A supervisor verifies the resolution before any complaint closes. Then feedback is scheduled, ratings are captured, and every response rolls into a Customer Satisfaction Index — so "we resolved it" becomes measurable, customer by customer, and Dhruv AI reads the remarks you'd never have time to.
Resolution is the middle of complaint handling, not the end. The last mile — verification and feedback — is what tells you whether the fix actually worked and the customer would buy from you again.
When the person who fixed the problem also decides it's fixed, quality systems drift. The Release Complaint step separates the two: the handler records the action and result, and a supervisor reviews and releases the complaint before it can close. It's a small gate with a large effect — closure means verified, every time, and your ISO 9001 record shows it.
Teams that "ask for feedback when they remember" collect feedback from nobody but the furious. Here feedback capture is a scheduled step after resolution: a feedback ticket is planned, the response and rating are recorded against the original ticket, and feedback-due alerts chase whatever hasn't been captured yet — on email, SMS and WhatsApp.
The Feedback MIS rolls every captured rating into the Customer Satisfaction Index — and breaks it open again as customer-wise detail feedback. Management review gets a trend; the account manager gets the specific customers sliding toward the exit; and every poor rating traces back to the ticket, category and root cause behind it.
Ratings tell you how much; remarks tell you why — but nobody has time to read a year of free text. Dhruv AI, the Fast Suite's own AI and BI layer, clusters complaint and feedback remarks into recurring themes with AI-generated labels, shows complaint views enriched with 8D CAPA data — root cause, corrective actions, lessons learned — and answers plain-English questions through a read-only, security-sandboxed query engine.
A supervisor verifies resolution and effectiveness before closure — the controlled gate that keeps "closed" honest.
Customer feedback recorded as its own tracked object, linked to the resolved complaint or service ticket it follows.
Capture planned after resolution with a due date — feedback becomes a step in the workflow, not an afterthought.
Email, SMS and WhatsApp alerts chase due and overdue feedback captures, so response rates don't depend on memory.
Satisfaction ratings recorded per ticket on the complaint dashboard — the raw material of the CSI.
The Customer Satisfaction Index plus customer-wise detail feedback — satisfaction measured, trended and traceable.
Resolution without verification and feedback is a guess. For the fundamentals of the full loop, read what is complaint management?
A Customer Satisfaction Index is a rolled-up measure of satisfaction built from the feedback and ratings customers give after their complaints and service requests are resolved. Ratings captured on tickets feed the Feedback MIS, which reports the CSI overall and customer-wise detail feedback — so satisfaction becomes a tracked number, not an impression.
Self-closed complaints hide unresolved problems. The Release Complaint step is a controlled verification: a supervisor reviews the resolution and its effectiveness before the ticket can close. That separation of doer and verifier is what makes the record defensible — including for ISO 9001 audits.
Feedback is scheduled after resolution rather than left to chance: a feedback ticket is planned, the customer's response and rating are recorded, and feedback-due alerts on email and SMS and WhatsApp chase the captures that haven't happened yet. Everything rolls up into the Feedback MIS.
The Feedback MIS provides the Customer Satisfaction Index and customer-wise detail feedback — satisfaction overall, per customer, and per feedback category, alongside the complaint dashboards' ratings view. Because feedback links to tickets, you can trace a poor rating straight back to the complaint behind it.
Dhruv AI, the Fast Suite's own AI and BI layer, clusters complaint and feedback remarks into recurring themes with AI-generated labels, shows complaint views enriched with 8D CAPA data (root cause, corrective actions, lessons learned), and answers plain-English questions through a read-only, security-sandboxed query engine — turning thousands of free-text remarks into a short list of things to fix.
Live demo of verified closure, scheduled feedback and the CSI — on your own complaint workflow. No generic slideshow.
