BIAS REDUCTION USING DATA FUSION OF HOUSEHOLD PANEL DATA AND TRANSACTION DATA

• US 20080319829A1
• Filed: 01/28/2008
• Published: 12/25/2008
• Est. Priority Date: 02/20/2004
• Status: Active Grant

• Current Assignee: Information Resources Incorporated
• Original Assignee: Information Resources Incorporated
• Inventors: Johnson, Arvid C., Bergeon, Cheryl G., Hudson, Gregory David Neil, Basilico, Andrea, Chapa, Craig Joseph, Yusko, Jay Alan, Griglione, Bradley Michael, Mason, Trevor, Hunt, Herbert Dennis, Agostinelli, Alberto, West, John Randall, Gibbs, Marshall Ashby Jr.
• Granted Patent: US 10,325,272 B2
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data

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.

Claims

1. A method comprising:

storing a consumer panel dataset in a data fusion facility;

storing a consumer point-of-sale fact dataset in the data fusion facility, wherein the fact data source is a retail channel dataset with limited data coverage;

fusing the datasets received in the data fusion facility into a new panel dataset based at least in part on a key, wherein the key associates the datasets in the data fusion facility based at least in part on consumers identified to be present both in the consumer panel dataset and in the fact dataset;

estimating a consumer behavior factor based on data for those consumers present in both the consumer panel dataset and the consumer point-of-sale dataset; and

applying the factor to adjust a model that uses at least one of the consumer panel dataset and the fact dataset.

2. The method of claim 1, wherein the consumer behavior is a product purchase.

3. The method of claim 1, wherein the fact data source is a retail sales dataset.

4. The method of claim 1, wherein the fact data source is a syndicated sales dataset.

5. The method of claim 4, wherein the syndicated sales dataset is a scanner dataset.

6. The method of claim 4, wherein the syndicated sales dataset is an audit dataset.

7. The method of claim 4, wherein the syndicated sales dataset is a combined scanner-audit dataset.

8. The method of claim 1, wherein the fact data source is point-of-sale data.

9. The method of claim 1, wherein the fact data source is a syndicated causal dataset.

10. The method of claim 1, wherein the fact data source is an internal shipment dataset.

11. The method of claim 1, wherein the fact data source is an internal financials dataset.