A leading retail brand based in Iran. The client offers a wide range of products over 500 SKUs.
The objective of client is to reduce the sales representative’s time while they visit the store & place the order and would like to accelerate the sales so that they can improve the bottom line by optimizing the supply chain.
Using ADF, Data Bricks and Spark we develop recommendation engine that make use of algorithms and SAP data to recommend the most relevant items to a sales representee for a particular store.
Customer-facing applications are mobile-based, whereas data is stored in an SAP database, therefore, we setup the data lake solution that ingests the source data from SAP to be furtheranalyzed holistically.
The data ranges from product data, retailer data, purchase order data, inventory data, deals & offers data. We categorized and segmented the top 50 SKU for each retail store and mapped deals & offers with each of them.This helped to easily access the top 50 SKU by the Sales Rep while placing the order.
We further cross-linked the recommended products for upselling and cross-selling the items. Apart from this based on the past purchase history, we applied predictive analytics algorithms to its data and used machine learning to optimize its insights overtime for the sales forecast and inventory planning.
