This project segments customers based on their purchasing behavior to identify different target groups.This project aims to identify customer segments using various data points such as customer purchase_Frequency behavior, Education, Gender, customer income, customer age and customer spendings. By leveraging Power BI, we create dynamic visualizations that provide insights into customer segments, enabling businesses to better understand and target their audience, improve customer engagement, and enhance marketing strategies.
⇒ Project Objectives:- To analyze customer data and identify meaningful segments using key variables. To create interactive Power BI visualizations to explore and understand each segment’s characteristics. To develop actionable insights for targeted marketing and customer relationship management. To provide a streamlined, interactive dashboard for business stakeholders.
⇒ Data Source:- The dataset used for this project includes customer Name, Income,Purchase_Frequency,Education, Country, Age, Gender and other behavioral indicators. The data typically contains fields like:- Customer Name Age, gender, location, income Purchase frequency and transaction amounts
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Size of Dataset:- The dataset contains 1,000 rows and 8 columns.
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breakdown of its structure and functionality:- The dataset includes 1,000 entries, which offers a moderate sample size to conduct meaningful analysis on customer-related metrics.
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Columns and Functionality:-
- name: Represents customer names; this may serve as an identifier or simply as sample data.
- age: Captures the customer’s age, useful for demographic analysis.
- gender: Indicates customer gender, allowing for segmentation.
- education: Specifies educational attainment (e.g., High School, Bachelor, Master).
- income: Annual income, essential for analyzing customer purchasing power.
- country: The country of residence, useful for geographic segmentation.
- purchase_frequency: The average number of purchases within a period, important for identifying high- and low-frequency buyers.
- spending: Represents the total spending, which can help in analyzing purchasing behavior and revenue contributions.
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Dataset Link - https://www.kaggle.com/datasets/dp1224/customer-segments-data
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Power BI Dashboarding Vizualisation Link - https://github.com/dpgitaccount/ProjectCustomerSegmentationAnalysiswithPowerBI/blob/main/Project%20%20Customer%20Segmentation%20Analysis%20with%20Power%20BI/Dashboard_of_Customer_Segmentation_Analysis.pbix
⇒ Tools and Technologies:- Power BI:- Used for data modeling, analysis, and creating interactive visualizations. Pandas, Numpy and Seaborn:- For cleaning Data and vizualization. Excel/CSV:- Used for initial data storage and upload into Power BI. Machine Learning Algorithms:- For finding the accurate segments of customers
⇒ Project Workflow:-
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Data Import and Preparation:- Load customer data into Power BI, ensuring data consistency and accuracy. Clean data to handle missing values, outliers, and inconsistencies. Use Power Query to transform the data as required.
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Exploratory Data Analysis:- Used for finding the statical and categorical data details in depth.
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Implement clustering techniques such as:-
- K-means clustering - computer to use unlabeled, unclassified data and enabling the algorithm to operate on that data without supervision. Without any previous data training, the machine’s job in this case is to organize unsorted data according to parallels, patterns, and variations.
- Selecting Relevant Features: Focus on age, income and spending for clustering.
- Standardizing the Data: Scaling the features to improve clustering results.
- Linear Regression:- using Python for Model Training and Evaluation and accurate calculations. Linear regression is a data analysis technique that predicts the value of unknown data by using another related and known data value. It mathematically models the unknown or dependent variable and the known or independent variable as a linear equation.
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Define and label segments based on key variables like purchasing frequency, spending amount, and demographic indicators. Visualization and Dashboard Creation
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Design a user-friendly and interactive dashboard that highlights:- Customer demographics across segments. Purchasing behaviors per segment. Key performance indicators like Maximum spend, purchase frequency, and customer Income. Enable filtering and drill-down options for detailed exploration of each segment. Insight Generation and Recommendations
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Analyze segment characteristics to generate targeted marketing insights.
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Provide recommendations to enhance customer engagement, loyalty, and retention based on segment-specific behaviors.
⇒ Key Visualizations and Metrics:-
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Segmentation Overview:- Displays the distribution of customers across different segments.
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Demographic Breakdown:- Shows age, gender, income, and location details for each segment.
- Purchase Patterns:- Visualizes metrics such as average transaction value, purchase frequency, and total spend per segment.
⇒ Results:- This dashboard provides valuable insights that can guide business strategies in areas such as:
Targeted marketing campaigns for high-value segments. Personalized offers and retention strategies for at-risk segments. Resource allocation and strategic planning based on segment profitability.
⇒ How to Use:- Open Power BI and load the provided .pbix file.
Interact with the Dashboard:- Use filters to view different customer segments, analyze individual metrics, and drill down into specific customer behaviors.
Export and Share:- Power BI allows exporting the report as a PDF or publishing to Power BI Service for online access.
⇒Dashboard Presentation of this Project-
⇒ Conclusion:- Customer segmentation using Power BI enables businesses to adopt a data-driven approach to understanding and engaging with their customers. By creating targeted, segment-specific strategies, businesses can optimize marketing, enhance customer satisfaction, and improve revenue.