Every business is about working with huge amounts of data and analyzing it in order to ensure informed decision-making and using opportunities to improve business efficiency and performance. Nonetheless, it looks easy on paper, while processing such huge quantities of data in real life is a difficult and effortful task, so you definitely require assistance and advice in this field.
What data should be collected? How should you collect it? What are the further steps regarding data analyzis and processing?
Working with call center data analytics starts from collecting data, analyzing it, segmenting and making decisions based on data collected. It is all done to collection valuable insights into contact center operations, and using interaction analytics to improve call center performance in real time, reach KPIs (Key Performance Indicators), handle inbound calls with higher operational efficiency, implement customer feedback and manager customer relationships with the brand effectively.
First of all, let’s find out what are the key call center analytics, what call center metrics should you focus on, how to organize call center analytics, and so on.
What is Call Center Data?
Modern call center now use contact center solutions to organize call center analytics, which means modern contact centers have access to more advanced call center analytics than their predecessors, and this also means that call center analytics can now be half-automated.
For instance, wide range of AI (Artificial Intelligence) services, such as Machine Learning, Natural Language Processing (NLP), Natural Language Understanding (NLU), speech analytics, and so on, modern call centers can automatically measure such advanced aspects of customer behavior, as customer sentiment, which was impossible a few years ago or required hours of manual work and didn’t guarantee precise results.
These actionable insights allow you to use predictive analytics for customer interactions, with the use of Customer Relationship Management software to improve contact center performance, boost the productivity of contact center agents, manage call volume trends, staffing levels, customer satisfaction levels, and so on.
Why Collect and Analyze Call Center Data?
To put it simply, there are three main reasons to collect and analyze call center data and that’s why call center analytics make difference. These three reasons include: resolving agent performance problems and identifying any cases of agent idling, call avoidance or absenteeism, providing more positive customer experiences, and improving sales performance. Anyhow, call center analytics can be used to improve any other aspect of call center performance, because avoiding to use call center metrics analytics and reporting is like acting blind – you can’t identify customer preferences, can’t design your IVR (Interactive Voice Response) system and other self-service channels, can’t deal with long average handle time or average hold time, and so on. All these actionable insights into customer behavior can transform your business success and help to find right strategic decisions.
Finding and fixing root causes for inefficiencies
For instance, there can be cases when you identify specific issue with your call center performance – let’s presume that you face high call abandonment rates, and what are you going to do about it without call center data collection? If you use call center data analytics, you can find the root cause of this issue: for example, it can be long average call wait times, or lack of active agents on the line, which makes it easy to find the appropriate solution and resolve the problem.
Let’s look at another example: you have problems with FCR rates, which is an essential metric, and this causes problems with customer satisfaction scores, which also results in losing happier csutomers and reducing overall quality of resolving contact center interactions.
Delivering better service and customer experience
Call center analytics are also about measuring key metrics of customer experience – without measuring and managing customer experience, you won’t be able to ensure customer retention. Call center analytics include such vital customer experience metrics as customer satisfaction (CSAT), customer effort score (CES), first call resolution rate (FCR), and net promoter score (NPS). All these metrics are used to measure different aspects of customer experience, combined to analyze overall experience and find effective ways to improve it.
How to Collect and Analyze Call Center Data
Accessing and improving agent performance
Vital call center metrics that relate to individual agent performance include average response time (ART), also known as average speed of answer (ASA) , average handling time, service level (SL), average talk time (ATT), agent occupancy and utilization, and so on. All these metrics, combined with measuring agent performance in action through call monitoring and recording, allow you to identify knowledge gaps and skill gaps, find agent individual strengths and weaknesses, plan training program in order to make it as efficient as possible, and understand key improvement areas for additional training in your team.
You can use contact center technology to get real-time analytics for making data-driven decisions to improve employee engagement and reduce agent turnover.
Keep track of your vital call center metrics and KPIs
One of the best ways to measure call center performance and organize call center analytics is to use the analytics capabilities of your contact center software. Advanced software solutions go far beyond basic call center metrics, and this is the option you should be looking for when choosing call center software. Let’s look at some key call center metrics to measure:
First Response Time (FRT)
First Response Time (also known as First Reply Time) measures the initial response time for each customer request regardless the communication channel that is used to contact the customer service team. Evn though the industry standard is considered to be within 3 minutes for all communication channels combined – some contact channels require faster response, including phone calls, it is vital to make sure that you have used all opportunities to reduce the first response time as much as possible.
The Average Speed of Answer (ASA)
Average Speed of Answer, or Average Response Time, is one of the most critical call center metrics that measure agent performance. This metric measures how much time it takes the agent to answer incoming calls from customers, and in most cases, keeping it below one minute is enough as a minute wait is considered as the maximum possible response time by customers. If not, it will case frustrated customers to consider switching to a competitor.
First Call Resolution (FCR)
This is a customer service metric that is both related to customer experience metrics and agent performance metrics. It measures the percentage of customer queries resolved within one contact with your customer support agents, regardless of the channel chosen for contact. The industry standard for first call resolution rate is 80%, with the possibility of keeping it lower for some complicated cases and keeping it higher for basic customer inquiries. This metric is one of the most important metrics to focus on, as inability to resolve a customer issue within one interaction is considered an example of poor customer service quality.
Let’s discuss other vital customer metrics in the part two of our topic!