Customer Segmentation Analysis using Jupyter Notebook
Budget: $30 – $250 AUD
Project Overview
In this project, I’m tackling the challenge of understanding customer demographics and behaviors to improve targeted marketing efforts. The goal is to segment customers into distinct groups based on their characteristics. To achieve this, I’ll explore the data, analyze patterns, and identify key clusters.
Exploratory Data Analysis
I’ll start by diving into the data with some basic exploratory analysis. This will include calculating essential summary statistics and visualizing the findings with charts like histograms and pie charts to highlight key trends.
Customer Segmentation
For the customer segmentation part, I’ll skip standardizing the data and instead focus on determining the optimal number of customer segments using the Elbow Method. Once I’ve identified the best number of clusters, I’ll apply both K-means++ and Agglomerative Clustering techniques to categorize the customers. This will involve presenting tables that show the cluster centers across all seven variables and the customer counts for each clustering method. I’ll also provide insights into what each cluster represents, profiling the typical customer in each group (e.g., a cluster of university-educated women in mid-sized cities). Additionally, I’ll compare the segments identified by both methods to see if there are any overlaps.
Recommendations
Based on the customer segments discovered through the K-means++ method, I’ll suggest some marketing strategies and techniques to effectively engage these groups.
Conclusion Finally, I’ll wrap up with a brief summary of the findings and insights from the project.
In this project, I’m tackling the challenge of understanding customer demographics and behaviors to improve targeted marketing efforts. The goal is to segment customers into distinct groups based on their characteristics. To achieve this, I’ll explore the data, analyze patterns, and identify key clusters.
Exploratory Data Analysis
I’ll start by diving into the data with some basic exploratory analysis. This will include calculating essential summary statistics and visualizing the findings with charts like histograms and pie charts to highlight key trends.
Customer Segmentation
For the customer segmentation part, I’ll skip standardizing the data and instead focus on determining the optimal number of customer segments using the Elbow Method. Once I’ve identified the best number of clusters, I’ll apply both K-means++ and Agglomerative Clustering techniques to categorize the customers. This will involve presenting tables that show the cluster centers across all seven variables and the customer counts for each clustering method. I’ll also provide insights into what each cluster represents, profiling the typical customer in each group (e.g., a cluster of university-educated women in mid-sized cities). Additionally, I’ll compare the segments identified by both methods to see if there are any overlaps.
Recommendations
Based on the customer segments discovered through the K-means++ method, I’ll suggest some marketing strategies and techniques to effectively engage these groups.
Conclusion Finally, I’ll wrap up with a brief summary of the findings and insights from the project.