AI Pricing Models for RFP Win Probability
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Solution Overview
Problem
Determining an optimal pricing strategy for a list of products in a competitive market is challenging, as it requires considering multiple factors such as customer relationship, demand, competitors, and market conditions, often resulting in subjective best guesses that leave uncertainty about pricing decisions.
Innovation Solution
The use of artificial intelligence through machine learning models to assign aggregate values and win probabilities for requests for quotation, based on historical data and market analysis, providing optimal price recommendations and reducing uncertainty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple factors are considered for pricing strategy, then pricing accuracy is improved, but analysis time and complexity increase
Solution Approach 1:
The system pre-calculates and stores pricing recommendations and win/loss probabilities in a database before actual quotation requests. When a quotation request arrives, the system quickly retrieves pre-computed results based on matching criteria, avoiding real-time complex calculations while maintaining high pricing accuracy across multiple factors
Solution Approach 2:
The system creates simplified representations of complex pricing scenarios by storing historical quotation data, competitor information, and market conditions in structured database tables. These copied and organized data structures enable rapid querying and comparison without re-processing the entire analysis from scratch
2Measurement precision
If multiple factors are considered for pricing strategy, then pricing accuracy is improved, but system complexity increases
Solution Approach 1:
The pricing system is divided into separate functional modules: data collection module, analysis module, database storage module, and output module. Each module handles specific tasks independently, making the complex system manageable and maintainable while still considering multiple pricing factors comprehensively
Solution Approach 2:
A database structure acts as an intermediary layer between the complex analysis engine and the user interface. The database stores structured pricing data, competitor information, and market conditions, transforming complex multi-factor analysis into organized, queryable results that simplify system interaction
3Productivity
If subjective best guesses are used for pricing, then decision speed is improved, but reliability decreases
Solution Approach 1:
The system incorporates feedback loops where actual quotation outcomes (won/lost) are fed back into the database to refine future pricing recommendations. This continuous learning process improves pricing reliability over time while maintaining rapid decision-making capabilities through automated rule-based systems
Solution Approach 2:
The system automatically generates pricing recommendations and win/loss probability assessments without requiring manual analyst intervention for each quotation. The automated system serves itself by retrieving data, performing analysis, and generating outputs, ensuring consistent reliable results while maintaining high decision speed
Data Source
AI summary
A method for optimizing aggregate values for a request for quotation is provided. The method includes: receiving data associated with a request for quotation for one or more items, the request for quotation including a respective quantity associated with each of the one or more items; assigning, using a first trained machine learning model, one or more aggregate values to the request for quotation; assigning, using a second trained machine learning model, a respective win probability for each of the one or more aggregate values; transmitting the one or more aggregate values and the one or more win probabilities associated with each of the one or more aggregate values to an electronic database; and transmitting the one or more aggregate values and the one or more win probabilities associated with each of the one or more aggregate values to a graphical user interface.