Customer support services can either make or break a customer relationship. A frustrated customer facing an issue can leave satisfied after a positive interaction with a customer support representative. Conversely, a potential customer can decide to take their business elsewhere after spending too much time on hold.
Fortunately, we now live in a data-driven society that gives businesses key insights into customer behaviors and potential improvement areas. Here are four ways to use data to create a winning call center improvement strategy.
Providing Emotional Cues
One of the most compelling call center improvement strategies uses AI to analyze speech patterns in both customer service reps and callers. These monitoring systems analyze the calls in realtime and prompt the agent to subtle speech cues.
For example, if the agent is starting to sound frustrated, they might get a gentle reminder to take a breath on their screen. If the customer is beginning to sound frustrated, the system will flag this tone to the rep with tips on how to course-correct.
These systems are also ideal for creating an environment of coaching and mentorship. Negative flags are an opportunity for improvement and helping staff find solutions to recurring issues. Similarly, positive flags throughout a conversation are an opportunity to acknowledge an agent’s success and boost morale.
Creating a feedback review strategy will help reduce turnover and provide the motivation and skills to interact positively.
Time from Queue to Resolution
Another data-driven metric to look at is the time customers spend on a call, from the initial contact to resolution. There are also various data points to consider within this analysis. Some common call tracking metrics at NICE inContact include Average Handle Time (AHT) and resolution time.
These analytics are best viewed in relation to customer feedback. As a business, you’ll only be able to reduce your AHT so far. Once you reach that threshold, it’s time to start looking at how you can make that timeframe as pain-free as possible.
It’s also important to look at the overall contact time required to resolve an issue. If customers are required to call back after an initial inquiry, there’s room to improve.
Call abandonment pertains to callers who hang up or are disconnected before an issue is resolved. In many cases, this number relates to those who never reach a service agent— ideally, the lower the abandonment rate, the better.
Some data points to consider when analyzing your abandonment rate include:
- Time to answer – how long your caller spends on hold before being acknowledged.
- Predictive analytic accuracy – how accurate your forecasting is (i.e., meeting the demand for high and low volume periods).
- Agent turnover and absenteeism – empty desks mean longer wait times.
Your abandonment rate will typically go hand-in-hand with customer satisfaction. Monitoring and improving this KPI is one of the most important data-driven strategies to incorporate into your call center.
Categorization and Self-Service
Finally, take time to look at why people are calling you. Commonalities indicate the potential for improvement in your communications and systems throughout the business. For example, if you have numerous people calling about holiday hours, making that information obvious on your website will reduce calls. As a result, wait times will be shorter, and customer satisfaction will increase.
Categorizing your inquiries by reason also presents an opportunity for creating self-service solutions. For many e-commerce businesses in the early days of the internet, a lot of time was spent answering calls to track shipments. Now, customers can readily access that information themselves using a tracking number. Use your call center data to explore ways to empower your customers and mitigate inquiries.
These four overarching data-driven strategies can help you improve your business processes and increase customer satisfaction.