Affinity Analysis System for Product-Consumer Segmentation
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Solution Overview
Problem
Current market research methods fail to accurately predict which products can be effectively marketed together to enhance sales, leading to inefficient marketing efforts and resource wastage, as they rely on incomplete data from loyalty programs, demographics, and past transactions.
Innovation Solution
The development of a system that identifies affinity between product characteristics and consumer segment attributes by analyzing transaction data to generate product and segment affinity rules, allowing for targeted marketing and product recommendations based on confidence levels and occurrence thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If market research entities rely on loyalty programs, demographics, and databases of past transactions to provide product recommendations, then they can generate some product recommendations, but the predictions are inaccurate and lead to inefficient marketing efforts
Solution Approach 1:
The patent segments consumers into distinct groups based on transaction data analysis, creating consumer segments with shared purchasing patterns. This segmentation enables more accurate product recommendations by targeting specific segments rather than using generic demographic approaches, thereby improving prediction accuracy while reducing marketing waste through focused campaigns.
Solution Approach 2:
The patent applies association rule mining algorithms to analyze only the necessary portion of transaction data required to identify meaningful product affinities and consumer patterns. By focusing computational resources on extracting key affinity relationships rather than processing entire databases, the system achieves accurate predictions with efficient resource utilization.
2Measurement precision
If comprehensive transaction data is analyzed to improve prediction accuracy, then product recommendation accuracy improves, but data processing complexity and resource requirements increase
Solution Approach 1:
The patent extracts specific affinity relationships and consumer patterns from large transaction datasets using association rule mining. By extracting only the relevant affinity rules and consumer segment characteristics needed for recommendations, the system achieves high prediction accuracy without requiring complex processing of entire databases, thus reducing computational complexity while maintaining precision.
Solution Approach 2:
The patent performs preliminary data processing to pre-segment consumers and pre-identify product affinities before generating recommendations. This preliminary action organizes data into structured segments and affinity rules in advance, reducing the complexity of real-time recommendation generation while maintaining high accuracy through pre-computed patterns.
3Adaptability or versatility
If generic demographic-based marketing is used, then marketing coverage is broad, but resource wastage occurs due to ineffective targeting
Solution Approach 1:
The patent creates dynamic consumer segments based on actual transaction patterns rather than static demographics. These segments adapt to changing consumer behaviors and purchasing patterns, allowing marketing coverage to remain broad and versatile while improving targeting precision. The system continuously updates segment definitions based on new transaction data, ensuring resources are allocated to currently relevant segments without wasting budget on outdated demographic categories.
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture are disclosed to identify affinity between segment attributes and product characteristics. An example method includes identifying, with a processor, a set of product characteristics from purchase transactions that exhibit a threshold product affinity, selecting, with the processor, a set of products having at least one product characteristic from the set of product characteristics that exhibit the threshold product affinity, the set of products associated with first segments, extracting, with the processor, segment attributes from the first segments, and improving a market success of the product of interest by identifying, with the processor, target segments based on ones of the extracted segment attributes exhibiting a threshold segment affinity.