An Overview of Relevant Metrics in Web 3.0

A high level overview of the Metrics we are tracking in Web 3.0 Networks from Bitcoin & Ethereum to MakerDAO and Beyond.

Max Mersch
Jul 9 · 8 min read

Following up on our post on what business models and revenue flows might arise from Web 3.0 we started thinking about how we might value and monitor such business models. Having worked through an aggregate of seven VC funds, we have no doubts about the importance that key metrics play in venture investing when evaluating the performance of a company:

  • “What’s the current MRR growth?”
  • “What churn do you have across different cohorts of customers?”
  • “How many DAUs do you have?”
  • “What’s your net contribution margin per customer?”

These are all metrics that measured very important aspects of Web 2.0 business models, but they do not apply to all Web 3.0 business models. Trying to forcefully apply them will not bring any results — just like one would not evaluate a lender by their number of “daily active mortgage users”, one shouldn’t analyse the healthiness of the MakerDAO ecosystem by its number of daily user interactions. Equally, MRR growth rates and churn rates don’t make sense to calculate when there is no central company making these revenues.

As investors in the Web 3.0 space, we have refined different types of metrics we use to evaluate networks based on their specific use cases and business models. From hash-rate, to gas usage, to value locked, this post will dive into some of the Web 3.0 native metrics.


The premier Proof of Work native asset, mainly used as a store of value

Hash-rate: the hash-rate of the Bitcoin network is the aggregate work put into the system by all the miners and hence is a representation of the network’s security. With the probabilistic finality of Bitcoin, any set of actors with 51% of the hash-rate can create the longest chain around which consensus forms: a higher hash-rate implies a higher difficulty to attack the network.

Number of miners controlling 51% of hash-rate: indicating the degree of centralisation of the network around certain miners & mining pools. The lower the number, the higher the risk of collusion for an attack on the network.

Number of transactions: can be used to represent the usage of the network as a means of exchange, but not to adequately represent the usage of the network as a store of value. Can be gamed by a set of bots spamming a high number of low value transactions, and can be a misrepresentation as one transaction can encode many transfers via batching or sidechains / Lightning Network.

Aggregate transaction value: the aggregate value of all transactions within a certain time period, showing the value transferred over the network.

Total market capitalisation: as a store of value, the total market capitalisation of Bitcoin matters as a representation of the amount of value it is entrusted with (with a caveat that some Bitcoins are lost or dormant).

Block rewards available to miners: main source of revenue for the miners securing the network. The block reward multiplied by the price of Bitcoin represent the value available in exchange for hash-power.

Transaction fees paid to miners: source of revenue for miners that should increase in importance as the block rewards continue halving. Expected to become the primary source of revenues for miners securing the network in the coming decades.

Lightning Network

Bitcoin’s Layer 2 scaling solution (written by Jeremy Welch from Casa)

Network Capacity: The total number of Bitcoins committed to the Lightning Network. Because channel transactions are completely private, this metric is the main indicator of how much wealth is being transferred on the system.

Number of nodes with active channels: Total number of Nodes indicates how many peers are actively trading on the Lightning Network, and also routing funds for other nodes on the network.

Number of payment channels: Total number of channels indicates how many direct node-to-node connections have been created. An increase in Nodes should also see an increase in channels, and as the number of use cases for Lightning Network grows, the rate of channel growth should increase faster than total nodes.

Average Channels per Node: This will likely increase as more use cases for Lightning emerge, then might decrease as the network density increases (and the ability to route via other nodes instead of direct becomes easier)

Number of Tor Onion Service Nodes: By default Lightning connection codes include the IP address and port of the Node. IP address can then be used to determine the physical location of nodes which is a major OpSec risk. Using Tor Onion Service, it is now possible to connect without revealing physical location.


The largest smart contract platform for application developers

Can be measured by some of the same metrics as Bitcoin around hash-rate and transactions, but includes many more:

Total gas usage: a metric that represents both the number of transactions / smart contracts used and the complexity of the smart contracts being used. Used to illustrate the total usage of the Ethereum network as a decentralised computation platform (with the caveat that transaction spamming can skew this metric heavily).

