Stablecoin retail payments account for only 0.6% of total transaction volume, but transaction counts make up half.
Author: Ethan Chan
Translation: Deep Tide TechFlow
Average transaction size on the Visa network is $45
Earlier this week, in a recent podcast with the Visa Crypto team, Cuy (Head of Cryptocurrency) and Noah (Head of On-Chain Data), we jointly introduced a simple and elegant method for analyzing stablecoin data, leveraging a unique key data point from Visa:
The average transaction amount for all Visa transactions over the past 12 months (as of March) is $45. (Source)
Unadjusted, adjusted, and retail-sized payments
Before diving into the data, let's quickly review the methodology we used for the Visa x Allium Stablecoin Analysis Dashboard.
We take the average of $45 and assume that most retail transaction amounts fall between $0 and $250. This makes sense, right? After all, how many retail transactions exceed $250 daily—people buy coffee, clothes, etc., and the amounts are usually not that high.
Motivation for the methodology: If we buy a $5 coffee with a Visa card, that $5 will flow through two different banks, one at the buyer's bank and the other at the seller's (the coffee shop) bank, and through intermediaries like credit card networks and processors. If all these activities are recorded on the blockchain, the transaction amount will be several times the $5, but in our analysis, we only focus on the $5 payment for that cortado.
Although retail-sized stablecoin transfers account for only 0.6% of the adjusted nominal transaction volume, they represent about 40% of all adjusted stablecoin transactions.
We can see that the "retail-sized" turquoise is almost invisible on the dashboard, as it only accounts for 0.6% of the adjusted stablecoin transaction volume.
Visa Onchain Analysis Transactions
This aligns very well with our expected retail payment environment—small, high-frequency behaviors. For example, buying coffee, sending money to friends, or paying bills. Although these payment amounts are small, they dominate in transaction counts, just like traditional consumer spending.
A closer look at the retail-sized distribution of USDC and USDT
About 80% of retail-sized stablecoin transfers are settled in USDT: We know that USDT is increasingly used in emerging markets, especially in everyday retail payments and cross-border transfers. This aligns with USDT's positioning as a unit of account and store of value in markets with local currency volatility or strict capital controls.
Note: Retail-sized transaction volume is a subset of adjusted transaction volume (0.6%)
About 65% of adjusted stablecoin transfer volume is settled in USDT, while only about 40% of all (unadjusted) stablecoin transfer volume is settled in USDT: This indicates that USDC has stronger penetration in enterprise use cases such as high-frequency trading, netting, and B2B payments.
Retail-sized transactions segmented by blockchain
Retail favors low fees + high throughput chains: Both USDT and USDC retail volumes occur on high-throughput chains, which is common.
76% of retail-sized adjusted USDC transactions occur on Solana, Polygon, Base, and Arbitrum, while 85% of retail-sized adjusted USDT transactions occur on BSC and Tron: This is because USDC is favored by U.S. platforms, while USDT dominates in emerging markets like Latin America and Southeast Asia, where BSC and Tron are more accessible and are often integrated into local wallets, exchanges, and payment applications (Binance P2P, TronLink).
Retail Size Adjusted Source: Blockchain
We are still in the early stages!
Through a simple $45 assumption and retail-sized filters, we can clearly break down the current landscape of "real-world" payments. These small transfers, while nominally low in value, account for nearly half of the adjusted transaction activity count!
We are still in the early stages of this journey and look forward to continuously tracking these metrics as the industry develops, while also welcoming feedback to explore the path forward together. If you're interested, we discussed some related data content in the podcast; for details, please check here:
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