Here are 10 ways AI improves pricing and revenue management:
Harnessing Pricing Power to Create Lasting Value, Bain & Company, February 24, 2020. Full report available for download here .
All marketers are increasingly relying on machine learning to define more competitive, contextually relevant pricing. Machine learning apps are scaling price optimization beyond airlines, hotels, and events to encompass product and services pricing scenarios. Machine learning is being used today to determine pricing elasticity by each product, factoring in channel segment, customer segment, sales period, and the product's position in an overall product line pricing strategy.
Each customer segment has a different price they are willing to pay for a given product or service. By using AI and machine learning to know the price by segment customers are most willing to pay for a given product, AI applications can suggest them to sales and revenue managers. Automating segment-specific pricing guidance using CRM and CPQ systems is pivotal to pricing segmentation strategies' success. The following graphic illustrates how pricing segmentation works.
Facing greater pricing pressure in sales cycles, they want to close quickly, and sales reps are quick to provide deep discounts that sacrifice margin. This is especially true in enterprise software. McKinsey found that using dynamic deal scoring indexed to discounts provides the guidance sales reps need in determining what level of discounting will win the deal and not sacrifice margin.
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