To leverage the power of call center analytics, the users must apply them to real-world business challenges. The right use of data allows companies to offer better customer experiences at all touchpoints. So, what exactly call/contact center analytics is? Well, it is simply the gathering, measurement, and reporting of performance metrics within a call/contact center. The analytics help in tracking call data and agent performance who are handling inbound and outbound calls.
Types of call analytics
Some of the key types of call analytics include:
- Call volume
- Handle time
- Customer satisfaction
- Hold time
Call/contact center analytics greatly influence the way calls are managed and the customer service delivered. Though the access to call/contact center data is usually limited to supervisors and team leads, most call/contact centers these days allow their agents to access real-time data so that they can handle increased call volumes and provide a superior customer experience.
Types of analytics in a call/contact center
There is numerous call data available starting from call duration, first call resolution, to speech analytics. By measuring a particular data point, users can deliver better customer experiences. Since so many analytics are available, it is possible to get overwhelmed. To prevent such a situation, it is advisable to focus on the top 10 metrics that matter the most instead of tracking 20 different metrics.
Based on the size of a call center and support department, users can collect some of the key analytics like:
Interaction analytics include historical data as well as real-time data related to the performance of a call center. It covers everything starting from the response and call hold times, call resolution time, calls transfer rate, abandoned calls, etc. Besides, interaction analytics are not only useful in tracking agents’ performance but can also be used to identify the latest trends.
As the name suggests, speech analytics is used to track agent-customer conversations. It is used to identify the positive and negative keywords used during the conversations. In the past, speech analytics required a whole team to listen to and analyze customer conversations for hours which was a very tedious and time-consuming task. However, today with the help of conversational Artificial Intelligence (AI) and Machine Learning technology the whole process can be automated and completed within no time.
Customer surveys are another effective and powerful source to enhance the customer satisfaction level. After every customer interaction, an automatic survey form can be sent to the customers to determine how their recent experience was. The completion of the survey itself is a measure to increase customer engagement.
With the help of predictive analytics, users can forecast staffing to become more efficient. They can easily analyze historical data and based on that they can prepare forward-looking models. For instance, by looking at the call volume report, the user can prepare the team’s schedules for more efficient results.
Omnichannel call/contact center analytics
In an omnichannel call/contact center, calls are not the only way that customers can use to get in touch with a business. Other than calls, they may choose chat, social media, or email options. In such a case, the users have to access more advanced analytics such as:
Business intelligence: Business intelligence let users analyze the RFM model which is based on 3 quantitative aspects:
- Recency: How recently a customer has purchased a product/service
- Frequency: How often a customer buys a product/service
- Monetary value: How much money a customer spends on purchases
The entire RFM analysis allows users to identify if customers are completing more purchases and generating better revenue.
Text analytics: As most customers these days prefer real-time chat to resolve their common issues, companies are having a lot of text-based data to analyze. Text analytics allow users to gather and analyze conversations and metrics from both live chat and Ai-based chatbots.
Self-service analytics: Apart from text, another popular method that many customers use to resolve their issues include self-service options. The majority of customers today prefer resolving their issues on their own before connecting with an agent or support department. With self-service analytics, the users can collect data from their most-viewed help docs to identify the potential issues. This way they can provide their agents with more context about incoming customer calls.
All these data analytics sources allow businesses to understand their customers better and help them in offering the best and more efficient customer support.
The ultimate aim of using call center analytics is to generate better business outcomes in the form of increased revenue, improved customer loyalty, and reduced service costs. Collecting data related to the customers is relatively easy. However, using the data in a more meaningful and profitable way is quite challenging. Therefore, it is important to use the right tools, technology, and strategy to make the most out of the call data.