Dynamic Pricing for B2B Contracts

Leading B2B organizations have demonstrated measurable success with AI-powered dynamic pricing. A machine learning model improved dynamic pricing at an energy company in a period of greater competition and price spikes. By determining the probability that each customer would renew a contract at different price levels, the company reduced contract churn by 5% to 10%.

The telecommunications and services sectors have pioneered sophisticated approaches. One B2B services company aimed to rein in its discount variance by using an AI tool to create a pricing structure based on hundreds of customer and deal parameters. The tool, packaged in an intuitive app for the sales team, analyzed and scored deals, providing a range of desirable discount options that gave instant visibility into how good a deal really was.

About one in five online sellers are believed to use dynamic pricing, and McKinsey has estimated that such tools can increase conversion rate by 5-15%. Wilbur-Ellis, a provider of agricultural products, implemented real-time pricing technology for over 6,000 SKUs and saw a 2% margin uplift. A global B2B petrochemical company captured around $100 million in additional earnings with a machine-learning-enabled dynamic pricing model that clustered customers into microsegments based on over one hundred characteristics.

Organizations relying on data-driven guidance are more confident in negotiating prices and report winning more deals at a rate twelve percentage points higher than other companies.