AI Mentoring Software for Demographic Performance Benchmarking
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Solution Overview
Problem
Existing financial advisor and insurance agent systems, such as Granum's One Card System, do not adequately address the need for personalized and demographic-specific benchmarking and coaching to improve client-building and performance metrics.
Innovation Solution
A method utilizing artificial intelligence (AI) to compare user data with benchmarks, adjust demographic information, and provide personalized coaching and training based on AI analysis of activity patterns, productivity, and skill sets, including regression models and neural networks, to optimize benchmark scores and improve performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional benchmarking systems are used, then basic performance measurement is provided, but personalized and demographic-specific coaching is not available
Solution Approach 1:
The system changes the parameters of benchmarking by introducing demographic-specific adjustments. Instead of using a single universal benchmark, the system modifies benchmark parameters based on user demographics (age, gender, race, experience level, education, marital status, family status, household income, geographic location, occupation, origin) to create personalized benchmarks for each user, thereby achieving adaptability without requiring a completely new system architecture
2Productivity
If AI analysis is applied to provide personalized coaching, then performance improvement is enhanced, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing demographic-specific benchmark data and performance thresholds before actual analysis is needed. This allows the AI system to quickly compare user performance against pre-prepared demographic benchmarks rather than calculating everything in real-time, reducing processing time while maintaining personalized coaching effectiveness
3Measurement precision
If multiple demographic factors are considered, then coaching accuracy is improved, but data collection and processing requirements increase
Solution Approach 1:
The system segments the user population into distinct demographic categories and creates separate benchmark datasets for each segment. By dividing the complex task of analyzing multiple demographic factors into separate, manageable segments, the system can process and store data more efficiently while maintaining high coaching accuracy through targeted analysis of relevant demographic characteristics
Data Source
AI summary
Included in the present disclosure is a method, including obtaining data associated with a metric of a user. In some embodiments, the method includes comparing the data to a benchmark associated with the metric, thereby determining a benchmark score. According to some embodiments, the method includes applying artificial intelligence (AI) to determine a course of action based on the benchmark score and demographic information. The course of action may include i) calibrating the benchmark, ii) determining an activity to adjust future data associated with the metric, or iii) both, so as to modify the benchmark score.