Stacked Machine Learning Models for B2B Product Recommendations
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Solution Overview
Problem
In the B2B industry, the time-consuming process of prospecting clients and aligning client needs with available products/services is hindered by disparate data sources and types, making it difficult to combine machine learning models and generate effective recommendations.
Innovation Solution
A computer-implemented method using stacked machine learning models that merge outputs from multiple datasets to detect convergence of target variables, generating product recommendations by integrating disparate data sources and types, thereby aligning client needs with suitable products/services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple separate machine learning models are used to analyze different datasets (territory plan and prospective-based data), then each model can be optimized for its specific data type, but the process becomes complex and time-consuming to integrate and generate recommendations
Solution Approach 1:
The patent combines multiple separate machine learning models into a single stacked model architecture. The first model processes territory plan data while the second model processes prospective-based data, and their outputs are stacked together to form a unified recommendation system. This merging maintains the specialized processing capabilities of each model while simplifying the overall system architecture and recommendation generation process.
2Measurement precision
If manual processing of territory plans and prospective data is performed, then data accuracy can be ensured, but the time and resources required for prospecting and analysis increase significantly
Solution Approach 1:
The patent replaces manual mechanical processing of territory plans and prospective data with automated machine learning models. These models automatically extract features, analyze patterns, and generate recommendations from the input data, eliminating the need for manual data processing while maintaining high accuracy through the sophisticated processing capabilities of the stacked model architecture.
3Ease of manufacture
If separate data types (firmographics, historical pipeline activity, digital footprint) are analyzed independently, then each data source can be processed with appropriate methods, but correlating client needs with products/services becomes difficult
Solution Approach 1:
The stacked model architecture provides a universal framework that can process multiple different data types including firmographics, historical pipeline activity, and digital footprint data. Each model in the stack is designed to handle specific data types appropriately, while the stacked structure enables correlation across all data sources to generate comprehensive product recommendations that adapt to various client needs.
Data Source
AI summary
A method, computer system, and a computer program for generating recommendations using stacked models is provided. The present invention may include receiving a first dataset pertaining to a territory plan associated with a user and a second dataset pertaining to prospective-based data. The present invention may then include detecting a plurality of target variables associated with the B2B party within the first and second datasets. The present invention may further include determining a convergence of at least two target variables of the plurality of target variables. The present invention may further include generating a product recommendation associated with the B2B party based on the convergence.