Mckinsey argues that- On average, a 1 percent price increase translates into an 8.7 percent increase in operating profits (assuming no loss of volume, of course). Yet we estimate that up to 30 percent of the thousands of pricing decisions companies make every year fail to deliver the best price. That's a lot of lost revenue. And it's particularly troubling considering that the flood of data now available provides companies with an opportunity to make significantly better pricing decisions. For those able to bring order to big data's complexity, the value is substantial.

We're not suggesting it's easy: the number of customer touchpoints keeps exploding as digitization fuels growing multichannel complexity. Yet price points need to keep pace. Without uncovering and acting on the opportunities big data presents, many companies are leaving millions of dollars of profit on the table. The secret to increasing profit margins is to harness big data to find the best price at the productnot categorylevel, rather than drown in the numbers flood.

How would big data help in getting pricing strategy right? Use your knowledge of pricing and your understanding of data to show how flexible pricing can be introduced on a particular toll plaza on a busy road to maximize revenue, manage peak traffic times and ensure smooth flow of transportation?