Meta-descriptors for Automated Marketing Offer Assignment
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Solution Overview
Problem
Marketers face challenges in identifying and assigning relevant offers to marketing deliveries due to poorly identified offers, manual tagging processes, and the need for cumbersome coded rules, which are not scalable and do not provide insights into successful delivery components.
Innovation Solution
The use of meta-descriptors to describe offers in marketing deliveries, including arrangement, products, themes, fonts, backgrounds, and color schemes, with a predictive modeling approach that identifies key performance indicators (KPIs) using a training and testing module, enabling automated tagging and performance prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual tagging and coded rules are used to identify offers, then marketers can assign offers to deliveries, but the process becomes tedious and does not scale well
Solution Approach 1:
The system enables automated self-tagging of offers and deliveries by using machine learning models that automatically analyze content, extract relevant features, and assign appropriate tags without requiring manual marketer intervention. This eliminates the tedious manual tagging process while maintaining accurate offer identification.
Solution Approach 2:
The patent replaces the mechanical manual tagging system with an automated computational system using machine learning algorithms. The system automatically processes delivery content, extracts features, and assigns tags through computational analysis rather than human manual effort, significantly improving scalability and efficiency.
2Adaptability or versatility
If marketers manually create coded rules to assign offers, then offers can be assigned based on consumer characteristics, but the process is cumbersome and does not scale to millions of consumers
Solution Approach 1:
The system performs preliminary automated analysis of delivery content and consumer characteristics before offer assignment. Machine learning models pre-process and pre-tag content, and pre-segment consumers based on their characteristics, so that when offer assignment is needed, the system can quickly match offers to appropriate consumers without time-consuming manual rule creation.
Solution Approach 2:
The patent transforms the static manual rule-based system into a dynamic parameter-driven system. Instead of creating explicit coded rules for each consumer segment, the system uses machine learning to automatically adjust and optimize assignment parameters based on consumer characteristics, delivery features, and performance data, enabling scalable adaptation to millions of consumers.
3Loss of information
If traditional delivery analysis is used, then deliveries can be sent to consumers, but marketers do not know which components made the delivery successful
Solution Approach 1:
The system implements comprehensive feedback mechanisms by tracking and analyzing which specific delivery components (offers, tags, content features) contribute to successful outcomes. Machine learning models continuously learn from performance data, providing feedback on what works and what doesn't, enabling marketers to optimize future deliveries based on proven successful patterns.
Solution Approach 2:
The patent segments delivery performance analysis into component-level evaluations. Instead of treating each delivery as a monolithic unit, the system breaks down deliveries into individual components (offers, tags, content elements) and analyzes the performance contribution of each segment, allowing marketers to identify specifically which components drove success.
4Ease of operation
If offers are not well identified, then marketers can send deliveries, but marketers must scroll through large numbers of offers to find appropriate ones
Solution Approach 1:
The system enables automated self-identification and self-tagging of offers based on their content and characteristics. Machine learning models automatically analyze offer content, extract relevant features, and assign appropriate tags without requiring marketer intervention, so offers are pre-organized and easily searchable when needed.
Solution Approach 2:
The patent replaces the manual offer browsing and selection process with automated machine learning-based offer identification. The system automatically matches offers to appropriate deliveries and consumers based on content analysis and pattern recognition, eliminating the need for marketers to manually scroll through large numbers of offers.
Data Source
AI summary
Various embodiments are directed to assigning offers to marketing deliveries utilizing new features to describe offers in the marketing deliveries. Marketing deliveries can be described at a finer level to thus enhance the effectiveness of building and conducting marketing campaigns. The approaches facilitate matching content to recipients, predicting content performance, and measuring content performance after dispatching a marketing delivery.