How often does your business conduct surveys of your customers, employees, and other parties? What types of questions do you use? Closed questions are those that have a predictable and limited set of answers. For example, any yes or no questions fall under this category. Open ended questions have broader responses and have the potential to give you new insights into your audience.

What are Open Ended Survey Questions?

An open ended question allows the respondent to use their own words to go into detail about what they’re thinking or feeling. This type of information is useful because it can give you hard data about assumptions you make on your audience, open up new opportunities, and give you feedback in a conversational tone.You end up with a lot of data to sort through and manually processing open ended survey questions requires a lot of resources. However, there are automated solutions available that make this process much easier.

Using Open Ended Survey Responses

Open-ended survey responses can be used in many types of research for your company, such as learning more about your customer experience, gauging why people responded the way that they did to NPS surveys, finding areas of discontent with employees, and countless other use cases.

Open Ended Survey Software

If you’re using an automated solution for working with this unstructured information, then you’re likely using a text analytics or sentiment analysis tool. These advanced solutions rely on artificial intelligence in the form of Natural Language Processing. The system translates what the respondents are saying into a form that the computer can understand and analyze. It accounts for many nuances of language, such as slang, different languages, grammatical rules, and identifies the underlying themes and emotion in the response.

The Benefits of Open Ended Survey Questions

Open ended survey questions may require additional tools to interpret the results, but this data offers a wealth of benefits.

Locate insights in verbatim comments

You never know what your audience might bring to light. Sometimes you have a strong understanding of the topics they’re likely to bring up, but in other cases, they come from left field. If you don’t let your audience have the opportunity to add details to their responses or make comments, then these insights would be lost.

Analyze social media feedback

Social media is a powerful tool for direct customer feedback. Whether people are responding to your content or sharing their opinions on their own pages, you’ll be able to see what they really think about your business. These posts can spur other conversations which may lead into completely new territory when it comes to your audience.

Document detailed feedback for products and services

You can be as broad or specific as you’d like with open ended surveys. Your audience may choose to expand on their comments or start talking about topics outside of the question. You may end up with a whole new line of survey questions following these replies.

Read between the lines

You learn as much from what people don’t say as what they do. Open-ended questions give you the opportunity to determine what the person is actually thinking, or what they’re implying in their response. This context can drastically change the tone of the feedback and the insights you get out of it.

Identify factors driving employee satisfaction

If you knew exactly what was wrong that was driving employee unhappiness, you could fix it. Open-ended surveys for employee engagement allow you to find out exactly what’s going on that drives dissatisfaction in the workplace, rather than guessing at the cause.

Discover the “whys” behind your audience’s choices

A simple yes or no question doesn’t show you why the person chose that response. They could be making the decision based on factors that you have already predicted, or for a completely different reason. You won’t know until you ask.

Allow your audience to speak with their own voices

You get a lot of value from this benefit, even if you don’t do anything with the unstructured data. By allowing your audience to engage with you as though they were having a conversation, they’re free to share their opinions in a form that works best for their needs. You can use this data for more than just sentiment analysis. You can also use it to make your sales and marketing efforts more relatable, by using their own voice and verbiage selection in the materials.

Using Text Analytics to Analyze Open Ended Survey Questions

You get a lot more out of your verbatim comments when you have technology helping you process them. Some of the most useful features that are available in this software category include:

Grouping topics

You can combine similar topics, create complex taxonomies, and learn more about the themes that come up in conversations with customers.

Creating Rule Sets

You have unique needs in your industry and using a custom rule set allows you to better analyze the data.

Scrubbing open ended survey data

You don’t want to worry about data breaches getting in the way of your text analysis or extraneous personal data getting in the way of results. You can also use this feature to clean up other parts of the responses that may not be relevant to your analysis, such as curse words.

Integrating with popular tools

Data sources come in many forms, and you’ll need an easy way to get that in and out of your solution. Great text analytics tools allow you to have multiple data sources, as well as APIs that give you a lot of flexibility in how you work with the solution.

Custom reporting and dashboards for visualization

Many people who aren’t data scientists need access to text analytics and the results. Powerful reporting and dashboard tools allow them to present the data in the way that works best for their job role, as well as create visualizations that are useful for non-technical users.

Comparing verbatim statements

You can see how your comments relate to one another through comparison tools. This option also allows you to see how comments change over time as you make improvements, introduce new products, and apply what you’ve learned to your operations.

Automating the translation process

When you’re working with more than one language, it’s convenient if translation can take place in the text analysis solution. You don’t have to worry about this feature if you’re only operating in areas that have a single dominant language. However, even demographics and regions that have a strong preference for an official language may feel more comfortable responding in another. Make sure that the languages you want to cover are supported by the solution.

Creating word clouds

This visualization tool allows you to see how frequently each topic is mentioned in the responses, with larger words denoting a greater number of responses pertaining to that topic.

Filtering unstructured data

You can focus on the unstructured data that is most relevant to your business, and filter out information that doesn’t have value.

How Do You Code Open Ended Survey Responses

The coding process varies based on whether you use a manual or automatic process. It does follow the same broad steps, just with a computer handling it and speeding up the process in the automated option.

Loading Open Ended Survey Data

The first step is to pull your open ended question data into the software that you’re using to process it. For manual coding, you may use a spreadsheet for keeping everything organized. Once this information is in the software, you’re going to want to eliminate the unusable data from it. This type of data includes incomplete responses, those typed in gibberish, and blank replies.

Cleaning Open Ended Survey Responses

By cleaning the data before you start to go through it, you can focus on the responses that matter. Look at each verbatim comment and identify any predominant themes and topics that stick out to you. Generally, you want to have a focused set of themes so that you can best understand the analysis. If you spread yourself too thin, then it may be difficult to get meaningful data from your responses.

Discovering Themes in Open Ended Surveys

You can see how many people comment on a particular theme, and then determine whether their response is a positive, neutral, or negative one in that area. Using text analytics software greatly speeds up this process and makes it possible to go through large data sets, which may be challenging for manual practices.

Free Text Questions

If you’re wondering what types of questions are commonly used to get open ended survey responses, you can try these ones out.

