It's hard to overstate the importance of getting pricing right. 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.

The secret to increasing profit margins is to harness Big Data to find the best price at the product-not category-level.

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 product-not category-level, rather than drown in the numbers flood .

Too big to succeed

For every product, companies should be able to find the ideal optimal price that a customer is willing to pay. Ideally, they'd factor in highly specific insights that would influence the price-the cost of the next- best competitive product versus the value of the product to the customer, for example-and then arrive at the best price. Indeed, for a company with a handful of products, this kind of pricing approach is straightforward.

It's more problematic when product numbers balloon. About 75 percent of a typical company's revenue comes from its standard products, which often number in the thousands. Time-consuming, manual practices for setting prices make it virtually impossible to see the pricing patterns that can unlock value. It's simply too overwhelming for large companies to get granular and manage the complexity of these pricing variables, which change constantly, for thousands of products. At its core, this is a Big Data issue (exhibit).

Many marketers end up simply burying their heads in the sand. They develop prices based on simplistic factors such as the cost to produce the product, standard margins, prices for similar products, volume discounts, etc. They fall back on old practices to manage the products as they always have, or cite "market prices" as an excuse for not attacking the issues. Perhaps worst of all, they rely on "tried and tested" historical methods, such as a universal 10 percent price hike on everything.

"What happened in practice then was that every year we had price increases based on scale and volume, but not based on science," says Roger Britschgi, head of sales services at Linde Gases . "Our people just didn't think it was possible to do it any other way. And, quite frankly, our people were not well prepared to convince our customers of the need to increase prices."

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