System and Method for Estimating Price Sensitivity and/or Price Aggregation for a Population Having a Collection of Items

• US 20140278803A1
• Filed: 03/12/2014
• Published: 09/18/2014
• Est. Priority Date: 03/13/2013
• Status: Abandoned Application

• Current Assignee: Opera Solutions, LLC.
• Original Assignee: Opera Solutions, LLC.
• Inventors: Diev, Vesselin, Huq, Shamima
• Application Number:
  US14/206,677
• Publication Number:
  US 20140278803A1
• Time in Patent Office: Days
• US Class Current: 705/7.35
• CPC Class Codes: G06Q 30/0206 Price or cost determination based on market factors
• CPC Class Codes: G06Q 30/0283 Price estimation or determination

Abstract

Provided is a system for estimating price sensitivities and determining aggregate price adjustments for a population of items, the population comprising a plurality of sub-populations. More specifically, provided is a system comprising a computer executing a price sensitivity engine and a price aggregation engine, the price sensitivity engine receiving time-series information, determining covariate coefficients to estimate a population price sensitivity average, modeling a first set of vectors based on the covariate coefficients, modeling a second set of vectors based on the covariate coefficients and an indicator variable, and estimating sub-population price sensitivities based on the first and second sets of vectors; and the price aggregation engine comparing each of the sub-population price sensitivities to the population price sensitivity average and/or to other sub-population price sensitivities, ranking, ordering, and/or clustering the sub-populations, and determining aggregate price adjustments to items in the sub-populations.

Claims

1. A system for estimating price sensitivities and determining aggregate price adjustments for a population having a collection of items, the system comprising:

a computer system in electronic communication with a database storing time-series information therein, the computer system executing a price sensitivity engine and a price aggregation engine, said price sensitivity engine performing the steps of;

electronically receiving from the database the time-series information;

determining, based on the time-series information, a set of covariate coefficients to estimate a population price sensitivity average for the collection of items, the population comprising a plurality of sub-populations;

modeling for the collection of items a first set of vectors based on the covariate coefficients;

modeling for the collection of items a second set of vectors based on the covariate coefficients and an indicator variable; and

estimating a sub-population price sensitivity for each of the plurality of sub-populations based on the first set of vectors and the second set of vectors, to generate a plurality of sub-population price sensitivities; and

said price aggregation engine performing the steps of;

comparing each of the sub-population price sensitivities to at least one of the population price sensitivity average or other sub-population price sensitivities of the plurality of sub-population price sensitivities;

based on said comparing, at least one of ranking, ordering, or clustering the plurality of sub-populations; and

determining aggregate price adjustments for items in one or more of the plurality of sub-populations based on the at least one of ranking, ordering or clustering.

2. The system of claim 1, wherein the time-series data includes information relating to at least one of a quantity of items sold, an average price of items sold, competitor prices, promotions-related variables, seasonal indicators, or trend data.

3. The system of claim 1, wherein the price aggregation engine further performs the step of adjusting prices for the items in the one or more of the plurality of sub-populations based on the determined aggregate price adjustments.

4. The system of claim 1, wherein the price sensitivity engine performs the comparing step by setting the population price sensitivity average to zero (0), wherein a positive price sensitivity estimate of a sub-population indicates that the sub-population is less price sensitive than the population, and a negative price sensitivity estimate of a sub-population indicates that the sub-population is more price sensitive than the population.

5. The system of claim 1, wherein the price sensitivity engine models at least one of the first set of vectors or the second set of vectors based on a non-normal Poisson-type distribution for a quantity of items of sold for a given time period.

6. The system of claim 1, wherein the applying aggregate price adjustments comprises applying a percentage and/or monetary increase or decrease in price applied collectively to the items in the one or more of the plurality of sub-populations.

7. The system of claim 1, wherein the population is a plurality of stores, and each sub-population is a department within each of the plurality of stores.

8. The system of claim 7, wherein the price aggregation engine clusters the plurality of departments to determine a first set of stores in a first virtual pricing zone and a second set of stores in a second virtual pricing zone.

9. The system of claim 8, wherein the applying aggregate price adjustments comprises adjusting by a first amount prices of all items within the department for the first set of stores, and adjusting by a second amount prices of all items within the department for the second set of stores.

10. A method for estimating price sensitivity and generating aggregate price adjustments for a population having a collection of items, comprising the steps of:

electronically receiving, by a price sensitivity engine of a computer system, time-series information from a database in electronic communication with the computer system;

determining, by the price sensitivity engine, a set of covariate coefficients based on the time-series information to estimate a population price sensitivity average for the collection of items, the population comprising a plurality of sub-populations;

modeling, by the price sensitivity engine, a first set of vectors for the collection of items based on the covariate coefficients;

modeling, by the price sensitivity engine, a second set of vectors for the collection of items based on the covariate coefficients and an indicator variable;

estimating, by the price sensitivity engine, a sub-population price sensitivity for each of the plurality of sub-populations based on the first set of vectors and the second set of vectors, to generate a plurality of sub-population price sensitivities;

comparing, by a price aggregation engine of the computer system, each of the sub-population price sensitivities to at least one of the population price sensitivity average or other sub-population price sensitivities of the plurality of sub-population price sensitivities;

at least one of ranking, ordering, or clustering, by the price aggregation engine, the plurality of sub-populations based on the comparing; and

determining, by the price aggregation engine, aggregate price adjustments for items in one or more of the plurality of sub-populations based on the at least one of ranking, ordering or clustering.

