Optimizing decision making in retail industry using analytics

Abstract

The goal is to optimize retail business processes using advanced scientific techniques for increased productivity. When a client gives his large volume of data, all the data is not relevant for analysis as they often contain errors and noise. We first try to see the high level trends of the data to know their business better so that we understand the type of business we are dealing with. Next we apply filtering on the data to refine it without losing the essence of it. Then we apply data mining techniques to discover patterns and relationships hidden in data. Subsequently, we turn our focus to optimizing specific areas. In the end, we use algorithms as to predict the optimal decisions required. We accumulate all the results and analysis to present a business case to the client as to how their overall revenue can be increased.