Lead Enhancement System with Propensity Scoring and Contact Verification
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Solution Overview
Problem
Businesses face challenges in effectively contacting potential customers due to incomplete or inaccurate contact information in lead records, leading to wasted resources and duplicate contacts, as existing methods fail to accurately verify and enhance lead data.
Innovation Solution
A method and system for enhancing lead records by determining a contactability score and propensity score using customized models, which verify and append accurate contact information from multiple data sources, and identify duplicates and spoof data, thereby improving the quality of lead records.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If lead records are acquired from multiple sources to increase lead quantity, then the quantity of leads increases, but the accuracy and completeness of contact information deteriorates
Solution Approach 1:
The system performs preliminary verification and enrichment of lead records by querying multiple data sources before the leads are used for marketing campaigns. This advance processing ensures contact information accuracy is established beforehand, preventing the deterioration that would occur with raw multi-source leads.
Solution Approach 2:
The patent introduces an intermediary lead verification system that acts as a mediator between multiple lead sources and the final marketing database. This intermediary layer cleans, validates, and standardizes contact information from various sources, maintaining accuracy while allowing quantity to increase.
2Measurement precision
If lead verification processes are enhanced to improve contact information accuracy, then the accuracy of contact information improves, but the time and resources required for processing leads increases
Solution Approach 1:
The system applies partial verification actions by querying only the most relevant data sources based on the lead's characteristics and the specific verification needs. Rather than exhaustively checking all possible sources, it performs sufficient verification to achieve acceptable accuracy levels, reducing processing time while maintaining quality.
Solution Approach 2:
The patent dynamically adjusts verification parameters such as the number of data sources queried, the depth of verification, and the types of checks performed based on lead priority, source reliability, and resource availability. This parameter optimization balances accuracy requirements with processing time constraints.
3Productivity
If duplicate lead identification is performed to reduce wasted contacts, then resource allocation improves, but the complexity of the lead processing system increases
Solution Approach 1:
The system extracts duplicate leads from the processing stream by comparing leads against existing databases and previously processed leads. By removing duplicates early in the process, it prevents wasted resources on repeated contacts without requiring complex ongoing analysis of every lead.
Solution Approach 2:
The patent uses copying techniques by creating simplified representations or hashes of lead records for quick duplicate detection. Rather than performing complex full-record comparisons, it uses copied key identifiers to efficiently identify duplicates, reducing system complexity while maintaining effectiveness.
4Measurement precision
If propensity scoring models are customized for specific businesses to improve lead quality, then the relevance and conversion potential of leads improves, but the time and computational resources required for model development and application increases
Solution Approach 1:
The system performs preliminary customization of propensity scoring models by pre-configuring industry-specific parameters, weightings, and criteria based on historical data and business requirements. This advance model preparation reduces the time needed for custom model development while maintaining high lead quality assessment accuracy.
Solution Approach 2:
The patent creates universal propensity scoring frameworks that can be applied across multiple businesses and industries with minimal customization. By designing a multi-functional model structure that adapts to different business contexts through configurable parameters rather than complete redesign, it reduces development time while preserving customization benefits.
Data Source
AI summary
A client transmits one or more lead records to a lead enhancement module that is configured to enhance the received lead records and return enhanced lead records to the client. The lead enhancement module may return a contactability score for each lead record, indicating a likelihood that the individual identified in the lead may be contacted using the contact information provided in the lead record and/or additional contract information located by the lead enhancement module. The lead enhancement module may also receive additional data items associated with leads from one or more data sources. Additionally, statistical models that may be customized for each client may be applied to information associated with lead records in order to determine one or more propensity scores for each of the lead records, where a propensity score indicates a likelihood that an individual will take a particular action, such as purchasing particular goods or services.