• How are we doing? Many customers know exactly the feedback they would like to provide to you. This simple question allows them to quickly provide that feedback, and helps you understand what is really on their mind.
• What interests you? Learn more about your audience and the things that they enjoy doing with their time. You can find potential new markets with this type of question, as well as being able to identify things that they value the most. See where you align and how you can use this information to create relevant sales and marketing material.
• What is your first impression of our company? How has this changed over time? You can learn more about your reputation in the marketplace, what customers think when they first encounter your brand, and ways that their impression changes over time. This question works well for your repeat customer base, especially those that have been with the company for months or years. If the shifts are favorable, then you can continue to follow those business goals and strategies.
• What reasons did you pick us over a competitor? Your unique selling proposition may be completely different from what you expected. Learn exactly why you end up getting picked over other companies in your market segment, and capitalize on that.
• What problems have we solved for you? You learn about the use cases for your product and how customers are actually using it, compared to the ways that you predicted they would use it. Sometimes you can discover completely new ways to use your products, which can inform research and development.
• What problems are we not solving for you? You get a better understanding about other pain points in the customer’s life. While you might not be able to solve all of these issues, you can potentially address them in future products and services.
• How do you use our products? This is another way to find out about the use cases that are in real-world situations. You may find that the use cases are much broader than you expected.
• What’s your feature wishlist for our products? You may not be able to use all, or even half of these suggestions, but it’s useful to see what your customers are thinking about your current offerings. Some feature requests may make it into an upgraded version of the product or as a completely new release.
• How did you feel during your last experience with our company? Learn more about recent interactions your audience had with your company. You can use this question to find out whether your customer experience remains strong, or if there are issues cropping up that could impact their overall impression of your company.
• What would you like to see our company do? This question is another one that will have a range of responses, covering everything from unexpectedly useful to off the wall outrageous. It’s great to have food for thought. This is another opportunity to find new markets to expand into and better learn how to serve your customers.

Analyzing your open ended survey questions is an excellent course of action to take your business to the next level. If you’re currently handling this process manually, an automated software solution for text analytics and sentiment analysis can boost your productivity significantly and allow you to process much larger data sets.

Uncover More Insights in Verbatim Comments

If you are looking to uncover insights from verbatim comments with ease, check out Ascribe’s fourth generation text analytics offering, CX Inspector. CX Inspector, is a customizable and feature-rich text analytics tool that provides topic and sentiment analysis from verbatim comments automatically.For a more comprehensive solution add X-Score, a customer measurement approach that provides a customer satisfaction score from open-ended comments.

Natural Language Processing, or NLP, allows computers to understand the natural language of humans through artificial intelligence technology. Solutions that use natural language processing products deliver significant value to businesses that understand how to harness its potential.The typical NLP implementation uses machine learning algorithms to process unstructured data and output it into a format that makes sense for computers. The two parts of language that NLP looks at in this data are the syntax and the semantics.The syntax focuses on the grammatical rules of language and looks for the intent of the text based on this information. It’s also useful for identifying words that have similar meanings, segmenting phrases into words and units, finding the parts of speech in a sentence, looking at the root form of a particular word, and parsing what the sentence as a whole means.Semantics looks into the meanings of words and phrases in context. You don’t always get the proper understanding of text based on purely grammatical analysis, especially when it comes to slang terms and phrases. When NLP tools look at semantics, it can interpret this data.

NLP Use Cases

Natural Language Processing NLP has many practical applications in the business world, no matter what your industry is. Here are some of the most common use cases you’ll find with this technology.

Sentiment Analysis

In an age of social media, your audience isn’t afraid to share their opinions of news. This type of direct feedback is market research gold, but only if you can process it to find trends, patterns, and overall impressions. Sentiment analysis accomplishes this by taking unstructured text data and analyzingit to determine key insights. Many companies have a wealth of data that they’re unable to access since sentiment analysis is not a process that you can manually do at scale. NLP automates a significant portion of the work so you can move forward with strategic decisions. It can create topics, run analytics on the text, and look for like words.

Chatbots

Chatbots are a popular way for companies to offer basic support and information 24/7 without hiring an exponentially growing amount of staff to do so. People who send the messages don’t need to use special keywords to interact with the bot. It picks up on the meaning of the person’s sentences to present them with relevant content. For more advanced requests, the bot is capable of connecting that individual with a staff member.You end up quickly helping a large portion of your audience with their basic needs, as many requests fall into this category for the typical business. This part of your audience can be happy with the immediate assistance they received, even if it happens to be in the middle of the night. Your staff members get their time freed up so they can focus on providing an excellent customer experience for people with more complex requests. They don’t have to feel rushed off the phone due to a queue that grows by the second, and that allows you to cement a reputation for a customer-centric company without straining your resources as you grow.

Structuring Unstructured Data

Think about all of the unstructured data that ends up being untapped because you lack a way to work with it efficiently. NLP opens up the possibilities for this information, and some NLP solutions also support a mix of structured and unstructured data. As more data sources develop in the future, having a plan in place for the unstructured data is key to maintaining a competitive advantage.

Digital Personal Assistants

Digital personal assistants rely heavily on NLP to understand user requests, with a strong focus on voice input. Microsoft, Google, Apple, and Amazon all have their own versions of this use case, and popularity continues to grow for it. Whether you want to get your content on one of these platforms or you have a product that could benefit from voice control, there are many ways to implement this option in your organization.

Automated Translation

Global companies may have text data in dozens or hundreds of different languages. The market needs in one country can be quite different from the next, especially when it comes to figuring out whether your marketing messages are appropriate for the region. Automating the translation process frees up staff time and increases their productivity when working with multi-language analysis. The automated translation feature may be included alongside other NLP tools or as a stand-alone solution.

Speech-to-Text Improvements

Dictation is heavily used in many industries, especially in the medical field. Misinterpretation of voice data can lead to mistakes on medical records and potentially deadly consequences, so accurate transcription is an important issue. NLP works to improve speech-to-text tools by looking at the context of the discussion and what the person is trying to convey.Voicemail transcription is another area where this use case shines. Rather than listening through each voicemail, you can get a basic overview of what someone is calling in about with NLP. This allows you to automatically assign messages to the right staff members, identify priority cases, and process the text for sentiment analysis and other insights.Live captioning of live streaming content is another example of this use case. This type of tool automatically creates subtitles for live videos to make the content more accessible to people who are hard of hearing, have difficulties processing speech, or are deaf. It’s also useful for video viewers who have their sound muted.

Interactive Voice Response

Interactive voice responses on phone lines have improved significantly over the years through advances in NLP. These changes make it possible to better route calls, provide callers with the information that they need without staff intervention, identify whether someone is frustrated or irate and would benefit from talking to specialists in de-escalation and to track call metrics based on the reasons for calling.

Recruiting NLP Specialists

NLP is a highly flexible and valuable technology, and as such specialists in this area are in-demand. It can be challenging to recruit NLP specialists, especially if you want to assemble a data scientist team. There are a few options when you want to recruit in this competitive area.

