Assessfy Industry Projects Lab Advanced 6 milestones 100 marks

Subscriber Churn Prediction and Retention Offer Engine for Indian Telecom Operators

Industry: Telecom Industry: Telecom Function: Marketing Type: Industry-vertical applied project Team: up to 4 Assessment: 6 milestones (100 marks)

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About this project

Objective: To build a predictive analytics solution that identifies at-risk subscribers and recommends targeted retention offers to reduce churn for Indian telecom companies.

Indian telecom operators face significant challenges with subscriber churn, especially in the highly competitive prepaid and postpaid segments. Retaining customers is crucial for profitability, given high acquisition costs and price-sensitive users.

The project involves collecting and analyzing subscriber data (usage patterns, complaints, recharge history, demographics) from telecom CRM systems and publicly available datasets. The team will use machine learning models to predict churn probability and design a rule-based engine to recommend personalized retention offers (discounts, bonus data, loyalty benefits).

Deliverables include a predictive model, a retention-offer recommendation engine, dashboard visualizations, and an actionable report analyzing churn drivers and offer effectiveness.

Business impact: The solution enables marketing teams to proactively engage likely churners with targeted offers, lowering churn rates and improving customer lifetime value. It informs campaign design and operational decision-making within Indian telecom firms.

Milestones

1. Problem Definition & Business Case

10 marks 21d

Submit a brief outlining the churn problem, its business impact, and a clear project scope tailored to Indian telecom. Reviewed for clarity, relevance, and understanding of industry context.

2. Domain Research & Data Gathering

15 marks 28d

Conduct secondary research on Indian telecom churn, collect CRM/recharge datasets or simulate subscriber data, and document data sources. Evaluated for domain depth, data quality, and completeness.

3. Solution Design / Methodology

15 marks 21d

Develop a detailed plan for churn prediction modeling and retention offer logic, including feature selection and business rules. Assessment based on feasibility, innovation, and alignment with business objectives.

4. Build / Analysis & Implementation

25 marks 35d

Implement machine learning models, offer engine, and dashboards. Deliverable includes annotated code, visualizations, and model documentation. Reviewed for technical accuracy, model performance, and actionable insights.

5. Validation & Results

20 marks 28d

Test model predictions, simulate campaign scenarios, analyze retention offer effectiveness, and quantify churn reduction. Evaluated for robustness, statistical significance, and practical applicability.

6. Final Report & Presentation

15 marks 21d

Prepare a comprehensive report and deliver a presentation to stakeholders summarizing findings, recommendations, and business impact. Assessed on clarity, professionalism, and persuasive communication.

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Skills you'll learn

TelecomMarketingTelecom domain knowledge (Indian subscriber behaviormarket dynamics)Data cleaning and preprocessing (handling CRM and recharge datasets)Machine learning model development (classificationfeature engineering)Statistical analysis and interpretationOffer design and business logic implementationDashboard creation and data visualization (Power BI/Tableau)Effective report writing and presentation skillsStakeholder communication (marketing and analytics teams)

Tools used

Python (Pandasscikit-learnNumPyMatplotlib)SQL (for querying CRM/recharge data)Power BI or Tableau (dashboard visualization)Excel (exploratory analysis)Jio/Airtel publicly available recharge data or simulated datasetsTRAI reports and telecom market datasetsBusiness logic frameworks for offer designCRISP-DM methodology for analytics project lifecycle

Prerequisites

Basic statistics and probabilityIntroductory machine learning (classificationregression)Database management (SQLCRM data)Business analytics or marketing fundamentalsPython programming (data analysis libraries)

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