📊 Customer Campaign Response Analysis

📌 Project Overview

This project analyzes customer data to uncover insights that can improve marketing campaign targeting.
The analysis focuses on identifying demographic and financial factors that influence campaign response rates.

🎯 Objectives

1. Identify customer segments most likely to respond to campaigns.
2. Understand the factors that influence response rates.
3. Provide recommendations for improving campaign targeting.

🗂 Dataset Description

Rows: 56
Columns: Customer ID, Age, Gender, Annual Income, Credit Score, Employed, Marital Status, No of Children, Responded.

Source: Kaggle Dataset

🔧 Tools Used

• SQL Server – Data cleaning, preparation, and analysis
• Tableau – Data visualization
• Excel – Preliminary data exploration

🛠 Data Cleaning & Preparation

1. Identifying Null Value
2. Identify how many rows are recorded
3. Identify the structure of the table
4. Create a new column that contains the Age Group (Young, Middle_Aged, and Older_Adult)
5. Create a new column that contains the Credit score group (Lower_credit, Middle_credit, and High_credit)
6. Create a new column that contains Income Group (Low Income, Middle Income, and High Income)

🔍 Analysis Questions

1. How does marital status influence campaign response?
2. Are higher credit scores linked to higher response rates?
3. Do customers with more children tend to respond more or less to campaigns?
4. Which segment (based on gender, age group, income group) has the highest conversion rate?
5. Is there a correlation between employment status and response rate?
6. What factors (income, credit score, marital status, etc.) are most common among customers who responded “Yes”?
7. Are there income groups or age groups where no one responded at all?
8. Based on the patterns, what type of customer is most likely to respond to a future campaign?

📈 Key Insights

1. Marital Status Impact: Marital status significantly influences campaign response rates, with married customers showing higher engagement.
2. Credit Score Correlation: Higher credit scores are associated with increased response rates.
3. Effect of Children: Customers with more children are more likely to respond positively to campaigns.
4. Top-Performing Segments: The highest conversion rates are observed among Male, Middle-Aged, and Middle-Income customers.
5. Employment Status Influence: Employed customers have a higher likelihood of responding to campaigns.
6. Common Traits of Respondents: Respondents tend to be Male, Married, Middle-Aged, Middle-Income, and have High Credit Scores.
7. Non-Responsive Segments: No responses were recorded from the following groups: Low Income & Middle-Aged, Low Income & Young, Middle Income & Young.
8. Ideal Target Profile: The most promising audience for future campaigns is Male, Married, Middle-Aged, Middle-Income, High Credit Score, and Employed.

💡 Recommendations

1. Prioritize High-Value Segments: Focus marketing efforts on Male, Married, Middle-Aged, Middle-Income, High Credit Score, and Employed customers to maximize conversions.
2. Leverage Credit Score Insights: Create exclusive offers for customers with high credit scores, as they show a greater tendency to respond.
3. Tailor Campaigns for Parents: Develop family-oriented promotions or loyalty benefits targeting customers with children.
4. Re-engagement Strategies: Implement special campaigns for Low Income & Young/Middle-Aged and Middle Income & Young groups to improve engagement in underperforming segments.
5. Employment-Based Targeting: Consider campaigns aligned with the schedules and needs of employed individuals, potentially leveraging workplace partnerships.
6. Data-Driven Personalization: Use the identified customer traits to personalize messages, offers, and channels for maximum impact.

📊 Tableau Dashboard

📜 License

This project is licensed under the MIT License – you are free to use, modify, and distribute this project, provided proper credit is given.