• In-house: You would recruit, train, and hire the employee on as an in-house, full-time or part-time employee. In addition to the difficulties in finding these candidates, you also have to offer a significant benefits package that leads to many overhead costs with each hire.
• Outsourced: You avoid the overhead costs of an in-house hire and have the option to bring in an outsourced data science service based on your current projects. However, that service works with multiple clients and may not be able to scale up with you as you grow.
• Upskilling: This is the most cost-effective way to bring on a data scientist. Look for potential candidates in your workforce and pay for their training and upskilling. They’re already familiar with your organization, and investing in their skills in an area they’re interested in leads to a more engaged and loyal employee.
• Flexibility: Do you really need the data scientist to be in-house? If you have the opportunity to look at remote candidates, then you open up more possibilities for specialists. This option also allows you to make the position more accessible to disabled applicants, who may be unable to go to a traditional workplace, along with candidates who have responsibilities that require them to be close to home. For example, someone caring for an aging parent may not be able to be on-site.

Choosing an NLP Solution

NLP solutions come in all shapes and sizes. When you’re evaluating your options, consider the type of data that you’re working with, your business goals, the type of insights you hope to gain from unstructured data, and the resources that you have on-hand to implement the solution.If you don’t have data scientists on-staff, for example, you probably don’t want to choose a barebone solution that requires custom development to use. All-in-one and comprehensive NLP solutions that are user-friendly can be excellent choices for non-technical business users and those that don’t need access to the machine learning engine.NLP is an exceptionally useful technology for your organization and allows you to harness the power of all of your data. Think about the ways that you can make it work to support your business goals.

The feedback you receive from customer surveys, market research studies, employee polls, and other sources can be generated from open ended and close ended questions. Both types have their benefits, but open ended questions excel in giving you deeper insight into the respondent’s thoughts.

What is the Difference Between Open Ended Questions and Closed Questions?

Closed questions have a predictable and specified set of responses. Typically the answers to these questions are either selecting from multiple choice or giving a yes/no reply. Open ended questions do not have set responses, and encourage respondents to give their thoughts and opinions in a freeform format.Closed questions work best for basic demographic questions and simple inquiries that have a binary choice. You end up with quantitative data.Open ended questions are excellent for diving deeper into why people respond the way they do. You’re able to explore a variety of topics and may encounter unexpected and unpredicted feedback. This is qualitative data.

The Benefits of Including Open Ended Questions on Your Surveys

Open ended questions allow you to start a conversation with your audience. You get a straight from the source look into the thoughts, emotions, values, interests, and other factors that influence their decisions.These questions provide you with added information on your close ended questions, since it gives the respondent a space to elaborate. The potential for discovering completely new information about your audience opens up opportunities in many areas.

Use Cases for Open Ended Questions

There are many places where open ended questions are helpful. Here are a few of the most common use cases.

• Evaluating audience reception of new products and services
• Learning about the features that your audience wants to see in products and services
• Finding out more about your customer experience
• Gauging the satisfaction of your employees and customers
• Getting more detail from a client or vendor
• Having conversations with current and former customers
• Understanding your company’s brand awareness and reputation

Examples of Open Ended Questions and Sample Responses

Here are a few open ended questions and responses that you might see when you start using this option.

What parts of your experience did you like the most and the least?

This question allows you to find out about problems in your customer experience in general or specific touchpoints. You get to learn more about what you’re doing right and wrong, and gain more information about how your audience perceives your brand.Some responses you may receive to this question include those that go into detail about their most recent or overall experiences, reviews of their last interaction, points of frustration, and where you’ve delighted the respondent.

What influenced your decision the most?

You’re learning more about the thought processes and emotions that are involved in the decision that you’re asking about. Some ways that you can use this question are learning more about a store visit, purchase, marketing plan, or employee interview.The responses to this open ended question often involve walking you through the thought process at each step, elaborating on the areas that are most important when making decisions, and the problems they ran into that influenced them.

What improvements could be made in the workplace?

If you’re having problems with employee turnover, a lack of engagement, or other issues with your workforce, you can use this question to discover the biggest complaints.The improvements could cover everything from needing the right technology to wanting more pay for the work that they’re doing. When you have happy, engaged employees, you see a benefit at all aspects of your organization, including with your customer experience. You may not be able to solve all of the problems overnight, but incrementally improving the workplace can lead to excellent results.

Where do you research product information?

You gain several benefits from asking this type of question. You get to learn about who the customer trusts to provide information about the products they want to purchase, find out how long it takes before the buyer gets in contact with you, and discover ways to position yourself as the trusted authority in your market segment.The responses could range from a list of websites and publications to a full description of the steps they take when they’re researching and evaluating their choices.

What are your main concerns about your decision?

Why did you choose us over another company?

What challenges do you want to overcome in your life?

What are your interests?

What values are most important to you?

Younger demographics are focusing more on value-based decision making when they look at who they want to do business with, the types of products they purchase, and the companies that they want to work for. If you want to optimize your customer experience for this audience, then you’re going to need to know what values are driving their decisions.The responses to this question will show you the values that are most important to your audience, and how they influence each decision made. For example, someone may feel strongly about animal welfare and seeks out cruelty-free products to purchase.

How to Analyze Your Open Ended Question Responses

You may have hundreds, thousands, or even millions of responses to your open ended questions. Since the respondents don’t have a uniform way of answering, they can come in many styles and formats. These responses are called verbatim comments, and your typical survey analytics tools won’t be able to work with this unstructured data. Manually sorting through all of the surveys with open ended responses can be prohibitively resource intensive. Thankfully, there’s a software category that can help.Text analytics tools use Natural Language Processing technology to work with this information and provide you with actionable insights. When you deal with large data sets of verbatim comments, using this type of solution automates significant portions of the process for cost-effective analysis.

Natural language processing, or NLP, is an exciting technology that holds the key to making sense of your ever-growing collection of unstructured data. Everyone from your customers to your employees are giving you data that’s critical for optimizing your operations and improving your business, but it remains locked behind a format that’s indecipherable for computers.NLP acts as the translator between the human and computer, and powers NLP analytics solutions. Once the computer has a way of working with text data that it understands, it can process the data and surface a variety of insights.

How is NLP typically used?

Human speech may follow certain rules, but there are many deviations from that pattern that require AI-powered NLP to step in. Just think of how hard it would be to program in all of the exceptions to rules, slang terms, and other linguistic differences that emerge over time.NLP uses machine learning as a way of gaining that understanding on its own. The system can adapt to language shifts over time and optimize itself for the type of unstructured data that your company commonly works with. You probably have a significant backlog of unstructured data that could use processing, so it’s not challenging to get the solution up and running with real-world data.The NLP solution can identify notable trends in text, which is helpful for a range of use cases. You end up with an accurate analysis that can drive your data-driven decision making. It’s capable of quickly coding responses into different topic areas, so you can see at a glance what continually gets mentioned in the feedback.

