Have you ever found yourself lost in a sea of hundreds of product reviews, desperately wanting to uncover the genuine customer insights buried within? The overwhelming volume of feedback can be a daunting puzzle, making it nearly impossible to pinpoint specific pain points or areas for improvement.
Enter text analytics—the unsung hero in deciphering this information overload. While the early days relied heavily on keywords to analyze text, today’s text analytics harness cutting-edge technologies like advanced filtration through text-vector models and generative AI. However, not all these sophisticated tools are created equal, and navigating the nuances of different text analytics tasks remains a compelling challenge.
Below, you’ll learn all about text analytics, including its various use cases and best practices to uncover the meaningful insights you’re looking for.
- What is text analytics?
- How does text analytics work? A technical overview
- Business applications of text analytics
- Steps in text analytics
- Text analytics best practices
- Idiomatic’s innovative approach to text analytics
What is text analytics?
Text analytics is an intelligent combination of machine learning (ML), statistics, and language processing techniques. It’s used to make sense of unstructured text to find patterns and insights.
Why is this important? Here’s an example.
When customers provide written feedback like comments or product reviews, they tell you exactly what they think without restrictions. They’re easy to go through and make sense of if you’re talking about a few dozen people, but when there are thousands or tens of thousands of customers, determining the main point of every sentence in every piece of feedback becomes a lot of work. It’ll take hundreds of man-hours just to go through all of them. Then, there’s the question of gathering actionable next steps from the feedback.
Text analytics uses AI to read and understand unstructured human-written text and extract meaningful insights. Text analytics tools can scan huge volumes of text to highlight key elements and common themes, making it easier for businesses and organizations to understand and act on what people are saying.
Due to its wide range of applications, the text analytics market is projected to grow exponentially, surging from $7.50 billion in 2023 to $40.20 billion by 2028. That reflects a compound annual growth rate (CAGR) of almost 40% throughout the forecast period.
How does text analytics work? A technical overview
Text analytics is a nuanced field that leverages several key techniques to extract meaningful insights from unstructured textual data. It does this by using four primary methods: Natural language processing (NLP), sentiment analysis, topic modeling (including latent Dirichlet allocation or LDA), and pattern recognition.
Natural language processing
At the core of text analytics lies natural language processing: a field of AI that focuses on the interaction between computers and human language. NLP enables machines to comprehend, interpret, and generate human-like text. It bridges the gap between the complexities of human language and the capabilities of computing systems. The purpose of NLP is to allow systems to understand the context, sentiment, and relationships within a given document or dataset.
Sentiment analysis, also known as opinion mining, is a specialized branch of NLP that aims to determine the emotional tone or sentiment expressed in a piece of text. The process involves using algorithms to analyze the words, phrases, and context of a piece of text and categorize the sentiment as positive, negative, or neutral. Businesses can gauge public opinion, employee concerns, and customer insights by discerning sentiment, identifying areas of concern, and adapting remedial strategies.
Topic modeling is a technique that uncovers hidden thematic structures within a collection of documents. Latent Dirichlet allocation (LDA) is a widely-used topic modeling approach that operates on the assumption that documents are mixtures of topics, and each topic is a mix of words. LDA works by figuring out which words often go together, grouping these words into topics, and finally revealing the main ideas that make up the content.
Pattern recognition is a broad field that identifies regularities or patterns within data. In the context of text analytics, it involves the automated recognition of recurring structures or features in textual information.
Machine learning algorithms play a pivotal role in pattern recognition as they can be trained to recognize patterns based on labeled examples. In text analytics, pattern recognition can be applied to various tasks. For instance, in the context of an e-commerce website, pattern recognition can be used to analyze customer reviews and automatically identify patterns related to product preferences, sentiment analysis, and common feedback themes.
👉 Learn how Idiomatic’s sentiment analysis software can help you supercharge your customer insights with advanced text analytics.
Business applications of text analytics
Businesses can harness the power of text analytics to glean actionable insights and enhance decision-making across various teams.
Here are 5 applications of text analytics:
Enhancing customer support through feedback analysis
Understanding customer sentiment is paramount for businesses, and text analytics is a game-changer in this context.
Businesses can decipher customer satisfaction levels, identify pain points, and quickly address concerns by analyzing customer feedback. The customer insight data can be gathered from surveys, reviews, or social media posts. For instance, a restaurant chain analyzing customer reviews might discover a recurring complaint about slow service. The business can address this issue promptly to enhance customer experience and loyalty. Integrating and leveraging robust customer feedback analytics software can help businesses gain actionable insights from customer data.
Recently, Amazon has taken a significant leap forward by employing generative AI for the analysis of feedback text, using the voice of customers for the benefit of customers. This innovative AI-powered feature generates a concise paragraph on the product detail page, summarizing frequently mentioned product features and customer sentiments from written reviews. This streamlined presentation lets customers quickly assess whether a product aligns with their preferences without diving into lengthy reviews.
