Intent Signal Scoring With Buying Funnel Stage Prediction
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Solution Overview
Problem
Existing intent data systems provide fine-grained, single-topic scores that overwhelm companies, leading to analysis paralysis and missed opportunities, failing to leverage the intent data for improved lead generation and sales efficiency.
Innovation Solution
A content consumption monitor (CCM) system that generates high-level intent signals across multiple topics of interest, integrating historical context analysis with real-time data to identify target companies' receptivity and preferred contact methods, using NLP and ML to optimize sales and marketing strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If fine-grained, single-topic intent data scores are provided for each target company, then comprehensive topic coverage is achieved, but data complexity and analysis difficulty increase leading to analysis paralysis
Solution Approach 1:
The patent combines multiple fine-grained, single-topic intent data scores into a single consolidated intent signal score for each target company. This merging process aggregates information across multiple topics and buying funnel stages, transforming overwhelming granular data into an actionable unified metric that indicates overall purchase intent and readiness.
Solution Approach 2:
The intent signal score serves multiple functions simultaneously: it indicates purchase intent, identifies buying funnel stage, and provides a prioritization metric for sales teams. This multi-functional approach replaces the need for customers to separately analyze multiple single-topic scores across different categories.
2Adaptability or versatility
If multiple sets of intent data scores for multiple topics and companies are generated, then comprehensive intent coverage is achieved, but usability and actionable insights decrease
Solution Approach 1:
The patent transforms the parameter structure of intent data by changing from multiple dimensional scores (one per topic per company) to a consolidated intent signal score with associated buying funnel stage classification. This parameter transformation makes the data more usable while preserving comprehensive intent coverage across multiple topics and companies.
3Measurement precision
If historical context analysis and multiple data sources are integrated, then prediction accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary analysis by pre-processing and organizing data from multiple sources (web browsing behavior, online research, social media activity, event attendance, job postings) before generating intent signals. Historical context is analyzed in advance to establish baseline patterns, enabling faster real-time intent prediction without repeated heavy processing.
Data Source
AI summary
Disclosed embodiments include a content consumption monitor (CCM) that receives a name or domain of a customer and historical context data. A set of topics are identified that are most relevant to the customer based on the name/domain. The set of topics is ranked in order of highest relevancy. The CCM determines which topics in the ranked set most closely match with a set of target topics for which a target company has shown interest, the target topics having a corresponding topic interest score indicating interest level. The matching topics are associated with the corresponding topic interest scores. The CCM identifies at which buying funnel stage the matching topics are in based on the topic interest scores, and the historical context data. The CCM generates an intent signal comprising the matching topics and corresponding topic interest scores, and associated buying funnel stage of the matching topics.