Sales Opportunity Scoring Using Combinatorial Optimization
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Solution Overview
Problem
B2B Sales Professionals face challenges such as being underserved by technology, lacking skills and resources to navigate a complex buyer landscape, and receiving ineffective sales training, leading to a longer and more complex sales cycle.
Innovation Solution
A novel system and method using a consistent scoring system that calculates the likelihood of closing sales deals by normalizing and applying combinatorial optimization algorithms to assign sales attributes, providing insights for sales professionals to allocate resources effectively and improve deal closure rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional CRM systems are implemented, then company benefits are improved, but sales professional workload increases due to administrative burden
Solution Approach 1:
The system automatically performs data normalization, similarity calculations, and opportunity matching without requiring manual sales professional intervention. The combinatorial optimization algorithm self-adjusts to find optimal matches between sales opportunities and representatives based on historical data patterns.
Solution Approach 2:
Manual administrative processes are replaced with automated computational systems. The patent uses computer-implemented algorithms to perform what would traditionally require manual analysis of sales data, opportunity assessment, and representative matching.
2Measurement precision
If sales professionals manually analyze each sales opportunity, then decision quality improves, but time consumption increases
Solution Approach 1:
The system pre-calculates and stores normalized attributes for all past sales opportunities and representative performances. When a new opportunity arises, the system quickly retrieves and compares pre-processed data using combinatorial optimization, eliminating the need for real-time manual analysis while maintaining evaluation quality.
Solution Approach 2:
The patent transforms qualitative sales assessment criteria into quantifiable normalized parameters. By converting deal attributes and representative characteristics into standardized numerical values, the system enables precise computational comparison and optimization.
3Loss of information
If comprehensive sales data is collected and analyzed, then insights quality improves, but system complexity increases
Solution Approach 1:
The patent divides the complex sales data analysis into distinct normalized attributes (e.g., deal size, industry, representative performance metrics). Each attribute is independently processed and weighted, allowing the system to handle comprehensive data without overwhelming complexity.
Solution Approach 2:
The normalized attribute system serves multiple functions: it enables comparison across different deal types, tracks representative performance, identifies optimal matches, and provides historical analysis. This universal framework handles diverse sales data through a unified approach.
Data Source
AI summary
In some embodiments, a computer implemented method for determining and generating an electronic recommendation and/or other outputs, such as observations and tasks, in which the method may include the steps of: receiving input from the user through a client device in which the input may include data for populating a key member or key player data record; identifying, via a computing device processor, a first rule corresponding to the key player data record; retrieving, via a computing device processor, a first observation in which the first observation is associated with the first rule; and displaying to the user, via a display screen of the client device, the first observation. In further embodiments of the method, an observation may be associated with a recommendation and the recommendation may include a pre-recorded video multimedia file specific for the observation.