This Langflow flow implements a multi-source Voice of Customer analysis pipeline.

It starts by collecting feedback from YouTube and Reddit based on configured sources (video URLs, channels, subreddits, threads) and filtering rules (keywords, time window, language). Ingestion components normalize the raw text into a consistent dataset and remove noise such as spam, duplicates, or irrelevant content.

A sentiment analysis stage classifies feedback into positive/neutral/negative categories and can optionally score intensity to distinguish mild concerns from high-frustration issues. A theme extraction stage groups feedback into recurring topics using semantic clustering and structured labeling, surfacing the most frequent pain points, feature requests, and moments of delight.

The workflow then extracts actionable insights by identifying root causes, affected personas, and impact signals (e.g., churn intent, blockers, workarounds). It selects representative quotes for each theme and produces a prioritized issue list with recommended next steps for product and support.

Finally, structured output components generate a comprehensive VoC report that is easy to share with leadership: executive summary, sentiment distribution, top themes with quotes, priority actions, and follow-up questions for further validation.

The flow can be extended into a full VoC program. Add scheduled runs to produce recurring reports, integrate with product analytics to correlate themes with usage drops, and connect to ticketing/roadmap tools to open issues automatically for top pain points. Store historical feedback and prior reports in a vector store to track trendlines and detect emerging topics earlier. For governance, add moderation filters, language detection, and a review gate for sensitive outputs before distribution. Advanced setups can support multi-channel ingestion (App Store reviews, X, LinkedIn, support tickets) and unified dashboards for sentiment and theme tracking.