Kiwi | what you do, how you feel



Tap to Pay: How Sensors Fuel Your Morning

Tap to pay is probably the most notable feature of your Starbucks app. It makes things a little quicker - you order, go in, scan your phone, get your coffee, on with your day.

 

What if there were ways to make this even more efficient so your morning fuel-up is a little more bearable?

 

My Starbucks Rewards

Starbucks recently revamped their loyalty program, and although the coffee chain is incredibly successful, public sentiment on the changes have been largely negative. Strengthening brand loyalty by attaining Starbucks Stars and the prestigious Gold Status had people buzzing when this fuel house launched their rewards program in 2009.

 

By constantly innovating on their brand, Starbucks has risen as one of the top coffee brands in the world. Instead of changing what people already love about the app, perhaps it's time to make changes that affect other things - like efficiency.

 

They even uploaded an instructional video

 

Adding value through motion software

Is there a way to use motion context to add even more value to the My Starbucks Rewards program?

 

Simple answer: Yes.

 

Imagine having your GPS set a radius boundary on a coffee shop, and if you are within the radius during a certain time of day, it would automatically order for you.

 

The GPS can be used to determine your distance from the store, and the accelerometer data from your phone can be used to detect if you're walking and on your way.  It can even go a step further and add Bluetooth payments when within radius for the pre-made drink. This would decrease overall time spent by consumers, which is vital especially during the morning rush.

 

This would also allow more baristas to focus on making drinks rather than taking orders, which would increas the efficiency for the baristas as well.


Combining GPS, motion sensors and Bluetooth payments can be a great way to increase overall efficiency in consumption and with patience in the mornings.



John David Chibuk

Toronto, Canada | http://kiwi.ai

Entrepreneur in machine learning and sensor based software development (>10 years). Background in engineering, tech with professional experience in North America and Europe.