How to make better pricing decisions using Big Data

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.

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 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.

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