Call Center Analytics

5 Tips for Root Cause Analysis in the Contact Center

The best way to solve a problem is to dig deep and find out where it started in the first place. Often, what you see of a problem is a symptom, not the cause. Here are five steps you can take to improve your contact center’s root cause analysis.

  1. Consider acoustic issues.

Root-cause analysis should take acoustic factors into account. For example, if the call has long periods of silence, this could point to a problem with the system. If the contact center agent can’t access data quickly enough or if there are problems with IVR, a slow system may be the problem.

  1. Flag conversations that are abnormally long.

Speech analytics will let you sort through calls based on parameters like duration and repeated calls. You can also find calls where specific keywords are mentioned, like those that are normally associated with a complaint. This will let you know which calls need the most attention.

  1. Monitor data in real time.

Accessing real time data can help you spot and stop issues early. If a new sales or marketing strategy launches and then phone calls start coming in within an hour or two, you’ll know that there’s a problem with the launch that must be fixed. Real time data lets you identify trends as they emerge, giving you the opportunity to stop a problem in its tracks.

  1. Sort problems into categories.

As you start to uncover the main problems customers are having, you can segment them into categories, such as product defects, customer education and marketing communication. Then, you can meet with specific teams to come up with targeted strategies to solve the problems.

  1. Understand the context of the situation.

Relying on word count frequency isn’t enough – the terms and phrases that are being used have to be understood contextually, too. Knowing the context of a problem instead of just the hard data will allow you to pinpoint the situation that caused or contributed to it.

Knowing the average number of complaints your contact center receives on a weekly basis is just a start. You have to figure out the root cause of the complaints in order to effectively tackle them and prevent them in the future. Root cause analysis is a way to solve prominent issues instead of merely putting a Band Aid on them.

4 Important Categories of Contact Center Analytics

 

Contact centers collect a lot of data. They can find out what their customers are doing on a daily basis. They can determine what time a customer contacted support and how long their contact lasted. They can listen carefully to conversations and decide if a customer is happy or angry based on certain keywords. All of this data helps the contact center do things like reduce call times and examine agent performance. To be competitive, contact centers have to stay focused on the customer. By keeping track of customer service metrics, contact centers can make decisions based on reliable data.

  1. Speech Analytics

Speech analytics help contact centers improve a customer’s phone call experience. Customer service agents are monitored to ensure they’re adhering to scripts and following regulations. This can also pinpoint the areas in which an agent needs additional training. Speech analytics will also segment hard-to-handle calls so that they can be dealt with by a supervisor or an agent with more experience. Furthermore, speech analytics can determine the reason for the customer’s call, what they hope to get out of the call, and if they are happy, upset, stressed, satisfied, etc.

  1. Interactive Voice Response (IVR) Analytics

Intuitive IVR systems improve the customer experience. Insights that can be gleaned from IVR analytics include the percentage of callers who want to speak with a live agent and their reason for doing so; the reason for the transfer of calls between departments; the percentage of callers who were not identified accurately; and the number of calls that were handled from start to finish by IVR.

  1. Overall Customer Satisfaction

Gauging overall customer satisfaction will give you an idea of how well you’re delivering the entire customer experience. In order to measure customer satisfaction, the CSAT score is often used. The contact center will ask the customer to rate their satisfaction with a specific experience, like an interaction with the company or a transaction. For example, the customer may be asked to rate their satisfaction on a scale of one to ten. Any answer that’s a six or above means the customer is satisfied. To figure out the percentage of satisfied customers, the number of customers who responded with a satisfied rating is divided by the total number of customers who were surveyed.

  1. Predictive Analytics

Tracking analytics isn’t worth much if you’re not going to take the information and figure out how to improve the contact center. Predictive analytics show the changes that will most impact the performance of the contact center. Management can then figure out the best way to communicate with customers, retain happy customers and resolve problems with dissatisfied customers.

One single metric will not give you a useful view of customer service quality. Instead, several metrics that are carefully chosen based on your customer service goals have to be followed. Tracking analytics allows the contact center to improve, update and revamp their programs on a regular basis.