Using big data to make better pricing decisions

It is 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). Up to 30 percent of the thousands of pricing decisions companies make every year fail to deliver the best price. That is a lot of lost revenue. And it is 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 number of customer touchpoints keeps exploding as digitization fuels growing multichannel complexity.

pricing

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.

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.

It is more problematic when product numbers balloon. About 75% 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 is 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.

The key to better pricing is understanding fully the data now at a company’s disposal. It requires not zooming out but zooming in.

To get sufficiently granular, companies need to do four things.

–          Listen to the data. Setting the best prices is not a data challenge; it is an analysis challenge. Good analytics can help companies identify how factors that are often overlooked—such as the broader economic situation, product preferences, and sales-representative negotiations—reveal what drives prices for each customer segment and product.

–          Automate. It’s too expensive and time-consuming to analyze thousands of products manually. Automated systems can identify narrow segments, determine what drives value for each one, and match that with historical transactional data. This allows companies to set prices for clusters of products and segments based on data. Automation also makes it much easier to replicate and tweak analyses so it’s not necessary to start from scratch every time.

–          Build skills and confidence. Implementing new prices is as much a communications challenge as an operational one. Companies need to work closely with sales reps to explain the reasons for the price recommendations and how the system works so that they trust the prices enough to sell them to their customers. Equally important is developing a clear set of communications to provide a rationale for the prices in order to highlight value, and then tailoring those arguments to the customer. Intensive negotiation training is also critical for giving sales reps the confidence and tools to make convincing arguments when speaking with clients.

–          Actively manage performance. To improve performance management, companies need to support the sales force with useful targets. The greatest impact comes from ensuring that the front line has a transparent view of profitability by customer and that the sales and marketing organization has the right analytical skills to recognize and take advantage of the opportunity. The sales force also needs to be empowered to adjust prices itself rather than relying on a centralized team. This requires a degree of creativity in devising a customer-specific price strategy, as well as an entrepreneurial mind-set. Incentives may also need to be changed alongside pricing policies and performance measurements.

To get the price right, companies should take advantage of big data and invest enough resources in supporting their sales reps—or they may find themselves paying the high price of lost profits

(McKinsey)