Details anonymised at client request.
Business Situation
The company’s customer-support operation was handling high volumes of repetitive service requests, including balance inquiries, transaction-status checks, and account-limit requests that required minimal human judgement but consumed significant agent capacity. As routine queries increased, customer-support teams struggled to maintain response quality and resolution speed for more complex cases involving disputes, fraud reviews, and account-related issues.
What the Consultant Did
An independent customer-experience consultant redesigned the support structure using a tiered service architecture that separated routine service interactions from higher-complexity customer cases. The engagement included chatbot implementation, workflow redesign, support-routing optimisation, conversation-history integration, escalation management, and refinement of agent-handling processes across the customer-support function. Particular focus was placed on reducing avoidable agent workload while improving first-contact resolution for cases requiring human intervention.
What Changed
The revised support model reduced routine-contact dependency on human agents and improved handling quality for higher-complexity customer issues. The company also established a more scalable support workflow with clearer separation between automated service interactions and specialised human support.
Evidence, not adjectives.
The measurable changes recorded during or following the engagement.
58% of routine customer contacts resolved through automation by month five
Human-handled support volume reduced by 34%
First-contact resolution improved from 69% to 81%
Tiered customer-support workflow implemented across operations