What is NLP data?

One of the most common forms of NLP data is responses to open-ended questions. Since this text input doesn’t fall into a predefined response, the computer needs help finding out the true meaning and intent of the person. Since many decisions can be driven by emotions, sentiment analysis allows you to get inside the audience’s head to better understand their decision-making processes. Another form of NLP data is spoken language, commonly used for solutions that have speech-to-text functionality.

Why NLP analytics is important for businesses

You can’t set your organization up for long-term success by flying blind in the face of text data. With NLP analytics, you can:

• Process unique responses to surveys without significant manual work involved: If you needed workers to manually go through each response, code it, and try to make sense of overall patterns, they could spend hundreds or thousands of hours on a single project. NLP analytics takes a fraction of the time to perform this functionality and eliminates human errors from the equation.
• Discover what people think about your products and services on social media: Sorting through hundreds and thousands of social media comments can be a tiring process, and important data could remain hidden in it. NLP analytics allows you to get to the bottom of the responses, whether you’re managing a social media crisis or want to know what people are saying about your latest announcement.
• Explore how people feel about new products and services: You don’t want to wait around for months to determine whether people enjoy your latest offerings. NLP analytics quickly gathers these insights so you can make decisions based on fresh responses.
• Identify high-priority customers who are irate, upset, frustrated, or otherwise in a state where your customer experience isn’t living up to expectations: This audience segment has one foot out the door and they’re going to loudly proclaim that to anyone who’s listening in their social circle. You can recapture these customers by offering fast resolution, specialized resources, and sending the case to an escalation team.
• Tap into employee feedback to identify areas of process inefficiency, potential investment, and other improvements: You can learn a lot about your business when you have the opportunity to hear from employees at all stages of the company. Your schedule might be too busy to individually talk to each person, but NLP analytics can take their open-ended responses and present you with findings on an on-going basis.
• Quickly respond to sudden changes in the market driven by shifting sentiment: It’s hard to predict where a game-changing technology, product, or business is going to come from. In some industries, you can see shifts coming from 10 miles away. In others, it seems like the industry changes overnight due to startups. Speed up your reaction time by watching trends in your unstructured data.
• Create a consistent structure for analyzing open-ended responses: Each person looking at verbatim responses and other unstructured data will have their own way of interpreting the information. You end up with inconsistencies that can muddy the data and make it less useful for analytics. Automated NLP analytics creates a consistent structure for evaluation, and changes to the model are applied throughout the system at the same time.
• Reduce biases in text data analytics: Bias comes in many forms in data analytics, and it can lead to many problems with your data. While you can’t completely eliminate biases, as algorithm development and learning data both have the potential to introduce biases, you can reduce the effect they have on the end result.

These are just a few ways that NLP analytics enriches your business and fuels your growth.

Working with NLP analytics data

Once your NLP system finishes processing and analyzing your unstructured data, you can pull it into reports, run it through other big data solutions, visualize the data, and create custom data dashboards.Combining NLP analytics data with other sources can provide a more complete view of the information that you’re working with. For example, if you combineopen-ended question data from surveys with the associated customer profile in your customer relationship management platform, you get a deeper look at the way they think and feel during their interactions with your company, products, and services.While a significant portion of the process is automated, humans still play an important role in NLP. The models used by machine learning text analysis are developed and maintained throughout the life of the software. As your business needs change, the algorithms that you use for your NLP solutions will also shift. For example, if you expand into new regional markets, you’ll want to make sure that regional dialects and slang are included. The machine learning models and quality source data are essential for getting the most out of an NLP analytics software, such as text mining tools. Learn more about the importance of text mining.

NLP analytics functionality

Each NLP analytics solution has its own feature set, tailored to the type of solution and the intended end user. However, this is a list of the most common features that are available in NLP analytics platforms:

• Accurately detecting languages in responses: The system can pick up on the language in the response and use the appropriate model for working with it.
• Translating and localizing text: Machine translation and localization take place on the backend, so the unstructured data doesn’t need to go through a different software before it’s translated.
• Picking out parts of speech: Before the application can look at the deeper meaning of open-ended responses, it needs to look at the grammatical structure of the text. This step allows the NLP application to begin putting together the context and sentiment of each response.
• Assembling a topic list: Quickly look over the common topics in the feedback. You can take a broad perspective or start drilling down into the data for a closer look.
• Considering context when analyzing text: The NLP solution looks at the question that the person responds to, the channel that the response was made, and other important context clues that could color the data.
• Surfacing named entities: If you want to see how often certain products, services, companies, and other named entities are mentioned, NLP analytics solutions support this use case. You have a lot of flexibility with this tool.
• Digitizing and analyzing hard copy documentation: The paperless office hasn’t happened for many industries, and valuable information could be hiding on these pages. Optical character recognition, or OCR, software converts hard copies into digitized documents that can be edited and manipulated. NLP analytics solutions can look at this text for valuable information.
• Providing appropriate assistance for customers without human intervention: Your audience might expect 24/7 responses but that doesn’t mean that it’s a feasible option for your company. Automated chatbots can leverage NLP tools to understand the context of the requests, as well as analyzing this unstructured data for details about your customers.
• Automatically creating structured data out of unstructured data sets: Structured data has a countless amount of software that can analyze it and otherwise work with it. Once your unstructured data is turned into structured data, you have the capability to look at it with many types of tools.
• Sentiment analysis: One of the biggest use cases for NLP is understanding exactly what someone means when they provide feedback, so it’s not surprising that sentiment analysis is frequently found in these tools.
• Creating a document synopsis: Some companies have a vast content library but it’s not organized or structured in a meaningful way. NLP can create synopses for these documents, as well as identifying overall topics and categories that they should be sorted into.

NLP analytics will continue to be a valuable tool for getting the most out of your company’s data. By understanding the role of NLP analytics in working with unstructured data, you can capture the insights that are hidden in these data sets.

How much of your business data is sitting around unused? You may have many ways to work with structured data, but your unstructured data gets overlooked due to the difficulty involved in the process. Relying on structured data alone means that open-ended comments and responses don’t factor into your reports and business goals. You could overlook opportunities, miss out on valuable customer feedback, and fall behind on optimizing the customer experience. Text mining fixes this problem through a variety of helpful capabilities that makes the most out of verbatim comments and other unstructured data.

