Prediction Of Consumer Purchasing In Retail Store

Abstract

In Retail Industry, to maintain and increase sales, retailer has to do some observation based on past sale data. But, sometimes instead of increase in sales, retailer faces exponential decrease in sales due to some parameters like, unsatisfied customers due to product quality, behavior of store attendees, etc. Decrease in sales causes loss to retailer. To retain and increase sales and productivity by Customer satisfaction, Oracle Retail provides solution i.e. Oracle Retail Sales & Productivity and Loss Prevention to Retailers. In this thesis, I have worked on part of the solution i.e. ”Prediction of Consumers Purchasing Pattern in Retail Stores” using data mining technique called Association Rule Mining. Based on past consumers purchasing transactions sample data. Using this prediction we can generate individual report for a particular product and link those individual reports based on different filters and parameters to predict likely-hood of buying particular product with other specific product or set of products. That will help to analyze the area where retailer can improve sales productivity and reports also help us identify unusual occurrences of transactions which shows fraud occurrence in retail store.