With the recent tech boom and subsequent rise of digital marketing, data analytics is now more popular than ever. Businesses want to better understand their customers, and call centers are no exception. That’s why call centers are employing analytics to improve the customer experience. And contact center agents are at the core of this business model, so providing superb customer interactions is crucial.

We’re all familiar with that experience of a call center agent not being able to answer our queries or not giving us the right information. Calling a customer helpline is seen as more tedious and time-consuming than helpful by the majority of the public. But this attitude can change simply with the power of analytics.

What is call center analytics?

Analytics tools extract data to better identify trends and patterns and provide insights. A variety of companies use analytics tools to track their business’s performance. But call center analytics can go beyond just tracking patterns and discerning key information about your customers’ needs. Call center analytics can deliver a personalized customer experience.

In many ways, call center analytics can help anticipate your customers’ needs — significantly improving their experience with you. Call center analytics can identify what questions are commonly asked so your company can establish ways to answer these questions without a customer having to dial a number. These analytics can pinpoint some of the most frequently reported issues so you can quickly eradicate the issue. And call center analytics can interpret a customer’s reaction and tone to help service representatives understand how to respond to their customers effectively.

Analytics data can be pulled from a variety of platforms such as social media, wait and hold times, the average length of a phone conversation with an agent, the proportion of calls handled and issues resolved, and customer surveys. These are all examples of important metrics to keep track of for a call center. Through interpreting this data, call center management can find knowledge gaps and consistent problems and correct these issues through training sessions or other alternatives. To help interpret the data more efficiently, Tableau training offers software that can easily present even the most complex business data in handy interactive reports, charts, or graphs.

Main types of call center analytics

Call center analytics have evolved throughout the years, from using voice-based data and artificial intelligence to analytics seeking to interpret the text and other written documentation, which is especially useful when paired with social media interaction.

Six main types of call center analytics exist. By analyzing calls using speech analytics software, call center management can determine where more training and knowledge are needed for their agents so agents provide better assistance to their customers. Speech analytics algorithms can also gauge the customer’s emotions to see where frustrations arise and how to mitigate this for future calls. Predictive analytics can (as the name suggests) help predict customer behaviors, and then use that data to improve sales and interactions. Working with predictive analytics can reduce call handling time.

In today’s tech-savvy culture, cross-channel analytics are key. These analytics extract data from the various channels customers interact with agents on mobile devices, desktop, social media, mobile apps, email, and (of course) calls.  Desktop analytics monitors agents’ desktop activity and the relevant systems used to track their performance. Self-service interaction analytics keeps track of customer experience in self-service channels like automated chat boxes and interactive voice response systems. Assessing this data helps smooth out any kinks with these applications. Similar to speech analytics, text analytics deciphers text communications and helps agents and management understand customer needs through written applications.

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