Customer Segmentation With Machine Learning: New Strategy For Targeted Actions

Abstract

Customers Segmentation has been a topic of interest for a lot of industry, academics, and marketing leaders. The potential value of a customer to a company can be a core ingredient in decision-making. One of the big challenges in customer-based organizations is customer cognition, understanding the difference between them, and scoring them. But now with all capabilities we have, using new technologies like machine learning algorithm and data treatment we can create a very powerful framework that allow us to best understand customers needs and behaviors, and act appropriately to satisfy their needs. In the present paper, we propose a new model based on RFM model Recency, Frequency, and Monetary and k-mean algorithm to resolve those challenges. This model will allow us to use clustering, scoring, and distribution to have a clear idea about what action we should take to improve customer satisfaction.