On Tech, Business, Society.

Tag: metrics

Pulse, Impact and Breadth (PIB): A Simple Framework of Metrics for Evaluating Cryptocurrencies and Tokens

The valuation of cryptocurrencies and tokens has been an ever challenging topic in the blockchain space. Numerous methods and formulas have been proposed. However, in my opinion, nothing has yet emerged as being generally accepted or a de-facto standard, in the same way the widely accepted “earnings-per-share” (EPS) is the common metric traditional financial markets adhere to. 

Before jumping to devising models and equations, I think we need to have good visibility on the basic units of “input data” that could be used to then construct such formulas or later develop quantitative methods. 

Last year, I enumerated a number of Blockchain Metrics for quantifying usage. It was a step in the right direction, but here I’m organizing 9 metrics as the essential ones on top of which valuation frameworks could be constructed. Here, I have focused on bringing visibility to few metrics that have potential characteristics of being absolute in the sense of clarity, and therefore, they could carry little ambiguity when being published.

I believe that the industry needs precise data points that cannot be challenged (similar to the EPS analogy). For example, the number of public shares a company floats or issues is known and can be trusted as the real number. This is why the EPS number is trusted and cannot be debated. The EPS multiples that analysts decide to project for a given stock to derive market capitalization is a subjective number that is chosen later. 

That is why, for the blockchain sector, as a starting point, we need fewer, but more essential data points, not a panoply of metrics with no head or tail. Furthermore, the language around the metrics should be clear, non-technical, and easily understandable. 

The following is my attempt to list the essential data points organized under the PIB moniker, referencing the following descriptive sub-categories: Pulse, Impact, Breadth. 

In the field of quantification methods, there are inputs and outputs. There is causal activity and there are resulting effects. These metrics I’m proposing are in the “input” and “causal” categories, which means they can be used to in a variety of analysis methods to derive resulting outputs and effects.


The metrics in this category pertain to the dynamics behind the network’s operations. 

P1 Number of (active) Nodes 

The nodes are an essential native unit for blockchains. 
What is the number of nodes that are servicing the underlying peer to peer network?

P2  Ownership distribution of nodes 

Decentralization is a key attribute, and it can be argued that a more even distribution of ownership is better for the future health of the network.
What is the ownership distribution of the nodes in operations? 

P3 Speed of transactions 

Although public blockchains (and many dApps) are not known for their transaction speeds, how quickly are transactions finalized is an important factor that indicates the actual throughput of a blockchain or app.


In this category, we look at the financial and economic aspects behind the cryptocurrency. 

I1 Transactions Volumes (in fiat denomination)

Here, we need to make a clear distinction between currency-to-currency transactions (C2C) where the user is just exchanging one cryptocurrency for another (I1a), versus on-chain transactions that are used for a given utility, eg. earning/spending, gas costs, staking costs, etc. (this excludes the native earnings from mining or minting activity (I1b). Here, I1 = I1a + I1b. 

I2 On-chain Revenues 

These are the revenues from mining, minting, or rewards related distributions.

I3 Values in Wallets

What is the fiat-denominated value of current holdings in all issued wallets? See B3 to also include the segmentation between user vs. non-user wallets. Here, I3 = I3a (users) + I3b (non-users).


Breadth pertains to the available token/cryptocurrency. It is closest to the number of shares (float, restricted, issued) in public stocks.

B1 Units in Circulation 

What is the total number of tokens/cryptocurrency units in circulation? (both in the hands of users or investors)

B2 Units Held

What is the number of units that are either un-vested, un-minted or not yet issued? (publishing a vesting schedule is useful)

B3 Number of Wallets 

A distinction should be made between the wallets being held by users versus non-users. Users include app end-users or developers that need to use the token or underlying cryptocurrency to run their programs. Here, B3 = B3a (users) + B3b (non-users).

It is my opinion that we need to start with the above metrics before constructing valuation models. Once these numbers are available, any analyst can decide what equations to fit them in, what multiples or weights to give them, and how to construct various methods to derive meaningful comparative metrics. These can apply for blockchain protocols, networks and applications. 

I encourage crypto projects, blockchains, analysts and data sites (I provided a list to some of them in this blog post, Where Are All the Decentralized Applications) to help make that data easily available so that we don’t have to spend time finding it. It is the responsibility of each issuer to make sure their data is visible and easily measurable with integrity. 

