Big Data Retail Analysis and Product Distribution (BREAD) Model for Sales Prediction

Abstract

Retailing is one of the most promising and significantcommercial sectors of the world. But within retail, there is scale difference in the way they operate, at both businesses and store level.People believed that marketing is an art, however, advent of big data analysis added a scientific flavor to marketing. Retail companies are now using big data and analytics to every stage- identifying the products with predicting drifts, evaluating customers purchase behavior, forecasting demand trends of each product, thereby, segmenting and targeting customers accurately. Companies are using algorithms and models for storing and using customer data for sales prediction, however, they are still facing difficulty to correctly map products with customers. In this study, a new technique called BREAD (Big Data Retail Analytics and Product Distribution) model is developed for product distribution for retailers. As an experiment, the model was used for product distribution of ABC Stores (name changed, as requested).The algorithm takes product details from each store unconnectedly (10 in the case) and maps it with demand forecasting and product visibility. After evaluating the two results, the algorithm further assesses the price for each product category (termed as price optimization) and devise strategies accordingly.