Closing the knowledge gap and streamlining warranty queries with AI and automation

Learn how an Australian tapware provider used AI to analyse emails, creating 16 support documents that bridged knowledge gaps, improved efficiency, and streamlined warranty queries for scalable customer service.

Client

Australian Wholesaler

Service

AI & Automation

Deliverable

Knowledge Base

Client Overview

The client is an Australian mid-sized provider of premium home and bathroom fixtures, specialising in high-quality tapware and accessories. They serve a diverse customer base, including retail and trade professionals, with a reputation for innovation and customer satisfaction.

The Challenge

The client faced significant inefficiencies in managing warranty and technical support queries, particularly those received through their primary customer service email. The departure of their Warranty Manager created a knowledge gap, leaving only 2-3 senior staff with the expertise to handle these queries. This forced them to divert their attention from high-value work to support the customer service team, who were not fully equipped to address technical and warranty issues.

"We were spending far too much time answering the same questions, which took focus away from our more important priorities," explained a senior team member.

These challenges led to delays, inconsistencies in responses, and if left unchecked there were concerns it would lead to a negative impact on customer satisfaction.

The Solution

To address these challenges, one year’s worth of emails from the former Warranty Manager was meticulously analysed using AI and automation. This provided both the information and the tone required to craft responses that aligned with the client’s communication standards. Given the large volume of data, AI was utilised to synthesise this into 16 comprehensive internal support documents. These documents provided detailed guidance on handling warranty and technical queries, establishing a best-practice knowledge base for the customer service team.

Results (Pending)

This work has been recently completed and is performing well. As time progresses, we anticipate establishing the following outcomes:

  • Response time for common queries significantly reduced, with staff potentially saving an average of 15 minutes per inquiry.
  • Improved customer satisfaction due to faster and more consistent responses.
  • The knowledge base serving as a cornerstone for future AI-driven customer service initiatives, providing a foundation for scalable operations.
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