Ranking of Store Locations Using Separable Features of Traffic Counts

• US 20150120392A1
• Filed: 10/25/2013
• Published: 04/30/2015
• Est. Priority Date: 10/25/2013
• Status: Abandoned Application

• Current Assignee: Cellco Partnership, Inc. (Verizon Communications Inc.)
• Original Assignee: Cellco Partnership, Inc. (Verizon Communications Inc.)
• Inventors: Gharachorloo, Nader, Mostoufi, Farshid
• Application Number:
  US14/063,805
• Publication Number:
  US 20150120392A1
• Time in Patent Office: Days
• US Class Current: 705/7.34
• CPC Class Codes: G06Q 10/0639 Performance analysis of employees; Performance analysis of enterprise or organisation operations
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0205 Location or geographical consideration

Abstract

A system may generate a matrix according to subscriber count data for a plurality of points of interest within a geographical area over a period of time identified from aggregate subscriber data, the matrix including counts per subset of the period of time arranged according to subset of the period of time and point of interest. The system may further perform a factorization of the matrix of subscriber counts to extract feature components of the subscriber count data, identify at least a primary feature component and a secondary feature component according to the factorization, and provide a ranking of at least a subset of the points of interest according to at least one of the primary feature component and the secondary feature component. The system may also receive a request for a report, generate the report according to the identified feature components, and provide the report responsive to a request.

Claims

1. A computing device configured to execute a software application on a processor of the computing device to provide operations comprising:

identifying, from aggregate subscriber data generated from subscriber network data records received from a subscriber network and representing usage of the subscriber network by subscriber devices, subscriber count data for a plurality of points of interest within a geographical area over a period of time;

generating a matrix according to the identified subscriber count data, the matrix including counts per subset of the period of time arranged according to subset of the period of time and point of interest;

performing a factorization of the matrix of subscriber counts to extract feature components of the subscriber count data;

identifying at least a primary feature component and a secondary feature component according to the factorization;

providing a ranking of at least a subset of the points of interest according to at least one of the primary feature component and the secondary feature component; and

sending a notification over the subscriber network to at least one of the points of interest, the notification including a suggested course of action determined according to the ranking

2. The computing device of claim 1, wherein the primary feature component is indicative of an overall variation in subscriber counts for each of the plurality of points of interest, and the software application is further executable by the computing device to provide operations comprising identifying, according to the primary feature component, at least one of a busiest day of the week and a slowest day of the week of a point of interest of the plurality of points of interest.

3. The computing device of claim 1, wherein the secondary feature component is indicative of a further variation in the subscriber counts independent of the primary feature component, and the software application is further executable by the computing device to provide operations comprising identifying, according to the secondary feature component, at least one of a weekend variation in subscriber counts and a holiday variation in the subscriber counts.

4. The computing device of claim 1, further comprising identifying at least one tertiary feature according to the factorization, wherein the tertiary feature is indicative of a further variation in the subscriber counts independent of the primary feature component and the secondary feature component, wherein the software application is further executable by the computing device to provide operations comprising identifying, according to the tertiary feature, at least one of a localized event and a variation in holiday celebration in the subscriber counts.

5. The computing device of claim 1, wherein the subset of the period of time is one of an hour, a day-part, or a day, the geographical area is one of a zip code, a section of a city, a city, a state, and a nation, and the plurality of points of interest are included in the matrix as being within a point of interest category.

6. The computing device of claim 1, wherein the factorization is performed according to principal component analysis using singular value decomposition.

7. The computing device of claim 1, further comprising:

receiving a request for a report regarding the plurality of points of interest within the geographical area over the period of time;

generating the report according to the identified feature components; and

providing the report responsive to the request.

