Retail Data Fusion System Correcting Panel Biases
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Solution Overview
Problem
Current retail data analysis methods, such as consumer panels, are plagued by biases and sampling errors, leading to inaccurate sales measurements, and lack coverage of non-tracked growth channels, limiting their usefulness for manufacturers.
Innovation Solution
A system and method that utilizes competitive and complementary data fusion to align and correct consumer panel data by comparing it with more accurate POS data, identifying and quantifying biases, and projecting values into areas with incomplete data, enhancing data accuracy and coverage across various retail channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If consumer panels are used to provide consumer-level data and insights, then consumer insights are improved, but bias and sampling errors increase
Solution Approach 1:
The patent combines consumer panel data with POS data and other data sources through data fusion techniques. This merging allows the system to retain the consumer-level insights from panels while correcting biases using the more accurate POS data, thereby improving overall measurement precision without losing valuable consumer behavior information.
Solution Approach 2:
The patent introduces data fusion algorithms and reconciliation processes as intermediary mechanisms between consumer panel data and POS data. These intermediaries process and harmonize the data from multiple sources, allowing the system to leverage consumer insights while mitigating panel biases through systematic data reconciliation.
2Measurement precision
If panel size is increased to reduce sampling error, then sampling error is reduced, but bias remains unaffected and may worsen
Solution Approach 1:
The patent extracts and separately addresses sampling errors and biases through different methodological approaches. Sampling errors are reduced through statistical methods and appropriate sample sizing, while biases are addressed through data fusion with POS data and reconciliation processes, allowing each type of error to be handled by the most appropriate technique.
Solution Approach 2:
The patent implements dynamic data fusion that adapts the weighting and integration of panel data and POS data based on the specific characteristics of each dataset. This dynamic approach allows the system to optimize the balance between reducing sampling error and mitigating bias, adjusting the contribution of each data source based on their respective strengths and weaknesses.
3Measurement precision
If traditional POS-based measurement is used, then measurement accuracy is improved, but consumer-level insights and coverage of non-tracked channels are lost
Solution Approach 1:
The patent merges POS data with consumer panel data and additional data sources to create a comprehensive measurement system. This combination preserves the measurement accuracy of POS data while incorporating the consumer-level insights from panels and extending coverage to non-tracked channels through data fusion and reconciliation techniques.
Solution Approach 2:
The patent creates a multi-functional data system that simultaneously provides accurate sales measurements, consumer-level insights, and extended channel coverage. The system is designed to perform multiple functions by integrating diverse data sources and applying various analytical techniques, making it universally applicable to different measurement needs.
4Measurement precision
If data fusion is performed to correct panel data, then data accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the data fusion process into distinct modules and stages, including data collection, data reconciliation, bias correction, and integration. This segmentation allows each component to be developed and optimized independently, managing system complexity while achieving accurate data fusion through a structured, modular approach.
Data Source
AI summary
A computer system and method is disclosed that analyzes and corrects retail data. The system and method includes several client workstations and one or more servers coupled together over a network. A database stores various data used by the system. A business logic server uses competitive and complementary fusion to analyze and correct some of the data sources stored in database server. The data fusion process itself is an iterative one—utilizing both competitive and complementary fusion methods. In competitive fusion, two or more data sources that provide overlapping attributes are compared against each other. More accurate/reliable sources are used to correct less accurate/reliable sources. In complementary fusion, relationships modeled where data sources overlap are projected to areas of the data framework in which fewer sources exist—enhancing the accuracy/reliability of those fewer sources even in the absence of the other sources upon which the models were based.