5 metrics to track the impact of AI on your customer service

As a dedicated customer service manager, you’ve convinced the higher-ups that AI is the coolest and fastest sidekick your team could ever have. Now, it’s time to throw on your CSI hat (Customer Service Investigator, of course) and uncover the mysteries of AI’s influence, and measure its outcome and ROI.

Why you should measure the impact of AI on your customer service performance

Tracking the impact of AI on your customer service isn’t just about numbers; it’s a strategic move in a landscape where customer expectations are always evolving. By monitoring key metrics, you get insights into how well AI processes are working. This helps you spot areas for improvement, streamline workflows, and, most importantly, boost the overall customer experience. This proactive approach lets you make smart decisions, use resources wisely, and keep your customer service strategy in sync with what your clients need. In a world where customer satisfaction is king, knowing how AI influences your operations is the key to staying ahead and providing top-notch support.

Also, don’t forget to flex those ROI muscles in front of your boss. It’s not just about impressing them with numbers; it’s about earning your credibility stripes. Demonstrating the return on investment isn’t just a pat on your own back – it’s your golden ticket to showcase that every dollar invested in customer service tools is a dollar invested in elevating team efficiency. So, when you’re on the budget battleground, armed with data-backed results, you’re not just asking for funds; you’re asking for the ammunition to increase your team’s productivity and boost your CS performance. Show them the digits, and watch your boss give you the nod of approval, knowing you’re not just spending but investing in a future of seamless customer service.

Which are the key KPIs to monitor when analyzing the ROI of AI-driven solutions

In customer service, every business has its own key metrics to track – what matters most varies from one company to another. For some, the focus might be on FCR, ensuring customer issues are resolved quickly in the initial interaction. Others might prioritize CSAT, aiming to gauge and improve how happy customers are with the service they receive. Ultimately, the right metrics to monitor depend entirely on the specific goals and needs of each business.

The crucial question then becomes: what impact does implementing AI features have on these chosen metrics?

But remember that when you check how well AI is doing in customer service, it’s not just about AI chatbots and automation handling tasks. It’s also about how AI helps human agents do their job better: instantly giving them all the information they need, helping them personalize their interactions or doing on-the-fly translations. So you should pay attention to two things: what the AI itself can do, and how much more effectively agents can work with AI’s help. 

It’s important to consider both of these improvements to really understand how AI is changing your customer service.

Based on our customers’ experiences with implementing AI, we’ve identified the following as the top metrics to track:

#1 Average Handling Time (AHT)

Once you’ve implemented an AI solution, track the AHT to assess how quickly your customer service resolves inquiries. A shorter AHT often indicates that your business is becoming more efficient in meeting customer needs. For a comprehensive view, it’s important to measure the handling time separately for requests resolved entirely by AI (without agent intervention) and for those that required human agent involvement. This will give you a clear picture of how AI is impacting your service efficiency and where it stands in complementing human efforts.

For instance, our customer tink, a frontrunner in European open banking, saw an impressive 39% reduction in AHT within just three months of deploying an AI agent assistant.

As a side note, after reducing AHT, we recommend you also evaluate how effectively AI tools free up human agents to tackle more complex issues and upskill to perform new tasks (like providing support in a new language). Improved agent utilization indicates increased efficiency and smarter use of resources.

#2 Response Time

If you’ve rolled out an AI chatbot or equipped your agents with an AI assistant, closely watch the response times to customer inquiries. Faster response times not only boost efficiency but also significantly enhance customer satisfaction.

For example, we’ve seen one of our customers, a leading pet tech company, experience a 30% reduction in response time by integrating an AI agent assistant for their agents.

#3 Ticket Volume Handled per Time Unit

Analyze how many customer tickets your team, regardless of size, manages daily, weekly, or monthly. This metric will help you understand if using AI tools, like an AI agent assistant, lets your team handle more without needing more people

It’s crucial for businesses, especially scale-ups, as it shows how well they can grow their customer service efforts without adding more staff, thanks to AI efficiency. 

Peak seasons are a particularly good time to monitor this metric, as a good implementation of an AI solution should allow you to navigate seasonal peaks without the need for new temporary hires.

#4 Resolved on Automation Rate (ROAR) 

Many companies are now using this innovative new metric to assess the effectiveness of AI in customer service. ROAR measures the percentage of customer inquiries resolved through automated systems, like AI chatbots or AI-driven tools, without human intervention.

It gives you insights into how well automation handles customer issues, reducing the workload on human agents and speeding up resolution times. By keeping an eye on ROAR, you’ll be able to monitor the success of your automation strategies and make informed decisions about further investments in AI technologies. 

So what can you expect? Take, for example, one of our customers, a European market leader in consumer tech subscriptions. They managed to automate 50% of their inbound conversations within just one week after launching their AI chatbot across all markets (US, Spain, Netherlands, and Germany), resulting in a ROAR increase to 50%. Additionally, they observed a 70% reduction in negative social media mentions!

#5 Customer Satisfaction (CSAT)

Gather customer feedback to evaluate the AI’s service quality. Positive experiences with AI will reflect in higher CSAT scores. Remember, the goal of AI is to speed up service without sacrificing quality. Regularly monitor your CSAT scores to ensure they meet expectations. 

We also recommend you compare scores from human and AI/automated interactions to gauge performance and strike the perfect balance between speedy service and happy customers.

Finding the sweet spot between efficiency and satisfaction

When looking at AI’s role in customer service, it’s vital to weigh both efficiency and customer satisfaction. Efficiency metrics, like how many queries AI can handle and its ability to manage several tasks at once, show the speed and capacity of our service. But that’s only half the story. You need to make sure that these services are not just fast but also meet customer expectations. This is where satisfaction metrics like CSAT, net promoter score (NPS), and customer effort score (CES) come into play. They give you a snapshot of how customers feel about the service, ensuring your push for efficiency doesn’t compromise the quality of their experience.


Dragos Andronic

Meet Dragos, our Director of Product Management, who’s all about innovating and making informed bets. He’s passionate about harnessing AI in the CX industry, revolutionizing the way CS teams work and perform.

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