Cognitive Opportunity Recommendation System
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Solution Overview
Problem
Business entities face challenges in discovering and acquiring new clients and opportunities due to inefficient decision-making processes, high turnover rates, and rapidly changing skill sets, leading to delayed and sub-optimal sales performance.
Innovation Solution
A cognitive system is implemented to provide intelligent opportunity recommendation and management by applying a channel selection model to identify and rank team candidates and entity partners based on alignment with opportunities, recommending team compositions, and automatically managing opportunity progression through historical data analysis and machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual decision-making processes are used for opportunity management, then flexibility and human judgment are maintained, but decision-making speed and accuracy deteriorate
Solution Approach 1:
The patent introduces a cognitive system as an intermediary between manual decision-making and automated processes. This system uses machine learning models and natural language processing to analyze opportunity data, generate recommendations, and assist human decision-makers, thereby improving decision-making speed and accuracy while maintaining human oversight and flexibility.
2Productivity
If traditional opportunity management methods are used, then simplicity is maintained, but sales productivity and efficiency deteriorate
Solution Approach 1:
The patent segments the opportunity management process into distinct functional modules including opportunity identification, analysis, recommendation generation, and progression management. Each module is handled by specific cognitive functions and machine learning models, allowing the system to process complex sales data efficiently while presenting simplified interfaces to users.
Solution Approach 2:
The system performs preliminary analysis and preparation of opportunity data using historical data and machine learning models before human intervention is needed. This includes pre-processing opportunity information, generating initial recommendations, and preparing progression strategies, thereby reducing the time and effort required for final decision-making.
3Reliability
If high turnover rates and changing skill sets are accommodated through manual processes, then adaptability is maintained, but decision-making quality and consistency deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where the cognitive system continuously learns from historical opportunity data, team member performance data, and outcome results. This feedback loop allows the system to adapt to changing skill sets and maintain consistent decision-making quality by comparing actual outcomes with predicted outcomes and adjusting recommendations accordingly.
Data Source
AI summary
Embodiments for implementing intelligent opportunity recommendation and management by a processor. A channel selection model mat be applied to a selected opportunity in view of a plurality of opportunity attributes to identify one or more team candidates of an entity or one or more entity partners ranked by alignment with the selected opportunity and determine a recommended opportunity owner from the one or more team candidates of the entity, the one or more entity partners, or a combination thereof.