Adaptive Learning System for Pricing Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing pricing optimization methods are limited by their ability to observe only discrete responses to simplistic stimuli, failing to account for complex stimuli and continuous responses in real-world scenarios, making it difficult to refine business processes effectively.
Innovation Solution
The use of adaptive learning systems that profile customers, select actions based on historical data and customer responses, and update forecasting algorithms to refine future actions, allowing for the analysis of complex stimuli and continuous responses across multiple profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If discrete stimuli are used in pricing optimization, then the measurement and observation are simple, but the ability to capture complex real-world customer responses is limited
Solution Approach 1:
The patent segments complex stimuli into multiple discrete components that can be independently observed and measured. Each component of the stimulus is tracked separately, allowing the system to handle complex multi-faceted stimuli while maintaining the ability to measure and observe each element's impact on customer response.
Solution Approach 2:
The patent transitions from discrete binary responses to continuous multi-dimensional response measurements. By introducing continuous response variables and multi-dimensional stimulus components, the system captures the complexity of real-world customer behavior while maintaining measurability through structured data collection.
2Ease of operation
If discrete binary responses are observed, then the data collection is straightforward, but the nuance of continuous customer behavior is lost
Solution Approach 1:
The patent changes the measurement parameter from discrete binary values to continuous variables. Customer responses are measured on continuous scales that capture nuanced behavior, while the system maintains ease of operation through automated data collection and standardized measurement protocols.
Solution Approach 2:
The patent introduces intermediary measurement mechanisms that translate complex continuous customer behavior into structured data that is easy to collect and analyze. Response proxies and intermediate metrics serve as mediators between the complex reality of customer behavior and the simplified data collection process.
3Adaptability or versatility
If multi-faceted stimuli are utilized, then the ability to capture complex customer interactions is improved, but the ability to identify which aspect generates the response becomes difficult
Solution Approach 1:
The patent segments multi-faceted stimuli into distinct components that can be independently analyzed. By breaking down complex stimuli into separate elements and tracking each component's contribution to customer response, the system identifies which specific aspect generates the response while maintaining the ability to present complex multi-faceted offers.
Solution Approach 2:
The patent implements feedback mechanisms that track the relationship between specific stimulus components and customer responses. Through iterative observation and analysis of response patterns, the system learns to identify which aspects of complex stimuli drive customer behavior, enabling targeted optimization of specific stimulus elements.
4Device complexity
If simple price variations are tested, then the experiment design is simple, but the ability to optimize complex pricing strategies is limited
Solution Approach 1:
The patent segments pricing strategies into multiple independent variables that can be tested and optimized separately. By dividing complex pricing strategies into component elements such as price levels, discounts, bundles, and timing, the system maintains simple experiment designs for each component while achieving comprehensive optimization of the overall pricing strategy.
Solution Approach 2:
The patent introduces dynamic pricing optimization that adapts to complex market conditions through iterative learning. The system dynamically adjusts multiple pricing parameters based on observed customer responses, enabling optimization of complex pricing strategies while maintaining manageable experiment designs through adaptive, data-driven adjustments.
Data Source
AI summary
Systems and methods are described which optimize a business process through adaptive learning. Incoming customers are profiled and based on the customer's profile, a corresponding set of actions may be generated. From this set of actions, an algorithm is used to select a discrete action which is then presented to the customer. The customer's response to the presented action may in turn be used to forecast future customer responses and update the selection algorithm. These methods and systems allow attainment of a specific objective through progressive optimization of a business process.