Today’s high tech companies are increasingly challenged with their knowledge of pricing, quoting and how to manage deals due to the complexity of products, product lines, and global channel dynamics. Many companies struggle with how to price more intelligently, and to remain competitive, relevant, and grow revenue while maintaining expected margins.
In an Accenture survey of 1500 C-suite executives, “84% believe they must leverage artificial intelligence (AI) to achieve their growth objectives, yet 76% report they struggle with how to scale.”
However, many high tech companies don’t grasp how AI can help or believe it can improve pricing. “Moving away from a static pricing approach means listening for changes in market conditions, competition, and demand, and then using those inputs to make the right pricing decision” shares BCG in Debunking the Myths of B2B Pricing.
Two Factors for Intelligent Pricing Success
BCG provides five things B2B companies can do to improve their pricing journey. Below are two key areas.
Technology and Data – “How much high-quality data do you have, and how automated are the current processes for data collection and processing? … A rich set of data to draw on: information on customers, products, costs, and competitor prices, as well as contract details, wins and losses, sales overrides and comments, escalations, and negotiation histories.”
Algorithms and Pricing Engine – “How does your current pricing logic work? What inputs and calculation logic could you add in order to derive optimal price recommendations? Some B2B companies have already made steps toward rigorous, data-driven pricing, and others have some history and experience with pricing tools for marketing and sales.”
What can AI Bring to Pricing Intelligence?
AI-infused pricing intelligence and the data supporting it enables pricing teams to stay ahead of the competition and to assess the pricing strategies that improve margins, revenue, and profitability. The objective of AI-driven price analytics is to provide insights into understanding profit drivers, identify operational ineffiencies, and pricing anomalies across customer segments, channels, product lines, geographies, etc. These insights will help users measure the impact of different pricing strategies, benchmark customers, and devise new contract and rebate strategies.
To learn how Model N can bring AI pricing intelligence to your organization, go here. To understand how high-quality data can improve pricing intelligence, click here. To speak to an expert about your pricing concerns, go here.
