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.
- 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