Systems and methods for contract based offer generation

Abstract

Systems and methods for a contract-based offer generator is provided. A contract for a promotional offer on a product is received. Data is extracted from the contract. An offer band is accessed, and a plurality of test offers are selected from the offer bank by scoring each offer in the offer bank against the extracted data. The promotional offer and the selected plurality of test offers are deployed in a plurality of retail locations. This is done by maximizing orthogonality between the following variables: store sales, store out of stock rates, number of relevant SKUs carried in each store, temporal effects, discount depth, buy quantity and offer structure.

What is claimed is: 
     
         1 . A computer implemented method for generating contract based offers comprising:
 receiving a contract for a promotional offer on a product;   extracting data from the contract;   accessing an offer bank; and   selecting a plurality of test offers from the offer bank by scoring each offer in the offer bank against the extracted data.   
     
     
         2 . The method of  claim 1 , further comprising deploying the promotional offer and the selected plurality of test offers in a plurality of retail locations. 
     
     
         3 . The method of  claim 2 , wherein the deploying is performed to maximize orthogonality between the following variables: store sales, store out of stock rates, number of relevant SKUs carried in each store, temporal effects, discount depth, buy quantity and offer structure. 
     
     
         4 . The method of  claim 1 , further comprising selecting a number of test offers to run in-market using reinforcement learning techniques. 
     
     
         5 . The method of  claim 4 , wherein Thompson sampling is used to select the number. 
     
     
         6 . The method of  claim 1 , wherein the offer bank is populated with forecasted offers. 
     
     
         7 . The method of  claim 6 , wherein the forecasts are based upon transaction logs of a plurality of retailers. 
     
     
         8 . The method of  claim 7 , wherein the transaction logs are adjusted for compliance by the given retailer, estimated out of stock events, normalized across stores to account for different store attributes, and adjusted for temporal effects. 
     
     
         9 . The method of  claim 8 , further comprising applying machine learning to the adjusted transaction logs to determine lift and standard deviation for a given test offer. 
     
     
         10 . The method of  claim 9 , wherein the forecasts are a baseline function of time from the transaction log data plus elasticity from cross store experiments times a change in price, where in the elasticity is calculated as a function of the lift, and a confidence for the forecast is calculated as a function of the standard deviation. 
     
     
         11 . A computer product comprising non-transitory computer readable medium, which when executed on a computer system causes the computer system to perform the steps of:
 receiving a contract for a promotional offer on a product;   extracting data from the contract;   accessing an offer bank; and   selecting a plurality of test offers from the offer bank by scoring each offer in the offer bank against the extracted data.   
     
     
         12 . The computer product of  claim 11 , further comprising deploying the promotional offer and the selected plurality of test offers in a plurality of retail locations. 
     
     
         13 . The computer product of  claim 12 , wherein the deploying is performed to maximize orthogonality between the following variables: store sales, store out of stock rates, number of relevant SKUs carried in each store, temporal effects, discount depth, buy quantity and offer structure. 
     
     
         14 . The computer product of  claim 11 , wherein when the computer readable product when executed further performs the step of selecting a number of test offers to run in-market using reinforcement learning techniques. 
     
     
         15 . The computer product of  claim 14 , wherein Thompson sampling is used to select the number. 
     
     
         16 . The computer product of  claim 11 , wherein the offer bank is populated with forecasted offers. 
     
     
         17 . The computer product of  claim 16 , wherein the forecasts are based upon transaction logs of a plurality of retailers. 
     
     
         18 . The computer product of  claim 17 , wherein the transaction logs are adjusted for compliance by the given retailer, estimated out of stock events, normalized across stores to account for different store attributes, and adjusted for temporal effects. 
     
     
         19 . The computer product of  claim 18 , wherein when the computer readable product when executed further performs the step of applying machine learning to the adjusted transaction logs to determine lift and standard deviation for a given test offer. 
     
     
         20 . The computer product of  claim 19 , wherein the forecasts are a baseline function of time from the transaction log data plus elasticity from cross store experiments times a change in price, where in the elasticity is calculated as a function of the lift, and a confidence for the forecast is calculated as a function of the standard deviation.