ML Classifier for Sales Prospect Prioritization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Marketing and sales groups face challenges in identifying and prioritizing new potential sales targets due to the complexity of analyzing various criteria such as business type, legal standing, location, and logistical requirements, which complicates the process of cross-selling, upselling, and new customer acquisition.
Innovation Solution
A system comprising a transceiver, processor, and memory that uses a classification engine with machine learning classifiers to identify attractive and unattractive company prospects by training on a database of company engagements, including firmographic data, to calculate probability scores for prioritizing leads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual analysis of multiple criteria (business type, legal standing, location, logistical requirements) is used to identify sales targets, then the analysis can be thorough and comprehensive, but the process becomes time-consuming and complex
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated machine learning classification system. The classifier engine processes multiple criteria (business type, legal standing, location, logistical requirements) automatically, eliminating time-consuming manual evaluation while maintaining comprehensive analysis through structured data fields and automated scoring mechanisms.
Solution Approach 2:
The system enables self-service lead scoring and prioritization through automated classification. The machine learning model independently evaluates prospects against predefined criteria without requiring manual intervention, allowing the sales team to receive pre-prioritized leads ready for action.
2Measurement precision
If multiple candidate data items (linkage, business type, location, growth rates, commercial credit scores, revenue) are analyzed for target identification, then the selection accuracy improves, but the device complexity and difficulty of operation increase
Solution Approach 1:
The patent merges multiple candidate data items (linkage, business type, location, growth rates, commercial credit scores, revenue) into a unified classification framework. The system consolidates these diverse data sources into standardized fields that feed into the machine learning classifier, simplifying the overall process while maintaining comprehensive evaluation.
Solution Approach 2:
The machine learning classification system serves multiple functions simultaneously: it evaluates business type, checks legal standing, verifies location constraints, assesses logistical requirements, and scores leads based on growth rates and revenue. This multi-functional approach reduces system complexity by using a single platform for all evaluation needs.
3Measurement precision
If multiple candidate data items are analyzed for target identification, then the prioritization accuracy improves, but the ease of operation decreases
Solution Approach 1:
The patent replaces manual prioritization operations with automated machine learning classification. The system automatically processes multiple data items and generates prioritized lead lists without requiring users to manually weigh or compare different criteria, significantly improving ease of operation while maintaining high prioritization accuracy.
Solution Approach 2:
The classification engine performs self-service prioritization by automatically evaluating prospects against all criteria and ranking them according to predicted conversion probability. Users simply need to input leads into the system, which then handles the complex evaluation and ranking independently.
4Reliability
If comprehensive criteria analysis is performed for each prospect, then the reliability of sales target selection improves, but the productivity decreases
Solution Approach 1:
The patent replaces slow manual analysis with high-speed automated machine learning classification. The system processes comprehensive criteria for each prospect automatically, maintaining reliable evaluation while increasing lead processing speed from days to seconds per lead.
Solution Approach 2:
The system performs preliminary classification and filtering of leads before they reach sales representatives. By pre-evaluating all criteria and prioritizing leads in advance, the system enables rapid processing while ensuring comprehensive analysis has already been completed.
Data Source
AI summary
Embodiments of a system and method for identifying and prioritizing company prospects by training at least one classifier on client company win/loss metrics. One or more classifiers can be trained on a training database compiled from company win/loss database for a client and firmographic data from a robust business entity database. Once trained, the system can employ Artificial Intelligence powered by the trained classifiers to classify and output customized prospect lists of thousands of profiled and scored companies that the AI has determined are likely targets for specific marketing and sales. The AI can also ingest databases of client targets and classify and score them based on the custom-trained classifier.