Promotion Recommendation Engine Clustering Correlation Metrics

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Solution Overview

Problem

Current methods for providing promotion recommendations in electronic marketing communications are inefficient and inaccurate, failing to leverage electronic marketing information effectively, leading to resource wastage and reduced marketing benefits.

Innovation Solution

A method and apparatus that analyze past transactions to determine correlation metrics for promotion clusters, generate valid promotion recommendations, and filter them based on validity, quantity, and availability, using a recommendation engine to select and transmit personalized electronic marketing communications.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If current methods for providing promotion recommendations are used, then electronic marketing communications can be sent to consumers, but the accuracy and effectiveness of the recommendations are low, leading to resource wastage

Engineering Contradiction:

Improveaccuracy of promotion recommendationsVSAvoidresource wastage in marketing communications

Core Design Contradiction:

Measurement precisionVSLoss of energy

Solution Approach 1:

The system performs preliminary analysis of transaction data to identify promotion clusters and correlation metrics before generating recommendations. This advance preparation ensures that only relevant promotions are recommended, improving accuracy while reducing wasted resources on irrelevant communications

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system uses past transaction data as feedback to continuously improve promotion recommendations. By analyzing historical redemption patterns and consumer behavior, the system learns from previous marketing outcomes to enhance future recommendation accuracy and resource efficiency

Inventive Principle:

Principle #23Feedback

2Productivity

If promotion recommendations are generated without filtering, then more recommendations can be provided to consumers, but the quality and validity of recommendations decrease

Engineering Contradiction:

Improvenumber of promotion recommendationsVSAvoidvalidity of promotion recommendations

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system segments promotions into distinct clusters based on correlation metrics derived from transaction data. This segmentation allows the system to generate numerous targeted recommendations within each cluster while maintaining high validity through data-driven grouping, resolving the contradiction between quantity and quality

Inventive Principle:

Principle #1Segmentation

3Measurement precision

If complex analysis methods are used to determine promotion recommendations, then recommendation accuracy improves, but system complexity and processing time increase

Engineering Contradiction:

Improveaccuracy of promotion recommendationsVSAvoidcomplexity of recommendation system

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system extracts key correlation metrics from transaction data to identify promotion clusters, rather than analyzing all possible promotion attributes. This extraction of essential features maintains high recommendation accuracy while significantly reducing system complexity and processing requirements

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS11830034B2Method and apparatus for providing electronic communications

Publication Date: 2023.11.28 BYTEDANCE INC

AI summary

The present disclosure relates to methods, systems, and apparatuses for providing electronic communications to client devices based on clustering and filtering candidates for inclusion in the electronic communications based on programmatically generated correlation metrics and thresholds associated with the clustering.