Systems and apparatus to determine shopper traffic in retail environments

• US 8,229,781 B2
• Filed: 04/25/2008
• Issued: 07/24/2012
• Est. Priority Date: 04/27/2007
• Status: Active Grant

• Current Assignee: The Nielsen Company LLC (MOOD Media Corporation)
• Original Assignee: The Nielson Company (US), LLC
• Inventors: Zenor, Michael John, Dupre, William J., Piotrowski, Frank W.
• Primary Examiner(s): PATS, JUSTIN
• Application Number:
  US12/110,095
• Publication Number:
  US 20080294475A1
• Time in Patent Office: 1,551 Days
• Field of Search: 705/7.31, 705/7.34
• US Class Current: 705/7.31
• 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
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0203 Market surveys; Market polls
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: G06Q 30/0205 Location or geographical consideration

Abstract

Methods and apparatus to determine shopper traffic in retail environments are disclosed herein. In a disclosed example method of generating shopper traffic data, in-person-based count data and sensor-based count data are obtained. The in-person-based count data is indicative of a first quantity of shoppers within a first zone of a monitored establishment is obtained. The sensor-based count data is indicative of a second quantity of shoppers within the first zone of the monitored establishment. The example method also involves determining a relationship value indicative of a relationship between the in-person-based count data, the sensor-based count data, and first sales data associated with the first zone of the monitored establishment.

Claims

1. A method of generating shopper traffic data, comprising:

determining a functional relationship between first in-person-based count data, first sensor-based count data, a product category corresponding to a first zone of a monitored establishment, and auditor-based count data corresponding to the first zone of the monitored establishment, the first in-person-based count data indicative of a first quantity of shoppers within the first zone of the monitored establishment, the first sensor-based count data indicative of a second quantity of shoppers within the first zone of the monitored establishment, the first in-person-based count data and the first sensor-based count data generated concurrently during a first time, and the auditor-based count data generated by an auditor instructed to move through the monitored establishment while counting shoppers in a plurality of zones of the monitored establishment;

estimating second in-person-based count data based on the functional relationship and second sensor-based count data, the second in-person-based count data and the second sensor-based count data corresponding to one of;

(a) the first zone of the monitored establishment during a second time different from the first time, or (b) a second zone of the monitored establishment during the first time;

determining, via a processor, a coefficient set indicative of a relationship between the estimated second in-person-based count data and first sales data associated with the monitored establishment; and

forecasting shopper traffic in a second establishment based on the coefficient set and second sales data associated with the second establishment without collecting shopper count data in the second establishment.

2. A method as defined in claim 1, wherein the monitored establishment is at least one of a retail establishment or a commercial establishment.

3. A method as defined in claim 1, further comprising obtaining the first in-person-based count data via a stationary human counter instructed to remain at a particular location in the monitored establishment for an extended duration while counting shoppers in at least one of the plurality of zones.

4. A method as defined in claim 1, wherein the first sales data includes a sales value of a product category corresponding to one of the first or second zones that corresponds to the second in-person-based count data and the second sensor-based count data.

5. A method as defined in claim 1, wherein the first sales data includes revenue data.

6. A method as defined in claim 1, further comprising obtaining the first sensor-based count data by detecting shoppers associated with electronic tags via stationary units located in the monitored establishment as the shoppers and their associated electronic tags move within proximity of at least some of the stationary units.

7. A method as defined in claim 1, wherein the first quantity of shoppers are the same shoppers as the second quantity of shoppers.

8. A method as defined in claim 1, wherein estimating the second in-person-based count data comprises fitting the second sensor-based count data to data representative of the functional relationship.

