The power of N=1, R=G has become very relevant in the Web 2.0 world, where a lot of end customer data is accessible and can be used to customize the experience for every individual visiting an online site, as opposed to the traditional retail model. The R=G part is an ecosystem.

In brick-and-mortar stores, traditionally, a lot of analysis was done to segment and drive the right traffic to the store, followed by advanced merchandising and pricing methods to drive up sales. However, in the new 1-to-1 online world, we are more advanced in how every movement of the visitor to a store can tracked.

Starting with what they typed into Google, where they came from, what they are doing in the store in real-time, to what they like and dislike on display, and hence what to do to keep them engaged, the whole experience can be predicted and optimized to produce the right results. And this can be predicted through advanced mathematical models. So one can go to the extent of predicting what the behavior of visitors will be on the website and then engaging with them on a one-on-one basis.

For example, one can predict, for any given visitor, the probability of purchase. So one can predict that a male visiting the store on a Wednesday afternoon between 3 pm and 5 pm, coming through a cable connection from San Jose, with a zip code ending with 42, is more likely to buy than a woman coming in from San Antonio, with zip code ending with 18, on a Thursday morning between 10am and 11am, through a dial up connection, browsing the jewelry section for more than 5 minutes. Typical N=1.

The main difference between the offline store and the online world would be that the N=1 is predictable and hence meets the need of the visitor who needs assistance. And the R=G part of it can be done all over. It is an ecosystem comprising the store’s business managers, who may be in the United States, the agents, who may be sitting in Mexico or anywhere else in the world, the mathematicians predicting the interaction, who may be sitting in India or Eastern Europe, all in one collaborative ecosystem that ultimately converges to provide a unique experience for every visitor who comes to the website.

Flip this on the service side -- the same logic applies. We can end up predicting who needs service in what channel, for what problem, when, and then drive an individualized end-customer service experience through an ecosystem of global resources.

N=1, R=G accurately summarizes how data and digitization are helping next-generation companies compete successfully. And it will define the lines between companies that get it and those that don't.