
San Francisco-based near-field communication (NFC) startup Tagstand, which produces and distributes customised NFC stickers, has secured USD1.1m in an initial funding round. Contributed by a host of angel investors and networks, including SV Angel, TEEC and Israeli venture fund Vaizra Investments, the cash will be used to maintain the company’s growth, which the firm says currently stands at 50% month-on-month. Over 20,000 Tagstand stickers have been sold to date, with a third of orders coming from business clients and the remaining two thirds from individuals.
NFC allows smartphone owners to access web content by tapping their phones on specially designed stickers. Tagstand’s hardware offers a URL-shortening and redirection service similar to bit.ly, which allows stickers to be re-configured for different addresses after being deployed. The technology is expected to take off with advertisers as it becomes integrated into an increasing number of handsets over the coming months and years, with significant growth already occurring in markets such as Australia, where Tagstand has worked with supermarket chain Coles to launch a series of ‘smart-posters’.
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