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Using Text Analytics in Customer Experience (CX) Management

9 min read

| March 6, 2023

Text analytics is becoming an increasingly important tool for customer experience (CX) professionals. With the right approach, text analytics can be used to gain valuable insights into consumer sentiment and behavior that help shape CX strategy. But how exactly does one use text analytics to improve their customer experience? In this blog post, we will explore what text analytics is, how it can be leveraged in CX initiatives, potential challenges of implementation as well as best practices for getting started. 

What is Text Analytics?

Text analytics is the process of extracting meaningful insights from large amounts of unstructured text data. It involves using natural language processing (NLP) and machine learning algorithms to analyze customer feedback, conversations, and other textual information in order to gain valuable insights about customers’ needs, preferences, sentiment, emotions, and more.

The benefits of text analytics for customer experience are numerous but starts with the notion that text from a variety of sources is likely the most abundant source of consumer feedback available to organizations. Text can be derived from customer satisfaction surveys (such as an NPS survey), customer support and service interactions (post-call surveys, support chats, etc.), as well as “in the wild” sources of feedback, like social media and online reviews. Companies are surrounded by unstructure text feedback from consumers, but often lack the ability to make sense of it all. 

By leveraging text analytics tools and techniques such as NLP and machine learning algorithms, organizations can quickly identify patterns in customer feedback that would otherwise be difficult or impossible to detect manually. This allows them to better understand their customers’ needs and expectations so they can provide an improved experience for them. Additionally, it helps companies save time by automating processes such as sentiment analysis or topic modeling which would otherwise require manual effort.

There are several types of text analytics available depending on the use case. The Canvs AI platform performs three types of analysis:

Keyword or Topic Extraction

Extracting the topic or main idea being expressed from from text. Essentially “What are people “talking” about.?” For example, customers of a hotel might be talking about rooms, stays, hotel, ammenities, spa, parking, restaurants, etc.

Thematic Summary

A thamtic summary in text analysis is the process of organizing what people are talking about into categories, typically organized in a two-tier hierachy. Hotel feedback might be summarized based on things like service, faciliities, amenities, and value. 

Sentiment Analysis / Emotion Measurement

Identifying the emotion (when applicable) being conveyed in a piece of text, and (optionally) whether that emotion conveys positive or negative sentiment towards a particular subject matter. Canvs also provides the overall emotion reaction rate for a body of text, providing a measurment for the level of emotion expressed around a particular topic or time period. 

Text analytics is a powerful tool for understanding customer behavior, sentiment and preferences. But for text anlaytics to truely be effective for in cusotmer experience management applications, the solution should be able to identify connections between the customer rating (e.g. an NPS score or score category) and the factors identified in the text. For example, which topics are customers who provided a low feedback rating talking about more than others. Let’s explore further.

How Can Text Analytics Improve Customer Experience?

Text analytics is a powerful tool for market researchers and customer experience (CX) leaders. Text analytics facilitates the exploration of vast amounts of unstructured data, such as customer feedback, social media comments, emails and surveys. This analysis helps organizations gain valuable insights into their customers’ needs and preferences so they can better serve them, and helps organizations more quickly identify the source or drivers of both positive and negative feedback.

Identifying Customer Needs and Preferences:

Text analytics can be used to identify patterns in customer feedback that reveal what customers want or need from a product or service. If numerous clients make mention of the same issue with a product or service, it could point to an opportunity for enhancement. By understanding these trends in customer sentiment, companies can adjust their offerings accordingly to better meet customer needs.

Understanding Customer Sentiment and Emotions:

Text analytics also allows CX professionals to understand how customers feel about certain products or services by analyzing their language use in reviews or comments on social media platforms and feedback surveys. Businesses can use the gathered sentiment data to make decisions on how best to respond to customer concerns and what features should be given priority when designing new offerings.

Text analytics, in combination with artificial intelligence (AI) and machine learning algorithms, enable organizations to automate processes related to CX initiatives. These include sentiment analysis of incoming messages from customers across multiple channels such as email and chatbot conversations; identifying key topics discussed by customers on online forums; and quickly responding back based on automated rules set up using natural language processing (NLP). This automation helps reduce manual effort while ensuring accuracy at scale for faster decision making that leads to improved overall CX outcomes over time.

