Using Big Data to Make Better Pricing Decisions
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.
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.
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 and so on. 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 operations 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.”
Four Steps to Turn Data into Profits
The key to better pricing is understanding fully the data now at a company’s disposal. It requires not zooming out but zooming in. As Tom O’Brien, group vice president and general manager for marketing and sales at Sasol, said of this approach, “The [sales] teams knew their pricing, they may have known their volumes, but this was something more: extremely granular data, literally from each and every invoice, by product, by customer, by packaging.”
In fact, some of the most exciting examples of using big data in a B2B context actually transcend pricing and touch on other aspects of a company’s commercial engine. For example, “dynamic deal scoring” provides price guidance at the level of individual deals, decision-escalation points, incentives, performance scoring, and more, based on a set of similar win/loss deals. Using smaller, relevant deal samples is essential, as the factors tied to any one deal will vary, rendering an overarching set of deals useless as a benchmark. We’ve seen this applied in the technology sector with great success — yielding increases of four to eight percentage points in return on sales (versus same-company control groups).
To get sufficiently granular, companies need to do four things.
Listen to the data. Setting the best prices is not a data challenge (companies generally already sit on a treasure trove of data); it’s an analysis challenge. The best B2C companies know how to interpret and act on the wealth of data they have, but B2B companies tend to manage data rather than use it to drive decisions. 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. Successful companies overinvest in thoughtful change programs to help their sales forces understand and embrace new pricing approaches. 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. The best leaders accompany sales reps to the most difficult clients and focus on getting quick wins so that sales reps develop the confidence to adopt the new pricing approach. “It was critical to show that leadership was behind this new approach,” says Robert Krieger, managing director of PanGas AG. “And we did this by joining visits to difficult customers. We were able to not only help our sales reps but also show how the argumentation worked.”
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.
We’ve seen companies in industries as diverse as software, chemicals, construction materials and telecommunications achieve impressive results by using big data to inform better pricing decisions. All had enormous numbers of SKUs and transactions, as well as a fragmented portfolio of customers; all saw a profit-margin lift of between 3 and 8 percent from setting prices at much more granular product levels. In one case, a European building-materials company set prices that increased margins by up to 20 percent for selected products. 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.
Walter Baker is a principal in McKinsey’s Atlanta office, Dieter Kiewell is a director in the London office and Georg Winkler is a principal in the Berlin office.
This article was originally published by McKinsey Quartlery. Copyright © McKinsey & Company. All rights reserved. Reprinted by permission.
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