Marketing Campaign Targeting Using Pre-Trained ML Models
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Solution Overview
Problem
Marketing campaigns face challenges such as uncertainty, high costs, and resource-intensive preparation due to the need for testing multiple messages, small sample sizes, and lack of standardization, which hinder accurate targeting and messaging in competitive markets.
Innovation Solution
A system and method that utilize pre-trained machine learning models from historical campaigns to estimate customer responses, prioritize target customers, and fine-tune content through a user-friendly interface, allowing for data-driven insights to streamline and customize marketing campaigns, reducing uncertainty and resource requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional phased marketing campaigns with test and control groups are used, then brand awareness and sales improvement goals can be pursued, but preparation time and costs increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-training multiple machine learning models on historical campaign data before the actual campaign launch. These pre-trained models are stored in a library and can be rapidly applied to new campaigns, eliminating the need for time-consuming test and control group experiments while maintaining reliable effectiveness predictions.
Solution Approach 2:
The system creates copies of successful campaign patterns by training models on historical campaign data and storing them in a library. When launching a new campaign, the system selects and applies relevant pre-trained models that capture proven effective messaging and targeting patterns from past campaigns, rather than starting from scratch with new experiments.
2Measurement precision
If multiple messages are tested in traditional campaigns, then messaging effectiveness can be optimized, but costs and resource requirements increase
Solution Approach 1:
The machine learning models perform self-service by automatically evaluating and ranking multiple messaging options based on historical performance data. The system autonomously identifies the most effective messages without requiring manual A/B testing, thereby maintaining precise measurement of messaging effectiveness while significantly reducing the resources needed for traditional test campaigns.
3Productivity
If traditional marketing campaigns are launched without standardized learning from past campaigns, then campaign execution can proceed, but uncertainty regarding effectiveness remains high
Solution Approach 1:
The system implements feedback by continuously learning from historical campaign outcomes. Machine learning models are trained on past campaign data including messaging performance, customer responses, and conversion metrics. This feedback loop enables the system to predict campaign effectiveness with high reliability before launch, reducing uncertainty while maintaining efficient execution.
4Measurement precision
If custom machine learning models are trained for each new campaign, then campaign-specific accuracy is improved, but preparation time and computational resources increase
Solution Approach 1:
The system achieves universality by creating a library of pre-trained machine learning models that can serve multiple campaign purposes. A single pre-trained model can be applied to various new campaigns with similar characteristics, providing accurate campaign-specific predictions without requiring separate training for each campaign. This multi-functional approach maintains precision while dramatically reducing preparation time.
Data Source
AI summary
A computer-implemented method is provided for identifying potential individuals to contact in a campaign of interest. The method includes receiving campaign data including description about the campaign of interest and information about the potential individuals to contact for the campaign of interest and selecting a plurality of trained machine learning models from a library of trained machine learning models based on the campaign data. The library of trained machine learning models is created from data of historical campaigns administered, and each of the selected plurality of trained models corresponds to a historical campaign that is within a similarity threshold from the campaign of interest. The method also includes scoring a pool of existing customers using the select plurality of trained machine learning models and identifying the potential individuals to contact in the campaign of interest by ranking the existing customers by their corresponding propensity scores.