25 examples of responsible AI: How to leverage AI while minimizing risk
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The Team at CallMiner
November 16, 2023
The best text analytics software and tools can turn customer conversations into insights through convenient and reliable automation. Often used in contact centers and customer service applications, text analysis software can also be implemented into finance, marketing, sales, and other business functions to improve efficiency and optimize business processes.
As artificial intelligence (AI) expands and becomes more accurate, text analysis tools evolve in their ability to detect words and phrases and analyze sentiment and emotion. As more and more software and tools offer such capabilities, it’s crucial for business leaders to understand what to look for in text analytics software to make the right choice for their operations.
In this buyer’s guide, we’ve pulled together expert quotes and tips to find the best text analytics software and tools and maximize their value, including:
Customers communicate with businesses using a variety of speech-based and text-based channels. While speech analytics software is designed to capture and analyze spoken interactions, text analytics tools analyze written interactions such as:
Open text is a type of unstructured data where customers can write or type out their own responses in their own words, rather than selecting from pre-defined options such as multiple-choice survey questions. It’s especially valuable for understanding customer needs, wants, sentiments, and emotions. However, reading, organizing, and analyzing a large quantity of open-text responses is incredibly time-consuming and complex, making it impractical—if not impossible—for humans.
Organizations have more customer data than ever, but unlocking its full potential isn't easy.— CallMiner, Inc. (@CallMiner) November 1, 2023
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Text analytics software leverages AI and machine learning techniques to analyze vast amounts of unstructured text-based data at scale, doing in seconds what would take human analysts days. Text analytics tools typically ingest data from a variety of sources, applying natural language processing (NLP) techniques to identify patterns and trends and translating them into actionable insights.
1. Choose software that detects emotions in text and speech. “What moves satisfaction and loyalty today is how customers feel about engaging with brands beyond the nuts and bolts of a transaction. As Forrester Research notes, ‘Emotion has a bigger impact on brand loyalty than effectiveness or ease in every industry.’
Leveraging the power of emotional metrics across every conversation allows customer experience and customer service professionals to operationalize customer emotion and sentiment with more speed and accuracy than traditional methods such as intermittent surveys. Benefits of the [CallMiner] Emotion Solution Suite include:
2. Help potential users understand the importance of text analytics. “[One] hurdle is getting internal teams to embrace the solutions, especially the agents who interact with the technology on a daily basis. It’s important to explain and show the people engaging with these tools most frequently how the insights that are uncovered are to their benefit. It’s not about monitoring them – it’s about helping to support them in their roles, making their jobs easier and giving them access to the objective, data-driven feedback it takes to improve.” - Frank Sherlock, VP of International, CallMiner, Speech and Text Analytics: CX Today Expert Round Table, CX Today; X/Twitter: @cxtodaynews
3. Understand the difference between text mining, text analysis, and text analytics when searching for software. “Text mining and text analysis are often used synonymously. However, text analytics is different from both. Simply put, text analytics can be described as a text analysis or text mining software application that allows users to extract information from structured and unstructured text data.
“Both text mining and text analytics aim to solve the same problem – analyzing raw text data. But their results vary significantly. Text mining extracts relevant information from text data that can be considered qualitative results. On the other hand, text analytics aims to discover trends and patterns in vast volumes of text data that can be viewed as quantitative results.
“Put differently; text analytics is about creating visual reports such as graphs and tables by analyzing large amounts of textual data. Whereas text mining is about transforming unstructured data into structured data for easy analysis.
“Text mining is a subfield of data mining and relies on statistics, linguistics, and machine learning to create models capable of learning from examples and predicting results on newer data. Text analytics uses the information extracted by text mining models for data visualization.” - Amal Joby, Text Mining: How to Extract Valuable Insights From Text Data, G2; X/Twitter: @G2DotCom
4. Text analysis software benefits multiple departments. “A company’s supply chain frequently has many touchpoints, and as a result, many data points. Everything from invoices to shipping information can be analyzed with this software. Therefore, employees working in operations and supply chain teams can use text analysis software to gain a better understanding of their departments and the text data that is generated, such as from ERP systems. These applications track everything from accounting to supply chain and distribution. By inputting supply chain data into this software, supply chain managers can optimize several processes to save time and resources.
