Case Study

Customer Churn Analysis

A retention-focused case study showing how customer attrition patterns can be translated into targeted action and clearer executive decision-making.

Industry Subscription / Customer Operations

Use Case Retention Analytics

Tools Excel, SQL, Power BI, Python

View GitHub Repo View Demo

Client Context

The business needed a clearer view of why customers were leaving, which customer segments were most vulnerable, and how management should prioritize retention efforts.

Business Problem

The business needed to understand why customers were leaving and which segments were most at risk.

My Role

I structured the problem as a business-facing analytics case study, translated the churn question into measurable dimensions, and shaped the analysis into an executive-friendly narrative.

Outcome

The case study produced a clearer view of churn drivers, at-risk segments, and the type of interventions leadership could prioritize to improve retention.

Situation

Customer attrition was reducing revenue predictability and making retention planning reactive. The central challenge was not only to report churn, but to identify the likely drivers behind it in a way that business stakeholders could act on.

Approach

I framed the work around four questions:

1. Which customer groups are most likely to churn?
2. What behavioural or service indicators correlate with churn?
3. How should the business prioritize interventions?
4. What should leadership monitor continuously going forward?

The analysis combined data cleaning, exploratory analysis, segmentation logic, and dashboard design so that the final output could support both diagnosis and decision-making.

Solution Narrative

Rather than presenting raw analysis alone, the output was shaped as a business-facing case study:

• a clear business problem statement
• a structured analytical path
• a dashboard-oriented summary of the most relevant indicators
• recommended actions aligned to customer retention priorities

This makes the work more useful in a presales context because it mirrors how an analytics use case would be explained to a buyer, sponsor, or decision-maker.

Business Relevance

This case study demonstrates the ability to:

• convert an ambiguous business problem into measurable analytical questions
• organize data work around stakeholder decisions
• communicate insight in a way that is commercially meaningful
• bridge technical analysis and business action