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How To Analyze Metrics To Reduce Customer Support Time
Customer support has become an integral part of any business. Everything from client satisfaction to customer retention hinges on the ability to quickly, professionally, and personably address the needs and concerns of clientele. A lot goes into an exceptional customer support system, but one major factor is analyzing and gaining insight into what works and what doesn't. These different aspects are commonly referred to as "metrics." A clear understanding of customer service metrics is one of the best ways to ensure your customer support and support agents are operating efficiently and effectively. While the complexity of these numbers and figures can be daunting to even seasoned professionals, there are clear and consistent methods to help us understand what they represent. It's not always about processing returns or phone calls. Smaller queues, faster average response times, and complaint counts are all things to consider when evaluating the performance of customer service teams.
Benefits of Improving Customer Support
Having a solid structure in place for your customer support model makes your business ready for the myriad of issues that can arise. Poorly planned customer support can lead to many open tickets and overloaded workloads for customer support teams, which can create angry customers. On the other hand, well-oiled contact centers and customer support systems can help bring in customers through a positive reputation and help with overall client retention, so you can ultimately grow and scale your business.
Increased repeat purchases or renewals
The last thing you need is a decline in sales or subscriptions, and a consistent and efficient support system for your existing clients is one of the best ways to reduce churn promote repeat purchases or renewals. When customers feel supported by your company, that trust can remove barriers to purchase.
Decreased customer churn
Satisfied customers are returning customers, and that means more revenue for your enterprise. Customers are quick to cancel subscriptions or turn away from a company if they feel they have been scorned by support or have issues resolving severe problems. A good experience ensures the customer will stick around and make continued purchases.
Higher customer retention
Keeping your existing clients happy with everything you have to offer makes them more willing to stay with your company. Companies can build loyalty with customers by showing they are reliable. In turn, this brand trust gives customers peace of mind when assessing the risks of purchasing your product.
Improved customer experience
If the client has a positive experience from the point of sale to continued support, they'll keep that positive outlook on your business. A good customer experience means they will be more willing to continue working with you and recommend you to other clients.
Increased upsells and cross-sells
Positive experiences with your company and brand trust lead to more sales. When your customer base trusts you, you will see this loyalty reflected in your revenue numbers. Increased communication also leads to increased opportunities to upsell or cross-sell.
Understanding Customer Support Metric Categories
First, let's break down the three customer support metric categories: Performance Metrics, Productivity Metrics, and Quality Metrics.
Productivity metrics measure how much customer support work is being completed. An example of a productivity metric is how many support tickets are cleared within a given period.
Performance metrics measure how well the work is being done. An example of a performance metric is first-time response (FTR), or how quickly a support rep responds to a support ticket.
Quality metrics measure the customer impact. The gold standard of quality metrics includes net promoter scores (NPS) and customer satisfaction scores (CSAT), which are generally collected via survey. We'll discuss both a little later.
Examples of Performance Metrics
Here are examples of performance metrics.
First response time (FRT)
FRT pertains to the amount of time between a customer raising a ticket and an agent responding to it. Another related metric is the First Contact Resolution (FCR), which shows how many tickets were resolved immediately after the first message to support.
Average resolution time (ART)
In association with FRT is Average Resolution Time, or ART. In this case, ART is the average time it takes an agent to resolve a customer conversation, most commonly a customer service query or an inbound sales conversation. Like examining FRT, time is money, and making the most of a customer's valuable time is sure to endear them to your business.
Average handle time (AHT)
Another similar metric is the Average Handle Time (AHT) which measures how long it takes for a ticket to finish, adding up all factors such as the hold time of a customer call, the phone talk time, and the reply time to a ticket. By understanding this time frame and these performance metrics, you can analyze trends in the number of support tickets concerning updates or find what time of day has the highest ticket volume.
Goal completion rate (GCR)
Goal Completion Rate (GCR) counts the number of visitors who have completed all components of a goal, divided by the total visitors. A goal is considered complete only when a lead or customer completes all actions associated with the project.
Backlog indicates the number of unfinished support requests or tasks that still need to be completed and how long the tasks have been sitting in the queue. By minimizing backlog and understanding your completion ratio, you can ensure your business stays consistent and efficient. One way to look into the source of the backlog is to measure the Mean Time to Resolution time (MTTR) which measures the time it takes for an agent to resolve a ticket once it opens.
Examples of Productivity Metrics
Now turning toward Productivity Metrics, we can analyze several different aspects relating to the ticket flow. Everything from new tickets to repeat users are valuable metrics to understand your customer support flow better.
