Dynamic Price Negotiation Using Social Influence Data
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Solution Overview
Problem
Negotiating product prices can be unsatisfying and time-consuming for users, often limited to face-to-face interactions that do not consider all relevant factors, making the process uncomfortable and inefficient.
Innovation Solution
A computer-implemented method and system that uses social influence data and purchase parameter data, processed by a trained machine learning model, to determine a user-specific price for a product, which is then communicated to the user, allowing for efficient and data-driven price negotiation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If face-to-face communication with merchant personnel is used for price negotiation, then personal interaction is maintained, but the process becomes time-consuming and unsatisfying for users
Solution Approach 1:
The patent replaces the mechanical system of face-to-face human negotiation with an automated computer-implemented system that uses machine learning models to determine prices. The system substitutes human interaction with algorithmic processing, eliminating the time-consuming nature of personal negotiations while maintaining the price determination function.
Solution Approach 2:
The system enables self-service price negotiation by allowing users to receive automated price determinations without requiring direct interaction with merchant personnel. The machine learning model autonomously processes user data and social influence metrics to generate price recommendations, making the negotiation process independent and efficient.
2Loss of information
If face-to-face negotiation is used, then direct communication occurs, but not all factors impacting negotiation results are considered
Solution Approach 1:
The system performs multiple functions simultaneously: it analyzes user purchase history, evaluates social influence metrics, processes real-time bidding data, and generates price recommendations. This multi-functional approach ensures all relevant negotiation factors are considered comprehensively, unlike limited face-to-face interactions.
Solution Approach 2:
The system incorporates feedback loops by continuously analyzing user responses to price recommendations and adjusting subsequent negotiations. Social influence data and purchase parameter data provide ongoing feedback about user behavior and preferences, enabling the system to refine price determinations based on accumulated information.
3Productivity
If traditional negotiation methods are used, then simplicity is maintained, but user satisfaction and negotiation efficiency are reduced
Solution Approach 1:
The complex negotiation system is segmented into distinct functional modules: data collection components gather user information, machine learning models process the data, and recommendation engines generate price suggestions. This segmentation manages system complexity by dividing the overall function into manageable, independent components that can operate efficiently.
Data Source
AI summary
A computer-implemented method for negotiating a price of a product for a user may comprise obtaining an identification of the user via a device associated with the user; obtaining social influence data of the user based on the identification of the user, wherein the social influence data of the user includes a net promoter score or a social ranking of the user; obtaining purchase parameter data of the user based on the identification of the user, wherein the purchase parameter data of the user includes a credit score, an income range, or a transaction history of the user; determining a user-specific price of the product based on the purchase parameter data and the social influence data using a trained machine learning model; and transmitting, to the user, a notification indicative of the user-specific price.