Stablecoin transfer rankings point merchants toward networks their customers rarely use, according to a comparison of blockchain flows and the payments that actually arrived at one processor’s checkouts.
Key Takeaways
- Tron and BNB Chain USDT took 75% of pay-ins.
- Those two rails moved 14% of on-chain value.
- Merchant pay-ins peak between 19:00 and 21:00 UTC.
- Three network fees sit between buyer and payout.
The numbers come from the November 10 public summary of Stablecoins in Commerce, the first report in Glassnode’s Money in Motion series with NOWPayments. The route comparison covers the 12 months from September 2025 to September 2026 and describes payments processed by NOWPayments, not the whole checkout market.
Where Shoppers Actually Paid
USDT on Tron and BNB Chain delivered about three-quarters of NOWPayments’ stablecoin pay-in value over those 12 months. On-chain, the same two routes carried 14% of combined USDT and USDC transfer value. Add USDT on Ethereum and the checkout share climbs to 86%, according to the processor’s companion post.
Run the comparison from the other end and the gap keeps its shape. The three coin-network pairs that moved the most value on-chain – USDC on Base, USDC on Ethereum and USDT on Tron – covered 54% of pay-in value. USDT on Tron sits in both lists, so the 32-point difference is made by what stands beside it: USDT on BNB Chain and Ethereum at checkout, USDC on Base and Ethereum on the blockchain.
Each percentage has its own denominator. The on-chain total counts every transfer of the two coins, including exchange and trading flows; the pay-in total counts only what this processor’s merchants received. The two measures answer different questions, which is exactly why one cannot stand in for the other.
USDC on Base, Sept 2025 – Sept 2026
45.2% of on-chain USDT+USDC turnover
0.6% of NOWPayments pay-in value
Which on-chain metric tracks checkout better
≈2×
The report says active-address distribution matched where merchants got paid about twice as well as transfer volume did.
Why the Volume Leaders Rarely Show Up at Checkout
The summary reports the gap without explaining it. Two factors visible in public data fit the pattern.
The first is where USDT lives. Tron held $87.9 billion of circulating USDT at the end of the second quarter of 2026 against $78.7 billion on Ethereum, according to Messari’s State of Tron report, and its transfers cost a fraction of an Ethereum transaction. A customer who already holds USDT is more likely to hold it on Tron, and many exchanges default withdrawals to that network for the same cost reason. BNB Chain plays a similar role for users who came in through Binance.
The second is what USDC on Base and Ethereum is doing when it moves. Those networks carry the bulk of lending, trading and treasury activity in the coin, large transfers between contracts, exchanges and market makers. The value is real, but it is not a person paying for a subscription.
Visa’s on-chain dashboard shows how much work filtering takes. Its adjusted volume drops any sending address that exceeded 1,000 transactions or $10 million in a 30-day window, strips bridge routing and internal exchange moves, and counts only the largest leg of a multi-step contract call. It also tags transfers under $250 as “retail sized”. Even after all that, its payments category still bundles goods and services with payroll and person-to-person transfers. A filtered blockchain figure narrows the field; it does not identify a shopper.
Count Orders, Not Just Dollars
Pay-in value answers a treasury question: how much money arrived? Order count answers an operating one: how often did customers pay? A store that receives one $10,000 payment through one route and 100 payments of $50 through another leads by value on the first and by customers on the second. Neither number alone says which route is worth keeping.
Active-address counts help pick routes to investigate, the report found them a better guide than volume, but an address is not a customer. One person can run several wallets, and a custodial exchange can push thousands of customers’ payments through a handful of addresses. Address data points toward a route; completed orders confirm it.
From Network Fee to Money in the Bank
The fee on a single blockchain transfer is the visible part of a longer journey. NOWPayments’ fee documentation describes a default flow with three network-fee stages – the customer’s transfer, processing and payout – plus a service commission. The same page says minimum payment amounts depend on the pay-in and payout pair, so a route that looks cheap in isolation may be unusable for a merchant’s smallest orders or for the asset it wants to settle in.
A fair comparison holds two things constant: the invoice size and the destination where the merchant wants its proceeds. It then lists everything deducted on the way there, processing, conversion, payout and off-ramp costs, and everything the customer paid on top. Shifting fees to the buyer moves the cost; it does not remove it, as Coindoo’s guide to the full cost of accepting crypto payments sets out.
The same comparison should carry a clock. A transfer confirmed on-chain in seconds is not usable money if conversion or a bank payout still has to happen, and those stages differ by route as much as fees do.
When Customers Pay, and Who Answers
Merchant pay-ins on the largest routes peak between 19:00 and 21:00 UTC, the summary finds, using the 39 full weeks to October 4, 2026, a different window from the route comparison. It describes broader on-chain activity as peaking hours earlier, without giving the hour.
For a merchant the question is practical: when do customers need help with an unpaid invoice, a delayed credit or a payment sent on the wrong network? The processor’s evening peak is a prompt to check whether support coverage matches a merchant’s own hourly exception log, not a timetable to adopt as is.
Run the Test on Your Own Checkout
Log the token and the network as separate fields, then follow each route from checkout selection to completed order and final payout. Four columns do most of the work:
- Demand: which routes customers choose, and which unsupported ones they ask for, an option that is not offered cannot show up in the data.
- Completion: how many payment attempts become completed orders, with the denominator defined before counting starts.
- Economics: what remains after all deductions, compared at similar order sizes.
- Operations: how long settlement and exception handling take, and how many tickets each route generates.
A higher completion rate on one route does not prove the blockchain caused it. Customers self-select, and ticket size, familiarity and checkout design travel with them. The report says where to look; a merchant’s own orders, usable proceeds and support records say whether a coin, network pair has earned its place at checkout.
Reporter at Coindoo
Kosta has reported on cryptocurrency markets and blockchain infrastructure since 2020, bringing over six years of hands-on experience in the crypto industry built through daily tracking of markets, trends, and emerging blockchain developments. Specializing in Bitcoin on-chain analysis, institutional ETF flows, and digital asset price action, his work at Coindoo has been cited by other news agencies and consistently covers market developments with a focus on data-driven reporting across Bitcoin, Ethereum, Solana, and XRP. Over the years, Kosta has contributed to multiple crypto media outlets in different regions, authoring over 6,000 articles across the sector. His reporting spans cryptocurrency markets and the broader fintech industry, tracking not only price action but also the technological and regulatory forces shaping the ecosystem. To support his analysis, Kosta actively leverages on-chain data and metrics from leading platforms such as Santiment, Glassnode, and CryptoQuant, enabling deeper, evidence-based market insights. He believes in the power of transparency and the data that underpins the blockchain ecosystem. His academic background in Marketing Management from Denmark further complements his analytical approach, adding a strong understanding of communication strategy and content positioning to his work.
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