15 Customer Service Metrics and KPIs to Monitor & Measure

In the B2B landscape, customer service is not just a function; it’s a cornerstone of lasting partnerships and business growth. Monitoring performance is vital, and while many metrics can gauge success, some stand out in capturing the essence of customer-centric B2B operations. Here are the top five indispensable customer service Key Performance Indicators (KPIs) for B2B companies:

1. First Contact Resolution (FCR)
2. Net Promoter Score (NPS)
3. Customer Satisfaction (CSAT)
4. Customer Churn Rate
5. Average Resolution Time

According to a survey that has been done by SuperOffice, if we don’t consider SLA (Service Level Agreement) as a customer service metric. Then CSAT is the most important customer service KPI for the B2B market. This KPI is followed by NPS and Resolution time and rate.

Data Source: SuperOffice

Let’s learn more details about all these important customer service metrics and how to measure and calculate them.

Tracking these 15 customer service metrics allows businesses to gain a comprehensive understanding of their performance, identify areas for improvement, and make informed decisions to enhance customer satisfaction and overall business success.

By continually monitoring and analyzing these fifteen top customer service metrics, companies can stay ahead in the competitive marketplace and build strong, loyal customer relationships. Here are the list of essential customer service metrics that businesses should consider tracking:

The First Response Time (FRT) metric measures the time it takes for a customer’s initial support ticket to receive a response from an agent. Promptness in response time is crucial for ensuring customer satisfaction.

Monitoring this metric is essential for businesses to ensure they are meeting customer expectations and delivering a high level of service.

Measuring first response time (FRT) requires tracking the time it takes from when a customer submits a support ticket to when an agent provides an initial response. This metric can be monitored in real-time or analyzed over a specific period to identify trends and areas for improvement.

By establishing targets based on industry standards or customer expectations, businesses can strive to meet or exceed these benchmarks, ultimately enhancing the overall customer service experience.

• Ensure your support team responds to customer inquiries within a specific timeframe.
• Implement automated ticketing systems for efficient ticket routing.
• Utilize customer service software with robust communication features.
• Regularly analyze and evaluate first response time metrics to identify areas for improvement.

In conclusion, monitoring and optimizing first response time is essential for businesses to provide prompt and responsive customer service. By prioritizing this metric and implementing strategies to enhance response times, companies can improve customer satisfaction, build trust, and ultimately drive business success.

In today’s fast-paced customer service landscape, First Response Time is a critical metric that can significantly influence customer satisfaction.

AI Help Desk Solutions elevate this aspect by utilizing intelligent algorithms to automate initial responses. This technology not only accelerates query handling but also frees up human agents to focus on more complex issues, thereby streamlining operations and enhancing customer experience.

Average resolution time is another important customer service performance metric. The average time it takes to resolve customer support tickets from start to finish is called the average resolution time. Lower resolution times indicate quicker resolutions and higher customer satisfaction.

When customers reach out to customer service representatives with an issue or question, they expect a timely resolution. The average resolution time measures how long it takes for support agents to fully resolve a ticket, from the moment it is received to the moment the customer’s issue is completely resolved.

This metric is crucial in measuring the efficiency of any customer service team. A shorter average resolution time means that support and customer service agents are able to handle customer inquiries more quickly and effectively, resulting in higher customer satisfaction.

Calculation of average resolution time:

Average Resolution Time=[Total duration of all resolved conversations / Total number of resolved inquiries] * 100%

AI Customer Service solutions drastically reduce average resolution time. The median time it takes for the virtual assistant to resolve a request is from the time the customer sends a request to the time it is resolved. AI Customer Service solutions drastically reduce average resolution time.

The escalation rate metric tracks the percentage of support tickets that require escalation to a supervisor or manager with specialized knowledge or expertise, providing insights into the complexity of customer issues and the need for higher-level support.

When customers encounter complex or challenging issues that cannot be resolved by front-line or customer support agents, the escalation rate becomes crucial. This metric helps organizations understand the level of expertise required to address certain customer problems and ensures that the right personnel are involved in the resolution process.

