Customer Retention Data Analysis

I’m sitting on a sizeable pool of customer data and need clear, actionable insight into why clients stay—or slip away—so I can sharpen our overall experience. The single metric I care about right now is retention rate; every query, chart, and model should trace back to that outcome.

Here’s how I picture the engagement:

• Clean and consolidate the raw customer datasets I’ll supply (CSV and database exports).
• Run the exploratory analysis and segmentation needed to surface churn-related patterns.
• Build predictive or descriptive models—whichever proves most reliable—to highlight the moments of greatest attrition risk.
• Present findings in a concise slide deck plus an interactive dashboard (Python, SQL, Tableau or Power BI are all acceptable) that lets me slice retention by cohort, tenure, and key behaviours.
• Conclude with two or three priority recommendations I can act on immediately to raise retention.

I’ll provide sample data before kickoff so you can scope effort precisely, and I’m happy to clarify field definitions or business rules along the way. Let’s turn the raw numbers into a roadmap for keeping more of our customers delighted and loyal.