9. An apparatus to generate shopper traffic data, comprising:

a traffic count calibrator to, via a processor;

determine a functional relationship between first in-person-based count data, first sensor-based count data, a product category corresponding to a first zone of a monitored establishment, and auditor-based count data corresponding to the first zone of the monitored establishment, the first in-person-based count data indicative of a first quantity of shoppers within the first zone of the monitored establishment, and the first sensor-based count data indicative of a second quantity of shoppers within the first zone of the monitored establishment, the first in-person-based count data and the first sensor-based count data generated concurrently during a first time, and the auditor-based count data generated by an auditor instructed to move through the monitored establishment while counting shoppers in a plurality of zones of the monitored establishment;

estimate second in-person-based count data based on the functional relationship and second sensor-based count data, the second in-person-based count data and the second sensor-based count data corresponding to one of;

(a) the first zone of the monitored establishment during a second time different from the first time, or (b) a second zone of the monitored establishment during the first time; and

determine a coefficient set indicative of a relationship between the estimated second in-person-based count data and first sales data associated with the monitored establishment; and

a traffic count generator to forecast shopper traffic in a second establishment based on the coefficient set and second sales data associated with the second establishment without collecting shopper count data in the second establishment.

10. An apparatus as defined in claim 9, wherein the monitored establishment is at least one of a retail establishment or a commercial establishment.

11. An apparatus as defined in claim 9, further comprising a data input interface to obtain the first in-person-based count data from a communication device used by a stationary human counter instructed to remain at a particular location in the monitored establishment for an extended duration while counting shoppers in at least one of the plurality of zones.

12. An apparatus as defined in claim 9, wherein the first sales data includes a sales value of a product category corresponding to one of the first or second zones that corresponds to the second in-person-based count data and the second sensor-based count data.

13. An apparatus as defined in claim 9, wherein the first sales data includes revenue data.

14. An apparatus as defined in claim 9, further comprising a data input interface to obtain the first sensor-based count data from stationary units located in the monitored establishment that detect shoppers associated with electronic tags as the shoppers and their associated electronic tags move within proximity of at least some of the stationary units.

15. An apparatus as defined in claim 9, wherein the first quantity of shoppers are the same shoppers as the second quantity of shoppers.

16. An apparatus as defined in claim 9, wherein the traffic count calibrator is to estimate the second in-person-based count data by fitting the second sensor-based count data to data representative of the functional relationship.

17. A tangible machine accessible medium having instructions stored thereon that, when executed, cause a machine to at least:

determine a functional relationship between first in-person-based count data, first sensor-based count data, a product category corresponding to a first zone of a monitored establishment, and auditor-based count data corresponding to the first zone of the monitored establishment, the first in-person-based count data indicative of a first quantity of shoppers within the first zone of the monitored establishment, the first sensor-based count data indicative of a second quantity of shoppers within the first zone of the monitored establishment, the first in-person-based count data and the first sensor-based count data generated concurrently during a first time, and the auditor-based count data generated by an auditor instructed to move through the monitored establishment while counting shoppers in a plurality of zones of the monitored establishment;

estimate second in-person-based count data based on the functional relationship and second sensor-based count data, the second in-person-based count data and the second sensor-based count data corresponding to one of;

(a) the first zone of the monitored establishment during a second time different from the first time, or (b) a second zone of the monitored establishment during the first time;

determine a coefficient set indicative of a relationship between the estimated second in-person-based count data and first sales data associated with the monitored establishment; and

forecast shopper traffic in a second establishment based on the coefficient set and second sales data associated with the second establishment without collecting shopper count data in the second establishment.

18. A machine accessible medium as defined in claim 17 storing instructions to estimate the second in-person-based count data by fitting the second sensor-based count data to data representative of the functional relationship.

19. A machine accessible medium as defined in claim 17, wherein the monitored establishment is at least one of a retail establishment or a commercial establishment.

20. A machine accessible medium as defined in claim 17 storing instructions to obtain the first in-person-based count data via a stationary human counter instructed to remain at a particular location in the monitored establishment for an extended duration while counting shoppers in at least one of the plurality of zones.

21. A machine accessible medium as defined in claim 17, wherein the first sales data includes a sales value of a product category corresponding to one of the first or second zones that corresponds to the second in-person-based count data and the second sensor-based count data.

22. A machine accessible medium as defined in claim 17, storing instructions to obtain the first sensor-based count data from stationary units located in the monitored establishment that detect shoppers associated with electronic tags as the shoppers and their associated electronic tags move within proximity of at least some of the stationary units.

23. A machine accessible medium as defined in claim 17, wherein the first quantity of shoppers are the same shoppers as the second quantity of shoppers.