Calibo Use Case Enrichment Template

Use Case Title	Use Case ID	Enriched By	Date	Business Function / Domain	Business Problem	Objectives & Value Proposition	Target Outcomes	Key KPIs / Metrics	Strategic Alignment	Current Gap	Competitive Benchmark	User Personas and Interactions	Functional Requirements	Non-Functional Requirements	Process Flow & System Architecture	Technical Dependencies	Risk & Mitigation Plan
Sentiment Analysis of Customer Product Reviews	UC-001	Priya Sharma	13-May-25	Customer Experience / Product Management	Inability to analyze large volumes of customer reviews leads to delayed product improvements and missed insights.	Automate sentiment analysis to provide real-time insights for product teams, improving customer satisfaction and responsiveness.	Faster feedback cycles, higher CSAT scores, improved product-market fit.	• Sentiment accuracy ≥ 90% • CSAT uplift ≥ 15% • Manual analysis reduction ≥ 70%	Aligns with customer-centricity and real-time feedback strategy.	Manual processing is slow and inconsistent.	Leading fintechs use ML-based sentiment dashboards.	Product Manager- consumes sentiment reports Data Engineer - maintains pipeline CX Analyst- interprets trends.	Automated review scraper, NLP engine, dashboard visualizer.	Accuracy ≥ 90%, latency < 5 sec, scalable to 1M+ reviews/month.	Ingestion ? NLP Sentiment Engine ? Visualization Dashboard	Python, NLP Libraries (spaCy, NLTK), React.js dashboard, SQL DB	Risk: Biased models – Mitigate by continuous model tuning using diverse datasets.