In the world of B2B pricing, there are continuous challenges for high tech finance, sales, and channel teams. Areas such as lost and missed opportunities, deal registration, global pricing complexity, the rise of subscription license models, discounting errors, and never-ending ad-hoc pricing requests are creating an internal team and external channel frustrations. Trying to manage pricing with outdated or in-house solutions that don’t use intelligence adds to these pressures. What can be done to improve the intelligence of B2B pricing?
The Importance of Pricing as a Growth Lever
According to Accenture research, “Pricing, the most important lever for revenue and margin growth, is undergoing significant disruption based on advances in analytics and artificial intelligence.”
To mitigate these challenges, over the past 10 years or so, pricing, deal, sales operations teams have sought to improve pricing processes through better management of price lists and price books and use this information to improve pricing practices. By adding data science resources and analytics. While this has been a good alternative, challenges are still prevalent. Evolving B2B pricing requires real-time intelligence grounded in AI.
Will AI mitigate pricing challenges?
“The increasing importance of AI is due to several factors, including data proliferation and continuous technological improvements (processing power and storage), leading to the democratization of AI and massive investments in these tools and technologies” shares PwC.
“Today, thanks to advanced technologies such as analytics and artificial intelligence, organizations can develop “intelligent pricing”— optimally calculating prices in real-time based on multiple customer and market variables, testing price points or entire pricing models, and improving them continuously. These new approaches to pricing allow for differentiated strategies with real benefits: more empowered sales (10 to 20 percent improvement), improved margins (up to two percentage points) and increased revenues (5 to 15 percent)” shares Accenture.
How Can High Teams Implement AI?
For high tech teams, the objective of AI-based price analytics should be to provide insights into understanding profit drivers, identify potential wins and product upsell, and pricing anomalies across customer segments, channels, product lines, geographies, etc. These insights help companies measure the effectiveness of pricing strategies, benchmark customers and market trends, and devise new contract and rebate strategies.
How Can Model N Help?
Model N Price Intelligence brings immediate business intelligence into the price performance for high-tech and semiconductor industries. Price Intelligence offers insights into price performance by customers, products, territories, contracts, and channel incentives providing granular visibility. Price Intelligence enables pricing teams to stay ahead of the competition and to assess the pricing strategies that improve margins, revenue, and profitability.