Text analytics solutions provide businesses with a fast and cost-effective method of making the best out of customer interaction data. For call centers, this means acquiring the tools needed to provide better customer service, while also simplifying the tasks of agents and managers.
What is call center text analytics and how does it work?
Call center text analytics is a series of practices that allow the efficient gathering of valuable insights. Artificial intelligence is leveraged by the software to review all the information delivered in a written form, including textual data from emails, transcripts, customer surveys, support tickets, and many other sources.
The capabilities of call center text analytics software include the following:
- The identification of relevant phrases and keywords in written data.
- The comprehensive analysis of bodies of text with the use of extraction and filtering techniques.
- The translation of text into a format that can be interpreted by artificial intelligence solutions.
- The performance of sentiment analysis, an operation designed to differentiate between negative and positive sentiment in customer opinions.
- The identification of developing trends and other changes to the patterns in customer behavior.
- The quantification of customer intention using data mining to search for the specific needs and desires of customers.
- The conceptualization of text conversations by cataloging and ranking textual data with the use of specific criteria. A call center can set up its own specifications on how it wants its data to be classified.
Why does your call center need text analytics?
Text analytics data can empower a call center to improve the efficiency of operations. Gathering actionable insights can help recognize and predict customer trends, understand the specific desires of a customer base, and create strategies to maximize customer satisfaction.
Using call center analytics allows a business to efficiently collect information about customers while being less intrusive. Customer reviews and other sources of information can be data mined automatically, without requiring active feedback from consumers.
The competencies of call center data text mining can give a business the following advantages:
- Being aware of the reasons why customers decide to use text-based channels to initiate communication with a brand, alongside other relevant information such as the customer’s method and time of contact.
- Examine text data from customer interactions to recognize patterns in customer behavior and predict trends.
- Track customer sentiments on products both new and old, giving a business an easy way of performing quality assessment and addressing customer complaints.
- Improve customer interactions by using aspect-based sentiment analysis to gauge the efficiency of the text conversations between customers and representatives.
- Identify self-service opportunities. This gives a call center the potential to scale customer service strategies by using automatic solutions.
- Real-time analysis of operations to detect potential flaws in the workflow that could be generating extra costs.
How does text analytics fit into the call center?
Call center analytics carry out the main task of a call center by tracking performance to quantify and improve the customer experience. By using text analytics software solutions businesses can comprehend the prevalence of customer trends, upgrade products and services based on feedback, and perform other improvement operations.
What data does text analytics provide?
Text analytics data can be gathered from all customer interactions that happen in a textual format. From data mining and executing sentiment analysis on a new product launch to measuring call center agent performance, there are a wide variety of uses for the data provided by text analytics software solutions.
How can text analytics improve call center performance?
Text analytics allows a call center to convert text interactions into actionable insights. Once data is properly analyzed and reported, it can be used to gain a deeper understanding of the needs and desires of customers, as well as to optimize operations.
Call centers are hectic environments. Customer support tickets come and go incessantly, making a meticulous review of every text interaction a hard task to perform. However, the automatic nature of artificial intelligence allows it to perform precise reviews of large volumes of data in the blink of an eye. Moreover, text analytics solutions give end-users accurate reports complete with visual representations of data in record time.
The accuracy of reporting by text analytics software is far superior to the self-reporting capabilities of agents and managers. Every person has their own biases and understands information differently, causing a tagging system based on self-reporting to be potentially inaccurate. For instance, something that an agent would consider a question may be thought of as an IT issue by another, leading them to tag the same query into different categories.
Text analytics software always uses the same reasoning for every interaction. Moreover, machine learning algorithms develop a better understanding of call center datasets the more they interact with them, improving their efficiency over time.
How can call center managers use text analytics?
Text analytics allows managers to make more informed decisions. New strategies can be devised by implementing the information gathered by analytics software. Data can be used by managers as evidence that the choices they’re making are the right ones.
What features does your call center text analytics software need?
Reporting tools
By using reporting tools, text analytics software can produce an automatic summary of the key elements of data, including graphs, tables, and other forms of visual support. Your call center text analytics software of choice should allow you to produce documents with concise details and visual representations of data.
How data visualization improves decision-making
Customer sentiment analysis
All text interactions between customers and a brand should be monitored and analyzed to understand the customer’s general feelings about a product, service, or the brand’s overall image. State-of-the-art sentiment analysis solutions use predictive analytics, natural language processing, and machine learning algorithms to observe and rank textual data.
Natural Language Processing (NLP) is a subfield of linguistics and computer science that aims to enhance the way computers and human beings interact. Using NLP and the correct translation algorithms, even multilingual sentiment analysis is possible.
Real-time analysis
The flow of incoming customer information is constant, and a single event can drastically alter customer opinions on a whim. A text analytics solution that can only display results at certain intervals runs the risk of falling behind the flow of information and delivering inaccurate data. In order to be efficient, the analysis of data sets should be immediate.
Automatic analysis of incoming information allows for the early recognition of potential issues that may affect the customer-brand relationship or the quality of call center operations. Real-time guidance in the form of automatically reported information can help improve agent performance by giving agents clear information about their engagements with customers.
Post-interaction analysis
Brand-customer interactions are not over at the end of a text exchange. Not only is a customer’s experience with the brand going to be shaped by the event, but the brand can also gather valuable insights from the communication.
A detailed analysis of the text should be performed after every conversation, as the information from customer exchanges can serve different purposes. Post-interaction analysis can generate data about customer issues, product mentions, sentiment evolution, satisfaction with business processes, and much more.
Custom data classification
All data gathered by text analytics solutions must be organized in a certain way to allow users to make proper use of it. However, classification methods and desired results will vary from business to business. The classification structures used to catalog data should be customized according to the specific needs of the call center implementing the software.
Integration capabilities
Whenever acquiring any kind of artificial intelligence-based solution, it is important to make sure that its architecture can be implemented into existing operation-critical systems. To save time and resources, a company should prioritize text analytics software that is supported by a team of experts in terms of deployment and support.
Semeon offers customers a turnkey text analytics solution. Our services team is ready to support text analytics projects from start to finish, allowing companies to effectively implement analytics software without any in-house expertise or coding experience.
