Forecasting Using Share Models And Hierarchies

• US 20100106561A1
• Filed: 10/28/2008
• Published: 04/29/2010
• Est. Priority Date: 10/28/2008
• Status: Abandoned Application

• Current Assignee: SAS Institute Incorporated
• Original Assignee: SAS Institute Incorporated
• Inventors: Peredriy, Sergiy, Chaudhuri, Arin, Xiao, Yongqiao, McGuirk, Ann Mary, Chien, Yung-Hsin
• Application Number:
  US12/259,676
• Publication Number:
  US 20100106561A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities

Abstract

Computer-implemented systems and methods are provided for forecasting product sales. Market shares associated with a product are estimated. Sales for a share group are forecast based upon a seasonality component and a trend prediction. A product sales forecast is calculated based upon the forecasted sales for a share group and the estimated product market share.

Claims

1. A computer-implemented method of forecasting product sales utilizing historic product data that is organized with respect to a geography hierarchy and a product hierarchy, said method comprising:

receiving the historic product data from a computer-readable data store;

estimating a seasonality component based upon aggregate data from a first level of the geography hierarchy and a first level of the product hierarchy;

forecasting a trend prediction based upon aggregate data from a second level of the geography hierarchy and a second level of the product hierarchy;

wherein the second level of the geography hierarchy is at an equal or more detailed level in the geography hierarchy than the first level of the geography hierarchy, or the second level of the product hierarchy is at an equal or more detailed level in the product hierarchy than the first level of the product hierarchy;

estimating, for a product within a share group, a market share associated with the product with respect to other items in the share group;

calculating weighted marketing mix effects of a share group based on product level marketing mixes and corresponding estimated market shares within a share group;

forecasting sales for a share group based upon the seasonality component, the trend prediction, and the weighted marketing mix effects, wherein a share group is a collection of related products;

calculating, for the product within a share group, a product sales forecast based upon the forecasted sales for a share group and the estimated product market share;

storing the calculated product sales forecast in a computer-readable data store;

wherein the receiving, forecasting a seasonality prediction, forecasting a trend prediction, forecasting sales, estimating a market share, calculating a product sales forecast, and storing are all performed on one or more data processors.

2. The method of claim 1, wherein the historic product data is hierarchically stored in a MOLAP multidimensional database.

3. The method of claim 1, wherein a share group is a group of related products that:

are in the same product family;

are in the same product choice set;

are in a set of competing products;

or are substitutes for other related products in the group.

4. The method of claim 1, wherein the estimated product market share for a product is a value between 0 and 1 representing a fraction of total sales for a share group that are expected to be represented by the product.

5. The method of claim 4, wherein the step of calculating a product sales forecast further comprises multiplying the forecasted sales for a share group by the estimated product market share value to determine a product sales forecast.

6. The method of claim 1, wherein the second level of the geography hierarchy is at a more detailed level in the geography hierarchy than the first level of the geography hierarchy, or the second level of the product hierarchy is at a more detailed level in the product hierarchy than the first level of the product hierarchy.

7. The method of claim 1, wherein the second level of the geography hierarchy is at an equal level with the first level of the geography hierarchy in the geography hierarchy, and the second level of the product hierarchy is at an equal level hierarchy with the first level of the product hierarchy in the product hierarchy.

8. The method of claim 1, wherein the second level of the geography hierarchy is at an equal or more detailed level in the geography hierarchy than the first level of the geography hierarchy, and the second level of the product hierarchy is at an equal or more detailed level in the product hierarchy than the first level of the product hierarchy.

9. The method of claim 1, wherein the geography hierarchy and the product hierarchy are modeling hierarchies generated from one or more physical hierarchies and/or attribute hierarchies stored on the computer-readable data store.

10. The method of claim 1, further comprising the step of:

estimating a price elasticity and a promotion elasticity based upon aggregate data from the second level of the geography hierarchy and the second level of the product hierarchy;

wherein calculating a product sales forecast is further based upon the estimated price elasticity and the estimated promotion elasticity.