Probably the biggest challenge associated with text analytics in customer experience is the ability to effectively analyze the large volume of consumer feedback text with consistency and scale. Previous generations of text analysis tools often disappointed uses due to being complex to use and ultimately not highly accurate. But new approaches that utilize artificial intelligence (AI) are lowing the barrier to usage while improving on the accuracy and depth of analysis. Additional challenges include data collection and preparation challenges. Consumer feedback text comes from a variety or disparate sources in a variety of formats. Text analytics tools that are data source agnostic can help overcome this challenge by providing a single “source of truth” for text analysis across the customer journey. 

Key Takeaway: Text analytics can be used to identify customer needs and preferences, understand customer sentiment and emotions, and automate processes related to CX initiatives in order to improve overall customer experience.

What Are the Challenges of Using Text Analytics for CX?

Data Collection and Preparation Challenges:

Collecting data for text analytics can be a challenge, especially when dealing with customer experience initiatives. This is because customers often provide feedback in various forms such as surveys, emails, social media posts, and more. It’s important to collect all relevant data sources in order to gain a comprehensive perspective. Additionally, the text and relevant meta data, such as customer demographics, location information and channel, need to be structured in a way that’s optimal for the text analytics solution to ingest. 

Language Processing Challenges:

Language processing can be difficult due to its complexity and nuances of human language. Text analytics tools need to be able to accurately interpret natural language (and un-natural language, like emojis) so they can extract meaningful insights from customer feedback. To do this effectively requires advanced algorithms that are capable of understanding context and sentiment within written words which can be a challenge for some technologies. Previous generation of text analytics solutions typically required complext Boolean logic statements to generate meanginful output. While Boolean is still a powerful tool for building customization, modern solutions are less dependent on it for analysis. 

Analyzing unstructured data such as customer reviews or social media posts presents unique challenges since these types of feedback don’t generally include a question “prompt” (like what did you like most about your stay?). This means there isn’t always an easy way to organize the information into themes. Additionally, social media comments are often imbued with colloqual expressions, abbreviations, emojis and slang. However, some text analytics software solutions like Canvs use AI to identify such “un-natural” language and interpret its meaning. 

Although the challenges of using text analytics for CX can be daunting, there are best practices to follow that can help ensure success. By defining goals and objectives, collecting relevant data sources, and choosing the right tools and technologies, you can use text analytics to improve your customer experience. 

What Are the Best Practices for Implementing Text Analytics in CX?

It can help organizations identify customer needs and preferences, understand sentiment and emotions, and automate CX processes with AI and machine learning. However, there are several challenges to implementing text analytics in CX initiatives. To ensure successful implementation of text analytics in CX projects, it’s important to follow best practices.

Define Your Goals and Objectives:

Before starting any project involving text analytics for CX, it’s essential to define your goals and objectives. Ask yourself what you want to achieve by using this technology? Are you looking to gain deeper insights into customer behavior generally or address a more specific issue impacting the business? Customer experience feedback is most often associated with customer satisfaction (CSAT) surveys, such as the Net Promoter Score (NPS) system. Text analysis can be a powerful tool within these programs to identify the topics, themes and emotions correlating with positve and/or negative CSAT or NPS ratings.  Pinpointing your ultimate ambition can assist in deciding which information sources to acquire, as well as what tools and technologies should be employed for maximum efficacy.

Collect Relevant Data Sources:

Once you have identified your goals, the next step is collecting relevant data sources that will provide insight into customer experience. This could include obtaining customer feedback via surveys, monitoring social media platforms for posts, sifting through emails from customers or scanning reviews on external websites such as Yelp and TripAdvisor. Collecting high-quality data sets is key when it comes to leveraging text analytics for CX initiatives.

When selecting a tool or technology for your project consider factors such as cost-effectiveness, scalability options and ease of use before making a decision so that you get the most out of your investment in text analytics solutions. Complexity has frequently been the culprit of poor adoption, so strongly consider the balance of functionality and accessibility when evaluating text analytics solutions.

