System and method for rule based forecasting in multichannel, multi-offer, and multi-customer-segment environments

Abstract

A demand rule can handle simultaneous retail purchasing options available to users such as brick and mortar, online, mobile, catalog, kiosk, coupon, loyalty, volume discounts, and multiples. The method can include a demand rule for each single purchasing option, a rule for marketing influence, and a rule for partitioning user demand across purchasing options. The demand rule for each single purchasing option, rule for marketing influence, and rule for partitioning user demand across purchasing options can each be measured independently to enable computationally efficient methods for forecasting and sales optimization.

What is claimed is: 
     
         1 . A system for generating retail forecasts, the system comprising:
 a processor; and   a memory storing instructions that, when executed by the processor, cause the system to:
 receive a purchasing choice calendar, a marketing calendar, and a sales history; 
 generate parameters of a partitioning rule, the parameters defining a shift in demand between multiple purchasing choices; 
 partition out multiple calculation paths based on the purchasing choice calendar, each path including a demand rule; 
 generate a demand rule for each of the multiple calculation paths, each demand rule based on the purchasing choice calendar, the marketing calendar, the sales history, the included demand rule, and the parameters; and 
 forecast one of demand and sales for multiple user choices for purchasing a product, the forecast based on one or more of the demand rules. 
   
     
     
         2 . The system of  claim 1 , wherein the partitioning rule comprises a shift of demand from a first purchasing choice to a second purchasing choice based on an attractiveness of one of the first purchasing choice and of the second purchasing choice. 
     
     
         3 . The system of  claim 1 , wherein each demand rule includes purchasing choices comprising one or more of an online store and a brick and mortar store, wherein one of pricing and incentives vary between the online store and the brick and mortar store. 
     
     
         4 . The system of  claim 1 , wherein the forecast is further based on offers comprising enforced multibuy offers. 
     
     
         5 . The system of  claim 1 , wherein the forecast is further based on offers comprising loyalty card discounts. 
     
     
         6 . The system of  claim 1 , wherein the forecast is further based on offers including volume discounts. 
     
     
         7 . The system of  claim 1 , wherein the forecast further includes a take rate on offers. 
     
     
         8 . The system of  claim 1 , wherein the forecast includes a marketing lift for a marketing plan. 
     
     
         9 . The system of  claim 1 , wherein the sales history includes one of a TLOG, a daily sales, and a weekly sales. 
     
     
         10 . The system of  claim 1 , wherein the memory further stores instructions that, when executed by the processor, cause the system to optimize a price based on the forecast. 
     
     
         11 . The system of  claim 1 , wherein the memory further stores instructions that, when executed by the processor, cause the system to optimize a promotion based on the forecast. 
     
     
         12 . The system of  claim 1 , wherein the memory further stores instructions that, when executed by the processor, cause the system to update inventory based on the forecast. 
     
     
         13 . The system of  claim 1 , wherein the memory further stores instructions that, when executed by the processor, cause the system to position inventory in a store based on the forecast. 
     
     
         14 . The system of  claim 1 , wherein the memory further stores instructions that, when executed by the processor, cause the system to produce a marketing plan based on the forecast. 
     
     
         15 . The system of  claim 1 , wherein the memory further stores instructions that, when executed by the processor, cause the system to plan a new site based on the forecast. 
     
     
         16 . The system of  claim 1 , wherein the forecast is further based on offers including bundled offers. 
     
     
         17 . The system of  claim 1 , wherein a first demand rule of the multiple calculation paths shares information with a second demand rule of the multiple calculation paths. 
     
     
         18 . The system of  claim 1 , wherein the forecast is further based on a stock out of a purchasing choice. 
     
     
         19 . The system of  claim 1 , wherein the forecast is further based on over purchasing of a product during a promotion. 
     
     
         20 . A method for generating retail forecasts, the method comprising:
 receiving, by a processor executing a partitioning rule, a purchasing choice calendar, a marketing calendar, and a sales history;   generating, by the processor, parameters of the partitioning rule, the parameters defining a shift in demand between multiple purchasing choices;   partitioning out, by the processor, multiple calculation paths based on the purchasing choice calendar, each path including a demand rule;   generating, by the processor, a demand rule for each of the multiple calculation paths, each demand rule based on the purchasing choice calendar, the marketing calendar, the sales history, the included demand rule, and the parameters; and   forecasting, by the processor, one of demand and sales for multiple user choices for purchasing a product, the forecast based on one or more of the demand rules.   
     
     
         21 . A computer-readable medium storing instructions, the instructions comprising:
 receive a purchasing choice calendar, a marketing calendar, and a sales history;   generate parameters of a partitioning rule, the parameters defining a shift in demand between multiple purchasing choices;   partition out multiple calculation paths based on the purchasing choice calendar, each path including a demand rule;   generate a demand rule for each of the multiple calculation paths, each demand rule based on the purchasing choice calendar, the marketing calendar, the sales history, the included demand rule, and the parameters; and   forecast one of demand and sales for multiple user choices for purchasing a product, the forecast based on one or more of the demand rules.