Predicting market trends and behaviors
Text analytics empowers businesses to decipher market trends and anticipate consumer behaviors. Companies can identify emerging patterns and sentiments using text analysis software to decipher insights from social media, forums, and reviews. For instance, a fashion retailer might analyze social media discussions to predict upcoming trends, ensuring they stock the right products at the right time (or manufacture the products to get ahead of the trend). This proactive approach allows businesses to stay ahead of market shifts and align strategies accordingly.
Text analytics plays a crucial role in brand monitoring by sifting through vast textual data to extract valuable insights. Through sentiment analysis, businesses can gauge public opinion about their brand in real-time, identifying positive and negative sentiments expressed in online conversations. This enables proactive reputation management. Additionally, text analytics helps track brand mentions, analyze customer feedback, and understand market trends. By monitoring social media, forums, and news articles, organizations can swiftly respond to emerging issues, capitalize on positive sentiments, and make informed strategic decisions. Overall, text analytics in brand monitoring empowers businesses to enhance their brand perception and customer relationships.
Understanding employee feedback and expectations
Text analytics benefits internal operations, particularly in understanding employee feedback and expectations. Businesses can gain insights into the workforce’s sentiments, concerns, and expectations by analyzing free-text responses in employee surveys or communication channels.
Text analytics revolutionizes ticket tagging by automating the categorization of support tickets. Through natural language processing, it identifies and assigns relevant tags to incoming tickets, streamlining ticket management processes. This enhances efficiency and ensures that tickets are routed to the right teams promptly. By leveraging text analytics in ticket tagging, organizations can optimize their customer support workflows and provide faster, more accurate responses to user inquiries.
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Steps in text analytics
The process of text analytics involve the following steps:
The journey begins with collecting relevant text data from diverse sources, such as customer reviews, support tickets, social media interactions, or survey inputs. The goal is to gather a comprehensive dataset.
Raw text often contains noise, irrelevant information, or inconsistencies. Data preprocessing involves cleaning and formatting the text. This step includes tasks like removing stop words (“a”, “the”, “is”,) and standardizing text formats.
Once the text is cleaned, it needs to be transformed into a format suitable for analysis. This may involve techniques such as tokenization, where text is broken into individual words or phrases. It also includes lemmatization, which breaks words down to their root.
The heart of text analytics lies in extracting meaningful insights from the processed data. Techniques like sentiment analysis, named entity recognition (key subjects of text like names, locations, and organizations), and topic modeling are applied to uncover patterns, trends, and relationships within the text.
To make the findings easily understandable and accessible, visualization techniques are employed. Graphs, charts, and word clouds are common tools to visually represent text analytics results, aiding in interpreting complex information.
Text analytics best practices
To successfully leverage text analytics, take a thoughtful approach and follow these tips to get the best results in your text analytics endeavors.
Proper tool selection
Selecting the right text analytics tool lays the foundation for streamlined processes and insightful outcomes. Consider specific analysis needs and goals while choosing from many text analysis tools. Open-source options like NLTK and spaCy offer flexibility, while commercial tools tend to provide robust, user-friendly solutions.
For instance, Idiomatic caters to customer support, customer experience, product and marketing teams with a suite of products developed with the unique needs of these business units in mind.
Text analytics projects often yield more meaningful insights when tailored to a specific domain or industry. Customize your analysis to account for industry-specific language, acronyms, or jargon. This ensures that the tool understands the context, enhancing the accuracy of sentiment analysis, categorization, and other text-mining tasks. For example, a healthcare-focused text analytics project would benefit from recognizing medical terms and contextual nuances specific to the healthcare domain.
Ensure data privacy and compliance
Text analytics involves handling sensitive text data. Prioritize data privacy and compliance, implementing anonymous data techniques to protect personally identifiable information (PII). Adhere to industry-specific regulations such as GDPR or HIPAA to ensure your text analytics practices align with legal frameworks. This safeguards against potential legal repercussions and fosters trust among users and stakeholders.
Idiomatic’s innovative approach to text analytics
Idiomatic’s text analytics approach revolves around turning customer feedback into a business strategic asset. The essence of our name lies in our ability to make customer feedback idiomatic, making it seamlessly understandable for the businesses we collaborate with. From product development and marketing to strategic decision-making, we believe all business decisions can benefit from a profound understanding of customers’ feedback data.
At Idiomatic, we take a distinctive approach by employing suitable models for specific tasks. For large-scale classification, we use text embedding and text vector models, while for summarization and granularity, we turn to generative AI. This unique and practical approach maximizes the value derived from text feedback.
Our suite of products is designed to enable businesses to process unstructured customer feedback data, decipher customer sentiment, and derive actionable insights from that data.. We especially empower customer support/experience and product teams to leverage customer feedback data effectively with models customized to your business.
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