The Problem Statement
Store Clustering has been treated for far too long as a necessary evil and nuisance in the fashion retail industry. The classical approaches of using grading stores and store size to cluster stores are outdated and do not reflect the retail complexity of today. Because store clustering mostly performed manually, analysis of rich retail data is impossible. Even excel cannot perform this task.
Before we go any further, let us first review the objective of store clustering, i.e. what represents a store cluster:
A cluster is a set of similar stores in terms of category and product distribution, meaning that the same selling behavior exists throughout the cluster HENCE should have the same assortment composition and width, only varied by depth depending on the sales volume and/or size.
ebp Global in conjunction with its technology partners Datacrag and Retailisation have pioneered the application of machine learning for store clustering, as an integral part of the merchandizing process both as a historical evaluation of assortment performance in the store cluster as well as a future determination of the best assortment going forward.
As such, it supports vital functions in the merchandize planning process namely sales planning, range planning and buy planning. The buy plan in particular requires an assortment at store level to determine the correct buying budget per store, initial allocation and open to buy management during in-season.