Dynamic Product Offerings via Entity Risk and Performance Matching
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Solution Overview
Problem
Current online marketplace systems fail to provide product offerings that accurately match customer requirements based on entity performance and risk profiles, leading to inefficient procurement processes and low conversion ratios due to generic keyword searches and inadequate analysis.
Innovation Solution
A method for dynamic product offerings that identifies entity characteristics and risk profiles, determines target rewards and risks, and releases tailored product offerings in target markets using Big Data analytics and AI, optimizing financial returns and risk mitigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If generic keyword search is used for product identification, then ease of operation is improved, but measurement precision of customer requirements deteriorates
Solution Approach 1:
The system changes the parameters used for product search from generic keywords to multiple dimensions including entity characteristics, performance indicators, risk profiles, and transaction characteristics. This allows customers to search using specific parameters that accurately reflect their requirements while maintaining ease of operation through automated parameter selection and presentation.
2Device complexity
If limited non-validated information is used for analysis, then device complexity is reduced, but reliability of product matching deteriorates
Solution Approach 1:
The system performs preliminary validation and verification of information before using it for product matching. Entity characteristics, performance indicators, and risk profiles are pre-validated through automated checks and data verification processes, ensuring that only reliable information is used in the matching algorithm, thereby improving reliability without proportionally increasing complexity.
3Device complexity
If product offerings are not scientifically matched based on entity performance and risk profiles, then device complexity is reduced, but manufacturing precision of product-customer match deteriorates
Solution Approach 1:
The system replaces manual or simple mechanical matching mechanisms with automated computational algorithms that scientifically match products to customers based on entity performance indicators and risk profiles. Machine learning models and data analytics automatically process and compare multiple parameters, achieving high precision matching without requiring complex manual intervention.
4Ease of operation
If generic product offerings are provided at initial contact, then ease of operation is improved, but loss of time in procurement process increases
Solution Approach 1:
The system performs preliminary product matching and preparation based on available customer information at the initial contact point. By pre-processing and pre-matching products using the customer's entity characteristics and requirements, the system reduces the time needed for subsequent procurement steps while maintaining ease of operation through immediate presentation of relevant offerings.
Data Source
AI summary
A method for dynamic product offerings includes identifying characteristics of an entity based on a set of performance indicators and a risk profile associated with the entity. The method further identifies criteria for dynamic product offerings for a target market using the identified characteristics of the entity. The method further includes determining a target reward and an expected risk for a new dynamic product offering using the criteria for the dynamic product offerings. The method further includes one or more actions that cause the new dynamic product offering to be released in the target market.