By tracking the escalation rate, companies can identify patterns and trends in customer issues that require specialized attention. This information can then be used to improve training programs, identify knowledge gaps, and allocate resources effectively.

Calculation of Escalation Rate:

Query escalation rate= [Number of inquiries that were escalated / Total number of inquirers]*100%

Reducing the escalation rate ultimately leads to higher customer satisfaction. By resolving issues at the initial support level whenever possible, companies can minimize delays, streamline the support process, and provide more customer support agents for faster resolutions. Utilizing a universal bot for triaging also unsupervised natural language processing in a conversational AI platform can help to reduce the escalation rate as one of the important customer service metrics.

However, in AI-powered customer service systems, not all conversations can be handled by a virtual assistant. It is important to sense when to hand off to a live customer service agent or escalate to a customer support channel such as a callback or case.

CSAT is crucial in customer service KPIs especially when it comes to the B2B market. it directly reflects customer perceptions and satisfaction.

A high CSAT indicates successful service delivery, fosters loyalty and promotes positive word-of-mouth. Conversely, low scores highlight areas needing improvement, enabling businesses to address issues proactively and retain valued customers.

CSAT Stands for Customer Satisfaction (Score). CSAT is one of the most important factors to evaluate the performance of a customer service team.

CSAT is commonly used as a key performance indicator to track how satisfied customers are with a company’s product, services, or experience. It is usually measured on a percentage scale: 100% being total customer satisfaction, and 0% total customer dissatisfaction. In general, It’s a key performance indicator for service quality.

Conducting surveys after customer interactions helps gauge the level of satisfaction and identify areas for improvement. Also, commonly measured through other post-interaction including asking customers to rate their experience on a scale, typically ranging from ‘Very Unsatisfied’ to ‘Very Satisfied.’
These scores are then aggregated to provide an overall percentage of satisfied customers, which can guide service improvements.

For the first time, Aisera’s Virtual Assistants offer reliable CSAT metrics by combining explicit customer feedback with real-time, implicitly derived insights. These AI systems analyze multiple data points like churn rate, service ticket volume, and customer sentiment to generate actionable CSAT metrics for customer acquisition and customer retention.

For enterprises with large customer support teams, harnessing the power of artificial intelligence to improve key customer service metrics like CSAT is a strategic move.

Leveraging AI-driven customer intelligence, Aisera’s Virtual Assistant elevates CSAT scores by providing highly personalized and responsive to customer service requests. Through real-time analysis of customer interactions and behavioral data, the AI tailors its responses to meet individual needs and preferences.

This enhances not only problem resolution but also customer engagement, capturing the nuances of ‘human-like’ language and tone based on perceived moods. In doing so, Aisera’s Virtual Assistant offers a seamless customer experience marked by efficiency, convenience, and accuracy, which are crucial factors for improved CSAT.

In the image below, you’ll find a report detailing the significant improvement in CSAT and the increase in auto-resolution rates brought about by Aisera’s AI customer service. View and download the datasheet.

Another customer service metric is CES which stands for Customer Effort Score. Customer Effort Score is used to measure customer service performance and how much effort a customer has to put in to get their issue resolved.

It can range in scale from “very easy” to “very difficult” The more friction there is for the customer to get their issues, the more likely they are to abandon their journey.

Average handle time (AHT) is a customer service metric and refers to the average duration taken to resolve a customer’s query, including talk time, hold time, and after-call work. AI-powered customer service, it gauges the efficiency of AI solutions in addressing and concluding various customer queries and interactions.

First Contact Resolution (FCR) is a key metric in customer service that shows how often customer problems are sorted out on the first interaction. A high FCR suggests that the service team is effective, in answering questions or fixing issues promptly.

On the other hand, a low FCR might mean that there’s a need for better training or a review of current processes. To find the FCR, we use the “first time right” formula. For instance, if a team receives 20 queries in a day and resolves 15 immediately, the FCR is 75%. Most call centers aim for an average FCR of around 70%.

Calculation of first content resolution (FCR):

First Contact Resolution=[Number of inquiries resolved in the first contact / Total number of inquiries] * 100%

But, it’s essential to remember that while having a high FCR is good, it shouldn’t come at the cost of the customer’s experience. Keeping a balance between maintaining a high FCR and sticking to service promises or agreements ensures that customers remain satisfied.

