Did you know? In 2024, the world generated 149 zettabytes, and that’s expected to reach 394 zettabytes by 2028. That’s equivalent to every person on Earth streaming 4K video nonstop for over three years or stacking enough books to reach the moon and back…100 times.

But that data means nothing if not properly analyzed. Businesses need text analytics done right to extract valuable insights that they can use for effective decision-making.

There are text analytics best practices to keep in mind:

• Setting clear objectives for analysis
• Choosing the right tools with NLP capabilities
• Cleaning and preparing data for accuracy
• Applying key techniques like sentiment analysis
• Continuously improving models for better insights

In this guide, we break down these text analytics best practices to help you turn raw text into actionable intelligence.

1. Setting Clear Objectives for Text Analytics

Before diving into text analytics, it’s essential to define clear objectives. Without a focused goal, businesses risk drowning in data without extracting meaningful insights.

One of the top challenges in text analytics is dealing with unstructured text. Without a well-defined plan, businesses may extract data that is too broad, irrelevant, or difficult to interpret, leading to inaccurate insights.

The book Artificial Intelligence and Evaluation highlights the importance of structured planning:

"As expected, well-defined categories and subcategories in the taxonomy (such as legal obstacles, political risk, and market pricing) tend to generate fewer false positives than broader ones. In contrast, where categories were imprecisely specified a priori, the model faced greater difficulties in converging on the correct categories and had to be further refined following an iterative process."

When goals are too vague—such as “analyze customer feedback” without specifying which aspects to focus on (e.g., pricing concerns, product quality, or customer service)—the results become inconsistent and require multiple refinements.

However, when businesses set specific objectives and categorize data properly, models can classify text more accurately, leading to actionable insights with fewer errors.

How to Set Clear Objectives for Text Analytics

So, how do you create objectives? To ensure success, apply the SMART framework when defining your text analytics objectives:

• Specific – Clearly define what you want to achieve. (e.g., “Analyze customer reviews to identify recurring product complaints.”)
• Measurable – Set quantifiable goals. (e.g., “Track sentiment trends across 10,000 support tickets over six months.”)
• Achievable – Ensure your goal is realistic, given your data and resources.
• Relevant – Align objectives with business priorities. (e.g., “Identify emerging trends to improve customer satisfaction scores.”)
• Time-bound – Set deadlines for analysis and action. (e.g., “Generate insights within 30 days to inform the next product update.”)

Setting objectives is a given in any business endeavor, but it helps to be reminded of this first in the list of text analytics best practices. Be smart, and make SMART objectives.

2. Choosing the Right Text Analytics Tools & Platforms

With the vast amount of text data available today, selecting the right text analytics tool is crucial for extracting meaningful insights. The right platform can help businesses understand customer sentiment, monitor trends, and optimize decision-making.

However, not all tools are created equal—some offer basic text processing, while others use advanced AI-powered natural language processing (NLP) or large language models (LLMs) to uncover deeper insights.

Key Features to Look for in a Text Analytics Tool

When evaluating text analytics platforms, consider these essential features:

• Natural Language Processing (NLP): AI-powered tools can analyze context, tone, and meaning beyond simple keyword matching.
• Sentiment Analysis: Helps determine whether text expresses positive, negative, or neutral emotions.
• Entity Recognition: Extracts important names, locations, and product mentions from text data.
• Thematic Analytics Tools: Identifies recurring topics and trends across large datasets, helping businesses pinpoint key themes.
• Customization & Scalability: The tool should adapt to your industry-specific needs and grow with your business.
• Data Integration: The ability to analyze data from multiple sources, including text analytics for social media, customer support tickets, and emails.

Traditional Text Analysis vs. AI-Powered NLP vs. LLMs

In the history of text analytics, you will see that traditional text analysis relies on rule-based keyword searches, which can be rigid and miss context. For example, a simple keyword-based tool may flag "great service" as positive but fail to detect sarcasm in "Oh, great service…".

In contrast, AI-powered NLP solutions can:

• Understand context and intent rather than just keywords.
• Detect sentiment variations and sarcasm.
• Classify text dynamically, making it more adaptable to different business needs.

