Advanced analytics tools utilised to drive 2-3% gross margin through SKU rationalisation


A leading price point retailer approached AArete to develop a SKU rationalisation strategy for its department stores. The client’s existing strategy focused on rationalising SKUs on the basis of simple KPIs without appropriately accounting for market basket characteristics, inter-departmental associations and other hidden relationships. 


AArete developed a custom SKU rationalisation model leveraging multiple algorithms, most notably an association rules algorithm, focusing on market basket analysis. Using an open-source analytics software called R, we developed product clusters to identify complementary products based on a number of variables. Through defining these relationships, we were able to determine which SKUs should be retained or removed, with a bias on enhancing overall assortment productivity. We then used Graph Theory to further enhance insights from the model and identified SKU hierarchies and relationships. This was incorporated into a custom dashboard visualisation tool to improve interpretation and streamline decision-making processes. 


The modelling output resulted in the ability for our client to visually represent findings to further understand SKU relationships, eliminate unproductive items from their assortment and improve product placement strategies to further capitalise on complementary item relationships. Ultimately, our model identified the potential to drive a gross margin increase of 2-3% while also improving profitability and efficiency. 

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