8. A method, comprising:

identifying, from aggregate subscriber data generated from subscriber network data records received from a subscriber network and representing usage of the subscriber network by subscriber devices, subscriber count data for a plurality of points of interest within a geographical area over a period of time;

generating, by a computing device executing a feature identifier module, a matrix according to the identified subscriber count data, the matrix including counts per subset of the period of time arranged according to subset of the period of time and point of interest;

performing, by the computing device, a factorization of the matrix of subscriber counts to extract feature components of the subscriber count data;

identifying, by the computing device, at least a primary feature component and a secondary feature component according to the factorization;

providing a ranking of at least a subset of the points of interest according to at least one of the primary feature component and the secondary feature component; and

sending a notification over the subscriber network to at least one of the points of interest, the notification including a suggested course of action determined according to the ranking

9. The method of claim 8, wherein the primary feature component is indicative of an overall variation in subscriber counts for each of the plurality of points of interest, and the software application is further executable by the computing device to provide operations comprising identifying, according to the primary feature component, at least one of a busiest day of the week and a slowest day of the week of a point of interest of the plurality of points of interest.

10. The method of claim 8, wherein the secondary feature component is indicative of a further variation in the subscriber counts independent of the primary feature component, and the software application is further executable by the computing device to provide operations comprising identifying, according to the secondary feature component, at least one of a weekend variation in subscriber counts and a holiday variation in the subscriber counts.

11. The method of claim 8, further comprising identifying at least one tertiary feature according to the factorization, wherein the tertiary feature is indicative of a further variation in the subscriber counts independent of the primary feature component and the secondary feature component, wherein the software application is further executable by the computing device to provide operations comprising identifying, according to the tertiary feature, at least one of a localized event and a variation in holiday celebration in the subscriber counts.

12. The method of claim 8, wherein the subset of the period of time is one of an hour, a day-part, or a day, the geographical area is one of a zip code, a section of a city, a city, a state, and a nation, and the plurality of points of interest are included in the matrix as being within a point of interest category.

13. The method of claim 8, wherein the factorization is performed according to principal component analysis using singular value decomposition.

14. The method of claim 8, further comprising:

receiving a request for a report regarding the plurality of points of interest within the geographical area over the period of time;

generating the report according to the identified feature components; and

providing the report responsive to the request.

15. A non-transitory computer-readable medium tangibly embodying computer-executable instructions of a software program, the software program being executable by a processor of a computing device to provide operations comprising:

identifying, from aggregate subscriber data generated from subscriber network data records received from a subscriber network and representing usage of the subscriber network by subscriber devices, subscriber count data for a plurality of points of interest within a geographical area over a period of time;

generating a matrix according to the identified subscriber count data, the matrix including counts per subset of the period of time arranged according to subset of the period of time and point of interest;

performing a factorization of the matrix of subscriber counts to extract feature components of the subscriber count data;

identifying at least a primary feature component and a secondary feature component according to the factorization;

providing a ranking of at least a subset of the points of interest according to at least one of the primary feature component and the secondary feature component; and

sending a notification over the subscriber network to at least one of the points of interest, the notification including a suggested course of action determined according to the ranking

16. The computer-readable medium of claim 15, wherein the primary feature component is indicative of an overall variation in subscriber counts for each of the plurality of points of interest, and the software application is further executable by the computing device to provide operations comprising identifying, according to the primary feature component, at least one of a busiest day of the week and a slowest day of the week of a point of interest of the plurality of points of interest.

17. The computer-readable medium of claim 15, wherein the secondary feature component is indicative of a further variation in the subscriber counts independent of the primary feature component, and the software application is further executable by the computing device to provide operations comprising identifying, according to the secondary feature component, at least one of a weekend variation in subscriber counts and a holiday variation in the subscriber counts.

18. The computer-readable medium of claim 15, further comprising identifying at least one tertiary feature according to the factorization, wherein the tertiary feature is indicative of a further variation in the subscriber counts independent of the primary feature component and the secondary feature component, wherein the software application is further executable by the computing device to provide operations comprising identifying, according to the tertiary feature, at least one of a localized event and a variation in holiday celebration in the subscriber counts.

19. The computer-readable medium of claim 15, wherein the subset of the period of time is one of an hour, a day-part, or a day, the geographical area is one of a zip code, a section of a city, a city, a state, and a nation, and the plurality of points of interest are included in the matrix as being within a point of interest category.

20. The computer-readable medium of claim 15, wherein the factorization is performed according to principal component analysis using singular value decomposition.

21. The computer-readable medium of claim 15, further comprising:

receiving a request for a report regarding the plurality of points of interest within the geographical area over the period of time;

generating the report according to the identified feature components; and

providing the report responsive to the request.