Automated Lead Scoring System for Sales Efficiency
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Solution Overview
Problem
Conventional lead generation strategies are resource-intensive, time-consuming, and often yield low-quality leads, inefficiently utilizing financial and agent resources.
Innovation Solution
A computer-implemented system and method that leverages data from various sources to identify and filter high-quality leads by categorizing, scoring, and profiling leads based on selected attributes, using data processing modules and machine learning techniques to automatically store and display qualified leads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional lead generation strategies (advertising, referrals) are used, then leads can be generated, but the process is resource-intensive and time-consuming
Solution Approach 1:
The patent replaces manual lead generation processes with an automated computer system that uses data processing modules to query external databases, collect lead information, and generate leads automatically based on predefined criteria and machine learning models, eliminating the need for manual advertising and referral processes
Solution Approach 2:
The system performs preliminary data collection and processing by querying external data sources and pre-filtering leads based on selected attributes before they are presented to agents, so that lead generation and initial filtering are completed in advance automatically
2Reliability
If conventional lead generation strategies are used, then leads can be obtained, but the quality of leads is not assured and financial resources are wasted on low-quality leads
Solution Approach 1:
The system changes the parameters for lead evaluation by using multiple selected attributes (such as demographic information, behavioral data, and engagement metrics) and assigning weights to each attribute to create a comprehensive scoring system that accurately predicts lead quality and purchase potential
Solution Approach 2:
The system uses machine learning models that are trained on historical data and feedback about lead conversion rates to continuously improve the scoring algorithm, allowing the system to learn from past performance and refine its lead quality assessment over time
3Productivity
If agents manually follow up on leads, then transactions can be completed, but agent time and efforts are inefficiently used on uninterested or financially unavailable leads
Solution Approach 1:
The patent extracts the lead filtering and scoring function from the agent's workflow and places it in an automated system, so that the computer independently performs the complex task of evaluating lead quality and preparing personalized follow-up information, leaving agents with only the high-value task of closing deals
Solution Approach 2:
The system performs self-service by automatically querying external data sources, collecting and processing lead information, scoring leads based on selected attributes, and preparing follow-up materials without requiring agent intervention, thereby freeing agents from routine lead evaluation tasks
Data Source
AI summary
A method and system for obtaining leads based on data derived from a variety of sources is disclosed. The method is executed by a system that includes a data processing module within a leads management system, among others system components. The data processing module scans one or more external data sources; collects lead data from those sources; analyzes the data collected; identifies attributes of interest about one or more potential quality leads; identifies one or more high quality leads; creates a profile for those high quality leads; stores those profiles in an internal database; and generates a list of one or more high quality leads.