AI Customer Feedback Analytics Dashboard from CSV

Most NPS and CSAT tools give you the score and a list of responses. Neither answers the question that actually drives a product roadmap, which is 'what is the most common reason a detractor is unhappy?'. To answer it, someone has to read every detractor comment, mentally cluster them, and write down the top three themes. That work is real and it is rarely done. Sreniq automates it: free-text responses are clustered into themes (semantically, not by exact word match), each theme gets a short label, and the themes are ranked by frequency so 'Pricing too high' sits above 'Confused by onboarding step 2'.

The second underused cut is the segment-by-score breakdown. Headline NPS is one number; NPS for enterprise customers, NPS by region, NPS by tenure are usually three different stories. Sreniq lets you ask for any of them in chat and re-renders the chart in place — useful when a board update needs the headline number alongside 'and here is the score for our largest customer cohort'.

Closed-loop follow-up is where the dashboard moves from interesting to actionable. Once you have the theme list, the next question is 'show me every detractor who mentioned theme X so I can email them'. That is a one-line ask in chat — Sreniq filters the response table to the matching rows, including any contact column you uploaded, and you have a triage list ready in seconds.

Sample size matters: theme extraction works best with a few hundred responses; with twenty, it just lists the comments back. NPS trend works once you have at least three response months. For monthly programmes the typical cadence is to upload the latest survey export, refresh the dashboard, and use the theme list as input to the next quarter's product planning meeting.