“Sales teams also seek to improve financial metrics and can benefit tremendously from being more data-driven. They can obtain insights into prospective accounts, sales performance, and pipeline forecasting, among many other use cases. Using analytics tools in a sales team can help businesses optimize their sales processes and influence revenue. Through the analysis of survey data, business leaders can find out the most effective way to sell products.
“For marketing teams, tracking the performance of campaigns is key. Since they run different types of campaigns, including email marketing, digital advertising, or even traditional advertising campaigns, these tools allow marketing teams to track the performance of those campaigns in one central location. Marketers can learn about how their audience is responding to their messages using sentiment analysis. In addition, they can evaluate their ad copy by tagging and classifying it to better understand what drives conversions.” - Best Text Analysis Software, G2; X/Twitter: @G2DotCom
5. Choose a tool with multi-language support. “Text data can be in different languages, making it challenging to analyze and extract insights from multilingual datasets.
“To address language barriers, you can use natural language processing (NLP) libraries that support multiple languages. For example, the spaCy library supports over 50 languages and provides pre-trained models for various NLP tasks such as named entity recognition, part-of-speech tagging, and dependency parsing.” - Sahel Eskandar, Challenges and Solutions for Text Mining. Performing Sentiment Analysis and Topic Modeling via Medium; X/Twitter: @Medium
6. Find a tool that analyzes text and structured data. “Fusion of text with structured data to achieve results that cannot be achieved by either data set alone. For example, we see increasing use of ensembles that combine text predictors (eg: naïve Bayes classifiers) with predictors based on structured data – often with superior results. We also see structured data being used to enhance the unstructured analysis. A good example of this is cell phones that interpret ambiguous sounding names by referencing the structured contacts list.” - Dave Tomala, Sr. Director of Analytics – Knowledge Solutions, Express Scripts Inc., The Challenges Of Text Analytics From Clients In The Trenches, Greenbook; X/Twitter: @GreenBook
7. Look for a tool that fits your budget. “Pricing for text analysis software tools varies according to features and amount of users required. Some vendors will require only an annual payment, while others will charge per month.
“Costs generally start at about $400 per year for plans with limited features and users. Monthly rates can be as high as $2000 per month for plans with unlimited users and in-depth data analysis. Some vendors offer a free version or free trial of their text analysis platform.” - Text Analysis Software, TrustRadius; X/Twitter: @trustradius
8. Seek support for various mining types. “Special algorithms are used to identify insights across userbases, buyer personas and more, yielding meaningful reports that companies can use to direct their growth without resorting to guesswork. Mining comes in a variety of forms, each characterized by a distinct function. These are as follows:
9. Use text analytics software for brand management, not just customer management. “The public perception of a business must be flawless, especially in today's cancel culture. By analyzing tweets, comments, news stories, and other feedback that mention it or anything or anybody associated with it, text mining enables you to interpret data acquired via social media listening and voice of the consumer (VoC) initiatives.
“Executives of the corporation, investors, employees, partners, political parties, and organizations the business supports are included in this. By taking action to avert the catastrophe, businesses can improve the state of their image in real time.” - Ashesh Anand, Top 10 Applications of Text Analytics in 2023, Analytics Steps; X/Twitter: @Analytics
10. Know what you want from a text analysis tool. “You’d be surprised how many large companies are still using a mixture of their current tools and Microsoft Excel for analysis of communications. No doubt, this is a quick way to start analysing, but it is not scalable in the long, or middle run.
“If you’re at this stage, it’s advised to quickly understand what it is that you want out of text analysis, and what you need in an analysis tool. Then, upgrade to that tool as soon as you can. The benefits are boundless. Hundreds of hours saved from all stages of the text analysis process, as well as faster business response for cost reduction or revenue generation.” - Michelle Chen, A Guide: Text Analysis, Text Analytics & Text Mining, Towards Data Science; Twitter: @TDataScience
11. Python libraries are the future of text analysis tools. “Python, a high-level, general-purpose programming language, can be applied to NLP to deliver various products, including text analysis applications. This is thanks to Python’s many libraries that have been built specifically for NLP.
