Customer Relationship and Sales Management using Data Mining Techniques

Abstract

A general store is a destination for customers to buy everything they require in a single spot. The Manager is responsible for administering and making decisions for developing and running the store. But it is a heavy task for a single person to make valuable decisions that do not affect the business. So, in this project, data mining techniques are applied to the store’s transactional data to help the manager make decisions. We use RFM, Recency Frequency and Monetary segmentation method and classification algorithms to classify the customers into loyal and not-so-loyal customers to make predictions based on the data of the valuable customers. The customers who are loyal to the store are selected and their data is used for further analytics. Association mining technique to obtain the products which are most likely to be bought together and other data mining and visualization techniques to display the valuable knowledge mined from the dataset. Then we cluster the products based on the product’s important keywords and then the customers are further classified by finding the number of products bought from the previously found clusters.