Results:

This project received a score of 16.5, delivering customer segments that enable a significant improvement in marketing strategy.

Customer Segmentation For a Shopping Mall

Context

This dataset was specifically designed for educational purposes to illustrate the principles of customer segmentation, also known as basket analysis. The goal is to demonstrate the use of the KMeans clustering algorithm, an unsupervised method, to divide customers into homogeneous groups in a simple and intuitive way.

Description

You are the owner of a shopping mall, and through the use of loyalty cards, you have collected key information about your customers: Customer ID, age, gender, annual income, and spending score. The spending score is a value assigned to each customer based on defined criteria such as their purchasing behavior and consumption habits.

Problem Statement

As part of managing your shopping mall, the objective is to identify customer groups that can be easily targeted for personalized marketing campaigns. You aim to segment your clientele based on their purchasing behaviors so that the marketing team can design tailored strategies for each segment and optimize commercial efforts.

Inspiration and Learning Objectives

By the end of this case study, you will be able to:

- Identify target customer segments for launching personalized and effective marketing strategies based on their propensity to engage in commercial actions.

- Understand how a data-driven marketing strategy based on segmentation can be implemented in the real world and how it can transform customer relationship management.