“Python libraries are a group of related modules containing bundles of codes that can be repurposed for new projects. These libraries make the life of a developer much easier, as it saves them from rewriting the same code time and time again.
“Python’s NLP libraries aim to make text preprocessing as effortless as possible so that applications can accurately convert free text sentences into a structured feature that can be used by a machine learning (ML) or deep learning (DL) pipeline. Combined with a user-friendly API, the latest algorithms and NLP models can be implemented quickly and easily so that applications can continue to grow and improve.” - Alexander T. Williams, Top 5 NLP Tools in Python for Text Analysis Applications, The New Stack; X/Twitter: @thenewstack
12. Ensure that your tool respects privacy. “To collect any personal data, consent is needed. This consent must be freely given, unambiguous and involve a clear affirmative action. After consent, individuals still need control of their personal data. This means they need to be able to:
“Finally, respecting individuals’ choices means gathering only the information they agreed to share and using it only for the purposes they agreed to. For instance, personal data collected for first-party use shouldn’t be shared with partners or third parties sometime later.” - Karolina Matuszewska and David Street, What is privacy-friendly analytics?, PIWIK Pro; X/Twitter: @piwikpro
13. Set up text analysis software the right way. “Implementation differs drastically depending on the complexity and scale of the data. In organizations with vast amounts of data in disparate sources (e.g., applications, databases, etc.), it is often wise to utilize an external party, whether it’s an implementation specialist from the vendor or a third-party consultancy. With vast experience, they can help businesses understand how to connect and consolidate their data sources and how to use the software efficiently and effectively.
“It may require a lot of people, or even teams, to properly deploy an analytics platform. This is because data can cut across teams and functions. As a result, one person or even one team rarely has a full understanding of all of a company’s data assets. With a cross-functional team in place, a business can piece together its data and begin the journey of analytics, starting with proper data preparation and management.” - Best Text Analysis Software, G2; X/Twitter: @G2DotCom
14. Text analysis software should prioritize NLP. “Natural language processing, which evolved from computational linguistics, uses methods from various disciplines, such as computer science, artificial intelligence, linguistics, and data science, to enable computers to understand human language in both written and verbal forms. By analyzing sentence structure and grammar, NLP sub-tasks allow computers to “read”. Common sub-tasks include:
15. Understand text context before digging into a tool’s insights. “The important part of text analysis using computational tools is interpreting the data. For this you need a good idea of the broader context of your text and its purpose. Otherwise you might get conclusions from text analysis that are not supported by a more traditional reading of the text. In practice, that means that the findings you have about word patterns in the text do not make sense or do not fit within the meaning of the text when it is considered back in its context.
“An example is using text analysis on a series of political speeches. Unless you have the context in which these speeches were given (perhaps they all come from debate on one issue or piece of legislation), the findings of your text analysis may not make sense. That is not to say you need to have personally read everything that you are putting through your text analysis tool; you just need to be specific about what you are looking for and have information to help you interpret your findings.” - Text Analysis: An Overview via Methodology; X/Twitter: @DigHumns_ANU
16. The right text analysis tools can help recruit candidates for company positions. “Helping a candidate get their dream job is a highly satisfactory but quite challenging job. What makes this job even more challenging is when there’s limited staff in the HR department and 100s of candidates to assess. Text analysis helps in automating processes, including:
- 5 Use Cases of Text Analysis In Business Management, CEO Monthly; X/Twitter: @CEO_Monthly
17. Your software should detect and correct noise. “Text data can contain noise, such as spelling errors, typos, and non-standard abbreviations. This can make it challenging to analyze and extract data insights accurately.
“You can use techniques such as spell-checking and regular expressions to clean and normalize the text data before performing analysis to manage noise. For example, you can use regular expressions to identify and replace non-standard abbreviations with their complete forms.” - Sahel Eskandar, Challenges and Solutions for Text Mining. Performing Sentiment Analysis and Topic Modeling via Medium; X/Twitter: @Medium
18. Consider your integration needs and workflows. “Text analysis never exists in a vacuum. You will need to import data from a variety of different sources and possibly also export it to other applications. If you are integrating your text analytics into another application, you will likely need a tool with an API. Make sure you identify all these integration needs ahead of time so that you can find a product that can fit into your processes without a lot of extra work.” - Cynthia Harvey, Text Analysis Tools, Datamation; X/Twitter: @Datamation
19. Use text analysis software for spam filtering. “In most organizations, emails are still seen as the most official form of communication. Spam is its negative side, which has gotten worse in the twenty-first century. At least nine of every ten emails in my inbox are spam. Spams not only take up space but also act as a gateway for scams, infections, and other threats.
