This title slide introduces the "Reducing Customer Churn: ML Canvas Framework," focusing on a Machine Learning approach to tackle customer retention. It is a presentation prepared for the Telecom Management Board, emphasizing strategic solution design.

Reducing Customer Churn

Machine Learning Canvas Framework

Presentation for Telecom Management Board Strategic Solution Design

Source: Telecom Management Board | Strategic Solution Design | [Your Name/Company]

High annual churn rates of 20-30% erode recurring revenue and result in an average lifetime value loss of €500 per churned customer. This creates a competitive disadvantage in the public mobile market and wastes millions on customer acquisition expenses.

Problem Overview: Customer Churn Impact

• High annual churn rates (20-30%) erode recurring revenue
• Lost lifetime value averages €500 per churned customer
• Creates competitive disadvantage in public mobile market
• Millions wasted on customer acquisition expenses

Source: High churn erodes revenue, lifetime value, competitiveness, and acquisition costs.

This slide introduces the "Machine Learning Canvas" as Section 03. It presents a structured framework that simplifies designing ML solutions for business problems.

Introduction to Machine Learning Canvas

03

Machine Learning Canvas Introduction

Structured framework simplifying ML solution design for business problems

Source: ML Canvas Presentation for Telecom Churn Reduction

The slide defines the churn prediction problem as forecasting which mobile contract customers will cancel service within 30-90 days, enabling proactive retention to prevent revenue loss from high-value cancellations. It outlines key business objectives: achieving 15% churn reduction, 3x ROI on retention initiatives, and actionable insights for targeted campaigns to boost customer loyalty and profitability.

Problem Definition & Business Objectives

Source: Telecom Churn Reduction Presentation - Slide 4

The slide provides an overview of data sources including usage, billing, and support tickets, along with key features such as contract length, payments, and usage patterns. It describes an XGBoost ensemble ML model achieving 85% accuracy, following a canvas flow from inputs through processing to outputs.

Data & Model Design Overview

• Data sources: Usage, billing, support tickets
• Features: Contract length, payments, usage patterns
• Model: XGBoost ensemble ML, 85% accuracy
• Canvas flow: Inputs → Processing → Outputs

The slide highlights key churn drivers, with late payments at 35% as the top factor, followed by low usage at 25% and poor support at 20%. It also notes a 60-day early warning window for pre-churn detection.

Key Churn Drivers & Insights

• 35%: Late Payments

Top churn driver

• 25%: Low Usage

Second major factor

• 20%: Poor Support

Key service issue

• 60 days: Early Warning

Pre-churn detection window

This workflow slide outlines a four-phase retention strategy: predicting churn risk with ML scoring by the Data Science Team, segmenting high-risk customers by the Analytics Team, deploying personalized interventions by Retention & Marketing, and monitoring impact by the Operations Team. Each phase integrates with the CRM through automated pipelines, segment syncing, campaign triggers, and real-time dashboards for seamless operational execution.

Retention Strategies & Operational Integration

Source: Workflow: Predict → Segment → Intervene (discounts, upgrades) → Monitor. Integrate with CRM for auto-alerts and campaigns.

ML Canvas delivers churn reduction ROI. Next steps include a Q1 pilot, Q3 full rollout, with a call to approve the pilot now to secure the future and unlock ROI.

Conclusion & Next Steps

**ML Canvas Delivers Churn Reduction ROI

Next Steps: Pilot Q1 | Full Rollout Q3

Questions?

Closing: Secure future, act now. Call-to-Action: Approve pilot to unlock ROI.**