Customer Retention Prescriptive Data Analysis

Our company has amassed a rich set of customer data from CRM, support tickets, and usage logs, and I need a full prescriptive analysis that pinpoints actions we can take to boost retention. The work goes beyond describing what has happened; I need to understand why customers leave, simulate what-if scenarios, and receive clear, prioritized recommendations I can implement right away.

You will have direct access to anonymized customer records, churn labels, engagement metrics, and marketing touchpoints. I expect you to apply statistical modeling or machine-learning techniques of your choice, validate findings rigorously, and convert them into concrete retention strategies—loyalty offers, upsell timing, personalized messaging, or workflow changes—complete with expected impact.

Deliverables:
• Cleaned and documented data set (notebook or SQL scripts included)
• Model code and explanation of feature importance
• A concise slide deck or report translating insights into actionable next steps, ranked by projected uplift and implementation effort
• Optional dashboard (Tableau, Power BI, or similar) illustrating key retention drivers

Acceptance criteria: the recommendations must be tied to measurable KPIs (e.g., churn rate, CLV) and supported by model accuracy metrics.

Please outline your proposed methodology, preferred tools, and a sample timeline so we can move forward quickly.