Bias reduction using data fusion of household panel data and transaction data
First Claim
1. A method comprising:
- using a computer, storing a panel dataset in a data fusion facility, the panel dataset including panel data obtained from inputs of consumers who are members of panels, and the panel data including household purchasing behavior for a set of pre-defined buyer and shopper groups;
using a computer, storing a fact dataset of consumer behavior from retailer point-of-sale data in the data fusion facility, wherein the retailer point-of-sale data includes transactional data for one or more retail locations;
fusing the fact dataset and the panel dataset received in the data fusion facility into a new dataset based on a key that associates the fact dataset with the panel dataset according to consumers identified to be present in the panel dataset and in the fact dataset;
storing loyalty card data for a number of retailers containing exact measurements of household purchases in one or more venues of the retailer;
generating a corrected dataset by correcting for bias in the new dataset using the loyalty card data;
generating a public view of the corrected dataset containing bias-corrected aggregated data adjusted to reduce bias according to the loyalty card data while obfuscating disaggregated data in the new dataset to disguise a most accurate form of the loyalty card data from the number of retailers; and
creating a private view of the corrected data set for one of a number of retailers containing the bias-corrected aggregated data while replacing estimated household-level purchases with loyalty card data for the one of the number of retailers.
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Abstract
In embodiments of the present invention, a method is described for reducing bias by data fusion of a household panel data and a loyalty card data. In embodiments, a method is provided for receiving a consumer panel dataset in a data fusion facility, receiving a consumer point-of-sale dataset in a data fusion facility, receiving a dimension dataset in a data fusion facility, fusing the datasets received in the data fusion facility into a new panel dataset based at least in part on an encryption key, estimating a consumer behavior using a first model based on the consumer panel dataset, estimating a consumer behavior using a second model based only on those consumers present in both the consumer panel dataset and the consumer point-of-sale dataset, and refining the first model based at least on the results of the second model.
80 Citations
20 Claims
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1. A method comprising:
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using a computer, storing a panel dataset in a data fusion facility, the panel dataset including panel data obtained from inputs of consumers who are members of panels, and the panel data including household purchasing behavior for a set of pre-defined buyer and shopper groups; using a computer, storing a fact dataset of consumer behavior from retailer point-of-sale data in the data fusion facility, wherein the retailer point-of-sale data includes transactional data for one or more retail locations; fusing the fact dataset and the panel dataset received in the data fusion facility into a new dataset based on a key that associates the fact dataset with the panel dataset according to consumers identified to be present in the panel dataset and in the fact dataset; storing loyalty card data for a number of retailers containing exact measurements of household purchases in one or more venues of the retailer; generating a corrected dataset by correcting for bias in the new dataset using the loyalty card data; generating a public view of the corrected dataset containing bias-corrected aggregated data adjusted to reduce bias according to the loyalty card data while obfuscating disaggregated data in the new dataset to disguise a most accurate form of the loyalty card data from the number of retailers; and creating a private view of the corrected data set for one of a number of retailers containing the bias-corrected aggregated data while replacing estimated household-level purchases with loyalty card data for the one of the number of retailers. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A computer program product comprising computer executable code embodied in a non-transitory computer readable medium that, when executing on one or more computing devices, performs the steps of:
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using a computer, storing a panel dataset in a data fusion facility, the panel dataset including panel data obtained from inputs of consumers who are members of panels, and the panel data including household purchasing behavior for a set of pre-defined buyer and shopper groups; using a computer, storing a fact dataset of consumer behavior from retailer point-of-sale data in the data fusion facility, wherein the retailer point-of-sale data includes transactional data for one or more retail locations; fusing the fact dataset and the panel dataset received in the data fusion facility into a new dataset based on a key that associates the fact dataset with the panel dataset according to consumers identified to be present in the panel dataset and in the fact dataset; storing loyalty card data for a retailer containing exact measurements of household purchases in one or more venues of the retailer; generating a corrected dataset correcting for bias in the new dataset using the loyalty card data; generating a public view of the corrected dataset containing bias-corrected aggregated data adjusted to reduce bias according to the loyalty card data while obfuscating disaggregated data in the new dataset to disguise a most accurate form of the loyalty card data from the retailer; and displaying a private view of the corrected data set to the retailer, the private view containing the bias-corrected aggregated data while replacing estimated household-level purchases with loyalty card data for the retailer. - View Dependent Claims (16, 17, 18, 19, 20)
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Specification