Consumer Data Generator for Retail Purchase Analysis
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Solution Overview
Problem
Current consumer research systems face challenges in generating accurate and comprehensive consumer data, as they often rely on limited and fragmented datasets from panelists, which may not represent broader population behaviors effectively.
Innovation Solution
The system integrates panel product data with membership card data through a consumer data generator that selects a representative sample, fuses datasets, updates linkages, and generates projections to provide a more comprehensive understanding of purchasing behaviors across multiple retailers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If panel product data is collected from limited panelists, then data collection cost is reduced, but data representativeness deteriorates
Solution Approach 1:
The patent merges panel product data with membership card data from multiple retailers to create a fused dataset. This combination allows the system to maintain cost-effective panelist collection while achieving broader population representativeness through the integration of complementary data sources that cover different retail contexts and consumer behaviors
Solution Approach 2:
The patent introduces a data fusion entity as an intermediary that connects panel product data with membership card data. This intermediary process matches panelists to membership households based on demographic and geographic characteristics, enabling the system to bridge the gap between limited panel data and comprehensive population insights without directly collecting from all population members
2Measurement precision
If comprehensive consumer data is collected across multiple retailers, then data representativeness is improved, but system complexity increases
Solution Approach 1:
The patent segments the data collection system into distinct components: panel product data collection, membership card data collection, and a data fusion entity. Each component handles specific data sources and processing tasks independently, then the fusion entity integrates them. This segmentation reduces overall system complexity by breaking down the complex task of multi-retailer data integration into manageable, specialized modules
Solution Approach 2:
The data fusion entity serves multiple functions: it matches panelists to membership households, fuses datasets from different sources, updates linkages, and generates projections. This multi-functional approach consolidates what would otherwise require separate systems for each task, reducing overall system complexity while maintaining comprehensive data representativeness
3Ease of operation
If panel data is used alone, then data collection simplicity is maintained, but measurement accuracy deteriorates
Solution Approach 1:
The patent combines panel product data with membership card data to create a more accurate representation of purchasing behaviors. The fusion of these datasets allows the system to maintain the simplicity of panel-based collection while achieving higher measurement accuracy through the complementary information from membership programs that track actual purchases across multiple retailers
Data Source
AI summary
Methods and apparatus to generate consumer data are disclosed. An example method of selecting a sample of transaction data corresponding to a membership program includes defining a first type of member of the membership program; defining a second type of member of the membership program; calculating, via a processor, a target for the sample; selecting, via the processor, a first portion of the transaction data for the first type of member in accordance with the target; generating, via the processor, an updated target by recalculating the target with the first portion of the transaction data removed from consideration; and selecting, via the processor, a second portion of the transaction data for the second type of member in accordance with the updated target.