The purpose of "big data" is to give managers a clearer vision of something so they can make decisions about products, pricing and distribution. In pricing, it is generally around how customers buy.  It uses statistical techniques to group customers into definable segments and helps you figure out how to sell to those segments.

There is nothing new about "big data"; the statistical techniques that drive it have been around for decades.  What is new about it is that company data bases are increasingly accessible for the analysis to be performed.

The results of using better analysis of customer information include the ability to increase prices, sell more efficiently, sell corresponding products and services and generally improve your go-to-market approaches.

Big data answers "what".  That is, it says what customers do or don't do.  The big problem is that it doesn't answer "why".  It doesn't say why customers do the things they do.  This is the critical element if you're trying to improve your sales process.  Sure, salespeople can sell insights gained from big data, but without the "why", those efforts are internally focused and ineffective.

Our advice is to use analysis of internal data to reveal the “what” of customer behavior and follow up with additional (and ongoing) efforts to determine the “why” of customer behavior.  The results provide rich insights which not only lead to more effective selling but better offering structures, distribution approaches and prices.  Sounds like a win to me.