Call centers of the past were just what the name implied: a place to make and receive customers’ calls. A strictly functional extension of your organization. Today, with so much data available to us and increasingly sophisticated ways to analyze it, the call center is a strategic place for making improvements to your organization through analyzing relationships with your customers.
Table of contents
Making improvements in the call center starts with data. With new ways to obtain data, there is more of it around than ever before. This is both good and bad news. More data paints a clearer picture of your business. But, more data also means more noise. The key is to accurately analyze data and make it meaningful through action because without analysis and action metrics is only a list of numbers.
Here we look at how to analyze call center data, what metrics to gather, and a few best practices for analysis. But first, let’s look at the reasons you might want to analyze your data.
How to Analyze Call Center Data
So, you have an idea about the data that can be useful but, how do you gather it? There are several collection methods that you can employ to accurately gather what is most important to you.
Methods of Data Collection
Call Recording
Recording each call gives you direct insight into the customer experience. It can reveal potholes in the customer service journey, show you what agents are doing right, and give you important insight into your customers.
Speech analytics
This tool allows you to analyze tone and sentiment to gauge customer emotion. Improvements in natural language processing make this toll more refined than ever before. You can target keywords in conversations as well as emotional indicators.
Text analytics
Using text analytics you can gather data from conversations that happen over live chat, email, or text message. While this type of data gathering does not allow you to gauge customer emotion very well, it still provides data about conversation type, resolution time, and demographic data.
Surveys
Getting direct feedback from your customers is the easiest way to gather data about their satisfaction. Ask specific questions and you will get more useful data.
CSAT surveys
CSAT surveys measure how your call center meets the needs of your customers. It gives customers a chance to give you feedback specific to their experience with your organization. The more detailed the feedback, the better your improvements can be.
NPS
Net promoter scores (NPS) measure the willingness of customers to recommend your organization’s goods and services. You can use this measurement as a proxy for gauging overall customer satisfaction. Increased NPS scores indicate greater customer satisfaction.
eNPS
eNPS is the employee net promoter score. This measures your employees’ enthusiasm for their place of work and can help with agent retention.
Ticketing
Your customer support system, such as ticketing and helpdesk software, can tell a data-rich story about the context of customer requests, common errors and issues, process recommendations and workflow suggestions, and even capture in-the-moment conversational details related to overall service satisfaction that won’t be included in CSAT surveys.
WFO & BI
Remember that you have to start with “why” when trying to establish how to analyze call center data. The most common “why” will be to become more agile as a business.
Workforce optimization and business intelligence tools like traffic dashboards and automated scheduling by volume assist with demand planning and agent engagement by centralizing information about processes as opposed to within your processes. This means that you can have more detailed reviews of disparate data.
Tips for how to analyze call center data
Identify and prioritize
You should never collect data without a goal in mind. Define what you want to improve and prioritize the collection of data that can potentially give you insight.
For example, say you want to improve average handle time. Gathering data on-hold time, the number of times agents use target words like “hold”, “holding”, or “wait”, how agents wrap up calls, and customer surveys can be useful data points.
Interpret
Once you have collected data you can begin to figure out the reason certain data points are popping up. Why might an agent put customers on hold more than another agent? It could be that the agent lacks knowledge. Look for patterns across your call center and begin to create an actionable plan to improve your target area.
Scale up with automation
Once you have tested your hypothesis you can begin to scale up your solution with automation.
To make the most out of your data you need to know how to target and analyze it. Ask yourself
- What are you trying to learn?
- What data points are necessary?
- How can you make the data you have more useful?
The end goal of data collection is actionable improvements to the call center. Identify patterns in the data you collect and find out why they happen. By leveraging the right data your call center can be a powerhouse for organizational improvement.
Improve average handle time
When you improve average handle time you streamline conversations and improve efficiency in the call center. This translates to a sleek experience and happier customers. You can use data to learn how to correctly route customers the first time, minimize hold times, and make sure your agents have a solid knowledge base. Improving average handle time has an effect that can be felt throughout the entire organization.
Increase self-service
Data can help you pinpoint call center tasks that are best accomplished through self-service. This cuts down on the time agents have to spend doing tasks that don’t necessarily require their specific skills. Analyzing call center data can also help you improve self-service options to make them smoother and more user-friendly. This will help make sure agents are more engaged and your customers are given more autonomy.
Improve customer satisfaction
As the acronym implies, data from CSAT surveys are collected to improve customer satisfaction. But, customer satisfaction can be improved by collecting and analyzing other data like agent skill level, average handle time, and emotional cues gleaned through sentiment analysis. When you improve customer satisfaction, you improve the likelihood of retention.
Improve agent engagement
A top reason for agent attrition is lack of engagement. Metrics like agent sentiment, idle time, and average escalation rate can tell you what’s keeping your agents engaged and also what needs improvement. Engagement keeps agents satisfied with their positions. That job satisfaction shines through in conversations with your customers and improves relationships for your business on both sides. CSAT data can also tell you how to better engage your agents by identifying which agents continually exceed expectations.
Metrics to Monitor
Metrics make the volume, variety, and velocity of call center data more manageable. They allow you to put all of this information into neat categories for meaningful analysis.
Average hold time
The amount of time your customer is on hold before or during interaction with an agent.
Average handle time
The amount of time, start to finish, a call takes. It includes hold time, talk time, and any after-call work like scheduling follow-ups or taking actions.
Resolution time
This is the number of times it takes to resolve a problem. It is a pretty good measure of customer satisfaction. When you can resolve the reason for your customer’s call the first time around, the more satisfied they will be. Lower contact resolution times are indicators of higher customer satisfaction.
Agent turnover rate
This measures how often your call center is losing agents. It can help you identify challenges your agents face and gaps in training. If you have a high agent turnover rate and can pinpoint why you will have to spend less time and money on training. You will also lose agents less frequently which makes for more consistent service.
How to choose the right call center data analysis platform
The call center data analysis platform you choose will have a big impact on the success of your data analysis. There are many different platforms available, and it can be difficult to know which one is right for you. Here are the main considerations for choosing a call center data analysis platform:
- Ease of use: The platform should be easy to use so your team can get started quickly. Don’t get stuck wasting time trying to figure out how to use the platform.
- Flexibility: No call center analysis platform is perfect out of the gate. Choose a platform that’s flexible, so you can customize it to your needs.
- Functionality: Consider the specific features you need in a call center data analysis platform. Software providers offer a range of different capabilities, so it’s helpful to get clear on what you need and what you don’t need for your contact center operations.
- Cost: One of the most obvious yet important factors is cost. You don’t have to break the bank to find a platform that provides a considerable ROI.
- Support: If you run into issues with your platform or need additional training for your contact center team, you need to be able to access the platform provider’s support. Don’t overlook this component in your buying journey.
Choosing the right call center data analysis platform is important, so take your time and choose the one that is right for you.
