A Proposal for Shelf Placement Optimization for Retail Industry using Big Data Analytics
Big Data analytics helps retail industries in improving their customer experience and make better decision in terms of sales of their products. It enables to understand as to when, where and why customers buy. Analytics of these data helps us in anticipating and connecting to the customer’s retail needs. In most part of the world, there is the rise of this new digital competition; a new generation of customers who are way too smart and stay well ahead of time with the information and are more demanding in terms of retail needs. This is where we introduce the usage of Shelf Placement in the retail industry. The arrangement of products on the shelves plays a vital role in this context and a key factor to the retailer’s competitive world. Another term that goes hand-in-hand with Shelf placement is the Shelf life. Location of a product on the shelves plays a critical role as to how much it can get engagement from the buyers. With the motivation of fixing the space allocation problem in the retail industry, we would like to suggest a proper optimisation method of shelf placement which would shoot up the sales and profit. Our Big Data Analytics system would be based on a retail system. This system would be based on the following processes such as Acquirement, Placement and Advertise. Now using Shelf Life, Shelf Placement and Inventory as the input parameters for the Big Data Analytics system, we propose to boost the profit of the retail system.