Customer Clustering Analysis

Developed a model for one of the retail store in Boulder to get sense of customers and classify them into different cohorts/groups so that suitable marketing strategies can be used for individual cohort.

Problem Statement

Walmart has asked us to develop a model for one of the retail store in Boulder. If they like your work, they will ask you to replicate the model for other Walmart retail stores as well You would like to develop a model to get sense of customers and classify them into different cohorts/groups You are given the following attributes for 2000 customers (based on the Loyalty card they use at checkout)

Customer ID
Sex/Gender
Martial Status
Education
Income
Occupation (Unskilled/Skilled/Highly skilled)
Settlement Size (The size of the city that the customer lives in)

The model should be able to classify customers into different cohorts so that suitable marketing strategies can be used for individual cohort

About Boulder:

Boulder is a small town located in Colorado. Many high school and university students come to Boulder for attaining education
After attaining education, students get placed in top-notch software companies in Boulder.
However, due to limited jobs in Boulder, some students look for job opportunity in their home-town cities or nearby big cities such as Louisville, Westminster, etc. These big cities are a few miles away from Boulder

Dataset

Data Source : https://github.com/keshavsood80/Customer-Clustering-Analysis/blob/main/segmentation%20data.csv

Understand the Variables

Variable	Data type	Range	Description
ID	numerical	Integer	Shows a unique identificator of a customer
Sex	categorical	{0,1}	0 - male , 1 - female
Marital status	categorical	{0,1}	0 - single , 1 - non-single (divorced / separated / married / widowed)
Age	numerical	Integer	The age of the customer in years
Education	categorical	{0,1,2,3}	Level of education of the customer -- 0 - other / unknown , 1 - high school, 2 - university , 3 - graduate school
Income	numerical	Real	Self-reported annual income in US dollars of the customer
Occupation	categorical	{0,1,2}	Category of occupation of the customer -- 0 - unemployed / unskilled , 1 - skilled employee / official , 2 - management / self-employed / highly qualified employee / officer
Settlement size	categorical	{0,1,2}	The size of the city that the customer lives in -- 0 - small city , 1 - mid-sized city , 2 - big city

Agenda

Step 1: Import Libraries
Step 2: Import Dataset
Step 3: Exploratory Data Analysis
Step 4: Univariate Data Analysis
Step 5: Bi-variate Analysis
Step 6: Data Preparation
Step 7: Modelling
Step 8: Evaluate Performance

Models used

Hierarchical Clustering
K-Means Clustering

Solution

jupyter Notebook : Customer_Clustering_Analysis.ipynb
Please Go through Customer_Clustering_HLDX.pptx for more info.

Analysis of Clusters

Cluster Name	Analysis
(0) Fewer opportunity	High school passed people employed mostly in low skilled jobs (living in small cities)
(1) Career Focused	Single men focusing on only careers earning 2nd highest salary
(2) Well Settled	Married elder people with University+ education and High Income
(3) Average	High school passed people employed in skilled job (living in small and medium cities)

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