Mobile payment firm Square is adding customer loyalty features to its app as it attempts to boost its value to both merchants and consumers in the face of increasing competition. The firm is rolling out the update to its ‘Square Register’ app, which allows users to transform an iPad into a cash register, and its ‘Pay with Square’ mobile app, which lets users pay for goods using their smartphones. Merchants can now offer deals such as first-visit reductions to their customers, as well as dolling out personalised loyalty cards to bring in new business and encourage repeat custom from existing customers.
The move comes a week after Square announced that it is now handling USD6 billion in mobile transactions annually, up 20% from the volume of payments in April, when the firm reported USD5 billion. The figures show growing demand for the firm’s plug-in card reader, which allows merchants to take payments from customers using a tablet or smartphone device. Although this milestone indicates that the startup is beginning to mature, Square faces mounting competition, in particular from PayPal, which recently launched its own plug-in card reader.
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