Clustering algorithms in the online retail industry

Abstract

The growth of internet causes an significant increase in the number of online retailers that need to understand the customers in order to have better marketing strategy. In this research, a case study of using data mining techniques with customer relationship management for an online retailer is presented. The main purpose of this analysis is to compare two algorithms and then help the business better understand its customers and therefore create the marketing strategy more effectively. Using the Recency, Frequency, and Monetary model, customers of the business have been assigned into various customer segments using the k -means clustering algorithm and k-medoids algorithms and the main characteristics of the consumers in each segment have been clearly identified. A set of recommendations is further provided to the business.