Customer Retention Data Analysis

I have a rich set of customer-behavior records—transaction logs, click-stream events, and engagement metrics—that need to be explored and transformed into clear, actionable insights aimed squarely at boosting retention. What I’m looking for is a concise, end-to-end analysis that not only quantifies churn and loyalty drivers but also surfaces the patterns that explain why our best users stay and others leave.

Here’s what I need from you:
• A clean, well-documented workflow (Python, SQL, pandas preferred) that ingests the raw data, handles missing values, and engineers any retention-focused features.
• Exploratory analysis that highlights cohort trends, lifecycle touchpoints, and at-risk segments, backed by visualizations in Tableau or a similar BI tool.
• A succinct report—slides or notebook—summarizing key findings, recommended interventions, and the estimated lift we can expect from each action.

Acceptance is straightforward: the code must run on my sample dataset, the visuals must clearly illustrate the insights you describe, and your recommendations should map directly to measurable retention metrics (e.g., churn reduction, repeat-purchase rate). Once delivered, I’ll test everything on a fresh data slice; if the numbers replicate, the job is done.