System and method for selecting promotional products for retail

Abstract

A computer system and computer-implemented method for retail merchandise planning, including promotional product selection, price optimization and planning. According to an embodiment, the computer system for generating an electronic retail plan for a retailer comprises, a data staging module configured to input retail sensory data from one or more computer systems associated with the retailer; a data processing module configured to pre-process the inputted retail sensory data; a data warehouse module configured to store the inputted retail sensory data and the pre-processed retail sensory data; a state model module configured to generate a retailer state model for modeling operation of the retailer based on the retail sensory data; a calibration module configured to calibrate the state model module according to one or more control parameters; and an output module for generating an electronic retail plan for the retailer based on the retailer state model.

What is claimed is: 
     
         1 . A computer-implemented intelligent agent method for simulating promotional performance, comprising:
 providing, in a memory, an intelligent agent, the intelligent agent comprising:   a retail promotional model;
 a simulation component, 
   a long-term memory, and   a short-term memory; and   determining, at a processor in communication with the memory using the intelligent agent, one or more candidate itemsets, each of the one or more candidate itemsets identifying two or more products in a plurality of product records;   executing a reinforcement learning algorithm to simulate, at the simulation component, based on the short-term memory of the memory component, the long-term memory of the memory component, a simulated state of an environment, and the retail promotional model, an expected reward for each of the one or more candidate itemsets, each expected reward simulating a sales metric of the plurality of product records based on the respective candidate itemset;   selecting, at the intelligent agent module, one or more selected itemsets from the one or more candidate itemsets, each of the one or more selected itemsets identifying two or more products in the plurality of product records;   generating a current action corresponding to the one or more selected itemsets from the one or more candidate itemsets and each corresponding expected reward;   applying the current action to the simulated state of the environment;   receiving, at the intelligent agent, sensor data comprising a measured reward, wherein the sensor data is representative of a measured state of the environment;   store the sensor data in the short-term memory;   store the sensor data in the long-term memory; and   correcting the simulated state of the environment based on the sensor data comprising the measured reward.   
     
     
         2 . The method of  claim 1 , wherein:
 the long-term memory is representative of a long-term frequency response, the long-term memory comprising a plurality of long-term sensor data and a plurality of long-term prior actions; and   the short-term memory is representative of a short-term frequency response, the short-term memory comprising a plurality of short-term sensor data and a plurality of short-term prior actions.   
     
     
         3 . The method of  claim 1 , wherein:
 the plurality of product records further comprises a product category hierarchy;   the simulating the expected reward is for a first level of the product category hierarchy; and   wherein a first product belongs to the first level of the product category hierarchy and a first candidate itemset in the one or more candidate itemsets comprises the first product.   
     
     
         4 . The method of  claim 2 , wherein:
 the simulating the expected reward further comprises simulating a second level of the product category hierarchy, the second level at a lower level than the first level in the product category hierarchy; and   wherein a second product belongs to the second level of the product category hierarchy and the first candidate itemset in the one or more candidate itemsets comprises the second product.   
     
     
         5 . The method of  claim 3 , wherein the simulating the expected reward further comprises:
 simulating a first product set in the one or more selected itemsets, the first product set having a first product in the first level of the product category hierarchy and a second product in the second level of the product category hierarchy.   
     
     
         6 . The method of  claim 4 , wherein the simulating the expected reward further comprises:
 determining one or more solution increments for the one or more time periods; and   simulating an addition or removal of product records to the one or more selected itemsets.   
     
     
         7 . The method of  claim 5 , further comprising:
 receiving, from a retailer system, retail data for a current time period; and   updating the retail promotional model based on the retail data for the current time period and the one or more selected itemsets.   
     
     
         8 . The method of  claim 1  wherein the reinforcement learning algorithm comprises a genetic algorithm. 
     
     
         9 . A computer-implemented intelligent agent system for simulating promotional performance, comprising:
 a server, comprising:   a memory, comprising:   an intelligent agent, comprising:   a retail promotional model,   a simulation component,   a long-term memory, and   a short-term memory;   a network device;   a processor in communication with the memory and the network device, the processor configured to:   determine one or more candidate itemsets, each of the one or more candidate itemsets identifying two or more products in a plurality of product records;   executing a reinforcement learning algorithm to simulate based on the short-term memory of the memory component, the long-term memory of the memory component, a simulated state of an environment, and the retail promotional model, an expected reward for each of the one or more candidate itemsets, each expected reward simulating a sales metric of the plurality of product records based on the respective candidate itemset;   select one or more selected itemsets from the one or more candidate itemsets, each of the one or more selected itemsets identifying two or more products in the plurality of product records;   generate a current action corresponding to the one or more selected itemsets from the one or more candidate itemsets and each corresponding expected reward;   apply the current action to the simulated state of the environment;   receive sensor data comprising a measured reward, wherein the sensor data is representative of a measured state of the environment;   store the sensor data in the short-term memory;   store the sensor data in the long-term memory; and   correct the simulated state of the environment based on the sensor data comprising the measured reward.   
     
     
         10 . The system of  claim 8 , wherein:
 the long-term memory is representative of a long-term frequency response, the long-term memory comprising a plurality of long-term sensor data and a plurality of long-term prior actions; and   the short-term memory is representative of a short-term frequency response, the short-term memory comprising a plurality of short-term sensor data and a plurality of short-term prior actions.   
     
     
         11 . The system of  claim 8 , wherein:
 the plurality of product records further comprises a product category hierarchy;   the simulating the expected reward is for a first level of the product category hierarchy; and   wherein a first product belongs to the first level of the product category hierarchy and a first candidate itemset in the one or more candidate itemsets comprises the first product.   
     
     
         12 . The system of  claim 9 , wherein:
 the simulating the expected reward further comprises simulating a second level of the product category hierarchy, the second level at a lower level than the first level in the product category hierarchy; and   wherein a second product belongs to the second level of the product category hierarchy and the first candidate itemset in the one or more candidate itemsets comprises the second product.   
     
     
         13 . The system of  claim 10 , wherein the processor is further configured to simulate the expected reward by:
 simulate a first product set in the one or more selected itemsets, the first product set having a first product in the first level of the product category hierarchy and a second product in the second level of the product category hierarchy.   
     
     
         14 . The system of  claim 11 , wherein the processor is further configured to simulate the expected reward by:
 determine one or more solution increments for the time period; and   simulate an addition or removal of product records to the one or more selected itemsets.   
     
     
         15 . The system of  claim 12 , wherein the processor is further configured to:
 receive, from a retailer system using the network device, retail data for the time period; and   update the retail promotional model based on the retail data and the one or more selected itemsets.   
     
     
         16 . The system of  claim 8  wherein the reinforcement learning algorithm comprises a genetic algorithm.