Churn-Analysis-Project

An end-to-end Python project analyzing customer churn for an OTT platform. Performed data cleaning, feature engineering, EDA, churn segmentation, revenue and CLTV analysis, and visualized key trends to identify high-risk customer segments and revenue-at-risk areas, providing actionable insights for customer retention strategies.

Business Problem:

Customer churn can reduce recurring revenue and customer lifetime value. The objective was to identify which customer segments have higher churn risk and quantify the potential revenue impact.

Analysis:

Analyzed customer demographics, subscription plans, contract types, tenure, complaints, escalations, churn scores and CLTV (Customer Lifetime Value).

Business Outcome:

Identified high-risk customer segments and potential revenue-at-risk areas that can be targeted through retention initiatives such as personalized offers, plan upgrades and loyalty benefits.

Key insights to present on GitHub

1. Basic plan customers show the highest churn rate, indicating a potential retention issue within the entry-level customer segment.
2. Monthly-contract customers are a higher-risk segment, while annual-contract customers demonstrate stronger retention.
3. The 31–35 age group appears to be the highest-risk customer segment, with approximately 50% churn in your analysis.
4. Referral-based subscription customers show the highest churn rate, while paid subscriptions generate the highest total revenue.
5. The analysis identifies revenue at risk from churned customers, helping quantify the potential financial impact of customer attrition.
6. Customer churn score and churn-risk categories were used to segment customers into low-, medium-, and high-risk groups.
7. The CLTV vs. churn score analysis helps identify customers who may have high lifetime value but also carry elevated churn risk—an important segment for targeted retention efforts.
8. Monthly churn trends were analyzed to identify periods with higher customer cancellations.

personalized retention offers could include:

Discount: 20–30% discount on the next 2–3 months for high-risk customers. Plan upgrade: Offer a temporary upgrade to Premium at the current Basic-plan price. Loyalty rewards: Give long-term customers reward points, free months, or exclusive content. Personalized recommendations: Recommend content based on their viewing behavior to increase engagement. Contract incentive: Offer a discount for switching from a monthly plan to an annual plan.