11. The method of claim 10, wherein the time-series data includes information relating to at least one of a quantity of items sold, an average price of items sold, competitor prices, promotions-related variables, seasonal indicators, or trend data.

12. The method of claim 10, further comprising the step of adjusting, by the price aggregation engine, prices for the items in the one or more of the plurality of sub-populations based on the determined aggregate price adjustments.

13. The method of claim 10, wherein the price sensitivity engine performs the comparing step by setting the population price sensitivity average to zero (0), wherein a positive price sensitivity estimate of a sub-population indicates that the sub-population is less price sensitive than the population, and a negative price sensitivity estimate of a sub-population indicates that the sub-population is more price sensitive than the population.

14. The method of claim 10, wherein the price sensitivity engine models at least one of the first set of vectors or the second set of vectors based on a non-normal Poisson-type distribution for a quantity of items of sold for a given time period.

15. The method of claim 10, wherein the applying aggregate price adjustments comprises applying a percentage and/or monetary increase or decrease in price applied collectively to the items in the one or more of the plurality of sub-populations.

16. The method of claim 10, wherein the population is a plurality of stores, and each sub-population is a department within each of the plurality of stores.

17. The method of claim 16, wherein the price aggregation engine clusters the plurality of departments to determine a first set of stores in a first virtual pricing zone and a second set of stores in a second virtual pricing zone.

18. The method of claim 17, wherein the applying aggregate price adjustments comprises adjusting by a first amount prices of all items within the department for the first set of stores, and adjusting by a second amount prices of all items within the department for the second set of stores.

19. A computer-readable medium having computer-readable instructions stored thereon which, when executed by a computer system comprising a price sensitivity engine and a price aggregation engine, cause the computer system to perform the steps of:

electronically receiving, by the price sensitivity engine, time-series information from a database in electronic communication with the computer system;

determining, by the price sensitivity engine, a set of covariate coefficients based on the time-series information to estimate a population price sensitivity average for the collection of items, the population comprising a plurality of sub-populations;

modeling, by the price sensitivity engine, a first set of vectors for the collection of items based on the covariate coefficients;

modeling, by the price sensitivity engine, a second set of vectors for the collection of items based on the covariate coefficients and an indicator variable;

estimating, by the price sensitivity engine, a sub-population price sensitivity for each of the plurality of sub-populations based on the first set of vectors and the second set of vectors, to generate a plurality of sub-population price sensitivities;

comparing, by the price aggregation engine, each of the sub-population price sensitivities to at least one of the population price sensitivity average or to other sub-population price sensitivities of the plurality of sub-population price sensitivities;

at least one of ranking, ordering, or clustering, by the price aggregation engine, the plurality of sub-populations based on the comparing; and

determining, by the price aggregation engine, aggregate price adjustments for items in one or more of the plurality of sub-populations based on the at least one of ranking, ordering or clustering.

20. The computer-readable medium of claim 19, wherein the time-series data includes information relating to at least one of a quantity of items sold, an average price of items sold, competitor prices, promotions-related variables, seasonal indicators, or trend data.

21. The computer-readable medium of claim 19, causing the computer system to further perform the step of adjusting, by the price aggregation engine, prices for the items in the one or more of the plurality of sub-populations based on the determined aggregate price adjustments.

22. The computer-readable medium of claim 19, wherein the price sensitivity engine performs the comparing step by setting the population price sensitivity average to zero (0), wherein a positive price sensitivity estimate of a sub-population indicates that the sub-population is less price sensitive than the population, and a negative price sensitivity estimate of a sub-population indicates that the sub-population is more price sensitive than the population.

23. The computer-readable medium of claim 19, wherein the price sensitivity engine models at least one of the first set of vectors or the second set of vectors based on a non-normal Poisson-type distribution for a quantity of items of sold for a given time period.

24. The computer-readable medium of claim 19, wherein the applying aggregate price adjustments comprises applying a percentage and/or monetary increase or decrease in price applied collectively to the items in the one or more of the plurality of sub-populations.

25. The computer-readable medium of claim 19, wherein the population is a plurality of stores, and each sub-population is a department within each of the plurality of stores.

26. The computer-readable medium of claim 25, wherein the price aggregation engine clusters the plurality of departments to determine a first set of stores in a first virtual pricing zone and a second set of stores in a second virtual pricing zone.

27. The method of claim 26, wherein the applying aggregate price adjustments comprises adjusting by a first amount prices of all items within the department for the first set of stores, and adjusting by a second amount prices of all items within the department for the second set of stores.