Benefits of Text Mining

The importance of text mining can not be underestimated. Text mining delivers significant value to your business, as you’re better able to harness the insights that are hidden in your current and future data sets. Here are the benefits your company can realize by adopting text mining solutions, from coding to visualization.

Delve Into Your Unstructured Data

You don’t get a lot of value out of open-ended comments that are discarded due to a lack of processing power. Text mining empowers your organization to quantify this information. Rather than getting bits and pieces of the sentiment behind comments, you gain visibility.

Optimize the Experience

Without data from your open-ended feedback and other verbatim comments, you’re not able to fully optimize experiences for users, customers, clients, employees, and other stakeholders. While you can make some improvements based solely on structured data, you can better understand what stakeholders want and how they react to changes with text mining.You don’t want to miss the mark with customer experience optimization, especially in competitive marketplaces. Keep your customer satisfaction high by fully understanding where they’re coming from, what they’re feeling, and what they want going forward.

Generate Reports Faster

Automated processing of many parts of the text analysis means that you don’t have to pour countless resources into the project. Coding, sentiment analysis, and other time-consuming parts of text mining get handled through the software itself, with the help of Natural Language Processing and other artificial intelligence tools.

Create Custom Views of Your Data

Cut through the signal to noise ratio by putting together custom dashboards with the most important information. Each person involved in the project can have their own view of the data to better inform their particular position. People who are not data scientists appreciate the useful visualizations and easy access to relevant data that’s provided in these types of dashboards.

Increase Productivity Through Automation

Manual, time-consuming processes can lead to employee disengagement, especially if you’re working with data scientists and other highly skilled data professionals. Not only is the productivity improved through a faster text mining method, but you also get better use out of your staff and their work hours.

Gain a Single Version of Data Truth Through Integration

Siloed data is an enemy to accurate data and reporting. If your unstructured data is spread throughout multiple platforms, machines, spreadsheets, and other files, then it’s possible that people are working on different versions of that information. Text mining tools that have native integration for pulling data sources into the platform, as well as those with APIs that make it simple to work with your sources, consolidate all of this information in one place. You won’t have version control issues and decreased data quality concerns.

Automating Translations

Global companies may receive feedback in multiple languages. If you manually translate these verbatim comments, then you’re adding a lot of time to the process. Text mining tools can automate the translations and have the capability to understand multi-lingual sentiments.

How Text Mining Improves Decision Making

Rather than guessing at why your audience picked the scores that they did on a survey, you can evaluate their verbatim comments to open-ended questions.Sometimes your audience will surprise you with unexpected use cases and feedback. This will help you identify new opportunities and markets, as well as better serve your customers and other stakeholders.If you roll out a new product, service, or campaign and there are problems with it, customer feedback also helps you speed up your reaction time to these issues. This benefit also allows you to make fast decisions when you’re running tests or putting new initiatives in place.Data-driven decision making is essential for getting buy-in in organizations. When you can point to hard data that backs up the need for improvements, changes, and other initiatives, you’re better able to make your case with your bosses.

Examples of Text Mining

Text mining comes in many forms, depending on your business needs. It’s a flexible technology that adapts to your goals in the short and long-term. You may use one or more sources for unstructured data, which will now deliver many insights that were previously inaccessible due to the difficulty of manually processing this information. Some text mining solutions also work with structured data so you can perform both types of analysis within the same software. Here are examples of tests, surveys, and other research that benefit from text mining technology.

• Net Promoter Score®
• Advertising campaign tests
• Voice of the customer
• Ad creatives tests
• Concept testing
• Satisfaction testing of customers, employees, clients, patients, and others
• Survey and social media comments
• Customer support logs
• Trouble tickets

Text mining these unstructured data sources is helpful in many parts of your organization. With companies becoming focused on the customer experience and employee engagement, sentiment analysis becomes a necessary part of your critical business infrastructure.Continual improvement is what sets companies apart from one another in the modern business world, and it’s difficult to achieve that without considering open-ended survey responses.Make the most out of your company’s data and achieve the competitive edge that’s needed to take you through 2020 and beyond. Incorporate text mining software into your analysis workflow and enjoy the power and flexibility it delivers.Net Promoter^®, NPS^®, NPS Prism^®, and the NPS-related emoticons are registered trademarks of Bain & Company, Inc., NICE Systems, Inc., and Fred Reichheld. Net Promoter Score^SM and Net Promoter System^SM are service marks of Bain & Company, Inc., NICE Systems, Inc., and Fred Reichheld.

You can have the most advanced market research solution in the world, but it won’t be effective if you fail to ask the right questions. The example market research questions featured below will give you a starting point when determining what you should ask your audience and internal stakeholders.

Questions to Ask Consumers

When you send out customer satisfaction surveys, Net Promoter Score surveys, and other initiatives to collect feedback, these questions provide a wealth of market research data.

Basic Demographic Information

Questions that fall under this category include the person’s age, household income, their job title, location, and other standard demographic data. You want to ask questions in this category so you can segment your buyers and learn more about the differences between each group.

Would You Recommend Us to Your Friends and Family?

This question is typically used when you’re conducting Net Promoter Score surveys. You can discover your customer advocates, detractors, and those in-between.

Are You a New Customer or a Returning One?

Gain a better understanding of whether you are retaining customers and driving repeat sales, or if you’re bringing in a higher proportion of people who are new to your brand.

How Long Have You Been Purchasing From Our Company?

If you have repeat customers, you want to get an idea of their lifetime value based on how many purchases they have made and approximately how many purchases they will make in the future. When you build up a highly loyal, long-term audience, you end up in a good position for sustainable business growth.

What Do You Do in Your Free Time?

You can learn a lot about your audience by discovering their hobbies and interests. Discover opportunities that you haven’t considered and new products and services that fit into these areas.

What Sources Do You Use to Learn About New Products and Services?

Do you know where buyers get pre-sales information from? You can identify weaknesses in your content marketing strategy and other parts of the sales funnel with this question. You may need to improve the resources available on your website and collaborate with leading publications and websites in your market.

How Do Our Products and Services Solve Your Problems?

You learn more about common use cases and can potentially identify problems that you haven’t considered. Your audience may end up using your offerings in unexpected ways, leading to new releases and features. They could also use help with the areas that your products overlook. Fill in the gaps and give your customers what they’re really looking for.

What is the Biggest Challenge You Face in Your Life?

Learn more about what challenges your audience faces on a day-to-day basis. You can make improvements to address these areas or move into new markets.

Why Did You Buy Our Products and Services Over a Competitor’s?

Buyers had some reason that they chose your company over others in the market. Find out what that is, whether it matches up to your unique selling proposition, and how you can capitalize on these competitive advantages.

