Data is our best bet for understanding the past, present and future.
In traditional finance, obtaining data is relatively difficult. On the other hand, blockchain has broken down data barriers, giving everyone access to a ledger of information. However, the amount of data available has created another problem: finding valuable information is complicated.
There are dozens of tools to help people analyze Challenge data with a variety of measurements. However, these measures are mostly quite basic. In this article, we will present the most useful metrics as well as the most advanced data available on the next Footprint Analytics tool, DeFi 360.
TVL is usually the first metric people look at and it reflects the total value of all assets locked by users. TVL growth is often used to judge whether the project is in an upward trend.
TVL is measured in dollars. Since cryptocurrency prices change rapidly, it is difficult to see if the rise or fall in TVL is due to the price change or the addition of additional investments. Therefore, in addition to the TVL, attention should also be paid to the adjusted TVL.
For example, the statistics of Footprint analysis show that TVL in ETH from Liquidity (a lending program that can only lock ETH to lend stablecoins) is down slightly in the black frame of the chart below, while USD TVL is up. This is due to the rising price of ETH, which creates the illusion that the project is on an upward trend.
Complex projects like Aave and Yearn, which offer both lock and loan functions, complicate the use of TVL as a metric and require combining it with others.
Net liquidity refers to the change in inflows and outflows from the previous day or month. Changes can be further analyzed in terms of entries and exits, and whether the primary source is user entry or loss.
DEX-based protocols cannot be measured by TVL alone, as swaps also generate revenue. The ultimate goal of these types of projects is to maximize profitability and the revenues reflect their operating results.
Most platforms issue governance tokens and some use atoken model. The token data somewhat reflects market acceptance of the platform.
Price is the most intuitive measure, and rising and falling are closely related to supply and demand in the market. Price is also the fastest metric to be affected when an important event occurs. For example, Cream faced its second major attack on October 27, losing 130 million. This caused the price of CREAM to fall off a cliff.
For those who issue double tokens such as ManufacturerDAO and Liquiditytracking the number of DAI and LUSD minted can also reflect the degree of user participation.
The market capitalization of a token is the multiplication of the price and the circulating supply, reflecting the market value of a project in the DeFi industry.
Number of token holders and holding period
The number of token holders reflects the number of users who endorse the platform’s token model. The number of tokens staked to obtain governance rights is particularly large, which reflects the DAO situation of the platform.
The waiting time indicates whether the project attracts more users who believe in the long-term value of the project compared to speculators.
Trading volume reflects a token’s activity in the market and its ratio to market capitalization is similar to the turnover rate. Higher traffic reflects a token with a higher level of attention, while lower traffic is a token with less attention.
The usefulness of the token is also worth noting, i.e. whether the mined token is staked on the platform to use the governance token, or deposited in other external protocols to capture revenue.
For example, 61% of Liquity’s stablecoin, LUSD, is deposited in its own platform’s Stability Pool. LUSD does not play its role as a stablecoin in circulation compared to the usefulness of DAI.
All TVLs in the DeFi project are made up of pools, so finding the reasons behind the metrics means looking at the pool structure of a given project. For example: pool sizes, TVL and volume changes.
The project always revolves around users, and accurately capturing target users is at the heart of project development. Data classification and layered analysis of users can target quality users faster.
Global users can be subdivided into new and active users. New users reflect market expansion, while active users reflect the project’s continued growth potential.
Analyzing the trend of trade amount, holding amount and duration per user helps us understand the average quality of users.
Although averages can provide an observation of general trends in user behavior, they are still lacking as a basis for project implementation. Averages often dilute important data, and a deeper user layer is needed to find the real issues and develop the right course of action.
Whales generate the most value for the platform and developers need to prevent these users from churning. By sorting users, it is possible to create user portraits and focus on large accounts.
Insight into users’ investment preferences can provide a deeper understanding of users and uncover potential user groups by analyzing all DeFi platforms in which target users have invested.
Cross analysis refers to the comparative analysis of several measures together. Analysts and developers can use it to find correlations between metrics and create business hypotheses.
For example, if we compare APY with the number of users or with TVL, we can analyze the results to see if increasing APY attracts more users.
In another example, an analyst can compare the price of a project with the price of BTC to determine whether price movements are caused by changes in the intrinsic value of the project or larger market forces.
The Lego attributes of DeFi should not be overlooked either, with metrics from closely related projects often being a big factor in the move. For example, Convex helped drive Curve’s TVL growth.
There are many analytics tools on the market, but they usually stop at surface metrics.
For stakeholders to make data-driven decisions, it is crucial to dig deep and apply advanced analytics to blockchain data.
Date and author: February 09, 2022, Simon
The data source: Footprint analysis
What is fingerprint analysis?
Footprint Analytics is an all-in-one analytics platform for visualizing blockchain data and uncovering insights. It cleans and integrates on-chain data so users of any experience level can quickly start researching tokens, projects, and protocols. With over a thousand dashboard templates and a drag-and-drop interface, anyone can create their own custom charts in minutes. Discover blockchain data and invest smarter with Footprint.
Get your daily recap of Bitcoin, Challenge, NFT and Web3 news from CryptoSlate
It’s free and you can unsubscribe at any time.
Get a Edge on the Crypto Market 👇
Become a member of CryptoSlate Edge and gain access to our exclusive Discord community, more exclusive content and analytics.
Sign up now for $19/month Discover all the benefits