Prospect Model Identifies High-Probability Customers
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Solution Overview
Problem
Conventional systems for marketing, sales, and service interactions in businesses require complex and time-consuming tasks to integrate data from different sources, such as CRM systems, databases, and networks, making it difficult to identify and engage with potential customers effectively.
Innovation Solution
A system and method for generating prospect profiles and reports using a prospect model that analyzes historical data and traffic data to identify potential customers with a high probability of successfully completing an opportunity, allowing for personalized content transmission and targeted marketing efforts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is extracted from separate databases, networks, or information technology systems using complex queries and APIs, then relevant information for marketing and sales activities can be obtained, but the process becomes time-consuming and complex
Solution Approach 1:
The patent merges multiple separate data sources (CRM systems, databases, networks, and other IT systems) into a unified data structure that can be accessed through a single interface. This consolidation eliminates the need for complex queries across multiple systems and reduces data access time while maintaining comprehensive information availability.
Solution Approach 2:
The patent introduces an intermediary component (the unified data structure with standardized schema) that mediates between various data sources and the marketing/sales activities. This intermediary layer handles data transformation and integration, freeing users from direct complex data extraction tasks while ensuring information quality and consistency.
2Adaptability or versatility
If data is transformed from one native format to another suitable form for use in a different environment, then data compatibility is improved, but the complexity and time required for data processing increases
Solution Approach 1:
The patent creates a universal data structure with a standardized schema that can accommodate multiple data formats and sources. This universal structure serves as a common interface for various marketing and sales activities, eliminating the need for repeated format transformations while maintaining adaptability to different data sources and uses.
3Productivity
If separate systems are used for marketing, sales, and service interactions, then each system can be optimized for its specific function, but integration between systems becomes complex and time-consuming
Solution Approach 1:
The patent segments the integrated system into distinct functional components (marketing activities, sales activities, service interactions) that can operate independently with optimized performance, while sharing a common underlying data structure. This segmentation allows each function to maintain its specialized capabilities while reducing integration complexity through the unified data layer.
Data Source
AI summary
Systems and methods are provided for identifying prospects based on a prospect model. A set of primary features are extracted from historical data for an opportunity between an organization and an entity. A data container is generated to represent the set of primary features and a set of secondary features associated with the entity. Neighboring data containers, within a set of data containers that includes the data container, are grouped into data container groups. A data container group is selected to represent a combination of features of the entity predicted to yield the opportunity for the organization. The combination of features are used to generate and transmit content to the entity.