More data, more money. Payments company Square could be the first Big Data Payments company. The potential exists for quite a few reasons, but here are a few:
Reason #1: Payments without Borders Think of Square as kind of a floating "Point of Sale" terminal. That means Square can take a payment in places that traditional Point of Sale terminals such as Radius, Aloha and Micros cannot. The fact that you can turn any Tablet or Smartphone into a PoS (industry slang for Point of Sale) means that Square can capture Yotta Bytes of unstructured data that previously went unused. Unstructured Data is information that exists, but is never captured, stored, categorised or analysed, but could be valuable to some party or another.
Example? At the Alcatraz Triathlon, a Cliff Bar employee was walking around promoting the new SHOT Blox endurance nutrition product. If you are not an endurance athlete, SHOT Blox are basically gummy bear-like chewable gels packed with nutrients. With a Square-enabled iPad, the Cliff Bar employee could allow people to buy SHOT Blox on the spot, right after sampling it, paying with a credit card.
Now Square, not the Bank of America the card issuer or VISA the transaction issuer, would know a great deal more about what happened in the sales funnel. Square can know every place the sales were made in relation to the nearest Blox retailer, which is structured data easily attainable from the Cliff Bar distribution team. Analysis would yield conclusions about when and where the "Conversion" event in the sales funnel took place, and allows for more accurate attribution of credit to advertising and promotional efforts. Square's analytics about the transaction quickly become more valuable than the transaction itself.
Reason #2: Merchant Analytics One of the most important elements of the (ART+DATA) Principle is that a beautifully designed product manages "playback" of data elegantly to its users. Data becomes a part of the Design itself. Square has put itself in the position to give the merchant (its customer) real time information on where, exactly, various products were sold to people at the Alcatraz Triathlon.
Maybe 20 percent of the shirts were sold near the finish line, whereas 75 percent of the caps were sold near the transition point from swimming to cycling. This previously unstructured data could be valuable. Amazon, AirBnB, EBay and YouTube are masters at playback analytics that inform and entertain customers about their own activity. Square can bring playback analytics to merchants and consumers that giant payments industry players cannot.
Reason #3: Proximity By separating the Point of Sales from a scheduled time and stationary place, Square could easily plot the real-time locations of similar merchants on a map on the Tablet or Smartphone. If greater than 60 percent of the endurance drink merchants are crowded near the "bike to running" transition point at the Alcatraz Triathlon, a given merchant could make the decision to move to another location, or flood the transition point with 200 percent more sales people in an effort to overwhelm the competition.
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