Of course there are other metrics, but I believe these are the ones where a differentiated analysis can be built upon. Almost everything else could be a derivative of the above numbers. Many other blockchain/protocol metrics are table stakes or vanity metrics not worthy of comparative studies.

In a subsequent post, I’m going to propose some meaningful derivative ratios to consider, in the same way that the EPS is a meaningful ratio and reference point. As usual, I welcome suggestions on what these ratios should be, and any feedback on the proposed PIB framework.


A Guide for Blockchain Usage Metrics

Token Used by William Mougayar We are in dire straits for token usage metrics, not just to vindicate the “utility token” moniker, but also to eventually bring some sanity into how we could evaluate some of these coins. In the long term, I believe that not all coins are equal. And not all coins deserve the same valuation metrics. Super tokens like Bitcoin or Ethereum are multi-purpose, and they might deserve extraordinary multiples in part because it helps them secure their sovereignty as bona fide consensus protocols with strong crypto-economic defenses against theoretical attacks. (although with POS the attack equation changes a bit.) Aside from technical protocol coins, most other coins will be more closely aligned with end-user traction as the primary correlation factors for their valuation. This includes all flavors of app coins including the upcoming variety of so-called governance coins. The purpose of a coin is to create economically valuable business models directly or indirectly via the ecosystems they engender. Blockchain-based transactions are arguably a key traction metric. Of course, market prices can be influenced by news instead of usage activity, or they can be artificially influenced by lopsided token distributions that affect price elasticity as a result of the scarcity of the trading pool. But these are short term, centrally manipulated tactics that eventually run their course. You can amplify and concoct your news for so long before that game is up, and you can lock your reserve tokens for so long until eventually your Token economic models catch-up with reality.

If you look at CoinMarketCap, you get a macro view of the market, but if you look hard at the project levels, the real traction will be revealed from the bottoms-up metrics related to real usage. Below is a list of the Macro vs. Traction View metrics, as I see them.

The Macro View is Top Down whereas Market Reality Traction happens Bottoms Up via Outcome and Activity metrics.

Macro View

  • Market Cap
  • Daily Trades
  • Amounts raised
  • Number of Coins
  • Number of Apps

Traction View

Outcome Metrics
  • Active Users (Daily, Weekly or Monthly)
  • Total transactions (number)
  • Value of transactions (value)
  • Balances in smart contracts (value)
  • Transactions to/from contracts (number)
  • Value of transactions to/from contracts (value)
  • Blockchain rewards to users (value)
  • Wallets value (value)

Activity Metrics (numbers)

  • Unique addresses
  • Active addresses
  • Wallets
  • Nodes
  • Transactions per second
  • Blocks per hour
  • Developers
  • API calls
  • Software downloads
  • Repositories
  • Commits
  • App downloads
  • Clients
Tracking Blockchain Metrics by William Mougayar Here is my favorite (short) list of traction metrics (Testnet transactions don’t count): Ethereum: 52% of transaction types going to smart contracts (Source: Amberdata) CryptoKitties: 2.8 Million smart contract transactions in first 6 months (Source: Dappboard) 0x: Over 100,000 lifetime trades (Source: 0x Tracker) Until there are real revenues and profits (if that applies), I’d like to see more traction metrics from blockchain companies. We need to see them soon.]]>

Y Combinator, Advocate Marketing, Product/Market Fit, Gamification, Customer Retention, Metrics, Boards, SEO, Product Mgt, Roundup#15 Oct 20 2013

Startup Management is a manual selection from the hundreds of weekly articles being curated. Previous issues are available here. There are 24 article links in this edition.

Y Combinator Startup School

Here’s the Google Docs with lectures notes from the seminal Y Combinator’s Startup School event that just happened on Oct 19 2013,

Software Engineering, Business Models, Culture, Management, Metrics, Analytics, Entrepreneurship, Growth, Marketing, Weekend Roundup#14 Oct 13 2013

Startup Management is a manual selection from the hundreds of weekly articles being curated. Previous issues are available here. There are 32 links in this mega edition. I couldn’t narrow it further. Too much good content! Have you been forwarding to friend, so they can sign-up and benefit too? You can help, by forwarding to 5 friends.

Powered by WordPress & Theme by Anders Norén