
Author: Brian Sherry
AI Empowered Pricing
It is without a doubt that artificial intelligence (AI) has become a more important topic in many companies following the launch of ChatGPT in late November 2022. With every passing day, artificial intelligence is gaining more attention, especially when it comes to its use in the business environment and its role in optimizing pricing strategies. Driven by this rising interest in data science for AI-based pricing solutions, the market for price management and optimization software has been experiencing rapid growth and is now projected to reach $2.7 billion by 2028. One of the main factors driving AI's growth in pricing is its wide range of use cases, which can help address many of the complex pricing challenges that B2B companies face today.
A Complex B2B Pricing Challenge
Many mid-sized B2B (business-to-business) companies, particularly in sectors like manufacturing, wholesale, and IT services, can process hundreds to thousands of quotes each month. One significant challenge B2B companies face today is the ability to provide their sales teams with accurate and timely price guidance, for example:
What prices do sales quote when a customer comes with a price request or wants to negotiate new contract pricing and how can we control this?
How can we make this an iterative process that ensures our pricing guidance remains data driven, consistent, profitable, and accurate over time?
Overcoming these challenges is essential for pricing departments if they want to improve their win rates, accelerate their sales cycles, enhance their negotiations, and elevate the overall customer experience.
AI Driven Price Guidance Solution
Nowadays to solve these complex challenges many B2B companies are now using advanced analytics tools and AI to automate their data collection and transactional analysis. AI driven price guidance solutions are now enabling pricing functions to provide their sales teams with structured pricing recommendations and boundaries, ensuring that pricing decisions are aligned with their company’s overall strategy.
By leveraging large datasets, advanced machine learning algorithms, and continuous improvement processes, businesses can now better understand their customers’ willingness to pay, allowing them to better optimize their price guidance further. The real value of these AI based solutions is further enhanced by their ability to continuously collect new data on sales and customer behavior, enabling the continuous learning and improvement of any price guidance modelling taking place.
Powering Up Your Price Guidance Solution
Another crucial point that is worth remembering is that price guidance solutions get even more powerful when used with other tools. For example, price guidance and Net Price Index (NPI) distribution analysis are powerful tools when used together. A NPI distribution analysis involves evaluating the distribution of net prices (actual transaction prices after discounts) across different deals and customer segments.
Price Guidance + AI Conclusion
Price guidance is a key pricing use case for AI, delivering significant benefits in accuracy, efficiency, profitability, and competitiveness. For example, according to research by McKinsey & Company, B2B companies that leverage advanced analytics and automation for their pricing and quoting processes can handle higher volumes more efficiently (+49%) and with greater accuracy (+30%).
In today’s volatile world an AI driven price guidance solution is now a crucial tool for businesses to ensure consistent, competitive, and profitable pricing guidance. By providing structured, data-based pricing recommendations, it helps your sales teams navigate complex pricing landscapes, improve negotiation efficiency, and align pricing strategies with overall business goals
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