What really matters in B2B dynamic pricing | McKinsey

Companies that succeed at analytics-based pricing build a strong foundation—and include their salesforce in developing it from the start.

Like many of its peers, a medical-technology company was struggling to adapt to changing market dynamics. Its traditional relationship-based sales model was coming under threat from increased competition and scrutiny from hospitals, while end users, especially doctors, were still expecting highly customized solutions.

Company leaders quickly saw that deploying a system that enabled them to reset price targets in real time at a customer/product level, based on actual facts, could help improve pricing. In other words, they realized that they needed dynamic pricing.

Leadership ran into a few issues with these tools, however: historic data was not clean, no competitive data were available, and sales teams didn’t trust the pricing recommendations and so wouldn’t use them, since previous pricing tools had earned a reputation for making “theoretical” recommendations that bore little resemblance to realities in the field.

This situation is all too familiar to many companies. Developing a dynamic-pricing capability needs as much emphasis on people and processes as it does on analytics and technology (Exhibit 1).

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The real benefits of dynamic pricing
Digital technologies and platforms are disrupting B2B sales models and forcing players to fundamentally rethink their pricing strategies. Fortunately, these same technologies are also enabling this new dynamic-pricing approach, which brings substantial benefits for those companies that can embed it successfully.

Although interest in dynamic pricing continues to grow, we find that leaders tend to have a vague understanding of what it actually is and what its true benefits are. Dynamic pricing allows companies to better understand and predict when to push prices higher to quickly capture the upside, or lower, to avoid volume losses. And it helps to improve and speed up the decision-making process while providing more granular insights, for example by scoring deals against peer groups and factoring multiple criteria into price recommendations, such as strategy, deal size, customer type, and product type and mix.

Dynamic pricing is also self-reinforcing: as sales teams test new pricing approaches, they can feed win and loss information back into the system to steadily improve its accuracy and uncover new insights. The most effective teams also use the insights generated to tailor their offerings more closely to customers’ needs, for example by making relevant cross-sell recommendations, which significantly improves loyalty.