SYSTEMS AND METHODS TO PREDICT PURCHASING BEHAVIOR

• US 20140278795A1
• Filed: 03/13/2013
• Published: 09/18/2014
• Est. Priority Date: 03/13/2013
• Status: Abandoned Application

• Current Assignee: NC Ventures, LLC
• Original Assignee: NC Ventures, LLC
• Inventors: Satyamoorthy, Subramaniam, Mooney, Stephen P., McGivern.Smith, William, Gerhart, Kyle
• Application Number:
  US13/801,401
• Publication Number:
  US 20140278795A1
• Time in Patent Office: Days
• US Class Current: 705/7.33
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data

Abstract

Example methods, systems, and computer readable storage media to predict purchasing behavior are disclosed. A disclosed example method includes creating a model based on first purchase data and demographic information. The first purchase data and demographic information is associated with panelists. The first purchase data is collected via both a home scanning system and via a frequent shopper system. The example method includes applying the model to consumer data to predict second purchase data. The consumer data corresponds to consumers participating in the frequent shopper system who are not panelists of the home scanning system. The example method includes creating a report based on the second purchase data.

Claims

1. A method to predict purchasing behavior comprising:

creating a model based on first purchase data and demographic information, the first purchase data and demographic information being associated with panelists, the first purchase data collected via both a home scanning system and via a frequent shopper system;

applying the model to consumer data to predict second purchase data, the consumer data corresponding to consumers participating in the frequent shopper system who are not panelists of the home scanning system; and

creating a report based on the second purchase data.

2. The method of claim 1, wherein the first purchase data includes data collected by the panelists scanning purchased items at a location different than a point of sale.

3. The method of claim 2, wherein the consumer data is collected via frequent shopper cards at the point of sale.

4. The method of claim 1, wherein the model compares a first subset of the first purchase data collected via the home scanning system to a second subset of the first purchase data collected via the frequent shopper system.

5. The method of claim 1, wherein the model weights the first purchase data based on the demographic information.

6. The method of claim 1, wherein applying the model to consumer data includes identifying the consumers as having demographic characteristics similar to demographic characteristics of the panelists based on the demographic information.

7. The method of claim 1, wherein the model is created using one or more of a regression technique, a decision tree, a business rule, or a neural network.

8. A system to predict purchasing behavior comprising:

a purchase information modeler to create a model based on first purchase data and demographic information, the first purchase data and demographic information being associated with panelists, the first purchase data collected via a home scanning system and via a frequent shopper system;

a purchase behavior calculator to apply the model to consumer data to predict second purchase data, the consumer data corresponding to consumers participating in the frequent shopper system who are not panelists of the home scanning system; and

a report generator to create a report based on the second purchase data.

9. The system of claim 8, wherein the first purchase data includes data collected by the panelists scanning purchased items at a location different than a point of sale.

10. The system of claim 9, wherein the consumer data is collected via frequent shopper cards at the point of sale.

11. The system of claim 8, wherein the model compares a first subset of the first purchase data collected via the home scanning system to a second subset of the first purchase data collected via the frequent shopper system.

12. The system of claim 8, wherein the purchase modeler is to weight the first purchase data based on the demographic information.

13. The system of claim 8, wherein to apply the model to consumer data, the purchase behavior calculator is to identify the consumers as having demographic characteristics similar to demographic characteristics of the panelists based on the demographic information.

14. The system of claim 8, wherein the purchase information modeler is to create the model using one or more of a regression technique, a decision tree, a business rule, or a neural network.

15. A tangible computer readable storage medium comprising instructions that, when executed, cause a computing device to at least:

create a model based on first purchase data and demographic information, the first purchase data and demographic information being associated with panelists, the first purchase data collected via both a home scanning system and via a frequent shopper system;

apply the model to consumer data to predict second purchase data, the consumer data corresponding to consumers participating in the frequent shopper system who are not panelists of the home scanning system; and

create a report based on the second purchase data.

16. The computer readable storage medium of claim 15, wherein the first purchase data includes data collected by the panelists scanning purchased items at a location different than a point of sale.

17. The computer readable storage medium of claim 16, wherein the consumer data is collected via frequent shopper cards at the point of sale.

18. The computer readable storage medium of claim 15, wherein the model compares a first subset of the first purchase data collected via the home scanning system to a second subset of the first purchase data collected via the frequent shopper system.

19. The computer readable storage medium of claim 15, wherein the model weights the first purchase data based on the demographic information.

20. The computer readable storage medium of claim 15, wherein applying the model to consumer data includes identifying the consumers as having demographic characteristics similar to demographic characteristics of the panelists based on the demographic information.

21. The computer readable storage medium of claim 15, wherein the model is created using one or more of a regression technique, a decision tree, a business rule, or a neural network.