How "Machine to Machine" and "Cloud Computing" Figure into Mobile Commerce

Machine to machine communications, sometimes referred to a new "Internet of things," is viewed as a major growth opportunity by most larger mobile service providers in developed markets, for obvious reasons. 

To create large networks of distributed, small sensors that often are mobile or untethered, and must operate at relatively low costs per unit, mobile networks are ideal. Much of the discussion about real-world applications now focuses on telemetry applications in the energy and transportation industries, for example. 

Separately, lots of companies and developers are working on mobile wallets, mobile payment systems and mobile commerce systems that aim to glean real-time intelligence about potential customers and shoppers, before, during and after a visit to a retail location.

Underneath it all, software and applications are designed to work with heavy reliance on external data center processing of data. So it already is possible to forecast that cloud computing, M2M networks, smart phones and 4G networks will be used together to create new mobile commerce opportunities and services. 

In retail environments, retailers are looking at mobile apps as ways to identify all shoppers, connect them with their shopping profiles, and either sell them something or at least gain enough data about them to help make a sale during the next visit, an article in the Harvard Business Review suggests. 

That typically involves ways to correlate past purchase data, current offers or loyalty systems with present location, for example.  But there might be new ways to combine mobile commerce systems with machine to machine networks, or make the mobile network itself use the phone as a sensor, to create more shopper intelligence. The issue, for some, will be privacy issues. 

When a family or group shops together, data theoretically can be gleaned from the person who checks out. Typically, nothing is learned about the others who do not actually check out, bur are exercising buying influence in the store.

Facial-recognition software might be used to identify groups' sizes and estimate members' ages, which could allow stores to provide the customers with targeted displays, without requiring any detailed personal knowledge. 

For example, a car dealership could put minivan ads on monitors as a family walks up to the showroom door.

In a more intrusive application, a RFID reader could, in principle,  wirelessly glean details from a credit card that never leaves a pocket or purse, as a person enters a store. 

Encouraging shoppers to use a shopping app while inside the store is one less objectionable way to correlate location inside the store with delivery of context-dependent coupons or suggested products. 

High-speed processing, typically using cloud-based mechanism, is a must, because customers don't linger long at any one physical spot when shopping.