Meanwhile, many modern tools, like Thematic, now incorporate large language models (LLMs) and generative AI to further enhance qualitative data analysis. These advanced models bring several advantages:

• Greater accuracy: LLMs can process text with a deeper understanding of language, tone, and context.
• Automatic categorization: Generative AI can create dynamic taxonomies, eliminating the need for manual classification.
• More human-like insights: LLMs can summarize trends and patterns in a way that is easy to interpret.
• Scalability: AI models can analyze millions of text entries in seconds, making them ideal for high-volume data processing.

By leveraging AI-powered text analysis tools with LLM capabilities, businesses can gain deeper, more accurate insights from text analytics while reducing the need for manual intervention.

Beyond NLP: How LLMs Transform Text Analytics

Is your Text Analytics solution still relying on B-Grade NLP? Discover how large language models are revolutionizing text analytics, offering deeper insights than traditional NLP approaches.

• Understand key NLP limitations and LLM advantages
• View real-world results of AI-driven text analytics
• Learn how self-learning AI eliminates manual updates
• Cut analysis time from weeks to minutes

Download your free copy

How to Evaluate Tools Based on Business Needs

To choose the right text analytics platform, businesses should:

1. Define their goals: Are you looking for brand monitoring, customer feedback analysis, or risk detection?
2. Assess data sources: Do you need insights from customer reviews, surveys, or social media conversations?
3. Check ease of use: Can your team use the tool without extensive technical training?
4. Consider cost vs. value: Advanced AI solutions may have a higher price, but they provide more accurate and actionable insights.
5. Test scalability: Ensure the tool can handle growing data volumes and adapt to your evolving needs.

With the right text analytics tool, businesses can automate data analysis, improve decision-making, and uncover valuable insights in real time.

Thematic

AI-powered software to transform qualitative data into powerful insights that drive decision making.

Book free guided trial of Thematic

3. Preparing Unstructured Data for Analysis

Before text analytics can generate valuable insights, raw text data must be cleaned, structured, and formatted for analysis. Since most business data arrives in an unstructured format, proper data preparation ensures accuracy and reliability.

Step 1: Data Cleaning – Removing Noise & Handling Missing Data

Unstructured data often contains irrelevant characters, typos, or incomplete sentences that can mislead analytical models. Data cleaning involves:

• Removing special characters and symbols (e.g., hashtags, punctuation, extra spaces).
• Filtering out stopwords (common words like "and," "the," "but" that add no value).
• Handling missing data by either removing incomplete entries or applying context-based replacements.
• Standardizing formats (e.g., converting dates, numbers, and units into a consistent structure).

By ensuring clean input, businesses improve data accuracy and model performance.

Step 2: Tokenization & Normalization – Structuring Text for Analysis

Once the data is clean, it needs to be converted into a structured format for meaningful analysis. This process involves:

• Tokenization: Breaking text into individual words or phrases to allow detailed analysis (e.g., splitting "customer service was terrible" into ["customer", "service", "was", "terrible"]).
• Stemming & Lemmatization: Reducing words to their root form (e.g., "running" → "run", "better" → "good") for consistency.
• Lowercasing & Spacing Adjustments: Standardizing text to remove case sensitivity issues.
• Coding Qualitative Data: Assigning labels to textual themes (e.g., categorizing "The checkout process is slow" under "User Experience Issues"). This helps track trends and make data-driven improvements.

By structuring data properly, text analytics tools can recognize patterns, categorize responses, and extract meaningful insights more effectively.

Step 3: Ensuring Compliance with Privacy Regulations

Handling sensitive customer data comes with legal responsibilities. Organizations must comply with data privacy regulations such as:

• GDPR (General Data Protection Regulation - Europe): Requires consent for data collection and mandates anonymization.
• CCPA (California Consumer Privacy Act): Gives users the right to access, delete, or restrict the use of their personal data.
• Other Industry-Specific Laws: Financial, healthcare, and legal sectors may have additional compliance requirements.

To stay compliant:

• Mask or anonymize personal information (e.g., removing names, emails, and phone numbers).
• Ensure encryption and secure storage of text data.
• Implement opt-in consent mechanisms when collecting customer feedback.

Among the text analytics best practices, data cleaning is one that must not be missed because it sets the groundwork. Remember, when the foundation is faulty, the structure might fall apart.

