Inquiry Analysis Models for Prioritized Sales Lead Scoring
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Solution Overview
Problem
Sellers face challenges in managing high volumes of sales inquiries with limited information on potential customers' interest and financing capabilities, making it difficult to identify fruitful leads.
Innovation Solution
An automated inquiry analysis platform uses predictive modeling, sentiment analysis, and content analysis to score leads based on inquiry content and sentiment, providing vendors with qualitative information to prioritize responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If sellers manually review all sales inquiries to assess customer interest and financing capabilities, then they can obtain detailed information about each lead, but the time and effort required increases significantly with high volumes of inquiries
Solution Approach 1:
The patent introduces an automated inquiry analysis system as an intermediary between sellers and sales inquiries. This system uses natural language processing, sentiment analysis, and predictive modeling to automatically evaluate customer interest levels and financing capabilities, extracting key information from inquiry text without requiring manual seller review. The intermediary process preserves information quality while dramatically reducing the time sellers spend on each inquiry.
2Reliability
If sellers respond to all inquiries with equal effort, then no potential customer is missed, but resources are wasted on low-quality leads that are unlikely to result in sales
Solution Approach 1:
The patent applies local quality by differentiating the level of effort and response quality based on the specific characteristics of each inquiry. The automated analysis system assigns quality scores to different leads based on sentiment analysis, keyword matching, and predictive factors. Sellers can then allocate their attention proportionally, providing high-effort responses to high-quality leads and automated or template responses to lower-quality leads, optimizing both reliability and productivity.
Solution Approach 2:
The system performs preliminary analysis of each inquiry before the seller responds, pre-evaluating customer interest levels, financing capability, and lead quality. This preliminary action provides sellers with prioritized information about which leads warrant immediate attention and which can be handled with standard responses, enabling more efficient resource allocation while maintaining reliable lead management.
3Measurement precision
If the automated analysis system uses multiple modeling techniques (content modeling, sentiment modeling, valuation modeling), then the accuracy of lead scoring improves, but the complexity of the system increases
Solution Approach 1:
The patent segments the lead analysis function into three distinct modeling components: content modeling (analyzing inquiry text and keywords), sentiment modeling (evaluating customer attitude and interest level), and valuation modeling (assessing financing capability and purchase potential). Each segment performs a specific analytical function and outputs a component score. These segmented models are then combined to produce an overall lead quality score, improving measurement precision while managing system complexity through modular design.
Data Source
AI summary
An inquiry analysis system and method are disclosed. An inquiry analysis system includes a computer configured to receive an electronic communication from a user, the electronic communication comprising an inquiry associated with an article of interest. The inquiry analysis system determines one or more measures including a first result of a first trained model based on one or more content elements of the inquiry, a second result of a second trained model based on one or more sentiment elements of the inquiry, and a valuation divergence model result based on a comparison of the inquiry with a reference value associated with the article of interest. The inquiry analysis system calculates and outputs a composite interest prediction which predicts a level of interest of the user associated with the respective article of interest.