Method and system for variety optimization in hyper-localized assortment

Abstract

The disclosure relates generally to assortment of products in a retail scenario and more particularly to variety optimization in hyper-localized assortment. In retail scenario, due to availability of limited space in the store, retailers have limited options for displaying a category based on the attribute values of important attributes of the category. In addition, due to increased customer expectations, the attribute values are increasing each day leading to challenges in variety optimization in assortment. The state-of-art techniques address the problem of identification of key attributes for a category, however identifying optimal attribute values among the numerous attribute values specific to location of stores is still challenging. The disclosure provides methods to enable variety optimization in hyper localized assortment by identifying optimal attribute values for each attribute which are influenced mostly by location of stores by creating and applying dynamic variables in canonical analysis using hierarchical templates in an iterative way.