METHODS, SYSTEMS AND APPARATUS TO SELECT STORE SITES

• US 20140278768A1
• Filed: 03/12/2013
• Published: 09/18/2014
• Est. Priority Date: 03/12/2013
• Status: Abandoned Application

• Current Assignee: Nielsen Company (US) LLC (RR Donnelley & Sons Company)
• Original Assignee: Nielsen Company (US) LLC (RR Donnelley & Sons Company)
• Inventors: Zenor, Michael J.
• Application Number:
  US13/795,464
• Publication Number:
  US 20140278768A1
• Time in Patent Office: Days
• US Class Current: 705/7.31
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities

Abstract

Methods and apparatus are disclosed to select retail store sites. An example method includes generating a list of first descriptor types associated with a plurality of existing store locations, calculating, with a processor, a set of analog principal components factors (PCFs) for corresponding ones of the plurality of existing store locations, calculating a set of candidate PCFs for corresponding ones of a plurality of candidate locations, calculating respective similarity values based on the PCFs associated with respective pairs of the plurality of existing store locations and the plurality of candidate locations, for corresponding ones of the plurality of candidate locations, calculating a sum of second descriptor types associated with the existing store locations based on the respective similarity value, and predicting the performance of the candidate store locations based on a ratio of a sum of second descriptor types and a sum of the similarity values for the corresponding existing store location.

Claims

1. A method to predict store performance, comprising:

generating a list of first descriptor types associated with a plurality of existing store locations;

calculating, with a processor, a set of analog principal components factors (PCFs) for corresponding ones of the plurality of existing store locations;

calculating a set of candidate PCFs for corresponding ones of a plurality of candidate locations;

calculating respective similarity values based on the PCFs associated with respective pairs of the plurality of existing store locations and the plurality of candidate locations;

for corresponding ones of the plurality of candidate locations, calculating a sum of second descriptor types associated with the existing store locations based on the respective similarity value; and

predicting the performance of the candidate store locations based on a ratio of a sum of second descriptor types and a sum of the similarity values for the corresponding existing store location.

2. A method as defined in claim 1, wherein the first descriptor types comprise physical characteristics associated with the plurality of existing store locations.

3. A method as defined in claim 2, wherein the physical characteristics comprise at least one of a store size, a number of employees, a proximity to competitors or a geographic location.

4. A method as defined in claim 1, wherein the second descriptor types comprise performance metrics associated with the plurality of existing store locations.

5. A method as defined in claim 4, wherein the performance metrics comprise at least one of annual dollar sales, annual profit or annual unit sales.

6. A method as defined in claim 1, wherein the sum of second descriptor types comprise a sum of weighted performance metrics.

7. A method as defined in claim 1, wherein calculating the sum of second descriptor types further comprises multiplying the similarity values by respective performance metrics to generate weighted performance metrics.

8. A method as defined in claim 1, further comprising substituting one of the plurality of existing store locations for a candidate location to generate a prediction of the performance of the candidate location.

9. A method as defined in claim 8, further comprising calculating a performance difference value between corresponding ones of the predicted performance of respective ones of the existing store locations and corresponding ones of empirical performance associated with the plurality of existing store locations.

10. A method as defined in claim 9, further comprising solving the set of PCFs with calibration weights to minimize the difference values.

11. An apparatus to predict store performance, comprising:

a physical characteristics manager to generate a list of first descriptor types associated with a plurality of existing store locations;

a principal components engine to;

calculate a set of analog principal components factors (PCFs) for corresponding ones of the plurality of existing store locations; and

calculate a set of candidate PCFs for corresponding ones of a plurality of candidate locations;

a similarity engine to calculate respective similarity values based on the PCFs associated with respective pairs of the plurality of existing store locations and the plurality of candidate locations; and

a prediction engine to;

for corresponding ones of the plurality of candidate locations, calculate a sum of second descriptor types associated with the existing store locations based on the respective similarity value; and

predict the performance of the candidate store locations based on a ratio of a sum of second descriptor types and a sum of the similarity values for the corresponding existing store location.

12. An apparatus as defined in claim 11, wherein the physical characteristics manager is to associate the first descriptor types with the plurality of existing store locations.

13. An apparatus as defined in claim 12, wherein the physical characteristics manager is to identify at least one of a store size, a number of employees, a proximity to competitors or a geographic location.

14. An apparatus as defined in claim 11, wherein the physical characteristics manager is to associate the plurality of existing store locations with the second descriptor types.

15. An apparatus as defined in claim 11, further comprising a weight analyzer to multiply the similarity values by respective performance metrics to generate weighted performance metrics.

16. An apparatus as defined in claim 11, wherein the prediction engine is to substitute one of the plurality of existing store locations for a candidate location to generate a prediction of the performance of the candidate location.

17. An apparatus as defined in claim 16, further comprising a difference analyzer to calculate a performance difference value between corresponding ones of the predicted performance of respective ones of the existing store locations and corresponding ones of empirical performance associated with the plurality of existing store locations.

18. An apparatus as defined in claim 17, further comprising a weight analyzer to solve the set of PCFs with calibration weights to minimize the difference values.

19. A tangible machine-readable storage medium comprising instructions stored thereon that, when executed, cause a machine to, at least:

generate a list of first descriptor types associated with a plurality of existing store locations;

calculate a set of analog principal components factors (PCFs) for corresponding ones of the plurality of existing store locations;

calculate a set of candidate PCFs for corresponding ones of a plurality of candidate locations;

calculate respective similarity values based on the PCFs associated with respective pairs of the plurality of existing store locations and the plurality of candidate locations;

for corresponding ones of the plurality of candidate locations, calculate a sum of second descriptor types associated with the existing store locations based on the respective similarity value; and

predict the performance of the candidate store locations based on a ratio of a sum of second descriptor types and a sum of the similarity values for the corresponding existing store location.

20. A machine readable storage medium as defined in claim 19, wherein the instructions, when executed, cause the machine to associate the plurality of existing store locations with the physical characteristics of the first descriptor types.

21. A machine readable storage medium as defined in claim 19, wherein the instructions, when executed, cause the machine to associate performance metrics of the second descriptor types with the plurality of existing store locations.

22. A machine readable storage medium as defined in claim 19, wherein the instructions, when executed, cause the machine to multiply the similarity values by respective performance metrics to generate weighted performance metrics.

23. A machine readable storage medium as defined in claim 19, wherein the instructions, when executed, cause the machine to substitute one of the plurality of existing store locations for a candidate location to generate a prediction of the performance of the candidate location.

24. A machine readable storage medium as defined in claim 23, wherein the instructions, when executed, cause the machine to calculate a performance difference value between corresponding ones of the predicted performance of respective ones of the existing store locations and corresponding ones of empirical performance associated with the plurality of existing store locations.

25. A machine readable storage medium as defined in claim 24, wherein the instructions, when executed, cause the machine to solve the set of PCFs with calibration weights to minimize the difference values.