What Do You Enjoy the Most and the Least About Our Products and Services?

Discover your standout features as well as those that aren’t meeting customer expectations.

What Features Would You Like to See Added to Our Products and Services?

Customer feedback can be a great way of putting new features on the roadmap.

How Would You Rate Your Last Experience With Our Brand?

While the overall customer experience with your brand is important, you also want to learn more about each touchpoint. You can discover customer experience trends, dig deeper into isolated bad experiences, or those that point to a need for improvement.

Questions to Ask Internally

Market research and competitive analysis also require asking a number of internal questions. By using customer and internal surveys, you develop a complete picture of the customer experience and engagement of both sets of stakeholders.

What Does the Competitive Landscape Look Like?

This question provides an open-ended opportunity for employees to discuss the marketplace, movers and shakers in the industry, significant product and service releases, changes in technology, business models, and other details that impact your sales potential.

Do Our Current Buyer Personas Serve Our Sales, Marketing and Product Development Needs?

Your audience changes over time and your buyer personas should do the same. If you try to stick with the same set of personas as when you first started your business, you fall behind the competition and create mismatched expectations.

What Products, Services, or Features are Customers Using More Than You Anticipated?

Sometimes products take off unexpectedly, whether they achieve viral success or they become more widely adopted than predicted. You can gain more information on which products are seeing high usage, whether you need to allocate more resources to those offerings, and how you can use the momentum to secure a competitive advantage.

How Does Our Customer Experience Match Up to the Competition?

The customer experience is a critical factor in gaining and growing your customer base. If you don’t know what your competition is doing and how you match up, then you won’t be able to optimize your operations to beat them. Pay close attention to the expectations that leading companies set in your marketplace, if you don’t already hold one of the top spots.Each customer brings a set of expectations with them when they interact with one of your touchpoints. If you build your sales funnel based on a strong understanding of the typical experience, you can improve on it.

How Can You Adapt to the Changing Needs of Consumers?

The customers you serve today may be significantly different than those who come years later. As the marketplace and technology change, you need to adapt to stay relevant. Have a plan in place to accommodate these needs. Your business processes and infrastructure should be flexible enough to evolve over time

Incorporating a live interviewer in your survey strategy is a tactic researchers have used for years. Yet, it remains a useful practice for collecting meaningful data from respondents today.

However, what is being done to experiment with the flexible capabilities of CAPI technology –  to approach face-to-face surveys uniquely?  

Advanced CAPI software can be a powerful tool that opens the door to dozens of new uses. Think of personal interviewing software that empowers teams to access more respondents from a wider sample.

In this blog, we’ll dive deeper into the concepts of CAPI survey software and learn how it is a significant tool in data collection.

Let’s begin with the basics.

What is a CAPI survey software?

CAPI stands for ‘Computer Assisted Personal Interviewing’, in which an interviewer enters survey questions onto a tablet or phone during the interview.

CAPI survey software is a tool that enables it. It is like any other survey tool, for example, online survey tools, IVR, etc.

What are the advantages of using CAPI survey software for data collection?

Using CAPI survey software for research has several benefits, some of which are:

Efficiency and Accuracy: CAPI software can help interviewers administer surveys more efficiently and accurately. The software can guide interviewers through the questionnaire, ensuring that questions are asked in the correct order and responses are recorded accurately.

Real-time Data Collection: Data collected through CAPI software can be instantly transmitted to a centralized database. This eliminates the need for manual data entry, reducing the chances of errors and allowing for faster data analysis.

Complex Question Logic: CAPI software can handle complex skip patterns, branching, and routing in questionnaires. Depending on a respondent’s answers, the software can automatically skip irrelevant questions or guide the interview in a specific direction.

Multimedia Integration: CAPI software can incorporate multimedia elements, such as images, videos, and audio clips, to enhance the survey experience and gather more detailed responses.

Data Validation: CAPI software can include built-in data validation checks. This helps ensure that respondents provide valid and consistent responses by flagging or correcting errors in real time.

Offline Capabilities: Some CAPI software can operate offline, allowing interviewers to collect data in areas with limited or no internet connectivity. Once a connection is available, the data can be synchronized with the central database.

Security: CAPI software can implement security measures to protect sensitive data. Encryption and access controls can be implemented to safeguard both respondent data and the survey itself.

Monitoring and Supervision: Supervisors can remotely monitor interviews in progress, providing support to interviewers if needed. This can lead to better quality control and consistency in data collection.

Remote Administration: In cases where face-to-face interviews are not possible, CAPI software can enable remote data collection through video conferencing, allowing interviewers to administer surveys virtually.

Customization: CAPI software often allows researchers to customize the survey layout, design, and branding to match their needs and preferences.

What are some of the most popular CAPI survey software use cases?

CAPI survey software can be used for a variety of purposes. Here are some of the most popular use cases of CAPI survey software:

1. Frontline for Respondents

Let’s begin with a more straightforward use case for the software: CAPI surveys can act as the first gateway to deeper data from engaged respondents down the line. For example, a quick in-person survey can lead to a follow-up self-complete survey and can eventually lead to an invite to join a curated panel.

When using CAPI as an ingredient in a richer multi-channel survey system, personal interviewing software can be used as a first step to a longer, more engaged path to getting quality insights from respondents in many forms.

2. Elderly Needs Assessment Surveys

As we age, it becomes challenging for some to remain independent in their homes. Healthcare researchers, government, and home care services often use CAPI software to collect needs assessment surveys and collect data.

The health care professionals conduct an in-house computer-assisted personal interview with elderly respondents and their caregivers to evaluate living situations and health needs. With CAPI insights and a resulting assessment, elderly and disabled individuals can be supported with a customized care plan.

3. Multilingual Self-Completion

Connecting with tourists worldwide can be difficult, as there is a hurdle of language for interviewers to overcome with international respondents.

However, the insights collected at these locations are incredibly important; they can allow market researchers and tourist boards to make informed decisions about what’s driving the local tourism economy. An effective CAPI tool should allow interviewers to seamlessly change the language of the survey and turn the device towards the respondent for a direct answer.

4. Live Event Dashboards

File under “another unexpected use case”: CAPI tools can be used for fun and engaging live results displays at events, whether a tradeshow or conference.

At these events, organizations can incorporate interesting questions into a face-to-face interview. As interviewers chat with event attendees, the responses to the questions are synchronized via Wifi, with the results on a live display.

The results start meaningful conversations between interviewers and respondents and encourage participation in further surveys later on. Not to mention, this tactic will drive brand awareness in its uniqueness!

The many uses of CAPI software are not necessarily limited to those mentioned above!

