As an Industry Architect at Microsoft, I worked on a number of projects to improve pricing and proposal management for some great companies, which created a lot of value (small price improvements on the top line become large improvements on the bottom line). We used a business rules engine to imbed logic in pricing workflows. That approach was good at the time (circa 2000), and definitely superior to using SAP conditions or custom code – but it had its challenges with complexity and dynamics (here’s one example).
Today, analytics platforms, like Azure Machine Learning (AML), have changed the pricing game. Algorithms can comprehend the dimensions of pricing in a way that is not humanly possible. And they are truly dynamic (continuous learning can be implemented). This is a huge leap forward for companies with complex and dynamic pricing, and most definitely a source of competitive advantage (an overloaded term, but justified here).
The images above show AML algorithms embedded in a proposal management workflow site (K2 workflow) and integrated with pricing strategy models in Excel, so pricing strategy is consistent from planning to execution (the circled section on the site displays recommended price, and probability of winning the deal, to help people assess opportunities as they log RFPs and decide on resource commitments and approval routings in RFP responses).
Note: This solution architecture can be applied to an array of scenarios (e.g., S&OP, supply chain, HR recruitment and onboarding…)