MasterCard is partnering with shopkick, a retail rewards app, enabling cardholders to earn ‘kicks,’ a reward currency, when they link their card to shopkick’s Buy & Collect programme and make qualified purchases such as for music downloads, gift cards and movie tickets (view press release).
An added feature allows consumers to receive 250 bonus ‘kicks’ for each MasterCard card they link to the Buy & Collect programme for a limited period. Shoppers who link their MasterCard card to Buy & Collect will also qualify for offers such as “spend USD 40 and receive 250 kicks” at retailers including American Eagle Outfitters, Arden B, Crate and Barrel and Wet Seal.
Central to the app is a patent-pending “shopkick Signal” location technology, which detects when the user is present in partner stores, automatically crediting rewards to their account and delivering relevant offers.
“We’ll be able to help consumers maximize rewards while also supporting their interests and passions, all through the simple download of the app and linking their MasterCard card. At the same time, our merchant partners will benefit from a deeper engagement with their customers,” said Mario Shiliashki, group head of Emerging Payments at MasterCard.
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