Telecom Churn Prediction MLOps

2022-01-09T05:20:35+00:00

Table Of Content</summary> </details>

Technical feasibility</strong> - assesses whether sufficient, high-quality data and appropriate tools are available or what tools needs to be useful.</li>
Economic feasibility</strong> determines whether the expected business benefits, such as reduced customer loss and increased retention.</li>
Operational feasibility</strong> evaluates whether the organization can effectively use </li>
Auditing & Governance feasibility</strong> focuses on establishing clear policies for data ownership, data sources, fairness and compliance thoughout it’s lifecycle</li> </ul>
Use Cases of Churn Prediction</p>

Stop-loss Intervention and Win-back Forensics

Identify customers who are likely to leave and take actionable items such as sending personalized emails, offering discounts, rewards just to encourage them to stay</li>
Analyze who already done it and understand why they left how to get them back.</li> </ul> </li>
Understanding the Drivers of Churn

Help the business make improvements based on data rather than assumptions</li> </ul> </li> </ol>
</span></a> Business Phase</h3>
Know your KPI, Know your Data</p>
KPI</strong></th> What it Measures</strong></th> Why it Matters</strong></th></tr></thead>

Gross Customer Churn Rate</strong></td> Percentage of customers who leave during a given period.</td> Measures overall customer loss and retention performance.</td></tr>
Net Customer Churn Rate</strong></td> Difference between new customer acquisitions and customer cancellations.</td> Indicates whether the customer base is growing or shrinking.</td></tr>
Daily Active Users (DAU)</strong></td> Number of customers actively using the product each day.</td> Declining DAU can signal poor customer experience or potential churn.</td></tr>
Weekly/Monthly Active Users (WAU/MAU)</strong></td> Number of customers active each week or month.</td> Measures long-term engagement and product adoption.</td></tr> </tbody></table>

Business Domain and Requirements Discovery</em> – Understanding the business problem, objectives, stakeholders, and success criteria.</li>
ETL Project</em> – Collecting, cleaning, integrating, and preparing data from multiple sources or formulating implementation of the new workflow.(step 6)</li>
Data Engineering and Data Wrangling Projec</em>t – Building reliable data pipelines, transforming raw data, and ensuring data quality. In many organizations, this effort takes significantly more time than developing the machine learning model itself.</li>
Feature Engineering Project</em> – Creating meaningful features that capture customer behavior and improve model performance.</li>
Machine Learning Project</em> – Selecting algorithms, training models, evaluating performance, and optimizing predictions.</li>
Business Implementation Project</em> – Deploying the model into production and integrating predictions into business workflows, such as CRM systems or marketing campaigns.</li>
Results Assessment Project</em> – Monitoring model performance, measuring business impact, validating assumptions, and continuously improving the solution.</li> </ol>
Build a standardized Advanced Analytics Data Model that is tailored to your business.</p> </blockquote>
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Measure what matters.</strong> Focus on a small set of meaningful metrics that drive decisions rather than tracking everything.</li>
Connect metrics to people.</strong> Metrics should be traceable to individual customers so that quantitative insights can be validated through real customer feedback.</li>
Measure business outcomes.</strong> Prioritize metrics that reflect business success, such as revenue, retention, or customer satisfaction, instead of intermediate metrics like clicks or page views.</li> </ul>
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</span></a> Evalution of Model</h3>
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principle</th> Strategy</th> Description</th> Example</th></tr></thead>

Listen continuously</td> Talk to Your Customers</td> Collect regular feedback to understand customer needs and pain points before they leave.</td> Send customer satisfaction surveys, provide in-app feedback forms, or use live chat to gather suggestions.</td></tr>
Fix root causes</td> Know Your Weaknesses</td> Identify product or service shortcomings and continuously improve them.</td> A SaaS company discovers users struggle with onboarding and redesigns the onboarding experience.</td></tr>
Position yourself</td> Focus on Your Competitive Advantage</td> Reinforce the unique value your product offers compared to competitors.</td> An online storage service reminds customers about its secure backup and cross-device synchronization features.</td></tr>
Learn from cancellations</td> Understand Why Customers Cancel</td> Capture cancellation reasons and analyze common patterns to reduce future churn.</td> Add an exit survey asking, “Why are you leaving?” with options like “Too expensive” or “Missing features.”</td></tr>
Educate customers</td> Improve Customer Education</td> Help customers realize the full value of your product through proactive guidance.</td> Send tutorial emails, onboarding videos, or feature walkthroughs after signup.</td></tr>
Reinforce value</td> Reassure Customers of Your Product’s Value</td> Regularly remind customers about new features and benefits so they don’t overlook your product’s value.</td> Include new feature announcements and success stories in newsletters or support responses.</td></tr> </tbody></table>
</span></a> MLOps Life Cyle</h1>
MLOps supports every stage of the ML lifecycle—from data ingestion, feature engineering, model training, deployment, and inferencing to monitoring.</p>
This project builds an end-to-end Telecom Churn Prediction pipeline using DVC, Hydra, Optuna, MLflow, Docker, PostgreSQL, Prometheus, and Grafana for reproducibility, experiment tracking, deployment, and observability.</p>
The primary objective is to show how modern MLOps tools work together to create a reproducible, scalable, and maintainable machine learning pipeline on local setup using Docker compose.</p>

Muthukamalan - Churn Prediction

Telecom Churn Prediction MLOps