By following best practices for implementing text analytics in CX, companies can gain valuable insights into customer sentiment and preferences and perform root-cause analysis of service deficits that can be used to improve the overall customer experience. Companies can now leverage the data gathered through text analytics to make better decisions that will lead to more positive outcomes. In summary, it is important to remember the advantages of using text analytics in CX and how this can help businesses make better decisions for their customers.

Key Takeaway: Text analytics can be a powerful asset for obtaining knowledge of customer experience; however, in order to reap the full benefits, it is imperative that one takes time at the beginning to define goals and objectives, source relevant data sets and pick suitable tools and technologies.


Text analytics in customer experience can help to identify customer needs and preferences, understand customer sentiment and emotions, automate CX processes with AI and machine learning, as well as uncover insights from unstructured data. However, there are challenges associated with text analytics such as data collection and preparation, language processing issues, and analyzing unstructured data.

In order to get the most out of text analytics for CX initiatives it’s important to have clear goals in mind before beginning any project. Additionally, it’s important to collect relevant data sources that will be useful in achieving those goals. Finally choosing the right tools and technologies is essential for success when implementing text analytics into your CX strategy. 

FAQs: How to Use Text Analytics to Improve Customer Experience

How to improve customer experience using data analytics?

Data analytics can be used to improve customer experience by providing insights into customer behavior and preferences. By analyzing data from various sources such as surveys, social media, website visits, etc., businesses can gain a better understanding of their customers’ needs and wants. This information can then be used to create more personalized experiences for customers that are tailored to their individual interests and needs. Analyzing data can also help pinpoint areas that need to be improved in order to bolster customer satisfaction and commitment. Data analytics can be a powerful tool for companies to gain insights into their customers and deliver them the best possible service.

What is the use of analytics in customer experience?

Analytics can be used to gain valuable understanding into customer experience, helping organizations comprehend how customers engage with their offerings and what motivates satisfaction or dissatisfaction. It helps organizations understand how customers interact with their products and services, what drives customer satisfaction or dissatisfaction, and where improvements need to be made. By leveraging analytics, companies can identify areas of improvement in CX such as customer service response times, product quality issues, website usability problems and more. Analytics can offer a deeper insight into customer behavior, enabling businesses to craft personalized experiences that fulfill the needs of their desired demographic. Ultimately, analytics enable organizations to deliver a better customer experience and drive increased loyalty.

How does text analytics support CRM?

Text analytics is a powerful tool for market research and customer experience (CX) that helps organizations gain insights from large amounts of unstructured data. By analyzing text-based conversations, surveys, emails, social media posts and other forms of customer feedback, it can uncover valuable information about customers’ preferences, needs and behaviors. Text analytics can also be leveraged to recognize potential opportunities for improvement in services or products, as well as new areas of expansion. Text analytics can be utilized to foster stronger connections with customers, providing them with customized experiences tailored to their particular requirements. CRM teams can use text analytics to observe customer sentiment changes over time, thus allowing them to devise more effective ways of engaging with customers in the future.

How is text analytics useful in real-life situations?

Text analytics is a powerful tool for market researchers and customer experience leaders, as it allows them to quickly and accurately analyze large amounts of unstructured data. Text analytics can utilize NLP techniques to extract significant knowledge from customer feedback, survey results, online discussions and beyond. Text analytics provides organizations with the capability to comprehend their customers’ desires and inclinations in order to craft superior services and products that meet those needs. Additionally, text analytics can be used to identify trends in the marketplace or detect emerging opportunities before competitors do. Learn how the containerized moving and storage leader PODS used text analytics to improve customer experience


By leveraging text analytics, organizations can better understand customer sentiment, identify areas of improvement in the customer journey and develop strategies to optimize the overall customer experience. While there are challenges associated with using text analytics for CX, new text analytics solutions like Canvs have dramatically lowered the barrier to adoption, accelerating the time-to-insights from open-ended text.

As businesses strive to understand their customers better and provide a superior customer experience, text analytics is the perfect tool. Text analytics can help uncover valuable insights from customer feedback that are difficult to capture with purely closed-ended surveys.  Investing in an effective text analytics solution now will result in improved engagement with customers, higher retention rates, increased loyalty, and ultimately more success for your business.

Ready to learn how to apply text analytics to your customer experience (CX) management initiatives?


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