1. Customer Segmentation
Using Supervised Learning
This presentation explores customer segmentation using supervised
learning techniques. It covers the project's background, objectives,
methodology, and results. We aim to provide a clear understanding of
how AI can enhance marketing strategies.
by Daulat Biswal
2. Introduction
Customer segmentation involves dividing customers into groups.
Groups are based on similar characteristics. This allows businesses to
better understand their customers. They can create targeted marketing
strategies.
Targeted strategies
Segmentation creates customer focused marketing.
Understand customers
Segmentation builds deeper insights.
3. Background & Motivation
Traditional segmentation methods are manual and inefficient. AI and
supervised learning automate the segmentation process. They offer
faster and more accurate results.
Automated Fast Accurate
4. Project Objectives
This project focuses on implementing a supervised learning model. The
goal is to segment customers effectively. We aim to classify customers
into value-based segments.
Automate segmentation
Apply in real-world scenarios.
Visualize important features
Understand key segmentation drivers.
5. Dataset Overview
The dataset used is the Marketing Campaign Dataset from Kaggle. It contains demographic and behavioral attributes. These
include age, education, income, and marital status.
Demographics
• Age
• Education
• Income
Behavioural Data
• Spending on products
• Number of children
• Marital Status
6. Methodology
The methodology includes data preprocessing, feature engineering, and model selection. We used a Decision Tree Classifier for its interpretability.
Preprocessing
Feature Engineering
Model Selection
Model Evaluation
7. Code Highlights
The code involves data loading, cleaning, and label encoding. We
created a target variable by computing total spending. The
DecisionTreeClassifier was used from sklearn.
Data Loading
Model Training
Evaluation
9. Conclusion
The model successfully segmented customers using supervised learning. Key features included income, age, and product
spending. The project demonstrates the value of AI in marketing.
Good Accuracy 1
Key Role
2
Marketing Value
3