Interactions per ticket is a metric that shows how many steps it takes to solve a customer’s problem. It counts things like agent replies, chat switches, or file sharing. This customer service metric helps understand the complexity of customer issues and how much conversation is needed to address them.

It’s more than just counting messages; it includes all ways customers connect, like chats, emails, or calls. For instance, if a customer calls, then emails, and finally gets a custom solution, all these steps count as interactions for one ticket. This is especially useful for services where customers talk to multiple teams or use different ways to reach out.

So, instead of just looking at how many tickets are received, seeing the average number of interactions per ticket can offer deeper insights into customer support quality.

The Net Promoter Score (NPS) serves as a key metric in this realm. NPS quantifies customer loyalty and satisfaction by asking customers to rate, on a scale of 1 to 10, how likely they are to recommend your business to others.

Those responding with a 9–10 are termed ‘Promoters’, indicative of satisfied customers likely to vouch for your services.

Scores of 7–8 classify respondents as ‘Passives’, who, while satisfied, might not actively recommend you.

Meanwhile, any score of 6 and below identifies ‘Detractors’ — customers unsatisfied enough to potentially dissuade others from partnering with your company.

Customer Churn Rate is a critical metric that reveals the percentage of customers or partners who decide to end their business relationship with your company over a specific time frame.

A high churn rate might indicate dissatisfaction with your product, service, or support. Monitoring and understanding this rate is essential as it directly impacts revenue and growth in the B2B sector.

Retaining existing clients is often more cost-effective than acquiring new ones, making this KPI pivotal for assessing long-term business health and sustainability.

Ticket volume is easy to measure but important to benchmark the customer support team’s performance. While it is good to measure how much of your incoming inquiries and requests are handled, it is good practice to correlate with the total case volume to see if the volume is trending down assuming all other factors are the same. Leveraging ticket assistance and AI-powered customer service helps to increase this metric significantly.

This metric is relevant when your customer service teams or support department utilizes automated customer service to handle inquiries. When deploying a virtual assistant, your North Star metric will be to measure the percentage of inquiries that are automated by AI. Auto-Resolution Rate can also be a proxy for the First Contact Resolution rate.

This metric identifies the main reasons customers contacted customer service (Mostly AI-powered). It can be a topic from password resets to order cancellations. Analyzing these intents helps businesses understand and address the most frequent user inquiries, ensuring effective assistance.

This metric measures the overall mood or tone of customer interactions, often derived from customer feedback, reviews, or communication. Utilizing natural language processing (NLP) and AI, sentiment analysis classifies responses as positive, negative, or neutral.

The Average Sentiment Score provides a numerical representation of this sentiment, offering insights into general customer feelings toward a product, service, or interaction. A high score typically indicates positive sentiment, while a low score suggests dissatisfaction or negative feelings. Monitoring this score helps businesses understand customer perceptions and adjust strategies accordingly.

Machine Learning and AI models can now extract the sentiment from natural language conversations so you can be on top of the overall sentiment score of your customers and users while setting up workflows to react to interactions where customers are frustrated.

In the realm of customer service, this metric quantifies the number of individual user interactions with a customer support platform or tool within a set period. Each session can represent a query, chat, or support ticket initiation.

Monitoring the total number of sessions can provide insights into customer engagement, the volume of support requests, and potential issues or concerns customers are facing. A spike in sessions might indicate a product issue or a surge in inquiries, while a decline could suggest improved product stability or effective preventive measures.

Customer service metrics are pivotal in gauging the effectiveness of support operations and understanding customer needs. Incorporating Conversational AI and automation streamlines the measurement process, ensuring accuracy and promptness.

Not only do these, AI-driven customer service, and AI ticket assistants help to reduce the customer service workload, refine strategies, enhance customer experience, and continuously elevate the standards of service.

In essence, leveraging AI transforms the realm of customer support, driving both precision in metrics and excellence in service delivery. You can book a free AI-powered customer service demo today!