Incentive Price Recommendation via Customer Embeddings
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Solution Overview
Problem
Consumers face challenges in determining fair market prices for services, as they lack transparency in pricing and may be overcharged, while service providers do not know the customer's willingness to pay, leading to potential undercompensation or missed opportunities.
Innovation Solution
A computer-implemented method using machine learning models to generate customer and service provider embeddings based on job descriptions and previous job data, allowing for the determination of incentive prices that reflect real-time market values, thereby facilitating transparent and competitive pricing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If consumers search for service providers through traditional methods (newspapers, advertisements, word of mouth), then they can find service providers, but they cannot determine fair market prices and may be overcharged
Solution Approach 1:
The patent introduces an online platform as an intermediary between consumers and service providers. This platform collects and analyzes historical job data, service provider profiles, and market pricing information to generate data-driven price recommendations. The intermediary processes and structures unstructured market information, making pricing transparency achievable without requiring consumers to manually search through traditional channels.
Solution Approach 2:
The system performs preliminary analysis of historical job data, service provider qualifications, and market conditions before a consumer makes a hiring decision. By pre-processing this information and presenting it in an organized manner with recommended price ranges, the system eliminates the need for consumers to conduct extensive manual research through newspapers and advertisements.
2Reliability
If service providers set high prices to ensure fair compensation, then they may be overcompensated, but they may not get the job at all
Solution Approach 1:
The system implements a feedback loop where historical job data, including accepted prices and job outcomes, is continuously collected and analyzed. This feedback mechanism allows the system to learn from past transactions and provide increasingly accurate price recommendations that balance fair compensation with market acceptance rates, helping service providers set optimal prices based on empirical evidence rather than guesswork.
Solution Approach 2:
The system dynamically adjusts recommended price parameters based on multiple factors including service provider qualifications, job complexity, historical market data, and demand conditions. By changing these parameters in real-time based on accumulated data, the system helps service providers optimize their pricing to achieve both fair compensation and high acceptance rates.
3Productivity
If service providers set low prices to increase job acceptance, then they get more jobs, but they may not be compensated fairly
Solution Approach 1:
The system uses feedback from historical data to identify the relationship between pricing, service provider qualifications, and job acceptance rates. By analyzing this feedback, the system can determine optimal price points that maximize acceptance while ensuring fair compensation based on the specific service provider's credentials and the job's requirements.
Solution Approach 2:
The system dynamically adjusts price recommendations by changing key parameters such as base rate, qualification premiums, and market adjustment factors. This allows service providers to set prices that are competitive enough to achieve high acceptance rates while still reflecting the true value of their services and ensuring fair compensation.
4Loss of information
If the system uses machine learning models to generate price recommendations, then pricing transparency is improved, but the system complexity increases
Solution Approach 1:
The system employs machine learning models that automatically process historical job data, extract relevant features, and generate price recommendations without requiring manual intervention. The models self-train on accumulated data and continuously improve their accuracy, reducing the need for complex manual data processing and analysis infrastructure.
Solution Approach 2:
The patent replaces manual information gathering, analysis, and price negotiation processes with automated machine learning systems. Instead of consumers and service providers manually researching and negotiating prices, the ML models automatically process unstructured data and generate informed recommendations, substituting mechanical human processes with computational algorithms.
Data Source
AI summary
A computer-implemented method of customer matching with service providers includes applying, by the computer system, one or more machine learning models to generate a customer embedding for a customer. The customer embedding is based on a job description for a category of service currently requested by the customer. The method also includes applying one or more machine learning models to generate a service provider embedding for a service provider. The service provider embedding is based on at least one previous job accepted by the service provider through the content provider. The method includes determining an incentive price for the category of service currently requested by the customer based on the customer embedding and the service provider embedding. The method also includes providing the incentive price as a recommendation for the customer to offer through the content provider for an available service provider to perform the service requested by the customer.