Aiming to change the way merchants engage with consumers, iMobile3 has partnered with Ingenico to deliver a comprehensive platform for frictionless mobile payments at the point of sale using Bluetooth LE (BLE) devices, or “beacons.”
iMobile3 will be presenting the second-generation of its popular PassMarket solution in the Ingenico booth during the National Retail Federation (NRF) Big Show, January 12–15, 2014 in New York City.
PassMarket’s cloud-based, multi-wallet platform supports Apple Passbook and Google Wallet. This year, iMobile3 introduces 100% frictionless mobile payments by integrating its US Patent Pending PassMarket Beacon technology into Ingenico’s Telium-based family of payment devices.
“iMobile3’s PassMarket Beacon solution tightly integrates into our omni-channel retail strategy by further enhancing the consumer experience across a variety of touch points,” said Thierry Denis, President of Ingenico North America. “Collectively with iMobile3 and their PassMarket solution, we are offering novel ways for retailers to engage with customers and improve their shopping experience before they even walk in the store.”
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