API Pricing Using Supervised Learning for Consumer Value
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Solution Overview
Problem
Current API pricing methods are based on speculative revenue and user expectations, leading to prices that do not accurately reflect the API's value to consumers, resulting in excessive computing resource utilization and failure to consider the API's capabilities and data quality relative to competitors.
Innovation Solution
A method using supervised learning models to identify API consumption parameters, determine reference pricing through machine learning, and derive an API pricing score to set a suggested price that reflects the API's true value to consumers, dynamically adjusting based on consumer feedback and competitor pricing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If API pricing is based on speculative revenue and user expectations, then pricing can be established without detailed consumer value assessment, but the price does not accurately reflect the true value of the API to the consumer
Solution Approach 1:
The patent implements feedback loops where consumer usage data, satisfaction metrics, and transaction information are continuously collected and fed back into the pricing model. This allows the system to adjust prices based on actual consumer value perception rather than speculation, improving pricing accuracy while using automated feedback mechanisms to manage complexity.
Solution Approach 2:
The patent replaces complex manual value assessment processes with automated machine learning models and algorithms. These computational systems analyze multiple data points simultaneously to determine API value, achieving high pricing accuracy without requiring complex human judgment processes, thus managing system complexity through algorithmic automation.
2Measurement precision
If traditional pricing models are used without considering API capabilities and data quality relative to competitors, then pricing can be set simply, but the price does not reflect the API's true value compared to alternative solutions
Solution Approach 1:
The patent creates a universal pricing framework that simultaneously evaluates multiple dimensions including API capabilities, data quality, competitive positioning, and consumer value. This multi-functional assessment system adapts to different API types and market conditions, providing accurate value assessment while maintaining versatility across various competitive scenarios.
Solution Approach 2:
The patent dynamically adjusts pricing parameters based on competitive analysis and value assessment. The system modifies pricing variables such as base rates, discounts, and pricing structures according to the API's relative capabilities and data quality compared to competitors, enabling accurate reflection of true value while adapting to market conditions.
3Productivity
If speculative information is used for pricing, then the pricing process can be completed quickly, but excessive computing resources are utilized in attempting to derive an acceptable price
Solution Approach 1:
The patent performs preliminary data collection, cleaning, and structuring before the actual pricing computation. By preparing data in advance and organizing it into standardized formats, the system reduces the computational complexity of the pricing calculation itself, improving efficiency while reducing the overall computing resources required for the complete pricing process.
Data Source
AI summary
A method, system and computer program product for determining API pricing. Consumption parameters are identified using a supervised learning model. The API consumption parameters refer to any parameters that can be used to describe an API (functionality or otherwise) and can be used to compare other comparable APIs in similar domains provided by other providers. Furthermore, reference pricing is determined using machine learning using the identified API consumption parameters. Additionally, the API price is determined dynamically using the identified API consumption parameters and the determined reference pricing. An API pricing score is then derived for the API price using the supervised learning model. The API price is selected as the suggested price for the API in response to the API pricing score exceeding a threshold value. In this manner, an API price is established that reflects the true value of the API assessed by the API consumer.