Method and system for generation of at least one output analytic for a promotion

Abstract

There is provided a method and system for generating an output analytic for a promotion. The method includes determining, using an optimization machine learning model trained or instantiated with an optimization training set, at least one determined parameter for the promotion which optimizes at least one of received input parameters, the optimization training set comprising received historical data; forecasting, using a promotion forecasting machine learning model trained or instantiated with an forecasting training set, at least one output analytic of the promotion, the prediction training set comprising the received historical data, the at least one received input parameter and the at least one determined parameter; and outputting the at least one output analytic to the user.

1 . A computer-implemented method for generation of at least one output analytic for a promotion, the method comprising:
 receiving historical data related to one or more products and a plurality of previous promotions;   receiving at least one received input parameter for the promotion from a user, at least one of the input parameters comprising a macroscopic objective of the promotion;   determining, using an optimization machine learning model trained or instantiated with an optimization training set, at least one determined parameter for the promotion which optimizes at least one of the received input parameters, the optimization training set comprising the received historical data;   forecasting, using a promotion forecasting machine learning model trained or instantiated with an forecasting training set, at least one output analytic of the promotion, the prediction training set comprising the received historical data, the at least one received input parameter and the at least one determined parameter; and   outputting the at least one output analytic to the user.   
     
     
         2 . The method of  claim 1 , wherein the promotion forecasting machine learning model comprises at least one of an average price model and a regression model, the average price model comprises a Random Forest model to predict an average effective discounted price of the promotion based on a category of products, the regression model incorporating covariates to predict demand. 
     
     
         3 . The method of  claim 2 , wherein the regression model is used to determine the discounted price prediction on a per-product basis on a group of products in the same brand or subcategory, or both. 
     
     
         4 . The method of  claim 2 , wherein the regression model incorporates indicator variables for the one or more products, determining the indicator variables comprising, for each product, normalizing absolute units by a mean for periods with no promotion, and where such mean is not available, normalizing by the mean of the product's entire history. 
     
     
         5 . The method of  claim 1 , wherein the promotion forecasting machine learning model comprises a first Ridge Regression model combined with a second Ridge Regression model, the first Ride Regression model comprising at least one training set feature different than the second Ridge Regression model. 
     
     
         6 . The method of  claim 1 , wherein the historical data comprises one or products in a similar category or brand. 
     
     
         7 . The method of  claim 1 , wherein the plurality of previous promotions are aggregated in a stacked relationship. 
     
     
         8 . The method of  claim 1 , wherein the historical data comprises transaction history for the product and one or more other products in the same product category, the transaction history comprising at least one of date sold, product, units sold, price sold. 
     
     
         9 . The method of  claim 1 , wherein the at least one output analytic comprises one of promotion lift, cannibalization, halo effect, pull forward, and price elasticity of demand. 
     
     
         10 . The method of  claim 1 , further comprising determining a confidence indicator to indicate the reliability of the forecast, determining the confidence indicator comprises:
 determining if the forecast is in a predetermined scope; and   determining, using an accuracy machine learning model trained or instantiated with an accuracy training set, the confidence indicator, the accuracy training set comprising previous forecasts and their respective actualized values.   
     
     
         11 . A system for generation of at least one output analytic for a promotion, the system comprising one or more processors and a data storage device, the one or more processors configured to execute:
 an input module to receive historical data related to one or more products and a plurality of previous promotions, the input module further receiving at least one received input parameter for the promotion from a user, at least one of the input parameters comprising a macroscopic objective of the promotion;   a machine learning module to build an optimization machine learning model trained or instantiated with an optimization training set and, using the optimization machine learning model, determine at least one determined parameter for the promotion which optimizes at least one of the received input parameters, the optimization training set comprising the received historical data, the machine learning module further building a promotion forecasting machine learning model trained or instantiated with an forecasting training set and, using the promotion forecasting machine learning model, forecasting at least one output analytic of the promotion, the at least one received input parameter and the at least one determined parameter; and   an output module to output the at least one output analytic to the user.   
     
     
         12 . The system of  claim 11 , wherein the promotion forecasting machine learning model comprises at least one of an average price model and a regression model, the average price model comprises a Random Forest model to predict an average effective discounted price of the promotion based on a category of products, the regression model incorporating covariates to predict demand. 
     
     
         13 . The method of  claim 12 , wherein the regression model is used to determine the discounted price prediction on a per-product basis on a group of products in the same brand or subcategory, or both. 
     
     
         14 . The method of  claim 12 , wherein the regression model incorporates indicator variables for the one or more products, the machine learning module determines the indicator variables by, for each product, normalizing absolute units by a mean for periods with no promotion, and where such mean is not available, normalizing by the mean of the product's entire history. 
     
     
         15 . The system of  claim 11 , wherein the promotion forecasting machine learning model comprises a first Ridge Regression model combined with a second Ridge Regression model, the first Ride Regression model comprising at least one training set feature different than the second Ridge Regression model. 
     
     
         16 . The system of  claim 11 , the one or more processors further configured to execute a confidence module to determine a confidence indicator, the confidence indicator indicates the reliability of the forecast, the confidence module determines the confidence indicator by:
 determining if the forecast is in a predetermined scope; and   determining, using an accuracy machine learning model trained or instantiated with an accuracy training set, the confidence indicator, the accuracy training set comprising previous forecasts and their respective actualized values.   
     
     
         17 . A computer-implemented method for generation of at least one output analytic for promotional materials, the method comprising:
 receiving historical data related to one or more products and a plurality of previous promotional materials;   receiving one or more input parameters related to the promotional materials from a user;   selecting, using a machine learning model trained or instantiated with a selection training set, a configuration and a layout for the one or more products on the promotional materials, the selection training set comprising the historical data and the one or more input parameters, the selection comprising:
 assigning a prominence weight to each of the one or more products; 
 normalizing the prominence weight for each of the one or more products; 
 determine a block structure for the promotional materials based on the prominence weight of each of the one or more products; and 
 determine a location for each of the products on the promotional materials based on the prominence weight of each of the one or more products; and 
   outputting the promotional materials based on the selection of the configuration and layout.   
     
     
         18 . The method of  claim 17 , further comprising selecting, using the selection machine learning model, the one or more products to be promoted on the promotional materials. 
     
     
         19 . A computer-implemented method for generation of at least one output analytic for per-store unit demand, the method comprising:
 receiving historical data related to one or more products, the historical data comprising historical inventory level of the one or more products at a retail store;   forecasting, using a demand machine learning model trained or instantiated with a demand training set, a demand for the one or more products at the retail store, the demand machine learning model comprising a first model for predicting the total unit demand for the retail store and a second model for predicting the demand in the retail store for the one or more products, the demand training set comprising the historical data, the forecast comprising multiplying the prediction of the total unit demand for the retail store for a predetermined time-period by the prediction of the demand in the retail store for the one or more products; and   outputting the at least one output analytic to the user.   
     
     
         20 . The method of  claim 19 , wherein the forecast further comprises adding a covariate for a stock out condition.