coefficient for vote distribution to quantify centralization.
javascript// Example: Calculating turnout from proposal data const totalVotingPower = 1000000; // From token supply snapshot const totalVotesCast = 150000; const voterTurnout = (totalVotesCast / totalVotingPower) * 100; // 15%
Tip: A consistently low turnout (e.g., <10%) may indicate systemic issues with proposal relevance or voter fatigue.
3
Evaluate Proposal Lifecycle and Execution
Assess the process from proposal creation to on-chain execution.
Detailed Instructions
Track the proposal lifecycle to evaluate process efficiency and bottlenecks. Measure the average time from proposal creation to a successful vote, and from vote conclusion to on-chain execution. Analyze the proposal state history (e.g., Pending, Active, Succeeded, Executed, Defeated, Canceled) to identify common failure points. Scrutinize the proposal success rate—the percentage of proposals that pass and execute successfully. For executed proposals, verify the on-chain transaction succeeded by checking the ProposalExecuted event. This reveals the practical effectiveness of the governance process.
- Sub-step 1: Query proposal objects to extract timestamps for creation, voting start, voting end, and execution.
- Sub-step 2: Calculate the delay between the vote ending and the execution transaction.
- Sub-step 3: Filter for proposals that reached quorum but failed execution due to revert or expired timelocks.
solidity// Example: Checking a proposal's state in a typical Governor contract function state(uint256 proposalId) public view returns (ProposalState) { // Returns enum: Pending, Active, Canceled, Defeated, Succeeded, Queued, Expired, Executed }
Tip: Long delays between success and execution can create operational risk, especially for time-sensitive proposals.
4
Assess Voting Coalitions and Delegate Influence
Identify patterns of aligned voting and the concentration of delegated power.
Detailed Instructions
Conduct a coalition analysis to uncover voting blocs and potential sybil attacks. Cluster voters based on their voting patterns across multiple proposals using similarity metrics. Identify delegates who consistently vote together, suggesting coordinated governance. Analyze the top delegates by total voting power received; calculate what percentage of proposals are decided solely by the votes of the top 5 delegates. Investigate if large delegates (e.g., venture funds, foundations) vote as a monolithic bloc or exhibit internal divergence. This step is critical for understanding the real power dynamics beyond raw token counts.
- Sub-step 1: Build a matrix of voters and their votes (For, Against, Abstain) across a set of proposals.
- Sub-step 2: Apply clustering algorithms (e.g., Jaccard index) to group voters with high vote correlation.
- Sub-step 3: Trace delegated voting power back to its source using delegate registry contracts.
python# Pseudocode for calculating vote similarity between two addresses from sklearn.metrics import jaccard_score # votes_a and votes_b are arrays of vote choices (e.g., [1, 1, 0, 2]) similarity = jaccard_score(votes_a, votes_b, average='micro')
Tip: A high concentration of power among a few correlated delegates reduces the effective decentralization of governance.
5
Synthesize Findings and Report Metrics
Compile analysis into key insights and actionable governance metrics.
Detailed Instructions
Synthesize quantitative data into a coherent governance health report. Create a dashboard of core metrics: Average Turnout, Proposal Success Rate, Average Time to Execute, Top Delegate Concentration Ratio, and Vote Distribution Coefficient. Contextualize these numbers against the DAO's stated goals and industry benchmarks. Highlight specific risks identified, such as low participation making the DAO vulnerable to attacks, or execution delays hindering agility. Provide actionable recommendations, which could include parameter adjustments (e.g., lowering quorum), implementing delegation incentives, or improving proposal tooling.
- Sub-step 1: Aggregate all calculated metrics into a summary table or visualization.
- Sub-step 2: Write a brief narrative explaining the implications of each key finding.
- Sub-step 3: Propose specific, measurable changes to the governance process based on the data.
json{ "metrics": { "average_turnout_last_10_proposals": "22.5%", "proposal_success_rate": "85%", "avg_execution_delay_days": 4.2, "power_held_by_top_5_delegates": "41%" } }
Tip: Frame recommendations around improving resilience, efficiency, and decentralization—the core tenets of robust DAO governance.
Governance Analytics Tooling
Essential platforms and dashboards for analyzing DAO participation, treasury health, and proposal outcomes.
Voting Power & Delegation Analytics
Vote delegation analysis tracks how token holders delegate their voting power to representatives. This reveals centralization risks and influential delegates.
- Maps delegation trees and identifies power concentration
- Tracks delegate voting history and consistency
- Measures voter apathy via undelegated token ratios
- Essential for assessing governance capture and designing incentive structures.
Proposal Lifecycle & Sentiment Tracking
Proposal sentiment analysis monitors discussion forums and voting patterns to gauge community alignment before a formal vote.
- Aggregates sentiment from forums like Discord and Discourse
- Tracks proposal state from temperature check to execution
- Correlates discussion volume with final vote turnout
- Helps predict proposal success and identify contentious issues early.
Treasury & Financial Health Dashboards
Treasury analytics provide real-time insights into a DAO's asset composition, runway, and expenditure patterns.
- Visualizes asset allocation across chains and token types
- Calculates treasury runway based on burn rates
- Tracks grant disbursements and major expenditures
- Critical for informed budgeting and long-term sustainability planning.
Participant Contribution & Reputation
Contributor reputation systems quantify member involvement beyond voting, including forum posts, code commits, and grant work.
- Scores contributions across governance, development, and community
- Often uses on-chain attestations or off-chain data oracles
- Helps surface active contributors for grants or roles
- Moves governance beyond simple token-weighted voting.
Cross-DAO Benchmarking
Benchmarking tools compare a DAO's metrics against peers to identify strengths, weaknesses, and governance trends.
- Compares voter turnout, proposal velocity, and treasury diversity
- Benchmarks against similar-sized DAOs or those in the same vertical
- Tracks adoption of new governance primitives like rage-quit
- Provides context for internal metric evaluation and goal setting.
SECTION-FAQ
Governance Analytics FAQ
Further Reading and Resources
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