4. Applying Key Text Analytics Techniques

Once data is cleaned and structured, the next step is to apply text analytics techniques to extract meaningful insights. Businesses use these methods to understand customer sentiment, uncover trends, and improve decision-making.

We’ll talk about three techniques, that when combined, can:
✅ Improve customer support by identifying common complaints
✅ Enhance products & services based on real user feedback
✅ Make data-driven decisions to stay ahead of market trends

1. Sentiment Analysis – Understanding Customer Emotions

Sentiment analysis helps businesses determine whether text expresses positive, negative, or neutral emotions. This is especially useful for tracking customer feedback, online reviews, and net promoter score (NPS) surveys.

Example: A company analyzing support tickets might find that words like "slow response" or "frustrating experience" correlate with lower NPS scores, signaling a need for service improvements.

How it helps:
✅ Measures customer satisfaction at scale
✅ Identifies pain points and areas for improvement
✅ Helps brands track public perception over time

2. Topic Modeling – Identifying Common Themes

Topic modeling groups similar words and phrases to reveal hidden patterns in text data. Instead of manually sorting through thousands of reviews, businesses can use this technique to detect recurring topics in customer feedback.

Example: An e-commerce brand may find that "shipping delay" and "damaged packaging" frequently appear together, highlighting an issue in logistics.

How it helps:
✅ Automatically discovers emerging trends
✅ Prioritizes areas that need attention
✅ Saves time by summarizing large datasets

3. Entity Recognition – Extracting Key Information

Entity recognition identifies important names, locations, brands, and product mentions in text. This technique is widely used for social media monitoring, competitor analysis, and brand tracking.

Example: A hotel chain analyzing customer reviews can extract mentions of specific locations (e.g., "New York branch") to assess performance across different regions.

How it helps:
✅ Tracks brand reputation across platforms
✅ Identifies competitor mentions
✅ Helps businesses personalize marketing strategies

5. Ensuring Data Security & Compliance

As businesses collect and analyze vast amounts of text data, data security and compliance must be a top priority. Mishandling sensitive information can lead to legal penalties, reputational damage, and loss of customer trust.

By prioritizing data security and compliance, businesses can:

• Prevent data breaches and legal issues
• Build customer trust and transparency
• Ensure ethical and responsible data analysis

A secure approach to text analytics helps organizations extract insights without compromising privacy, ensuring compliance while maximizing business value.

Key Privacy Concerns in Handling Customer Data

Text analytics often involves customer feedback, support tickets, emails, and social media interactions—which may contain personal or sensitive information. The biggest privacy risks include:

• Unauthorized access – Poor security measures can expose customer data.
• Re-identification risks – Even anonymized data can sometimes be traced back to individuals.
• Data retention issues – Keeping data longer than necessary increases exposure risks.

Best Practices for Secure Data Storage & Processing

To protect customer data while using text analytics, businesses should:

• Anonymize and mask personal data (e.g., remove names, emails, phone numbers).
• Encrypt data both in storage and during transmission.
• Limit access using role-based permissions.
• Regularly audit and monitor data usage for compliance.
• Establish data retention policies to delete outdated information securely.

Regulations to Consider for Global Compliance

Different regions have strict data privacy laws that businesses must comply with:

• GDPR (General Data Protection Regulation - Europe): Requires user consent, limits data storage, and enforces the right to be forgotten.
• CCPA (California Consumer Privacy Act - U.S.): Grants consumers rights to access, delete, and opt out of data collection.
• HIPAA (Health Insurance Portability and Accountability Act - U.S.): Regulates the handling of healthcare-related data.PIPEDA (Personal Information Protection and Electronic Documents Act - Canada): Ensures data collection is lawful, limited, and secure.

6. Monitoring & Continuous Improvement of Models

Text analytics models are not set-and-forget solutions. Over time, language evolves, customer expectations shift, and data patterns change, which can cause machine learning models to degrade in accuracy.

As a closing to our list of text analytics best practices, remember to continuously monitor, refine, and update their models.

Why Machine Learning Models Degrade Over Time

Text analytics models rely on patterns in language, but these patterns can shift due to:

• New slang, industry jargon, and evolving customer sentiment
• Changes in product offerings or business policies
• Bias or drift in training data that affects accuracy

For example, a customer satisfaction model trained a year ago may struggle to interpret new product names, emerging issues, or shifts in sentiment expression. Without regular updates, insights become outdated, leading to poor decision-making.

