APPROACH

We took a four-phased approach to this challenge:

  • Data from disparate sources were consolidated into a single data warehouse, improving the usability of data
  • The analytical data layer was then prepared after carrying out data treatment procedures and applying business rules that were appropriate for the client’s business
  • Elementary data analysis helped classify stores based on potential products they could house
  • A regression model was then applied to identify the impact of changing assortment quantities on the top-line sales and the correlation model helped identify the affinity between different products
  • The combined insights from the models helped us arrive at the optimal assortment strategy for the client.

KEY BENEFITS

  • Our easy-to-use solution enabled the client to identify closely related products and plan assortments accordingly
  • It included recommendations on the mix of products that a store should carry and store-level revenue prediction based on the assortment mix

RESULTS

Our collective efforts paid off when the client’s Stock Keeping Unit (SKU) level prediction enhanced the weekly sales by 6%.

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