Mobeam, a mobile couponing and ticketing company, is partnering with ProLogic, a coupon processor and clearing house, to enable the processing of digital coupons on mobile devices. The companies aim to integrate their technologies to close the gap that exists between the current digital coupons available on consumer mobile devices and their redemption at point of sale. By combining Mobeam’s light-based technology and ProLogic Digital Coupon Clearing solution, the companies will create a digitized coupon system for consumers and retailers. Consumers will be able to acquire and redeem coupons digitally, while retailers can digitally process and clear the coupon to complete the transaction loop.
Mobeam’s technology makes digital coupons and other content presented on a mobile device universally scannable by a POS system. The company’s technology turns barcode data for coupons, content, or offers into a beam of light that can be read by barcode scanners presented at store checkout counters. When a coupon is presented at the POS and is digitally redeemed by a shopper, ProLogic’s Digital Coupon Clearing solution will complete the digital coupon process.
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