Customer feedback, analyzed automatically
Comments, reviews and surveys analyzed continuously: topics, sentiment and alerts before isolated cases become a problem.
Why feedback evaporates unheard
The problem: Your customers have long been telling you what annoys them and what they love: in comments, reviews, surveys and support requests. It is just that nobody reads all of it; there is too much and it is too scattered. So gut feeling decides, the loudest single voice gets the most weight, and a real trend only becomes visible once it has reached the ratings average.
The solution: Our system collects the voices from all channels and analyzes them continuously. An AI clusters the topics, scores the sentiment and counts instead of guessing: which problems are piling up, what customers genuinely praise, how that shifts over time. The result is not a raw-data dump but an analysis that product, marketing and support can work with.
What happens next: Decisions rest on what many are saying, not on what one person says loudly. If a topic starts piling up, you know early and can react before it drags down your ratings. And the same lens works outward too: the identical analysis runs over the competition’s comment sections while you monitor your competitors automatically.
Typical scenarios
From social comments to NPS free text. These are the most common scenarios from our projects.
- Social comments under control: Comments from Instagram, TikTok and YouTube are collected continuously and analyzed by topic and sentiment.
- Review radar: Ratings from Google to Trustpilot in one analysis: what customers praise, what annoys them, where it is heading.
- Surveys without the analysis project: NPS and CSAT answers including free text are summarized automatically and compared with the previous period.
- Early-warning system: If a topic piles up noticeably, an alert goes out before it shows in the ratings average.
