Lead Valuation Modeling for Enterprise-Value Marketing Decisions
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Solution Overview
Problem
Current marketing systems lack the ability to automate the processing of customer leads to identify the most valuable leads based on their potential impact on company valuation.
Innovation Solution
A method and system utilizing ensemble machine learning models to assess transaction values for potential customers, considering factors like cost of customer acquisition, customer lifetime value, and return on capital, to determine marketing actions that positively impact enterprise valuation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated lead processing is implemented using machine learning models, then lead identification accuracy and enterprise valuation impact are improved, but system complexity and computational resources increase
Solution Approach 1:
The system segments the lead processing task into multiple independent machine learning models (e.g., lead scoring model, valuation impact model, prioritization model) that can be developed, trained, and maintained separately. Each model focuses on a specific aspect of lead evaluation, reducing the complexity of any single model while achieving high overall accuracy through ensemble methods.
Solution Approach 2:
The patent introduces an intermediary optimization system that acts as a mediator between lead generation sources and the seller's CRM system. This intermediary layer handles the complex machine learning computations and decision-making, shielding the rest of the system from complexity while providing simplified interfaces for data input and output.
2Productivity
If comprehensive data analysis is performed to assess transaction value, then marketing decision quality is improved, but processing time and computational cost increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and pre-scoring leads as they are generated, rather than waiting for batch processing. Lead scoring models evaluate basic lead attributes immediately upon receipt, and high-priority leads are identified and routed in real-time, reducing the time loss associated with comprehensive analysis by doing critical evaluations upfront.
Solution Approach 2:
The patent applies partial action by implementing a tiered analysis approach where not all leads receive the full comprehensive analysis. Instead, leads are initially screened by lighter models, and only those meeting certain thresholds undergo more intensive valuation impact assessment, reducing overall processing time while maintaining decision quality for the most promising leads.
3Speed
If real-time lead assessment is implemented, then marketing response speed is improved, but computational resource consumption increases
Solution Approach 1:
The system implements dynamic resource allocation where computational resources are adjusted based on lead volume, priority, and current system load. During periods of high lead volume, the system dynamically scales computing resources or implements queueing mechanisms, while during lower volume periods, more intensive analysis can be performed. This dynamic approach maintains real-time response capability for critical leads while managing overall resource consumption.
Data Source
AI summary
Systems, methods and computer readable media for automated marketing decisions based on company valuation results derived from ensemble machine learning models include collecting potential customer data for a set of potential customers from a lead source and transmitting the potential customer data to an optimization system. An ensemble machine learning model that establishes a transaction value for each of the set of potential customers based on an enterprise valuation of the seller. A marketing action is then taken. The marketing action may include one or more of budgeting for a transaction with the lead source, targeting communications to members of the set of potential customers, accepting or rejecting members of the set of potential customers, or offloading members of the set of potential customers.