Automating Customer Insights with AI: What We Learned

We had thousands of customer support conversations, user interviews, and feedback surveys. Extracting insights manually was impossible at scale. Here's how we automated it and what we learned.

Categorization came first. AI classified feedback by theme, sentiment, and urgency. This alone revealed patterns invisible in aggregate—a specific feature generated 5x the negative sentiment of others despite similar mention volume. The tool worked; the insights surprised us.

Trend detection watched for emerging issues before they became crises. When complaints about a specific integration spiked, we knew within hours instead of waiting for support ticket reports. Early warning let us respond proactively.

The synthesis layer produced readable summaries for product meetings. Instead of "52% negative sentiment on feature X," we got "Users struggle with X because of Y, common workarounds include Z, suggested improvements focus on W."