Design a System to Handle Customer Feedback at Scale

To handle feedback at scale, I would first unify ingestion across all channels—App Store reviews, support tickets, and NPS surveys—into a centralized Azure Data Lake. The core challenge is noise; therefore, the next step involves an NLP pipeline using Azure Cognitive Services to perform real-time sentiment analysis and entity extraction. We would cluster topics automatically, identifying recurring themes like 'login latency' or 'UI confusion.' Once processed, we'd feed these insights into a prioritization matrix that weighs frequency against business impact. For example, a spike in negative sentiment regarding a specific feature would trigger an automated alert to the relevant Product Manager and Engineering Lead. Crucially, we must close the loop: when a fix is deployed, the system should re-scan feedback to validate resolution. This approach ensures we don't just collect data but actively drive product decisions, aligning with Microsoft's focus on empowering every person and organization on the planet to achieve more through data-driven innovation.