Business Case: How to Increase First Contact Resolution in Customer Service

Welcome to this month’s edition of Business Case. Every month we explore a common challenge in business process management and frame it through a realistic scenario. Our goal is to help managers and decision-makers see both the cost of inaction and the opportunities for improvement.

This month, we turn our attention to Customer Service—specifically, how to increase First Contact Resolution (FCR). Balancing efficiency, cost savings, and customer satisfaction is never easy. But as support volumes grow and customer expectations rise, FCR is becoming a critical performance metric.

The Business Case: A SaaS Company’s Customer Service Challenge

Imagine a SaaS company with 150 customer support agents handling about 120,000 tickets per year.

At first glance, the operation looks solid. Yet, digging deeper reveals a costly inefficiency: around 40% of tickets require multiple touches before resolution. Some tickets are unnecessarily escalated, others bounce back and forth between teams, and many require repeated clarifications.

Let’s anchor this in numbers:

• Tickets per year: 120,000
• Average handling cost per ticket: €8
• Share of tickets with multiple touches: 40% (48,000 tickets)

The cost formula is straightforward:

Savings = (Reduction in multiple handling) × (Cost per ticket) × (Number of tickets)

If the company could bring the rate of multiple touches down from 40% to 25%, it would eliminate 18,000 unnecessary re-handlings. At €8 per ticket, that translates into an annual savings of €144,000.

Why This Matters: The Problems Behind Low FCR

Numbers tell only part of the story. Let’s look at the broader impacts of low FCR:

• Financial impact: Extra handling means extra cost. What looks like a small inefficiency per ticket scales into six-figure annual losses.
• Operational impact: Support agents spend more time reworking cases instead of solving new ones. Productivity drops, and burnout risk rises.
• Customer impact: Customers have to explain their issue multiple times, wait longer for resolution, and lose confidence in the service.
• Strategic risk: Over time, these inefficiencies erode trust. In competitive industries, dissatisfied customers have low switching costs and may churn.

For a SaaS provider—or any service organization—the combination of cost pressure and customer dissatisfaction makes FCR more than just a metric. It’s a lever of both efficiency and loyalty.

What Can Be Done: From Process Improvements to GenAI

Traditionally, companies have approached FCR improvement through process mining and operational fixes:

• Better training: Equipping agents to handle a wider range of issues at first contact.
• Knowledge base updates: Ensuring that both agents and customers have access to accurate, up-to-date information.
• Process audits: Identifying bottlenecks and unnecessary handoffs that drive rework.

These remain essential. But increasingly, technology—especially Generative AI (GenAI)—is becoming a complement to traditional approaches.

Here are three ways GenAI can strengthen FCR:

1. Root cause analysis at scale
   By analyzing ticket histories, AI can identify recurring themes: which types of requests are most frequently escalated, which customer segments encounter the most friction, and where gaps in the knowledge base exist.
2. Real-time response suggestions
   Agents can receive automated, context-aware suggestions that reflect best practices from past resolutions. This reduces resolution time and raises the likelihood of solving the issue on the first try.
3. Self-learning knowledge base
   AI can detect patterns in incoming queries and proactively update knowledge articles or FAQs. Customers benefit from improved self-service, reducing the need for tickets in the first place.

Together, these measures can transform FCR from a lagging indicator into a driver of operational efficiency and customer satisfaction.

Food for Thought

• How much hidden cost in your organization stems from rework in Customer Service?
• What is the opportunity cost of not resolving tickets at first contact?
• If GenAI can reduce escalations by even 10–15%, what would that mean for your budget and for your customers’ loyalty?
• Could AI evolve from a back-office support tool into a customer experience differentiator?

Conclusion

Inefficiencies in Customer Service are rarely dramatic in isolation, but they add up quickly across thousands of transactions. In our SaaS example, the price tag of inaction is over €140,000 per year in unnecessary support costs. More importantly, it places customer trust at risk.

The solution is not one-dimensional. It requires process mining to uncover root causes, training and operational improvements to fix them, and AI-driven tools to support agents and empower customers.

Improving First Contact Resolution is one of those rare initiatives where efficiency and customer satisfaction align. The savings are measurable, but the long-term benefits—higher retention, stronger trust, and more engaged employees—are even more valuable.

As you reflect on your own organization’s processes, ask: where are we paying the price for inefficiencies, and how could new technologies help us bridge the gap?

FAQ

What is First Contact Resolution (FCR) in Customer Service?

First Contact Resolution measures the percentage of customer issues that are resolved during the first interaction with support, without requiring follow-ups, escalations, or rework. It is a key indicator of both efficiency and customer satisfaction.

Why is FCR important for organizations?

High FCR rates reduce operational costs, improve agent productivity, and enhance customer satisfaction. Low FCR, on the other hand, drives up costs and risks damaging customer trust.

How can Process Mining support FCR improvement?

Process Mining helps identify where tickets are repeatedly escalated, delayed, or reworked. By mapping out actual workflows, it uncovers inefficiencies and points to specific areas for improvement.

What role can Generative AI play in improving FCR?

GenAI can assist agents with real-time response suggestions, automate root cause analysis of escalations, and build self-learning knowledge bases. These capabilities help agents solve more tickets on first contact and reduce the need for rework.

What are the hidden costs of low FCR?

Beyond direct support costs, low FCR leads to longer handling times, customer frustration, and potential churn. These indirect costs can be far greater than the direct operational expenses.

Is improving FCR only relevant for large organizations?

No. While the financial impact is more visible at scale, small and medium-sized organizations also benefit. Even modest improvements in FCR can free up resources and improve customer loyalty.

How should companies start improving their FCR rates?

Organizations should begin by measuring current FCR levels, using process mining to diagnose problem areas, and combining process improvements with tools like AI-assisted responses. Pilot programs with clear KPIs are often the most effective way to start.

Can customers themselves play a role in improving FCR?

Yes. By providing clear problem descriptions and using self-service options like FAQs or chatbots, customers can often resolve issues faster. A well-designed knowledge base, powered by AI, can guide customers toward solutions without requiring escalation.