Retail Data Analysis - Product Recommendation through Association Rule Mining and RFM Customer Segmentation

Author: Thulasi Amaraneni
Chair: Tae Oh - thoics
Committee: Dave Patric - dkpvcs
Submitted On: Nov 1, 2021
Data Analysis

Retailing plays a crucial role in every individual’s day-to-day lives to meet their everyday needs. It is like an open house for trading. As it became a part of everyone’s life, full focused attention is needed in that business to make things easy and smooth for both retailers and customers. Every retailer wants to provide his customers with a personalized easy shopping experience. But to achieve that retailer needs to understand his customer in a very efficient manner. That is when my project model can come into consideration.

The Retail Data Analysis project model analyzes the customer’s shopping item transactions and understands the behavior of customer shopping. Recommendation Engines are built using Apriori and FP growth algorithms which help in extracting the frequent item sets’ with help of which we can recommend customers about the relevant products of their purchased targeted product. Also, Customer Segmentation using RFM analysis is implemented to divide the customers into different categories so that retailers can come up with strategies to target the groups individually and maximize profits in the retail business.