The Role of Human Feedback in Improving AI Models

Even with advanced AI, human oversight remains essential. Businesses should:

• Manually review sample predictions to catch misclassifications.
• Use human-in-the-loop (HITL) training to refine model accuracy.
• Incorporate customer feedback to improve sentiment detection and topic relevance.

For instance, if customers start using “mid” as a negative review term, human feedback ensures that the model adapts to new expressions of dissatisfaction.

Thematic’s approach allows analysts to edit and refine AI-generated themes, ensuring insights are both accurate and actionable​. This human-in-the-loop system provides the best of both worlds: AI efficiency with expert-driven accuracy, leading to better decision-making and improved customer insights.

Regular Updates & Performance Checks

To keep text analytics models performing well, companies should:

• Retrain models periodically with fresh data.
• Track performance metrics (e.g., accuracy, precision, recall).
• Test models against real-world data to ensure relevance.

Implementing continuous monitoring and improvement helps businesses ensure text analytics remains accurate, responsive, and aligned with evolving customer needs.

Thematic

AI-powered software to transform qualitative data into powerful insights that drive decision making.

Book free guided trial of Thematic

Real-World Applications of Text Analytics

Successful companies don’t just collect customer feedback—they apply text analytics best practices to transform raw data into meaningful action. The case studies below illustrate how top businesses implemented key text analytics strategies to drive customer satisfaction and business growth.

1. Atom Bank: Reducing Support Calls & Improving Customer Experience

Best Practices Applied:

• Setting Clear Objectives – Atom Bank’s goal was to unify fragmented feedback data and identify areas for customer experience improvements.
• Choosing the Right Tools – They integrated text analytics tools across seven different feedback sources to ensure a holistic view of customer pain points.
• Applying Key Techniques – By using sentiment analysis and topic modeling, they pinpointed recurring issues in device and mortgage-related support calls.

Results:

• 40% fewer calls related to device issues
• 69% fewer mortgage-related complaints
• 110% customer growth

By following best practices, Atom Bank transformed scattered, unstructured data into focused insights, leading to higher efficiency and customer satisfaction​.

2. DoorDash: Enhancing Driver Satisfaction & Efficiency

Best Practices Applied:

• Preparing Unstructured Data – DoorDash used text analytics for social media, surveys, and in-app feedback to capture driver sentiment.
• Applying Sentiment Analysis & Thematic Analytics Tools – They analyzed recurring pain points (e.g., unfair scheduling, long wait times) to identify what mattered most to their delivery drivers.
• Monitoring & Continuous Improvement – By tracking sentiment trends, they discovered that flexible scheduling was a top driver satisfaction factor.

Results:

• New reward system based on data insights
• Higher driver retention rates
• More effective marketing campaigns based on driver priorities

By consistently monitoring feedback and refining their approach, DoorDash increased driver engagement and improved retention​.

3. Instacart: Solving Customer Support Challenges Across Multiple Stakeholders

Best Practices Applied:

• Choosing the Right Text Analytics Tools – Instacart used entity recognition and topic modeling to categorize feedback from four distinct customer groups (consumers, personal shoppers, retailers, and advertisers).
• Ensuring Data Security & Compliance – They handled millions of customer interactions while ensuring privacy and data protection standards were met.
• Continuous Model Improvement – Instacart used text analytics insights to track the impact of product changes, ensuring that new updates improved customer experience rather than introducing new issues.

Results:

• 360-degree insights across all customer groups
• Faster issue resolution in customer service
• Reduced app crashes by detecting high-priority issues

By integrating structured text analytics across multiple feedback sources, Instacart improved its platform, leading to better usability and overall satisfaction​.

These businesses applied text analytics best practices and succeeded in their text analytics journey.

Your Next Steps

Text analytics is a powerful tool for uncovering customer insights, improving operations, and driving business growth. By following text analytics best practices, companies can set clear objectives, choose the right tools, clean and structure data, apply key techniques like sentiment analysis, and continuously refine their models.

These steps ensure that businesses extract actionable, high-impact insights from unstructured text data.

Ready to see the impact of text analytics on your business? Try Thematic on your own data and unlock deep customer insights with AI-powered analytics. Turn feedback into action and drive measurable results today!