Personalized Message Generation Through Automated Lead Scoring
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Solution Overview
Problem
Businesses face challenges in identifying and communicating with prospective customers in a personalized manner, as existing methods often result in mass emails that are recognized as spam and fail to engage recipients effectively.
Innovation Solution
A system utilizing crawlers, information extraction, and machine learning to identify relevant recipients and generate personalized messages by analyzing public and private data sources, including entity and event extraction, lead scoring, and content generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If mass emails with simple templates are used to reach many customers, then the quantity of communications is improved, but the personalization and relevance to recipients deteriorates
Solution Approach 1:
The patent segments the communication process into multiple stages: recipient identification, lead scoring, and personalized content generation. By dividing the mass communication task into discrete segments with specific functions, the system can process large volumes of communications while maintaining personalization through automated recipient discovery and scoring algorithms that evaluate individual recipient characteristics.
Solution Approach 2:
The system enables self-service through automated recipient discovery and scoring. The lead scoring algorithm automatically evaluates and ranks potential recipients based on their characteristics and likelihood of interest, eliminating the need for manual recipient selection while maintaining high personalization standards. This allows the system to serve itself in identifying and personalizing communications at scale.
2Ease of operation
If simple templates with placeholders are used for mass emails, then the ease of operation is improved, but the effectiveness in engaging recipients deteriorates
Solution Approach 1:
The patent introduces an intermediary lead scoring algorithm that acts as a mediator between the simple template system and the recipient. This intermediary automatically evaluates recipients and personalizes content selection based on their characteristics, bridging the gap between simple template operations and effective engagement. The intermediary layer maintains ease of template usage while significantly improving engagement effectiveness through automated personalization.
3Adaptability or versatility
If automated recipient discovery and personalized message generation are implemented, then the personalization and engagement are improved, but the device complexity increases
Solution Approach 1:
The patent implements a universal lead scoring algorithm that performs multiple functions: recipient identification, interest level assessment, and content personalization. This multi-functional approach consolidates what would otherwise require separate complex systems into a single versatile component, reducing overall system complexity while maintaining high personalization capabilities. The universal algorithm serves as a core engine that drives personalized engagement across diverse communication scenarios.
4Measurement precision
If sophisticated recipient filtering and scoring algorithms are used, then the precision of recipient selection is improved, but the loss of time in processing increases
Solution Approach 1:
The patent applies partial action by implementing a tiered scoring system that processes recipients in stages. The lead scoring algorithm performs rapid initial filtering to identify high-potential recipients, then applies more sophisticated evaluation only to those who pass the initial threshold. This partial processing approach maintains high selection accuracy for the most promising leads while reducing overall processing time by avoiding exhaustive analysis of all potential recipients.
Data Source
AI summary
A system includes a set of crawlers that find and retrieve documents from an information network, an information extraction system, a knowledge graph storing nodes and edges that connect them, wherein each node represents a respective entity of a corresponding entity type of a plurality of entity types, and wherein the knowledge graph further stores event data relating to events detected by the information extraction system, a machine learning system that trains models that are used in connection with at least one of entity extraction, event extraction, recipient identification, and content generation, a lead scoring system that scores the relevance of information to an individual and references information in the knowledge graph, and a content generation system that generates content of a personalized message to a recipient who is an individual for which the lead scoring system has determined a threshold level of relevance.