Number of new tickets
Quite simply, new tickets indicate your business activity, which is a great way to gauge whether you need to generate some new traffic for your enterprise. New tickets can also mean something wrong with a recent update or product, which can be a crucial red flag for your company.
New and repeat users
Ticket numbers also relate to new and repeat users, which you could use as a Productivity Metric. See who's continually utilizing your services and doing so for the first time to ensure you're engaging all of your clients equally with the same attention to detail. Repeat clients deserve attention for their continued business, so too do new customers you need to impress with your services. Knowing these numbers can help you address both.
Number of tickets resolved
Knowing the number of tickets resolved is a great way to understand your productivity metrics' depth further. The number of tickets resolved is a great metric to link to your GCR as well, as this will give you a clear idea of just how efficient your system is.
Average time spent per ticket
Another example to link back to the previous topic of ART is the average time spent per ticket. Knowing how long an issue takes to be resolved can direct your focus to areas that may need revamping to serve better the needs of both your existing customers and your business while making the most of everyone's time. A related, excellent metric for productivity and ticket response timing is your customer support agent's average first response time (FRT).
Lastly, consider your bot deflection rate as it pertains to your productivity metrics. Bots can be helpful, but if overutilized to address too many tickets or issues, they can quickly dissolve a customer relationship, as lack of personal communication can make many customers feel neglected. Having a grasp of this number can help you measure how to improve your customer experience with a personal touch.
Examples of Quality Metrics
Taking a closer look at quality metrics and some aspects of this concept, you can start to understand your customer satisfaction and experience in a measurable way.
Net promoter score (NPS)
NPS measures the likelihood that a customer will recommend your company's services to others. It's a great way to measure customer loyalty. Happy clients and word-of-mouth referrals are some of the most powerful ways to generate new business. A simple review, a quick email, or even a text can bring in new business, tying into another quality metric called the Customer Satisfaction Score or CSAT.
Customer satisfaction score (CSAT)
CSAT is a standard Quality Metric to measure the overall satisfaction rate of customers, often generated by surveys or customer follow-ups. Typically presented as a percentage, these numbers are perfect for understanding how people generally think about your business. Customer support is an excellent opportunity to further engage with your customers and make sure they feel valued and have their needs met. CSAT scores help you evaluate how helpful your live chat and self-service experiences are when resolving customer problems.
A related metric is the Customer Effort Score (CES) which tries to measure how hard it is for a customer to get help, so for instance, having to open multiple tickets for one problem would lead to a worse score.
Generally, CSAT measures customer satisfaction after a recent interaction. NPS is used to measure long-term, overall customer loyalty.
Service Level Agreement (SLA)
Another measurement is a Service Level Agreement (SLA) metric, such as the percentage of resolved tickets within your policy boundaries. For example, if your policy stipulates that employees respond to tickets within the first hour of receiving them, you can track this goal.
Tips for Improving Support Metrics
Now that we've covered the critical aspects of understanding customer support metrics, how can you best improve your business with this new knowledge? There are a few key ways to implement tools and strategies to improve your customer support experience and overall metrics.
Use automation balanced with personalization
While automation is integral to improving efficiency, it must be balanced with personalization to reduce customer frustration. When bot deflection or knowledge base reflection rates are too high, often, customers can be frustrated with the inability to resolve their unique problems.
Use chatbots to complement support staff
If things start getting overwhelming for your current staff, consider implementing chatbots to help out. Often, a simple issue can be answered by automation. Chatbots can also help you pre-collect information to prepare your staff to help them address those issues when speaking directly with the customer.
Keep knowledge base and educational resources up-to-date
Ensure that your staff is equipped with an up-to-date knowledge base regarding your business's practices and policies to make their tasks that much easier and faster to resolve. An updated knowledge base can also be a valuable tool for customers to solve issues or answer questions themselves. It's an absolute must-have component of a successful customer self-service strategy.
Establish processes and train for common customer problems
Having a clear and established sense of protocol to train your agents is vital to a consistent experience. Training is especially crucial regarding the more common issues your customers are bringing to your attention, something that all of your support staff should be readily aware of at any time.
Hire more and invest in training for support reps
Sometimes the answer is to hire more support representatives. While quantity balance is crucial, invest in the hires you make by providing continued education and training to help them solve any customer question or appropriately route the ticket to the next support level.
Train reps to specialize in specific problem areas
Sometimes, training specific representatives to specialize in resolving particular problems can help your team resolve tickets faster. Practice makes perfect. Since repetition and familiarity breed expertise and speed, training specialists to resolve more complicated but common problems can accelerate team performance by using automation to triage issues and then directing them to the proper specialist who has the skillset to fix the problem with alacrity.
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