Dynamic Outcome-Based Pricing Framework for Cloud Services
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Solution Overview
Problem
Cloud computing pricing models face challenges in measuring and distributing costs effectively across different service layers and organizational units, leading to reduced customer satisfaction and revenue due to inflexible pricing schemes that do not account for service value.
Innovation Solution
A machine learning-based dynamic outcome-based pricing framework that provides a two-step approach, offering customers a list of probability distributions and corresponding price quotes, allowing for real-time feedback to train the model and improve pricing accuracy based on service outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional fixed pricing models are used in cloud computing, then organizational profit and cost control are improved, but customer satisfaction and service quality perception deteriorate due to inflexible pricing that does not account for service value
Solution Approach 1:
The patent implements dynamic pricing by using machine learning models to continuously adjust pricing based on service outcomes, customer feedback, and value delivered. The system transitions from static fixed pricing to dynamic pricing that adapts in real-time to changing conditions and service quality, resolving the contradiction between profit reliability and pricing flexibility.
Solution Approach 2:
The system changes pricing parameters dynamically based on service outcomes and customer satisfaction metrics. The ML model adjusts pricing parameters (such as price quotes and probability distributions) based on learned patterns from customer feedback and service performance data, allowing the organization to maintain profit while adapting to customer value perception.
2Measurement precision
If machine learning models are trained with real-time customer feedback data, then pricing accuracy and customer satisfaction are improved, but data processing time and storage requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing customer feedback data as it is collected, preparing it for future ML training cycles. This includes validating, cleaning, and organizing feedback data in advance, so that when training is needed, the data is already ready for efficient processing, reducing actual training time.
Solution Approach 2:
The ML model training operates continuously in the background using incoming feedback data streams rather than batch processing. This continuous learning approach allows the system to maintain pricing accuracy while distributing processing load over time, preventing large spikes in processing time and enabling real-time pricing adjustments.
3Ease of operation
If cloud providers offer customized pricing schemes for different service layers, then customer satisfaction and service quality are improved, but device complexity and cost distribution challenges increase
Solution Approach 1:
The patent implements a universal ML-based pricing framework that handles multiple service layers and customer types through a single system. The ML model automatically adapts to different service configurations, customer preferences, and organizational structures, providing customized pricing without requiring separate complex pricing schemes for each scenario. This resolves the contradiction by making the system multi-functional rather than multiplying complexity.
Solution Approach 2:
The system enables self-service pricing where the ML model automatically determines appropriate pricing for different service layers based on customer feedback and service outcomes, without requiring manual configuration of complex pricing schemes. The system learns and adapts pricing parameters autonomously, reducing the operational complexity of managing customized pricing across multiple service layers.
Data Source
AI summary
A service request is received at an intelligence service server from a user, where the service request includes a number of required inputs associated with the user. The number of required inputs are executed by the intelligence service server to generate an inference, an outcome probability distribution and a price quote, where the price quote corresponds to the outcome probability distribution. The outcome probability distribution and the price quote are returned by the intelligence service server to the user. It is determined by the intelligence service server that whether the user accepts the price quote based on a response from the user. If so, the inference is returned by the intelligence service server to the user. Otherwise, the response from the user is logged in a database associated with the intelligence service server by the intelligence service server.