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AI-enhanced Customer Service

  • Writer: Pius Schmid
    Pius Schmid
  • Sep 22
  • 3 min read

A fast growing insurance business needed to optimize its customer support processes to handle an increasing number of inquiries. With well over 20 internal and external support agents, the company faced a surge in tickets, especially after launching new products and onboarding new clients. This led to a backlog of up to 8.000 tickets, making it difficult to provide timely support and resulting in frustrated customers.


Challenge

The company’s primary challenge was managing the high volume of support inquiries, which peaked due to issues like payment discrepancies, fines despite prior payments, refund requests, and account activation queries. The ticketing system, managed through Freshdesk, was overwhelmed, and the existing automations lacked structure and clarity, making the processes inefficient and chaotic.

With over 100 poorly structured native automations in Freshdesk, the support team struggled to maintain transparency and efficiency. These automations were resource-intensive to manage, failed to meaningfully reduce the workload, and only added to the overall confusion. As a result, the company needed a systematic approach to streamline ticket handling, reduce the backlog, and improve overall response times.


Solution

The solution focused on optimizing and restructuring the existing support processes through automation.


Automation Reduction and Streamlining

The number of automations in Freshdesk was reduced from 100 to just 7 effective workflows, eliminating redundancies and providing much-needed clarity on process flows.

MAKE.com Integration for Advanced Routing

  • Automated assignment of tickets based on keywords and email recipients, ensuring accurate distribution to the responsible support groups.

  • AI-based data extraction for key information such as case numbers and customer data, along with automated checks for existing conversations to prevent information loss.

  • Automated prioritization of cases from VIP requesters like lawyers or business clients to ensure quick responses

AI-driven Customer Communication

  • Integrated OpenAI’s ChatGPT to handle less complex inquiries. Responses were generated based on predefined templates, reducing the workload on agents.

  • Multiple AI checks ensured higher accuracy and less processing load, making it an ideal solution for handling FAQs and repetitive queries.

Process Optimization

  • AI-powered data extraction and automated input into the ticketing system significantly reduces manual efforts and minimizes errors.

  • Further enrichment of ticket information through automated data retrieval from sources like the data-backend or the CRM (Hubspot).

  • Automated privacy checks and keyword scanning to ensure compliance and quick identification of critical cases.


Business Results

The automation overhaul delivered impressive business results:

Backlog Reduction

Open tickets dropped from 8.000 to 4.500, allowing the team to focus on new inquiries in a more timely manner.

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Faster Resolution Times

The average resolution time decreased from 9 days to 3.5 days, greatly enhancing customer satisfaction.

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Improved Automation Efficiency

Around 40% of all incoming tickets are now categorised and resolved automatically, including follow-up actions such as updating information. 70% of all tickets are processed and categorised automatically.

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Error minimization and enhanced efficiency

Automated data processing reduced common mistakes related to case numbers and customer information. Structured processes, clear automation rules and a significant reduction in repetitive work helped agents handle tickets more effectively, improving their efficiency and motivation.


UPDATE AFTER 6 MONTHS: AUTOMATION LEADS IN ALL PERFORMANCE CATEGORIES

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All automations of the insurance company run through a dedicated “Make Automation” account. This account consistently earns the highest total points across all ticket-handling activities, leading in both fast resolution and first call resolution points.



Project Financials

Also from a financial perspective, the decision to collaborate with NOA proved highly successful for the mobility company. Below is a detailed analysis of the costs, savings, and ROI:

Project Overview

  • Total Project Costs: 17,500 €

  • Project Duration: 8 weeks

Running Costs of the Automated Workflow

  • Make (Enterprise License): 1,500 € per month (shared across multiple processes)

  • OpenAI-API: 165 € per month (at the time of project conclusion - with roughly 6,500 tickets processed monthly)


Total Running Costs Per Month: 1,665 €

Savings

  • Salary of One FTE (Customer Care Agent): 4,000 € per month (before taxes)

  • Number of FTEs Saved: 2 FTEs (at the time of project conclusion - according to the Head of CRM of the company)


Total Savings Per Month: 8,000 €


BOTTOM LINE

By using low code to enhance their customer success processes, the company secured monthly net savings of over 6,335 € after accounting for operating costs. With a project timeline of only 8 weeks and an initial investment of 17,500 €, the break-even point was reached in under 3 months. In the first year alone, this led to total net savings of 58,520 €, achieving an exceptional ROI of 334%.

Beyond the financial impact, the company successfully transformed its customer support operations through strategic automation refinement and AI integration. The optimized workflows and AI-driven responses enabled the team to manage higher ticket volumes, reduce backlogs, and provide a superior customer experience.


Overall with NOA's support, the company set a solid foundation for future growth.


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