“To filter out more spam emails and provide the user with a better experience, businesses are working hard to filter out more and more spam using intelligent text analytics as opposed to the keyword matching utilized earlier.” - Ashesh Anand, Top 10 Applications of Text Analytics in 2023, Analytics Steps; X/Twitter: @Analytics
20. Prioritize tools with real-time insights and guidance. “Beyond being able to analyze vast amounts of data, one of the most powerful outputs of AI and ML is the ability to impact contact center interactions in real-time. Speech and text analytics tools can recognize key conversation indicators, such as customer emotion or compliance requirements.
“When those indicators are identified, such as a customer becoming increasingly irate during a conversation, AI-powered real-time features can deliver guidance to agents on how to handle those customer emotions or when it’s time to escalate to a supervisor. Similarly, if there are specific compliance statements that need to be said by an agent during a conversation, AI and ML can give agents reminders if they’ve missed those statements. This support increases compliance and reduces risk.” - Frank Sherlock, VP of International, CallMiner, Speech and Text Analytics: CX Today Expert Round Table, CX Today; X/Twitter: @cxtodaynews
21. Consider tools with visualization features or add-ons to make data digestible. “Extracting reliable and actionable insights into qualitative data, such as keywords, is complex and challenging, especially if you’re starting out. You can easily get overwhelmed.
“You need a tool than can pore through the qualitative data for low-hanging insights. Going manual is not an option. This is where text visualization charts come in. The charts use simple text analysis to help you visualize and summarize qualitative data, such as customer feedback and search terms.” - Top 4 Text Visualization Examples, ChartExpo; X/Twitter: @ChartExpo_
22. Determine whether a tool fits the needs of its users. “To capture the most value from Big Data, you need to implement a strategy that involves everyone in the company, from the C-suite to your customer-facing teams. Consider how analytics applies to different roles within your organization. Which users need simplified solutions to support decision-making? Do you need sales or marketing-specific tools? Do you have data science capabilities?” - How to Select the Right Data Analytics Tools & Platforms,3Pillar Global; X/Twitter: @3PillarGlobal
23. Ensure that your software can handle the amount of data you need to collect. “The ability of a tool to handle your data volume is similar to choosing a vessel that can carry your cargo without sinking. Small businesses may need a nimble speedboat while larger enterprises require an ocean liner. Understanding the volume and complexity of your data and ensuring that the chosen tool can handle it efficiently is imperative in navigating the vast ocean of information.” - Kathy Haan, The Best Data Analytics Tools Of 2023, Forbes Advisor; X/Twitter: @ForbesAdvisor
24. Consider supervised vs. unsupervised learning. “Text analysis often relies on machine learning, a branch of computer science that trains computers to recognize patterns. There are two kinds of machine learning used in text analysis: supervised learning, where a human helps to train the pattern-detecting model, and unsupervised learning, where the computer finds patterns in text with little human intervention. An example of supervised learning is Naive Bayes Classification.” - Text Mining Tools and Methods, Illinois University Library; X/Twitter: @IllinoisLibrary
Text analytics is a crucial element for understanding the customer journey. A comprehensive conversation analytics solution like CallMiner captures and analyzes all interactions across channels, combining analysis of text-based and speech-based interactions for valuable customer experience insights.
Yes, several AI tools can analyze text to help marketers, finance managers, contact centers, and other teams and departments extract and use important data. For example, CallMiner a conversational intelligence tool powered by AI that digs deep into customer and call agent conversations to improve customer, employee, and brand experience.
AI-powered tools that use natural language processing can quickly analyze text to provide businesses with the insight necessary to improve products, operations, and customer experiences.
To find the best text analytics software for your specific business needs, look for a tool with the necessary features, like the use of natural language processing, multi-language support, and the ability to analyze text from multiple sources. Start with a free trial or demo to ensure that the tool is right for you before committing to a subscription.