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Home | Contact Center Metrics | What is Contact Center Text Analytics: How Does It Work?

What is Contact Center Text Analytics: How Does It Work?

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Text Analytics for Contact Center How Does It Work

Text Analytics for Contact Center: How Does It Work?

An essential component of the relationship between a brand and its clients is text-based communication. More people than ever are willing to voice their thoughts about the goods and services they use, and businesses can utilize that information to learn how to increase customer satisfaction. Every email, social media comment, live chat conversation, and support ticket provides important details about a brand’s reputation and consumer expectations and experiences.

Businesses can quickly and cost-effectively use text analytics technologies to extract the most value from customer contact data. Call center data analysis entails equipping call centers with the resources required to deliver superior customer care while streamlining the duties of managers and agents. This article provides an introduction to call center analytics and outlines a brief idea about how call center analytics software and its technology can be useful for your call center business.

What Is Text Analytics?

Text analytics for call centers is a set of procedures that allows businesses to acquire important information quickly. The software uses artificial intelligence to examine all written information, including text from emails, transcripts, customer surveys, support tickets, and various other sources.

What Are the Benefits of Text Analytics?

The use of text analytics data can help call centers run more smoothly. By identifying and predicting client trends, comprehending a customer base’s unique needs, and developing strategies to increase customer happiness, businesses gather actionable information. A company can efficiently and discreetly gather client information using  call center text analytics software. Without the need for active consumer feedback, data can be automatically extracted and utilized for ‘reporting for call center’ through customer reviews and other sources of information.

The following benefits can be obtained by a company using text mining techniques for call center data:


  • Knowing the factors influencing a customer’s decision to contact a company via text and other pertinent details like the customer’s method and time of contact.
  • To identify patterns in consumer behavior and forecast trends, analyze text data from customer interactions.
  • To keep track of consumer opinions regarding new and used products to give your company a simple way to evaluate the quality of your products and to address any client complaints.
  • By measuring the effectiveness of text exchanges between consumers and representatives, aspect-based sentiment analysis can enhance customer interactions.
  • Determine self-service possibilities. As a result, a call center has the opportunity to scale customer service tactics through the use of automated solutions.
  • Real-time operational analysis to find potential workflow problems that might be adding to expenses.


Improving Interactions With Customers

Additionally, call reports can evaluate more general trends in measuring call center agent performance. It is essential to monitor this, even when representatives aren’t speaking with clients. The percentage of callers who hang up before speaking to an agent is called the abandonment rate, for instance. It is advantageous in some circumstances, such as after hearing the location or business hours on the IVR. However, the industry average for most businesses is less than 10% abandonment rate. You can use “speed of answer” as your main statistic. More useful call center reports are provided below:

  1. According to incoming call data, customers anticipate a prompt answer when they call. This report displays historical and current information about your call-handling practices, such as wait times, queued calls, and missed calls.
  2. Service level report: Determining where your support team is falling short might be challenging. The service level report demonstrates how successfully your contact center can respond to calls at any given time.


How Does Contact Center Text Analytics Software Work?

The following are some of the capabilities of call center text analytics software:

  • The process of locating pertinent words and phrases in written material.
  • The thorough examination of text bodies using extraction and filtering methods.
  • The conversion of text into a format that artificial intelligence programs can understand.
  • Using sentiment analysis, a process intended to distinguish between negative and positive sentiment.

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What Are the Applications of Contact Center Analytics?

A call center may transform text exchanges into useful insights using text analytics. Data can be utilized to improve operations and better understand customers’ wants and preferences once properly examined and reported. Most call centers have more customer-related tasks that keep them busy. The constant influx of customer support tickets makes it challenging to evaluate each text exchange carefully. 

The primary function of a call center is carried out by text analytics software, which monitors performance to measure and enhance the client experience. Businesses can understand the prevalence of client trends, update products and services based on feedback, and carry out other improvement operations using text analytics software solutions. However, artificial intelligence can also quickly and accurately review vast amounts of data due to its autonomous nature. Additionally, text analytics solutions provide end users with precise reports that include data visualizations in a flash.

Compared to agents’ and supervisors’ ability to self-report, text analytics software’s reporting is far more accurate. A tagging system based on self-reporting may be erroneous because each person has unique biases and interprets the world differently. For instance, what one agent might perceive as an inquiry may be perceived by another as an IT issue, causing them to categorize the identical query under various headings, causing inaccurate data.

Software for text analytics always bases its decisions on the same logic. Additionally, as they interact with call center datasets more frequently, the machine learning algorithms get a deeper grasp of them, increasing their effectiveness over time.

All client interactions in text form can yield text analytics data. There are numerous applications for the data produced by text analytics software systems, ranging from call center agent performance measurement to data mining and sentiment analysis on the debut of a new product.

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Text Analytics for Contact Center: Bettering Businesses in Many Ways

Many businesses are now relying on data analytics to provide excellent customer service. What took many years for businesses to establish in terms of customer satisfaction is now able to be achieved easily. This is due to the required information being available at their fingertips. Many businesses can demonstrate their willingness by cultivating a culture of first-rate customer service. 

Client experience is increasingly taking the place of pricing as the deciding factor in business transactions and the source of customer loyalty. Using the appropriate data to determine customer calls’ “what” and the “why.” The chances for developing a good customer relationship and growing your business effectively are on your side if you utilize calling analytics. According to a McKinsey report, adopting call center analytics can help businesses reduce average call handling time by 40% and improve conversion rates by over 50%. Standing out from the competition requires an appropriate call center solution that includes call center software and monitoring & reporting for call center that can speed up a lot of processes in your business. 

Click to discover all the details about how text analytics software can improve call center performance and customer satisfaction.

Reference Links: 

Call Center Analytics: Types, KPIs & Metrics (Complete Guide 2024)

Guide to text analytics for call centers