Customer-Sign-Up-Behaviour-Data-Quality-Audit

Business Intelligence analysis of SaaS customer acquisition patterns and data quality audit using Python and Pandas.

🎯 Project Overview

I analyzed 300 customer records from a SaaS platform (Jan–Oct 2024) to understand:

1. How customers are signing up
2. Which subscription plans are most popular
3. Regional performance
4. Support patterns and data quality issues

Goal: Identify trends, improve data quality, and provide recommendations to boost revenue by 15-20%.

🔑 Key Findings

1. YouTube Leads Acquisition : 58 customers (19.4%) came from YouTube, significantly outperforming other channels. Recommendation: Increase video content budget by 25-30%.

2. Premium Plan Most Popular: 99 customers (33.1%) chose the Premium plan, indicating strong product-market fit. Customers are willing to invest in advanced features.

3. Regional Performance Gap: North region has 65 customers while Central has only 39—a 67% difference. Opportunity exists for targeted regional expansion campaigns.

4. Pro Plan Support Issues: Pro users contacted support 26 times within 2 weeks vs 14 times for Premium users. The onboarding experience needs improvement.

🛠 Data Cleaning

The original dataset had quality issues that were addressed:

Missing data: 30 regions, 34 emails, 19 ages filled with appropriate values Duplicates: 1 duplicate record removed Outliers: Unrealistic age (206 years) corrected to median Standardization: Plan names and formats made consistent

Result: Clean, reliable dataset for analysis.

📊 Tools Used

Python 3.8+ - Core programming language Pandas - Data manipulation and analysis NumPy - Numerical operations Jupyter Notebook - Interactive analysis environment Matplotlib - Data visualization Seaborn - Statistical visualizations

Analysis performed:

1. Customer demographics & segmentation
2. Acquisition channel performance
3. Regional distribution analysis
4. Support pattern identification

Key Statistics:

1. Average customer age: 35.6 years
2. Gender distribution: Nearly equal
3. Marketing opt-in rate: 45.7%
4. Customers who opt in are slightly older (36.1 vs 35.3 years)

💡 Business Recommendations

1. Invest more in YouTube marketing (top acquisition channel)
2. Improve Pro plan onboarding and tutorials (reduce support contacts)
3. Launch campaigns in Central and West regions (address performance gap)
4. Make location data mandatory during signup (fix 10% missing data)

Potential Impact: 15-20% increase in revenue

👩 About Me

Charu Madaan

Data Analyst | QA Software Tester | Business Intelligence

Email: charumadaan88@gmail.com

LinkedIn: https://www.linkedin.com/in/charu-madaan-7100b2210/

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