Customer Segmentation and Clustering

Overview

This is a data analysis portfolio project to perform customer segmentation on a specific group of mall customers.

Aim: Divide the mall's target market into approachable groups. Create subsets of a market based on demographics and behavioural criteria to better understand the target for marketing activitie

Problem Statement:

Understand the Target Customers for the marketing team to plan a strategy

Context:

The manager wants to identify the most important shopping groups based on income, age, and the mall shopping score. Preferably the ideal number of groups with a label for each category.

Tools:

• Perform some quick EDA,
• Use the KMeans unsupervised machine learning algorithm to create the segments,
• Use Summary statistics to find the univariate, bivariate, and multivariate clusters.
• Then visualise to identify the best marketing group.

Recommendations:

Target Cluster

• The target group would be cluster 1 which has a high spending score and high income
• 60% of cluster 1 shoppers are Female. We should look for ways to attract these customers using a marketing campaign targeting popular items in this cluster.
• Cluster 2 presents an interesting opportunity to market to the customers for sales events on popular items, therefore there is a need for more strategic clustering for product items in cluster 2.
• Cluster 4 is target for ongoing marketing spend (high income and acceptable mean age).