Average gas usage per transaction: illustrating the complexity of smart contracts / computations used on the Ethereum network.

Average gas price: dependent on the number of transactions at a given point in time— representing the usage and congestion of the Ethereum network.

Value locked up in DeFi: for the current core set of applications built on Ethereum in the Decentralised Finance space (DeFi), ETH is the main source of collateral being used. The amount of value locked in DeFi represents the usage of DeFi applications and positively influences the price of Ethereum (less liquid supply). Largely dominated by MakerDAO, but with traction from Compound, Uniswap, Dharma and Synthetix amongst others.

Number of developers using Truffle/Ganache/Zeppelin: as a smart contract platform, the number of developers working on Ethereum based applications is one of the most important metrics to track. The best quantifiable proxy could be the number of Truffle/Ganache/Zeppelin users (developer environments and libraries), while keeping in mind that every download does not equate an additional user / active developer.

MakerDAO & DAI

The largest decentralised stable coin & lending platform

Total value of loans issued: representing the total usage of the MakerDAO platform, comparable to other Web 2.0 lending providers.

Number of open CDPs: as a proxy for the total number of users of the platform, considering that an open CDP is the closest approximation to an “active user” of MakerDAO.

Value of stability fee: derived from the stability fee (currently 16.5% annually) and the total value of loans outstanding, the value of stability fee is burnt in MKR, leading to a theoretic increase in value of MKR tokens (lower supply).

Collateralised value: the value of all assets (currently only ETH) locked up in CDPs as collateral for outstanding loans.

Collateralisation ratio: the ratio between total value locked up in CDPs and the value of outstanding loans, representing the risk rate of the overall system (CDPs that reach below 150% are automatically liquidated).

Default rate of CDPs: percentage of CDPs that fall below 150% collateralisation — used to evaluate the optimal stability fee and collateralisation ratio required.

MKR voting participation: involvement from the MKR holder community in governance decisions around stability fee increases. For a token that largely bases its value on its governance features, this will become a useful metric to track in the future.

DAI stability/peg: the result of the market incentives set by the MakerDAO system to keep the supply and demand of DAI in similar ranges. Can be moved by increasing or decreasing the stability fee, and with the future Daily Savings Rate.

Work Tokens

Supply-side coordinating tokens such as Keep, Augur or Livepeer

Staking participation rate: a metric that tracks how many token holders are active within the economy of the network. Networks aiming to get above a certain threshold of participation can increase their block rewards whenever staking participation rate dips below a certain threshold (e.g. Livepeer increasing its inflation daily until it reaches 50% active participation).

Staking yield from block rewards: the yield provided to the active participants of the network, acting as a rebalancing method from passive holders to active holders. In a network with a token purely used to incentivise the supply side, the passive holder is not value additive and as a result should be diluted.

Work activity: the amount of work being done on a given network: securing Keeps on Keep Network, resolving markets on Augur or transcoding videos on Livepeer. This metric shows the real usage of the network and is currently still very low or nonexistent in most networks as they are just getting started & launching

Aggregate revenue flows to validators: the value of fees paid by users of the network to work providers of the network. The aggregate value paid for securing keeps in Keep, resolving markets in Augur and transcoding video in Livepeer. This should be the most important metric when valuing a network — by performing a discounted future cash flow analysis of these revenue flows aggregated across all validators one can assign a fair & rational value to holding these tokens whose sole purpose is granting the right to perform profitable work.

All these metrics come with different caveats and restrictions — they do not tell a clear story by themselves, but they do give a good indication of the direction a certain network is growing into.

This post serves as a high level introduction to quantifiable metrics we believe everybody should be tracking, and we look forward to the development of both the networks and the metrics that accompany them.

With thanks to Jeremy Welch and Nic Carter for their insights, contributions and corrections.

Written in collaboration with the whole team at Fabric Ventures, with numerous corrections from Julien Thévenard.

Fabric Ventures

Investing in decentralised data networks powering the shift towards human-centric computing

Thanks to Julien Thevenard

Max Mersch

Written by

Building Fabric Ventures || Imperial College & OpenOcean Alumni

Fabric Ventures

Investing in decentralised data networks powering the shift towards human-centric computing

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