Assessfy Industry Projects Lab Advanced 6 milestones 100 marks

Customer Sentiment Analytics for Indian Telecom Call Centre Optimization

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

Real-world project · AICTE-aligned · AI-graded · Audit-ready certificate

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About this project

Objective: To analyze customer interactions and sentiment from call centre data to identify key drivers of dissatisfaction and improve customer experience in the Indian telecom sector.

Problem Context: The Indian telecom industry faces high customer churn and frequent service complaints, with call centres being a crucial touchpoint. Understanding the root causes of negative experiences and sentiment is essential for reducing churn and improving service quality.

Approach: The team will collect and analyze call centre audio/text logs, applying natural language processing (NLP) and sentiment analysis to classify customer sentiment and identify frequent issues. Comparative analysis will be conducted across regions and customer segments using real or simulated datasets reflecting the Indian context.

Deliverables: The project will deliver a detailed report with sentiment analytics dashboards, issue heatmaps, root-cause analysis, and actionable recommendations. Prototypes may include interactive Power BI/Tableau dashboards and Python-based analysis scripts.

Business Impact: The solution will help telecom management prioritize service improvements, train agents on sensitive issues, and inform data-driven decisions to reduce churn, enhance customer satisfaction, and optimize call centre operations in the Indian market.

Milestones

1. Problem Definition & Business Case

10 marks 18d

Deliverable: 2-page document outlining business problem, Indian telecom context, objectives, and impact. Reviewed for clarity, context, and practical scope.

2. Domain Research & Data Gathering

13 marks 22d

Deliverable: Summary of Indian telecom CX landscape, call centre processes, data source identification, and sample dataset preparation. Reviewed for relevance and data readiness.

3. Solution Design / Methodology

12 marks 21d

Deliverable: Detailed methodology document covering NLP pipeline, sentiment classification approach, analysis plan, and dashboard design. Peer and mentor reviewed for feasibility.

4. Build / Analysis & Implementation

28 marks 35d

Deliverable: Python scripts for data processing and sentiment analysis, initial dashboards, and analytical findings. Reviewed by faculty/industry for correctness and depth.

5. Validation & Results

22 marks 28d

Deliverable: Validated results with accuracy metrics, issue heatmap, and business insights. Reviewed for analytical rigour, actionable findings, and stakeholder utility.

6. Final Report & Presentation

15 marks 21d

Deliverable: Comprehensive report, presentation deck, and live dashboard demonstration. Assessed for business value, communication, and integration of feedback.

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Skills you'll learn

TelecomData AnalyticsTelecom industry operations and customer lifecycle understandingText and speech data preprocessingNatural language processing (NLP) and sentiment analysisData visualization and dashboard creationStatistical and root-cause analysisStakeholder communication and reportingPython programming for analyticsTeam-based problem solving

Tools used

Python (NLTKspaCyTransformers)Jupyter NotebookPower BI or TableauSQL for data extraction/aggregationExcel for initial data explorationSample call centre datasets (e.g.KaggleTelecom datasetssimulated Indian call logs)Speech-to-text APIs (e.g.Google SpeechAzure Cognitive Services)CRMs (simulated or anonymized data)

Prerequisites

Introduction to Data Analytics or Business AnalyticsBasic Python programmingStatistics for Business or EngineeringFamiliarity with Indian telecom sector (preferred)

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