Business Analytics • Customer Retention • Excel • Power BI

🎯Objective

To diagnose customer churn for a UK-based SaaS company by identifying high-risk segments, behavioural drivers, and actionable retention opportunities using quantitative analysis and qualitative signals.

🧠 Business Context & Rationale

• SaaS businesses rely on recurring revenue
• Early churn increases CAC pressure
• Retention is cheaper than acquisition
• Analysis supports proactive decision-making

🧰 Tools & Methods

• Excel: data cleaning, pivot-based diagnostics, analysis
• Power BI: interactive churn dashboard (KPIs, visual segmentation)
• Qualitative analysis: theme coding from churn patterns

🔍 Process Overview

1. Data Preparation: Cleaned and structured a public customer churn dataset.
2. Metric Development: Computed churn and retention rates across key segments.
3. Diagnostic Analysis: Identified high-risk churn drivers using pivot-based analysis.