The results of MasterCard Advisors’ first PayPass Adoption Study reveal that PayPass accounts spent almost 30% more than the average in the first twelve months of contactless transactions (view press release). There was also a correlation between contactless adoption and preference for a particular card, signifying that contactless payments may help drive ‘top-of-the-wallet’ behaviour. The study divided the accounts surveyed into low, medium and high spend segments based on monthly spend prior to adoption. The 30% lift was consistent across all three segments.
“In our highest spend segment, this lift translates into approximately USD600 per month in incremental spend,” said Jonathan Orndoff, principal at MasterCard Advisors and study lead. “Increases like this can have a significant impact on the issuer business case for contactless.”
Comprising the 30% lift for general contactless payments, recurring payments saw lifts of 11.8-28.5% depending on segment, e-commerce saw lifts of 8.8 – 33.3% and, most notably, cross border spend saw lifts of between 53.1- 79.1%.
The PayPass Adoption study is a quantitative analysis of US account transaction behaviour over the first 12 month period of contactless payments, beginning July 2009.
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