AI Lead Recommendation System Using Fine-Tuned LLMs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies for processing lead-related data are inefficient and inaccurate, particularly when dealing with diverse formats, styles, and data types, and they do not utilize fine-tuned models derived from pre-trained Large Language Models (LLMs) for improved accuracy and efficiency.
Innovation Solution
An AI-based automated system that uses fine-tuned models derived from pre-trained LLMs to process and analyze sales lead data in real-time, incorporating language indicators to parse data, querying historical customer databases for relevant data, and generating feature vectors for predictive modeling to provide accurate lead response recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of lead-related data is performed by CRM specialists or basic applications, then some analysis can be conducted, but the accuracy is insufficient because historical data for similar customers and service representatives is not considered
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models on extensive historical customer data and service representative performance data before actual lead analysis. This pre-processing of historical data enables the models to automatically consider patterns from similar customers and representatives, improving accuracy without requiring manual setup for each analysis task.
Solution Approach 2:
The patent introduces machine learning models as intermediaries between raw lead data and analysis results. These models act as mediators that automatically process lead-related data while incorporating historical customer and representative information, eliminating the need for complex manual analysis procedures and achieving high accuracy through automated pattern recognition.
2Productivity
If manual analysis of recorded lead data is performed, then analysis can be conducted, but the efficiency is low because it requires manual work that takes a long time
Solution Approach 1:
The system implements self-service by enabling automated lead analysis through machine learning models that independently process lead data without requiring manual intervention. The models automatically retrieve historical data, perform analysis, and generate recommendations, allowing the system to serve itself and eliminate time-consuming manual work while maintaining high processing efficiency.
Solution Approach 2:
The patent replaces the mechanical system of manual analysis with an automated electronic system based on machine learning. This substitution eliminates the need for human specialists to manually examine each lead, significantly reducing analysis time while improving consistency and accuracy through automated pattern recognition across large datasets.
3Reliability
If manual lead analysis is performed, then analysis can be completed, but leads may not be addressed properly by the most qualified customer service rep or may be missed completely
Solution Approach 1:
The system implements feedback mechanisms by continuously analyzing historical data about which service representatives successfully handled similar leads and customers. This feedback loop enables the machine learning models to automatically identify and assign leads to the most qualified representatives based on proven performance patterns, ensuring reliable lead assignment while automatically managing the complexity of evaluating multiple representative qualifications.
Solution Approach 2:
The patent utilizes parameter changes by transforming unstructured lead data and historical performance data into structured feature vectors that can be processed by machine learning models. This parameter transformation enables automated comparison of lead characteristics with historical patterns, reliably identifying the most suitable service representative without manual evaluation of multiple qualification parameters.
4Measurement precision
If conventional lead-related data extraction and processing techniques are used, then processing can be performed, but accuracy and efficiency are insufficient when dealing with diverse formats, styles, and data types
Solution Approach 1:
The system achieves universality by developing machine learning models capable of processing multiple data formats, styles, and types through a single unified framework. The models are trained on diverse historical data and can automatically adapt to different lead formats, eliminating the need for separate processing procedures for each data type while maintaining high accuracy and processing speed across all variations.
Data Source
AI summary
A system for generation of recommendations based on a sale lead including a processor of a recommendation server (RS) node and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: acquire sales lead data from the sales lead entity node comprising data entered into a sales form by a customer; derive a language indicator from the sales lead data; parse the sales lead data based on the language indicator to derive a plurality of features; query a local customers' database to retrieve local historical customers'-related data related to previous customers' engagements associated with previous lead data based on the plurality of features; generate at least one feature vector based on the plurality of features and the local historical customers'-related data; and provide the at least one feature vector to the ML module for generating a predictive model configured to produce at least one recommendation lead response parameter for generation of a lead-related recommendation for the at least one CRM entity node.