Reducing Telecom Churn Through Predictive Analytics and Power BI | by Delphin Kaduli | Jun, 2025

Buyer churn is likely one of the largest revenue leaks within the telecom trade. When subscribers depart for rivals, you not solely lose their month-to-month income, however you additionally incur acquisition prices to interchange them.

Utilizing a mixture of machine studying and interactive visualization, I constructed a churn-prediction mannequin and a Energy BI dashboard to uncover the drivers of churn and suggest focused actions to maintain prospects engaged.

1. Information Assortment & Cleansing

• Supply: Telecom buyer information (5,000+ subscribers)
• Key fields: demographics, service utilization, billing, contract sort, add-on options
• Cleansing steps: imputed lacking prices, standardized classes, created calendar dimension

2. Churn Prediction Mannequin

• Finest algorithm: Random Forest classifier (tuned by way of grid search CV)
• Efficiency: ~82% accuracy, 0.78 ROC-AUC
• Key predictors: tenure, cost technique, web service sort, contract size

3. Energy BI Dashboard

• KPIs: Total churn (25.6%), whole churned income ($11.6 M), common tenure of churners (18 months)
• Pages:

1. Overview: Excessive-level metrics and development strains
2. Demographics: Churn by age, gender, companion/dependent standing
3. Service and subscription: Web sort, multi-line, streaming providers, Tech Help, and On-line safety
4. Monetary conduct: Month-to-month prices, Fee Strategies, Paper billing, and Whole prices.
5. Contract and tenure: Tenure and contract sorts.
6. Perception and advice: Key takeaways paired with strategic suggestions

All code, data-prep notebooks, and deployment scripts can be found on GitHub

1. Total Well being
   • 5,000+ prospects, 26.5% churn
   • $11.6 M income in danger
   • Avg tenure: 33 mo vs 18 mo
2. Early Tenure Threat: Clients of their first 6 months churn at 52.3%.
3. Fee Methodology Impression: Digital-check payers churn at 44.6% vs ~16% for auto-pay.
4. Service Vulnerability: Fiber-optic subscribers churn at 42.3% vs DSL 18.0%.
5. Senior Citizen Churn: Senior residents churn at 63.8%, almost double non-seniors.
6. Contract Sort Impact: Month-to-month plans account for the best churn quantity (2,744 prospects).

1. Speed up Onboarding (Months 1–3): Automated welcome collection: utilization ideas, tutorials, limited-time add-on reductions.
2. Senior-Targeted Bundles: “Senior Join” plan: lower cost tiers, simplified billing, devoted hotline.
3. Enhance Auto-Pay Enrollment: $5/month low cost or loyalty factors for credit-card/bank-transfer sign-ups.
4. Improve Fiber-Optic Expertise: Actual-time community monitoring + rapid-restore promise for outages.
5. Promote Longer Contracts: Spotlight 12- and 24-month financial savings vs month-to-month and supply loyalty bonuses.

Pairing predictive modeling with an interactive Energy BI dashboard and aligning every key perception with a concrete advice can’t solely forecast churn but in addition take the exact actions wanted to cut back it. Dive into the small print:

Github: Customer churn repo

Energy BI: Live Dashboard

Drive churn down, one information level at a time.

Delphin K.