Whether you’re using CAPI software for the tried and true purpose of in-person fieldwork or something a little different, working with these outreach tools will streamline your data collection processes and allow you to further become an information leader within your organization.

For those interested in broadening their toolkit, discover other market research tools that can complement and expand upon the capabilities of CAPI software.

What are the limitations of CAPI survey software?

Even though Computer-Assisted Personal Interviewing (CAPI) survey software offers various advantages for data collection, it also has its limitations. Here are some potential limitations of using CAPI survey software:

Technical Requirements: CAPI software requires electronic devices (such as tablets or laptops) for data collection. This can be a limitation in areas with limited access to such devices or where respondents are uncomfortable with technology.

High Cost of Implementation: Implementing CAPI software requires an initial investment in hardware (devices for interviewers and respondents) and software licenses. Additionally, ongoing costs may be associated with software maintenance, updates, and technical support.

Requires Training: Interviewers must be trained to use the CAPI software effectively. This training can take time and resources, and there may be a learning curve for interviewers unfamiliar with technology.

Data Security and Privacy Concerns: Storing sensitive respondent data on electronic devices raises concerns about data security. Proper encryption, data storage, and privacy protocols must be in place to prevent unauthorized access or data breaches.

Digital Divide: CAPI surveys assume that respondents are comfortable with technology and have access to devices and the internet. CAPI may exclude certain populations from participating in regions or communities with limited technological infrastructure.

Interviewer Bias: Even with CAPI software, interviewers can still introduce bias through their tone, body language, or behavior. Additionally, interviewers might inadvertently influence respondents during the survey.

Response Authenticity: In face-to-face interviews, there may be challenges in verifying the authenticity of responses. Interviewers might be unable to determine if respondents provide accurate information, which could affect data quality.

Complexity: While CAPI software can handle complex skip patterns and branching, designing and implementing these features can be time-consuming and prone to errors if not set up correctly.

Technical Glitches: Electronic devices and software can experience technical glitches or

malfunctions during interviews, leading to data collection disruptions or data loss.

Limited Interaction: CAPI surveys might limit the scope for open-ended responses or in-depth qualitative data collection compared to other survey modes like face-to-face interviews or focus groups.

Cultural and Language Barriers: CAPI surveys might not be suitable for populations with diverse languages, dialects, or cultural backgrounds, especially if the software doesn’t support these variations effectively.

Limited Access to Internet: While some CAPI software can work offline, certain features or functionalities might require an internet connection, limiting data collection in areas with poor or no connectivity.

Lack of Non-verbal Cues: In face-to-face interviews, interviewers can gather insights from respondents’ non-verbal cues. CAPI surveys may miss out on these cues, potentially affecting the interpretation of responses.

Sample Bias: The use of electronic devices may lead to a bias in the sample, as respondents who are more comfortable with technology might be overrepresented.

Considering these limitations is important when deciding whether to use CAPI survey software for data collection. Depending on the research context, some of these limitations might be mitigated with careful planning, training, and adaptation of the survey methodology.

How to Use CAPI survey software?

Using Computer-Assisted Personal Interviewing (CAPI) survey software involves several steps, from designing your survey to collecting and managing data. Here’s a general guide on how to use CAPI survey software:

Choose a CAPI Software: Research and select a CAPI software that suits your research needs. Look for question logic, multimedia integration, offline capabilities, and data security features.

Survey Design: Design your survey questionnaire using the software’s interface. Create questions, add response options, set skip patterns, and include multimedia elements like images or videos.

Device Setup: Set up the devices (tablets or laptops) that interviewers will use to administer the survey. Install the CAPI software on these devices and ensure they’re properly configured.

Training: Train your interviewers on how to use the CAPI software. Ensure they understand the questionnaire flow, know how to navigate the software, and any troubleshooting steps.

Pretest: Conduct a pretest or pilot study to identify any issues with the survey design, question logic, or software functionality. Make necessary adjustments based on the feedback.

Data Management: Set up a database or cloud storage system to store the collected data securely. Ensure that data is encrypted and backed up regularly.

Offline Setup (if applicable): Configure the software for offline use if your survey will be conducted in areas with limited internet connectivity. Ensure that collected data can be synchronized with the central database once a connection is available.

Interview Administration: Here’s the typical process for administering interviews using CAPI survey software:
a. Interviewer logs in to the software on the device. b. Selects the respondent from the list provided or enter respondent information. c. Administers the survey following the software’s guidance, which may involve reading questions aloud to the respondent and recording their responses.

Data Validation: The CAPI software may include data validation checks to catch real-time errors. Ensure that interviewers understand how to address validation errors during the interview.

Monitoring and Support: Supervisors can remotely monitor interviews in progress to provide assistance if needed. This helps maintain data quality and consistency.

Data Synchronization: If interviews were conducted offline, synchronize the collected data with the central database once an internet connection is available.

Data Analysis: Once data collection is complete, export the collected data from the CAPI software to a compatible format for analysis in statistical software.

Data Security and Privacy: Adhere to data protection and privacy regulations. Ensure that sensitive respondent data is handled securely and that proper encryption and access controls are in place.

Quality Assurance: Conduct regular data checks to identify inconsistencies or errors. Cross-reference collected data with source materials if necessary.

Documentation: Maintain detailed documentation of the survey design, software settings, and any issues encountered during the data collection. This documentation can aid in replication and future research.

Reporting: Generate reports or summaries based on the collected data to present your findings. Visualize the data using charts, graphs, and tables as needed.

Each CAPI software might have its own specific interface and workflow. It’s important to consult the software’s user guide or documentation for detailed instructions tailored to the software you’re using. Additionally, adapt these steps to suit your research goals, target population, and specific requirements.

Choosing the Right CAPI Software

Once you decide to use CAPI survey software for data collection, you’ll be faced with the dilemma of choosing the right one, as plenty are available in the market. No worries, we’re here to help. Read on.

Factors to consider when selecting CAPI software

Project Requirements and Scope: Understand the specific needs of your research project. Consider factors like the complexity of your survey, the type of data you’re collecting (quantitative, qualitative), the target audience, and the geographical locations where data will be collected.

User Interface and Ease of Use: The software’s interface should be intuitive and user-friendly for interviewers and respondents. Complex or confusing interfaces can lead to errors during data collection and increase training time for interviewers.

Compatibility with Different Devices: Ensure that the CAPI software is compatible with various devices, including smartphones, tablets, and PCs. This flexibility lets you choose the most suitable device for your data collection context.

Data Security and Encryption Measures: Data security is crucial to protect the confidentiality of respondent information. Check if the software provides robust encryption during data transmission and storage. Look for compliance with relevant data protection regulations.

