Case Study
AI-Powered Web Data Mining and Sentiment Analytics Tool for Automotive OEMs
Reducing costly test cycles and enabling faster root cause analysis through issues-to-system relationships
50%
Improvement in tracking and resolving vehicle part issues
80%
Increase in dynamic data collection and coverage
90%
Accuracy in sentiment and trend detection
Background
A leading South Korean automotive OEM partnered with Tata Elxsi to implement an AI-powered web data mining and sentiment analytics tool. The solution was designed to address the challenges of scattered customer feedback, evolving site structures, and delayed decision-making due to unstructured insights.
The initiative aimed to:
- Aggregate customer reviews, forum discussions, and social media feedback using GenAI-powered crawlers.
- Apply Natural Language Processing (NLP) and LLMs for real-time sentiment analysis and trend detection.
- Deliver dynamic dashboards and export-ready reports that provide structured, actionable insights for engineering, product, and marketing teams.
- Enable data-driven decisions across vehicle design, customer engagement, and market positioning.
The transformation focused on:
- Sentiment & Trend Analysis to detect early warning signals and identify customer pain points.
- Automated Feedback Structuring from 42+ online platforms into usable intelligence.
- Real-Time Issue Tracking to prioritize and resolve part-level and system-level problems faster.
- Exportable Dashboards for management-friendly summaries, ensuring insights are directly usable by decision-makers.
Challenge
The automotive industry requires faster customer sentiment detection, structured insights, and accurate trend analysis to improve product quality and responsiveness. With feedback dispersed across multiple online platforms, OEMs struggled with scattered data and slow manual tracking. Enterprises faced key issues:
- Scattered Feedback – Insights spread across forums, blogs, and social media hindered timely visibility.
- Manual Tracking Delays – Changing site structures and anti-scraping barriers slowed monitoring.
- Unstructured Data – Reviews and comments lacked consistent formats, delaying insight generation.
- Missed Trends – Limited real-time analysis prevented early detection of emerging issues.
- Decision-Making Gaps – Lack of structured insights hampered product and marketing strategies.
This initiative introduced an AI-powered solution with GenAI crawlers and LLMs, enabling faster trend detection, structured insights, and data-driven decisions.
Solution
Tata Elxsi partnered with a South Korean automotive OEM to design and deploy an AI-powered web data mining and sentiment analytics tool, built on its TEDAx advanced data analytics platform. The approach integrated GenAI crawlers, NLP-driven sentiment analysis, and LLM-based trend detection to automate the collection of scattered feedback, transform unstructured data into insights, and deliver real-time dashboards. Powered by TEDAx, the solution enabled faster issue resolution, improved product decisions, and data-backed marketing strategies.
Key Solutions:
- GenAI Crawlers – Adaptive crawlers to extract structured insights while overcoming anti-scraping barriers.
- NLP & LLM Models – Automated sentiment and trend analysis across 42+ online platforms.
- Dynamic Dashboards on TEDAx – Real-time visualizations with export-ready reports for engineering, product, and marketing teams.
- Data-Driven Decisions – Actionable insights for proactive issue detection, part improvement, and customer engagement.
Impact
Tata Elxsi’s AI-powered web data mining and sentiment analytics tool significantly enhanced the OEM’s ability to capture customer feedback, detect trends in real time, and resolve product issues faster. By automating data extraction across 42+ platforms, applying NLP and LLM-driven analysis, and delivering export-ready dashboards, the solution improved responsiveness, customer satisfaction, and marketing ROI through actionable insights.
Key Achievements:
- 50% Improvement in Issue Tracking – Better identification and resolution of part-level problems.
- 40% Reduction in Resolution Time – Accelerated detection and fixing of issues.
- 90% Accuracy in Sentiment Detection – Reliable insights across social media, forums, and reviews.
- 20% Higher Customer Satisfaction – Improved trust through proactive engagement.
- 25% Greater Efficiency in Part Improvements – Streamlined engineering cycles with feedback-driven updates.
Services Rendered
- AI-Powered Web Crawling
- Sentiment & Trend Analysis
- Automated Feedback Structuring
- Dynamic Dashboards & Reporting
- Real-Time Issue Tracking
