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    How Text Analytics Works in Your Quality Assurance Framework

    Establishing a solid and loyal customer base is the ultimate goal of any call center operation. However, the road you choose to get there separates good call centers from the great ones. There are mountains of data you can sort through when analyzing your customers and your agent interactions with them, but having accurate data that helps you understand their sentiments and emotions gives you truly actionable insights about your customers.

    This is how text analytics works.

    If your goal is to improve the customer experience, there are few better ways to do this than applying AI text analytics as part of your QA framework. Text analytics software allows your operations to complete real-time analysis of everything your customers are saying via text, including email, chat, and SMS. And your customers now prefer contacting your company via text-based messages as almost three-quarters of consumers use texting to communicate with businesses.   


    Using data to improve customer satisfaction webinar


    Text Analytics for a Connected World 

    In today’s hyper-connected digital world, every organization accumulates volumes of data from their customer interactions, chatbots, social media, server logs, etc. A considerable part of this data is in the form of unstructured text. The analysis of this unstructured content from these interactions provides precious information about your customers' needs and satisfaction levels and the quality and efficiency of the interactions they are having with your agents. 

    Text analytics enables you to turn the content from all these interactions into actionable information. Using text analytics, this textual data undergoes treatments such as information extraction, theme classification, and the sentiment mentioned above, emotion and intention analysis, with two main goals: 

    • To gain deep customer understanding: identification of needs, problems, attitudes, opinions, drivers of satisfaction or dissatisfaction or perception of our brand. 
    • Contact center management and optimization: from real-time supervision and support of interactions to better serve them, control adherence to procedures and regulatory compliance, or discover good practices.


    How Text Analytics Works  

    Text analytics software analyzes text to extract deeper customer insights beyond general information such as phone, email, zip code, etc. Through natural language processing and machine learning, the software automatically reviews every text-based channel—live chat, email, transcript, and customer support tickets—to provide a holistic view of the customer experience. 

    The software uses AI for real-time analysis of every text interaction and monitors conversations to detect and analyze critical information. It can segment and see trends in customer behavior and opinions to offer real-time conversational guidance. In addition, it aggregates and creates a repository of your text interactions to build predictive models for successful call center operation. 

    Text analytics works by:

    • Identifying keywords and phrases within text conversations.
    • Extracting and filtering this critical text for analysis.
    • Transforming the extracted text into a readable format that AI can interpret.
    • Mining the text through unique algorithms to identify essential insights:
    • Sentiment: This categorizes text conversations as positive, neutral, or negative.
    • Intention: This mines text conversations for specific desires among users and consumers.
    • Trends: This takes sizeable textual data sets and identifies significant shifts in consumer behavior.
    • Concept: This classifies and ranks text conversations by predetermined service and operational improvement criteria.

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    Text Analytics Effect on QA 

    The bottom line with customer data is that knowledge is power, especially for insights regarding your customers and their experiences with your call center. The more you know about your customers, what they want, and need, and how you can help them, the better every interaction will be.  

    The information you glean from text analytics helps strengthen your call center quality assurance program as your agents are better equipped to provide customers with consistently positive experiences. The deep dive data obtained from text analytics supply your agents with information that allows them to interact with their customers more effectively on a more personal level as they: 

    • Better understand how, why, and when your customers contact you. 
    • Garner insight from live conversations, including details about sentiment, behavior, product mentions, intent, emotional evolution, and more.
    • Optimize workflows by organizing customer interactions by keyword, topic, emotion, and volume.
    • Gain authentic customer feedback to adjust future interactions and increase customer satisfaction.
    • Save time by directing customers to appropriate self-service channels for automatic troubleshooting. 

    And when it comes to quality assurance, one of the biggest struggles is the generally inadequate coverage of the volume of interactions with customers and prospects. As a QA manager, there is only so much time to analyse text conversations manually, emails, SMS and help tickets. More often than not, you’re forced to select a few interactions and cross your fingers randomly. You’re able to gain some quality insight via that method. 

    You can automatically analyze all text-based interactions through natural language processing with text analytics. This means that you can extract insight on topics and trends without additional manual effort in the customer’s own words. And you won’t miss critical understanding due to a lack of resources or time. 


    The Text Analytics impact on SMS 

    Recent customer behavior research backs up the value of integrating text analytics, as an Avochato report claims your customers now prefer companies that offer messaging as a communication channel. Almost two-thirds (63%) of respondents to the study claimed they would switch to a company that provided text messaging as a communication channel. 

    Interactive messaging and immediate communication are now clearly desired. The vast majority of consumers (92%) say they expect to wait for 5-minutes or more on hold before they speak to a natural person on a customer service call. 

    There are significant drawbacks to lacking an SMS strategy. One in three consumers claim they have sent messages to a business but received no reply (mainly because the business didn’t have two-way texting). This reflects poorly on the customer experience offered by the company.  

    Additionally, 65 per cent of consumers feel more positive about businesses with messaging than they do about companies without it. Messaging through mobile phones has become a fast, easy way to gain points with customers and plays nicely with your text analysis strategy.  

    And amazingly, a recent McKinsey study revealed that only 37% of organizations feel that they are using advanced analytic methods to create value for their customers. Talk about missed opportunities; wow. The type of data obtained from text analytics can be a substantial strategic differentiator that can dramatically improve customer satisfaction, not to mention your organization's bottom line.

    Find out more about Scorebuddy’s text analytics software that can work for you. Request a demo now

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