Reporting and Analytics Features: Consider the reporting capabilities of the software. Can it generate real-time reports? Does it offer customizable data visualization options? Good reporting features can streamline your analysis process.

Customization Options for Questionnaires: The software should allow you to customize your questionnaire according to your research objectives. Check if it supports various question types (multiple choice, open-ended, Likert scale) and if it can handle complex skip patterns and branching.

Cost Considerations: Evaluate the software’s pricing structure. Some software options might have a one-time purchase fee, while others may charge based on usage or number of users. Factor in both upfront and ongoing costs.

Offline Capabilities: If data collection occurs in areas with limited internet connectivity, ensure that the software supports offline data collection. This allows interviewers to collect data without a live internet connection and sync it later.

Support and Training: Consider the level of customer support provided by the software company. Is there technical assistance available if interviewers encounter issues? Are training resources, tutorials, or documentation available?

Multimedia Integration: If your survey requires multimedia elements such as images, videos, or audio, ensure the software supports their integration. This can enhance respondent engagement and data accuracy.

Scalability: If your project involves many respondents or interviewers, ensure that the software can handle the scale without performance issues.

Data Backup and Recovery: Check if the software has data backup and recovery mechanisms. This is essential in case of device malfunctions, accidental data loss, or other unexpected situations.

Survey Logic and Skip Patterns: Ensure the software can handle complex survey logic and skip patterns. This is particularly important for surveys with conditional branching or skip instructions based on previous responses.

Language and Localization: If you’re conducting surveys in multiple languages or diverse regions, check if the software supports different languages and allows for the localization of survey content.

User Experience: It’s important to gather feedback from potential users about their experience with the software. Look for user reviews and testimonials to understand how the software performs in real-world scenarios.

By carefully evaluating each of these factors, you can choose a CAPI survey software that aligns with your research goals, maximizes data quality, and minimizes potential challenges during the data collection.

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Your business has access to countless data sources, including feedback from your clients, customers, employees, and vendors. These open-ended responses can be any text comments, such as social media posts, customer reviews, survey responses, and more, but analyzing it properly is challenging.Text analytics, also known as text mining or text analysis, is the process of extracting meaningful insights and patterns from unstructured text data. It involves various techniques from natural language processing (NLP), machine learning, and computational linguistics to analyze and understand the content of open end comments. The main goals of text analytics are to derive actionable insights, discover trends, and extract useful information from large volumes of text responses.

Key Components of Text Analytics

The text analytics process starts with a data set that has open end responses which may or may not include closed end responses, also called quantitative data. Text analytic solutions have the ability to work with data sets that are far too large to process manually, enabling your business to gain important research information that can drive your marketing strategies, customer service policies, budget allocation, product development, and countless other operations. Key components of text analytics include,

• Topic Analysis: Identifying topics or themes within the text data.
• Sentiment Analysis: Determining the sentiment (positive, negative, or neutral) expressed in the text.
• Clustering: Grouping similar texts together based on their content.
• Text Summarization: Generating a concise summary of the text data.
• Visualization: Visualizing the insights derived from text analytics through charts, graphs, word clouds, or other visual representations to facilitate interpretation.

How Is Text Analytics Used By Companies?

Text analytics is used by companies in various ways to extract valuable insights from large volumes of unstructured text data. Here are some key applications:

1. Customer Sentiment Analysis

• Purpose: Understand customer opinions and feelings about products or services.
• Application: Analyzing customer reviews, social media posts, and survey responses to gauge satisfaction and identify areas for improvement.

2. Industry Research

• Purpose: Identify market trends and consumer preferences.
• Application: Analyzing news articles, blogs, and online forums to understand industry dynamics and emerging trends.

3. Product Improvement

• Purpose: Enhance product features based on customer feedback.
• Application: Analyzing feedback from various channels to identify common issues and areas for improvement.

4. Customer Support

• Purpose: Improve customer support services.
• Application: Analyzing support tickets, chat logs, and email communications to identify common issues and streamline support processes.

5. Human Resources

• Purpose: Enhance employee experience and recruitment processes.
• Application: Analyzing employee feedback, performance reviews, and recruitment data to improve HR practices and employee satisfaction.

Capabilities of Text Analytics

Each text analytics tool has its own set of capabilities, but there are a number of features that you’ll commonly find in leading solutions on the market:

• Able to Analyze Both Structured & Unstructured Data
• Generates Clear & Descriptive Insights
• Processes Datasets of Any Size Quickly & Affordably
• Groups Data Together Logically
• Able to Drill Down to the Original Response
• Able to Trend Data
• Leverages the Latest AI Technologies
• Able to Adjust the Level of Generative AI by Project
• Produces Customizable Visualizations & Reports
• Offers Automatic Translation
• Enables Cross Tabs and Further Analysis
• Provides Data Scrubbing
• Has API Connectors
• Easily Imports & Exports Data

The Benefits of Text Analytics

Text analytics delivers many advantages to your organization. It's a critical part of extracting value from data sets with open end responses that you’re otherwise unable to process.

• Works with open end comments in many types of media or language.
• Gives you insights to improve experiences for customers, employees, and other stakeholders.
• Gives you insights to help increase your company’s revenue.
• Reduces the time and effort needed to analyze unstructured data.
• Gives you the data you need to better control your costs.
• Helps you make more data-driven decisions.
• Enables you to act quickly on new opportunities.

What’s the Difference Between Text Mining and Text Analytics?

Though often used interchangeably, text mining and text analytics have distinct meanings and applications. Text mining is the process of discovering patterns and extracting useful information from large sets of unstructured text data.It focuses on extracting information and knowledge from text using information extraction, categorization, clustering, association rule learning, and pattern recognition. The primary application of text mining is in data mining, where the goal is to uncover new patterns and insights.On the other hand, text analytics encompasses a broader range of techniques used to analyze text data and extract meaningful insights. It includes the interpretative aspect of the results obtained from text mining, focusing on applying and interpreting these results to solve specific business problems.Techniques used in text analytics include sentiment analysis, topic modeling, trend analysis, predictive analytics, and natural language processing (NLP). These techniques are employed in market research analysis, customer sentiment analysis, product review analysis, and other applications.

Find the Best Text Analytics Solution with Ascribe

If you are looking for a text analysis solution, check out CX Inspector with Theme Extractor and Generative AI. It’s our full-featured interactive software with generative AI that instantly analyzes open end responses, and lets you group ideas together, and explore data with sentiment, filters, crosstabs, and trend reports.Or contact us for a free demo with your data.