Customer Segmentation and Transaction Analysis

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Project Overview

This project aims to transform transaction data into actionable business insights through customer segmentation and behavior analysis.

Importance

Understanding customer behavior is crucial for businesses to:

• Tailor marketing strategies
• Improve customer retention
• Increase overall profitability

Dataset

The dataset has been taken from UCI Online Retail https://archive.ics.uci.edu/ml/machine-learning-databases/00352/Online%20Retail.xlsx

Customer Segmentation and Transaction Analysis: Part 1

Methodology: K-Means Clustering

In this initial phase, we'll employ K-Means clustering to group customers based on their total spending.

Benefits

• Identify distinct customer segments
• Understand purchasing patterns
• Enable targeted marketing campaigns
• Create personalized offers

Data Preprocessing Challenge

Our starting point is invoice-level data, which requires transformation into customer-centric data.

Process

• Convert transaction-based rows to customer-based rows
• Aggregate purchase details for each customer

Context in Modern Business

• This type of analysis is often integrated into business intelligence software
• Many companies are incorporating these techniques into their platforms
• Some are exploring machine learning applications to refine the process

Project Goals

By completing this analysis, we aim to:

1. Identify key customer segments
2. Provide actionable insights for business decision-making
3. Enhance customer satisfaction and loyalty

This project engages with current methodologies in data-driven business strategy, contributing to the evolving landscape of customer analytics.

Customer Segmentation and Transaction Analysis: Part 2

Introduction to DBSCAN Clustering

In this second phase of our project, we'll leverage DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to identify customer clusters and outliers based on spending patterns and purchase intervals.

Key Features of DBSCAN

• Handles clusters of varying shapes and sizes
• Effectively identifies outliers
• Automatically determines the number of clusters based on data density

Advantages over K-Means

Unlike K-Means, DBSCAN doesn't require pre-specifying the number of clusters. This allows for the discovery of natural groupings within the data.

Objectives

By applying DBSCAN to our customer data, we aim to:

1. Uncover organic customer segments
2. Identify customers with unusual purchasing patterns
3. Gain insights into the density and distribution of customer behaviors

Potential Insights

This analysis may reveal:

• High-value customer segments
• Customers at risk of churn
• Opportunities for personalized marketing strategies

The DBSCAN results will complement our earlier K-Means analysis, providing a more comprehensive understanding of customer behavior and potentially uncovering new business opportunities.