11. The method of claim 1, wherein the calculated product sales forecast is at a Store level in the geography hierarchy and an SKU level in the product hierarchy.

12. The method of claim 1, wherein the forecast of the seasonality prediction is based on a predictive model selected from the group consisting of:

An Unobserved Components Model (UCM), ARIMAX, UCM and Winters smoothing methods combined models, ARIMA and Winters smoothing methods combined models, and combinations thereof.

13. The method of claim 1, wherein the forecast of the trend prediction is based on a same predictive model that the forecast of the seasonality prediction is based.

14. The method of claim 1, wherein market shares are modeled using a regression model that utilizes product attributes and marketing mix information that includes price and promotions.

15. The method of claim 1, further comprising the step of estimating a halo effect for the share group based on pricing and promotions in a complementary share group;

wherein the step of estimating a halo effect includes correlating unexplained sales in the share group in previous time periods with pricing and promotions in the complementary share group;

wherein the step of forecasting shares for a share group utilizes the estimated halo effect for the share group.

16. The method of claim 1, further comprising the step of estimating a cannibalization effect for the share group based on pricing and promotions in a competing share group;

wherein the step of estimating a cannibalization effect includes correlating unexplained sales in the share group in previous time periods with pricing and promotions in the competing share group;

wherein the step of forecasting shares for a share group utilizes the estimated cannibalization effect for the share group.

17. A computer-implemented apparatus for forecasting product sales utilizing historic product data that is stored with respect to a geography hierarchy and a product hierarchy, said apparatus comprising:

a computer-readable data store for housing the historic product data;

a seasonality estimator configured to operate on a data processor and to estimate a seasonality component based upon aggregate data from a first level of the geography hierarchy and a first level of the product hierarchy;

a trend forecaster configured to operate on the data processor and to make a trend prediction based upon aggregate data from a second level of the geography hierarchy and a second level of the product hierarchy;

wherein the second level of the geography hierarchy is at an equal or more detailed level in the geography hierarchy than the first level of the geography hierarchy, or the second level of the product hierarchy is at an equal or more detailed level in the product hierarchy than the first level of the product hierarchy;

a market share estimator configured to operate on the data processor and to estimate, for a product within a share group, a market share associated with the product with respect to other items in the share group;

a sales forecaster configured to operate on the data processor and to forecast sales for a share group based upon the seasonality component and the trend prediction, wherein a share group is a collection of related products;

a product sales forecast calculator configured to operate on the data processor and to calculate, for the product with a share group, a product sales forecast based upon the forecasted sales for a share group and the estimated product share;

wherein the calculated product sales forecast is stored in a computer-readable data store.

18. The apparatus of claim 17, wherein the geography hierarchy and the product hierarchy are modeling hierarchies generated from one or more physical hierarchies and/or attribute hierarchies stored on the computer-readable medium.

19. The apparatus of claim 17, further comprising:

an elasticity estimator configured to estimate a price elasticity and a promotion elasticity based upon aggregate data from the second level of the geography hierarchy and the second level of the product hierarchy;

wherein the sales forecaster calculates forecast sales for a share group that is further based on the price elasticity and the promotion elasticity.

20. The apparatus of claim 17, wherein the calculated product sales forecast is at a Store level in the geography hierarchy and an SKU level in the product hierarchy.

21. The apparatus of claim 17, further comprising a halo effect estimator configured to estimate a halo effect for the share group based on pricing and promotions in a complementary share group;

wherein estimating a halo effect includes correlating unexplained sales in the share group in previous time periods with pricing and promotions in the complementary share group;

wherein the sales forecaster calculates forecast sales for a share group that is further based on the estimated halo effect.

22. The apparatus of claim 17, further comprising a cannibalization effect estimator configured to estimate a cannibalization effect for the share group based on pricing and promotions in a competing share group;

wherein estimating a cannibalization effect includes correlating unexplained sales in the share group in previous time periods with pricing and promotions in the competing share group;

wherein the sales forecaster calculates forecast sales for a share group that is further based on the estimated cannibalization effect.