Sample case studyMeridian Finserv is an illustrative composite, not a Geckolyst customer. Figures show the shape of a deployment and the metrics reported, not a result achieved for a named client.
Banking & NBFC · 412 branches

A grievance desk that closed 96% of cases inside the regulatory clock.

Meridian ran feedback in one tool, complaints in a spreadsheet and collections in a dialler that had no idea a customer was already disputing a charge.

96.4%Grievances closed within SLA
-38%Repeat complaints
+21%Retention on at-risk accounts
100%Collections calls scored

Illustrative composite. Ninety days, pre- versus post-deployment, same-cohort like-for-like.

The problem

Three systems, three versions of the same angry customer.

A customer disputing a charge would raise it on WhatsApp, get no acknowledgement, call the branch, be told to email, and then receive a pre-approved loan offer the same week. The grievance register was a shared spreadsheet with no clock on it. Nobody could say how many complaints were open, let alone how old the oldest one was.

The collections dialler was the sharpest edge. It called customers who were in active dispute, because it had no way of knowing.

What changed

One identity, then a clock on everything

Every channel was resolved onto one customer record first. Complaints stopped being a spreadsheet row and became a ticket with an owner and a regulatory SLA. Sentiment scoring was switched on across calls and chat, and the campaign engine was gated on it, so nothing promotional could reach an account with an open case.

The last step was the one that changed behaviour: every closed ticket triggered a re-poll to the customer. A case was not closed because an agent said so.

Where it landed

The regulator’s question became answerable

Ninety days in, the grievance desk could produce an audit trail per case on demand. Repeat complaints fell because root causes were named and assigned rather than counted. The collections team stopped calling disputing customers, and the recovery rate went up rather than down.

See it on your own data.

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