🧹 Customer & Product Text Analysis for Marketing Insights
📌 Project Overview
This project focuses on cleaning and validating customer and product text data using Python. The goal was to improve data quality for a simulated marketing and operations use case where accurate store information is critical for reporting, analysis, and customer engagement.
Poor data quality can lead to misleading insights, broken systems, and lost trust. This project demonstrates how analysts can prevent those issues using simple but powerful automation techniques.
🧠 Business Problems Addressed
The project answers the following questions:
How can we correct ZIP Codes when leading zeros are missing?
How can we identify invalid ZIP Codes that do not follow U.S. standards?
How can we validate store URLs to ensure correct protocols and ID formats?
How can we automate these checks to avoid manual data cleaning?
🔧 Tools & Technologies
Python
String manipulation
Conditional logic (if / else)
Custom functions
Data validation rules
🔍 Analysis & Development Process
1️⃣ ZIP Code Validation Function
A custom function was created to:
Check ZIP Code length
Add missing leading zeros when appropriate
Flag invalid ZIP Codes that do not meet formatting rules
Why this step was done:
ZIP Codes are often imported as numbers, which removes leading zeros and creates inaccurate location data.
2️⃣ ZIP Code Testing
Multiple test cases were used to confirm:
Valid ZIP Codes are preserved
Fixable ZIP Codes are corrected
Invalid ZIP Codes are flagged
Why this step was done:
Testing ensures the logic works before applying it to real datasets.
3️⃣ Store URL Validation Function
A second function checks:
Whether the URL uses the correct https: protocol
Whether the store ID is exactly seven characters long
Why this step was done:
Invalid URLs and IDs can cause broken links, failed tracking, and inaccurate reporting.
4️⃣ URL Testing & Error Messaging
The function clearly identifies:
Invalid protocols
Invalid store IDs
Valid URLs that pass all checks
Why this step was done:
Clear error messages make it easier for teams to fix issues quickly.
📊 Key Insights
Data errors often come from formatting issues, not missing data
Automating validation saves time and prevents repeated mistakes
Clean text data improves downstream analysis and reporting accuracy
🎯 Business Impact
This approach helps organizations:
Improve reporting accuracy
Reduce manual data cleaning
Increase trust in dashboards and insights
Catch errors early in the data pipeline
🚀 Next Steps
Apply functions to larger datasets
Log validation errors for reporting
Extend validation rules to emails, phone numbers, or product SKUs