Paper Title (use style: paper title)
Traditionally, retailers identified similar store clusters based on demographic and physical attributes. They are ingested into tools like Planograms to determine the right assortment, develop aesthetic merchandising strategies or run mass-scale promotional offers across similar stores. The approach has an inherent focus extrinsic to consumer needs and more on parameters like location and physical attributes of the store like region, store size, shelf space etc. It completely neglects product demand within a store. This paper aims to develop intelligent store clusters prioritizing consumer demand for categories. Firstly, it provides a comparative view of coverage versus contribution perspective on how best to create category-wise store performance data. Secondly, it employs the ensemble clustering algorithm on this dataset that outputs high-accuracy partitions of performance-driven clusters. Using a normalized similarity matrix as a consensus function to combine results from multiple clustering models yields a more robust view of frequently grouped stores. It demonstrates ensembles’ increasing usability and supremacy within the unsupervised space too. Finally, it provides insights into indivisibility observation, where the groups could not be further broken down even after forcibly increasing the number of clusters. It highlights the potential of this approach to set up regional boundaries within a nation based solely on consumption patterns independent of social, political, and economic causes.