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Big Data in Contact Centers: 5 Types of Analytics Services

by | Jun 3, 2020 | Contact Center Technology

Analytics for call centers provide a way for businesses to make sense of big data collected in everyday operations. To retain their competitive edge, brands must stay up to date with the latest trends in analytics.

Gartner defines of Big Data as “High-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.” On top of transactional data, surveys and financial records, businesses should develop capabilities around analyzing unstructured social media data, voice data and big data from website or app behaviours to create a complete customer profile for personalization.

Thanks to big data analytics we have the capability to do this now. Brands can better personalize conversations around customer experience, allowing them to build customer loyalty, boost customer engagement, and maximize conversions.

Analytics for call centers also helps enterprises big and small, measure performance and see the exact ways their agents can improve.

#1 Call Center Speech Analytics

For speech analytics, recorded calls are the primary data source. This data focuses on identifying common problems customers are discussing and the tone and intonation of the customer’s voice. In this way, emotions are recognized and tagged automatically by the software.

Though this area of analytics for call centers is entirely new, users are finding success as they try it. By analyzing the data collected on calls, analytics can see shortcomings in the current scripts and update them with more effective ones. They can also develop new systems to improve customer experience and achieve desired results.

#2 Call Center Desktop Analytics

Call center desktop analytics can combine with real-time call monitoring to record inefficiencies, improve security, and pin down feedback opportunities for agent performance. The efficiency of both the agent and the systems can be measured by capturing the activity on the agent’s desktop and monitoring the call.

As a whole, using call center desktop analytics can improve the optimization of the customer and agent experience. Repetitive, simple tasks can be monitored and assigned to process automation, freeing up the employees’ time for cognitive tasks.

#3 Self Service Analytics

While some customers from older demographics are often initially resistant to self-service, they quickly realize its benefits. Many tech-savvy businesses are optimizing specific tasks with self-service options. For example, instead of calling to update your address or check the status of your order, you can do so online.

Self-service analytics can reduce the chance for human error and the volume of incoming calls a call center receives. Ultimately, these reductions cut overhead costs and result in more engaged employees and satisfied customers. Self-service or chatbots requires little human interference once it’s set up with your company’s technological structure. Read more on the future of chatbots here.

#4 Predictive Analytics

Predictive analytics provides a vital tool for contact centers. It tracks and files customer satisfaction, wait time, call volume, and service level. Using predictive analytics can help customer care departments solve current problems with historical data.

For example, predictive analytics can assist in forecasting for staffing, allowing managers to decide how many employees are needed on certain holidays based on call volume. It can also track and record how a new product rollout affects call volume and demand. You can better plan for the future with this tool that looks at past results and intervention measures used to solve issues.

#5 Text Analytics

Text analytics mainly focuses on written communication, such as web chats, emails, documents, and social media comments. In the past few years, the use of social media has exploded, causing text analysis of social media comments to be exceptionally informative. With so many brands online, social media is one of the primary forms of communication.

Call center text analytics tools monitor and assign values to words and phrases. Data mining functions then identify patterns and relationships in the sets of data. This data can make conclusions about the messages being sent by your company and your customers, pointing out any issues from the customer’s mindset.

Understanding Your Metrics

By measuring the data of your call center in real-time, you can see the areas that require immediate attention. Understanding the unique metrics to your call center, such as satisfaction, call volume, wait time, and even customer emotions, helps identify weak points.

Pinning down the root cause of customer care issues helps your customer experience be the best it can be. Customer experience is a big differentiator between companies and crucial aspect of playing to win the market. It’s vitally important to stay on top of technological advances and implement them in your customer contact centers.

Measuring Agent Performance

Agent performance can be the most significant driver for exceptional customer care—or it can be the biggest deterrent. It’s vital to utilize call center analytics to monitor performance in real-time.

Using advanced performance analytics takes many variables out of creating the best customer experience possible. By using a combination of the above-listed methods, you can determine which language and behaviors are helping agents reach their target goals and key performance indicators (KPIs). This helps businesses reduce Average Handle Time (AHT), increase First Contact Resolution (FCR) and reduce costs of operating call centers. 

On the other hand, you can easily see your customers’ pain points and specific ways to improve agent performance.

At ContactPoint 360, we offer comprehensive solutions for call center analytics. Get in touch with us today to get started.

Jessica Johnson
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