Valuable insights into customer perspectives, expectations, and requirements are plentiful. Companies just need to know where to look.
Thankfully, text analytics helps is here to help, gathering and transforming unstructured textual information into meaningful, actionable insights.
With text analytics, businesses can map a better view of their customer’s journey and inform interventions to fuel future growth.
What Is Text Analytics? An Introduction
Text analytics combines natural language processing (NLP), machine learning, and various other technologies into a system capable of drawing insights from “unstructured” text.
Unstructured text is any text not formatted for analysis, such as a call transcript, email, or live chat conversation.
Often, experts separate various kinds of text analytics into different groups, including:
- Descriptive Analytics: This form of analytics gathers data from unstructured text to give insight into trends. As such, businesses get a broader view of the customer journey, especially when paired with data visualization tools.
- Predictive Analytics: Through the evaluation of historical information, companies gain a better insight into the needs and expectations of customers. The more data collected and the deeper the understanding of the customer becomes, the more businesses can “predict” future trends and outcomes. For instance, a contact center can forecast customer demand, preferences, and sentiment changes.
- Prescriptive Analytics: This type of text analytics assists in defining a contingency plan. Businesses may leverage predictive analytics and transform them into practical plans for the future. Some tools even come with AI algorithms that can suggest data-driven strategies and recommended actions.
Each form of analytics is commonplace in the experience management world, in conjunction with voice of the customer (VoC) and voice of the employee (VoE) programs.
These programs combine written information from various sources, such as call center notes, support tickets, emails, survey responses, social media posts, and reviews.
They then enter this information into an “analytical” system, which can derive descriptive, predictive, and prescriptive insights from the data provided.
To do so, text analytics tools usually leverage several different processes to deliver results, such as:
- Language Identification: The ability to identify written language instantly.
- Tokenization: The process of breaking words into “tokens”, such as words and punctuation
- Sentence Breaking: A machine learning strategy that separates large blocks of text into sentences.
- Part-of-Speech Tagging: This technique categorizes words based on definition and context.
- Chunking: The ability to connect specific phrases.
- Syntax Parsing: This technique uses technology to determine the structure of a sentence.
- Sentence Chaining: A method of linking sentences by context.
How Does Text Analytics Work?
To analyze a text using a text analytics program, most companies begin with a process called “text mining”.
Text mining uses statistical techniques to retrieve quantifiable data from unstructured data. After, it detects topics and categorizes words and sentences.
As such, companies can rapidly find patterns and trends in vast volumes of information using text-mining algorithms.
For instance, a text mining strategy may help detect customers’ most common questions when calling a contact center.
In addition, text mining tools can assist companies in checking reports and transcriptions for signs of compliance or security risks.
Text analytics may also feature more advanced technologies, such as natural language processing (NLP). This form of AI analysis allows technology to read or understand text from natural human language.
Moreover, NLP tools within text analytics can help companies detect the intent behind a message or analyze sentiment in conversation transcripts.
In most cases, all these tools come together, allowing companies to gain insight into not just which topics customers talk about but also how they talk about them (i.e., positively or negatively).
What Are the Benefits of Text Analytics?
Companies leverage text analytics to generate meaningful insights from unstructured data. By harnessing these, businesses may take targeted actions to improve employee or customer experience.
For instance, the organization may delve deep into employee reviews on social media and other channels to find the root cause of turnover, determining potential issues within the agent lifecycle.
Alternatively, it could identify trends in conversations where customer sentiment levels are particularly low and act on that information.
Other examples of how it may improve customer experience include:
- Identifying the Causes of Churn: Text analysis can pinpoint words and phrases commonly associated with churn. For instance, the system may link the word “shipping” with an adverse change in customer sentiment. As a result, the company may conclude that there is an issue with its shipping process. Mining additional text details can help show what the core problem is.
- Examine Emerging Trends: Text analysis allows companies to keep up-to-date with emerging trends. They can see which common topics customers and employees talk about most often and detect patterns in communication data. Such insight supports better decision-making when developing new products or improving service.
- Unlocking New Opportunities: Using text analytics, it’s possible to see which strategies lead to positive results. For instance, companies can track whether customers are more likely to respond positively to a specific upsell or cross-sell offer. If customers frequently complain about the lack of a feature in reviews of a product or service, this can inspire new product design efforts.
- Track Brand Awareness: Marketing teams often analyze web and social media text to determine whether people are actively talking about their company, service, or product. As a result, they may quantify brand awareness and track its impact on growth. Moreover, it may also help companies gauge online sentiment towards their solution and look for trends.
- Improve Marketing and Sales Campaigns: With text analysis, companies uncover phrases and terms consumers are likely to mention when searching for a service or product. This can make creating highly targeted marketing campaigns and sales pitches easier.
Leveraging Text Analytics in the Contact Center
As the contact center strives to create an omnichannel service experience, the opportunities for text analytics grow. Some NLP tools even convert spoken conversations into written data for analysis.
Such innovation is helpful. After all, the more data a company collects, the deeper it may understand its customers and contact center processes.
Businesses can also better identify the points that matter most to their customers and employees through text analytics, extracting valuable data from each call or interaction.
The software mines text resources for insights into customer sentiment – across various contact reasons – and how it affects the buyer journey.
Moreover, it’s also possible to envision and predict trends before they appear in the market by sorting through text-based conversations.
Whether offered as part of a real-time reporting strategy, or a historical trend analysis tool, text analytics could be a crucial step forward in understanding the customer journey and voice of the customer. Combined with customer conversation transcripts, text analytics gives business leaders a clearer view of their audience.
