📊 Client Acquisition, Retention & LTV Analysis

This project explores key business metrics—Customer Acquisition Cost (CAC), Client Retention, and Lifetime Value (LTV)—using simulated data to mimic a real-world customer lifecycle. It’s designed to demonstrate data analysis skills relevant for roles in marketing analytics, business intelligence, and customer lifecycle management.

✅ Built in Python using pandas, matplotlib, seaborn, and Plotly.

🔍 Objective

Analyze how marketing spend, acquisition channels, and customer behavior influence revenue outcomes and client value. This is especially aligned with roles involving:

Customer retention and churn analysis
Marketing performance evaluation
LTV vs CAC strategy
Monthly revenue tracking

📂 Datasets

The project uses three synthetic datasets:

campaigns.csv: Marketing spend by acquisition channel
clients.csv: Client info including acquisition channel and signup date
transactions.csv: Transaction history per client

💡 Key Business Questions

What is the Customer Acquisition Cost (CAC) by channel?
How does Lifetime Value (LTV) vary across acquisition channels?
What is the monthly client retention rate by signup cohort?
How does revenue trend over time?

🧪 Analysis Overview

📉 Customer Acquisition Cost (CAC)

Calculated as:

CAC = Total Cost per Channel / Number of Clients Acquired

📌 Insight: Helps evaluate channel efficiency.

💰 Client Lifetime Value (LTV)

Total value of all transactions per client, merged with acquisition channel.

📌 Insight: Highlights the most profitable channels based on average LTV.

🔁 Retention Analysis

Built a retention matrix by comparing clients' transaction months to their signup month.

📌 Insight: Visualized with a heatmap to identify how long clients remain active.

📈 Monthly Revenue Trend

Tracked revenue over time by aggregating transaction values monthly.

📊 Visualizations

CAC by Channel (Bar Chart – Plotly)
Distribution of LTV (Histogram – Seaborn)
Avg LTV by Channel (Bar Chart – Plotly)
Client Retention Heatmap (Cohort Analysis – Seaborn)
Monthly Revenue Trend (Line Chart – Plotly)

You can preview these visualizations directly in the notebook

🛠️ Tools & Technologies

Python 3.10
pandas, numpy
seaborn, matplotlib, plotly.express
Jupyter Notebook

📌 Results Summary

Channel C had the lowest CAC and highest average LTV → Most efficient.
Client retention dropped sharply after the first 2 months, indicating potential churn issues.
Monthly revenue showed steady growth, likely driven by new acquisitions.

🧠 Skills Demonstrated

✅ Data Cleaning & Transformation
✅ Business Metrics Calculation (CAC, LTV)
✅ Cohort Retention Analysis
✅ Data Visualization
✅ Insight Communication for Business Impact

Client Acquisition, Retention & LTV Analysis