USD275 per month
Mobile payments provider, Square, is introducing a new pricing plan targeted at small businesses (view press release). Square is looking to gain an advantage over its rivals by offering smaller merchants a flat monthly usage subscription for its mobile payment products instead of making them pay for every transaction, in an effort to streamline its service and boost uptake. Square is giving shops and restaurants making less than USD250,000 annually the option to pay USD275 per month, instead of paying 2.75% commission on every m-payment they take using Square’s plug-in card readers and smartphone apps. As a result, businesses taking more than USD10, 000 m-payments per month would pay less to use Square, and this is likely to raise stakes in the m-payments space as a rising number of services compete for merchant loyalty.
“For 62 years, merchants have suffered complicated, expensive processing fees. Square is the first company to rethink electronic payment pricing with the merchant in mind. We are giving merchants affordable, predictable pricing,” said Square CEO and co-founder Jack Dorsey. “With one monthly price, merchants know that the sales they’ve processed in a day is the same amount deposited in the bank.”
Whitepapers
Related reading
Central banks best suited to issue digital currencies
By Aaran Fronda A recent report by the Official Monetary and Financial Institutions Forum (OMFIF) said that central banks rather than private ... read more
Instant payments: innovations inbound for corporates
In 2020, instant payments look set to continue their current trajectory to become the biggest trend in payments. While these schemes already offer numerous benefits to corporates, leveraging innovations such as APIs and request to pay will go some way to unlocking their full potential, argues Michael Knetsch
Obstacles exist for banks to meet ECB’s instant payments goal
The cost of joining instant payment platforms will be one of many hurdles banks and payment services providers must overcome to meet ... read more
Banks must be aware of “biases” in data used to train ML models
Financial institutions need to be conscious of biases in the historical data that is being used to train machine learning (ML) models, ... read more
