CASE STUDY — AUTOMOTIVE
Social Media Insights Platform
Understand automotive customer sentiment at scale.
Sector:
Automotive
Focus:
Sentiment Analysis & AI
BUSINESS CHALLENGE
Thousands of videos, no clear voice of the customer
User feedback on automotive reviews is scattered across thousands of YouTube videos. The lack of centralized analysis, high volume of comments, and the need for qualitative insights (beyond views or likes) made it difficult to understand real user sentiment, detect recurrent issues, or identify market preferences.
SOLUTION IMPLEMENTED
Turning raw comments into structured, actionable insight
Built a secure, scalable application to analyze YouTube reviews using AI-driven sentiment analysis and topic extraction. The solution automatically retrieves, processes, and categorizes user opinions (e.g., on fuel efficiency, design, comfort), turning raw feedback into actionable insights.
TECHNOLOGY
An AI-driven pipeline built on Azure
DATA PLATFORM
Azure Functions & Azure OpenAI
Used to process comments with NLP for sentiment analysis, topic detection, and opinion mining.
SERVING
Next.js & Azure Container Apps
A web app developed to collect and manage YouTube video lists and associated metadata.
INTEGRATION
Azure Maps API
Queue management with Azure Storage and secure login via Microsoft Entra ID to ensure authenticated usage and scalable workloads.
Results & ROI
What the platform revealed once it went live
Insights
Surfaced patterns in user feedback across thousands of videos, helping manufacturers understand user perception per model or brand.
Decision
Enabled marketing and product teams to base decisions on real customer voice, leading to improved targeting and product positioning.
Scalability
Automated processing pipeline supports any number of new video lists, unlocking reuse across models, languages, and regions.
