Method and system for generating customer decision tree through machine learning

A system (100) for generating a Customer Decision Tree (CDT) in accordance with an attribute value (AV) based Demand Transfer (DT) estimation for a product category, the system (100) comprising:

• a data lake (106), as data source for aggregating data at a plurality of aggregation levels;
• a CDT system (102), connected with the data lake (106), comprising a data matrix generator (212), a model generator (214), a prediction model (216), a scenario generator (218), a DT Estimator (220), a hierarchy generator (222) and a CDT generator (224), wherein;
• the data matrix generator (212) is configured to:
• aggregate data associated with a plurality of AVs of the product category managed by an entity, wherein aggregation of the data is at a plurality of aggregation levels and the product category is identified with a plurality of attributes (ATs) with each AT having the plurality of AVs:
• generate a data matrix providing a multivariate multi-dependent structure from data aggregated at a predefined aggregation level for the product category for the plurality of AVs, wherein:
• a plurality of columns of the data matrix correspond to entity identity (ID) of the entity, the pre-defined aggregation level, sales drivers and recorded AV sales, wherein the sales drivers comprise demographic data associated with the product category, inventory availability of the AV and inventory stock of the AV;
• and
• a plurality of rows of the data matrix correspond to the data aggregated for a plurality of branches of the entity for the predefined aggregation level;
• the model generator (214) configured to:
• generate the prediction model (216), using the data matrix with the multivariate multi-dependent structure, to determine predicted AV sales for each AV of the product category at the predefined aggregation level, wherein the prediction model is a multivariate multi-structure machine learning model;
• optimize the prediction model (216) to reduce error between the predicted AV sales and the recorded AV sales to minimum, wherein the optimization comprises:
• generate the data matrix for each aggregation level among the plurality of aggregation levels;
• determine the predicted AV sales at each aggregation level using the trained prediction model;
• determine error between the recorded AV sales and the predicted AV sales at each aggregation level;
• identify an optimum aggregation level from the plurality of aggregation levels based on the determined error, wherein the determined error for the identified aggregation level is minimum;
• and
• fine-tune the trained prediction model to obtain the optimized prediction model using the data matrix associated with the optimal aggregation level;
• and
• the CDT generator module (224) configured to:
• generate the CDT utilizing the optimized prediction model.

One or more non-transitory machine readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:

• aggregating data associated with a plurality of AVs of the product category managed by an entity, wherein aggregation of the data is at a plurality of aggregation levels and the product category is identified with a plurality of attributes (ATs) with each AT having the plurality of AVs (302);
• generating a data matrix providing a multivariate multi-dependent structure from the data aggregated at a predefined aggregation level for the product category for the plurality of AVs, wherein
• a plurality of columns of the data matrix correspond to entity identity (ID) of the entity, the pre-defined aggregation level, sales drivers, and recorded AV sales, wherein the sales drivers comprises demographic data associated with the product category, inventory availability of the AV and inventory stock of the AV;
• and
• a plurality of rows of the data matrix correspond to the data aggregated for a plurality of branches of the entity for the predefined aggregation level;
• generating a prediction model, using the data matrix with the multivariate multi-dependent structure, to determine predicted AV sales for each of the AV of the product category at the predefined aggregation level, wherein the prediction model is a multivariate multi-structure machine learning model;
• optimizing the prediction model to reduce error between the predicted attribute value (AV) sales and the recorded AV sales to minimum, wherein the optimization comprises:
• generating the data matrix for each aggregation level among the plurality of aggregation levels;
• determining the predicted AV sales at each aggregation level using the prediction model;
• determining error between the recorded AV sales and the predicted AV sales at each aggregation level;
• identifying an optimum aggregation level from the plurality of aggregation levels based on the determined error, wherein the determined error for the identified aggregation level is minimum;
• and
• fine-tuning the prediction model using the data matrix associated with the optimal aggregation level;
• and
• generating the CDT utilizing the optimized prediction model.