The Voxco Answers Anything Blog

What is Systematic Sampling?

Systematic sampling is a type of probability sampling method used in research to select individuals from a target population at regular intervals. Unlike non-probability sampling, where not every individual has an equal chance of being chosen, systematic sampling ensures that each member of the population has a known and equal probability of selection. The process involves choosing a random starting point and then selecting every kᵗʰ individual from a structured list, where k is the sampling interval determined by dividing the population size by the desired sample size. This method offers a simple, efficient way to create representative samples—especially when working with large populations and well-defined sampling frames.
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How to Implement Systematic Sampling in Your Research

Systematic sampling can be implemented in just two main steps:

1. Calculate the sampling interval
   Divide the total population size (N) by the desired sample size (n) to determine the sampling interval (i). If the result is a decimal, round it to the nearest whole number.
   
   
2. Select a random starting point
   Choose a random starting point (r) between 1 and the sampling interval (i). From there, select every i-th element in the population list until the desired sample size is reached.
   
   

Before proceeding, it’s crucial to ensure that the sampling frame is not arranged in a cyclical or repetitive pattern. If it is, using a fixed interval may introduce bias.

Researchers often use survey platforms or social research tools with built-in sampling capabilities to streamline this process. For instance, Voxco’s survey platform offers advanced features that allow users to easily generate systematic samples through its panel management tools.

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Example of Systematic Sampling

Let’s say a researcher wants to select a sample of 25 individuals from a population of 1,000:

• Population size (N) = 1,000
  
  
• Sample size (n) = 25
  
  
• Sampling interval (i) = N / n = 1,000 / 25 = 40
  
  

This means the researcher will select every 40th individual from the list.

Next, a random starting point (r) must be chosen between 1 and 40. Suppose the researcher picks 17. The sample will then include the 17th person, the 57th, the 97th, and so on, continuing in 40-unit intervals until 25 participants are selected.

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Types of Systematic Sampling

There are three primary types of systematic sampling methods:

• Systematic Random Sampling
  The most common form, where a random start is followed by selection at fixed intervals.
  
• Linear Systematic Sampling
  In this method, the list is treated linearly. Once the end is reached, the sampling stops—even if the desired sample size isn’t met.‍
• Circular Systematic Sampling
  The population list is treated as a continuous loop. After reaching the end, the count continues from the beginning until the sample size is completed.

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1. Systematic Random Sampling

This is the most common and straightforward type. Here's how it works:

• Calculate the sampling interval using the formula: i = N / n
  
  
• Choose a random starting point (r) between 1 and i
  
  
• From that point onward, select every i-th element until the desired sample size is reached
  
  

2. Linear Systematic Sampling

In this method, the population list is treated as a linear sequence. Once the end of the list is reached, sampling stops—even if the full sample size hasn’t been met. Steps include:

• Create a sequential list of the population
  
  
• Determine your desired sample size (n) and compute the skip interval: k = N / n
  
  
• Pick a random starting number (r) between 1 and k
  
  
• Add k repeatedly to r to select the remaining units

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3. Circular Systematic Sampling

Here, the list is treated as circular, allowing the sampling to continue from the beginning if the end of the list is reached before the full sample is drawn:

• Calculate the interval: k = N / n
  
  
• Select a random starting point (r) between 1 and N
  
  

Move forward in k steps, looping back to the start of the list as needed, until n units are selected

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When Should You Use Systematic Sampling?

Systematic sampling is especially useful in the following research scenarios:

• When the population list is already randomized: If the sampling frame is randomly ordered, systematic sampling provides a quick and unbiased way to select a representative sample.
  
  
• When the population is large and well-defined: It's ideal for large-scale surveys where listing and selecting every individual manually would be time-consuming. The method simplifies the process without compromising accuracy.
  
  
• When resources or time are limited: Systematic sampling requires less effort than simple random sampling while still maintaining the principles of probability sampling, making it efficient for researchers with tight deadlines or limited staff.
  
  
• When you're using a structured list (like customer databases or employee rosters): As long as the list isn’t organized in a cyclical pattern, systematic sampling is a great choice for drawing samples from such structured data.
  
  
• When consistent intervals are meaningful or necessary: If your research benefits from evenly spaced sampling (e.g., time-based studies or product quality checks), systematic sampling can provide consistency in selection.

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Advantages of Systematic Sampling

• Simple to implement when a complete and ordered sampling frame is available
  
  
• Easy to understand and execute, even for researchers with limited statistical training
  
  
• Efficient and organized, especially compared to more complex sampling methods like stratified sampling
  
  
• Minimizes bias when the list is randomly ordered, ensuring a fair and representative sample

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Disadvantages of Systematic Sampling

• Risk of systematic bias if the population list is ordered in a repeating or cyclical pattern, which may align with the sampling interval and distort results
  
  
• Potential for data manipulation, as researchers could intentionally choose intervals or starting points that skew results
  
  
• Lower randomness compared to methods like simple random sampling, which can increase the risk of selecting similar types of units repeatedly
  
  

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Conclusion

Systematic sampling offers a practical, efficient, and widely-used approach for drawing representative samples—particularly when dealing with large populations and organized sampling frames. While it comes with a few limitations, especially regarding potential bias in non-random lists, its simplicity and speed make it a valuable tool in both academic and commercial research. When paired with the right tools, like Voxco’s survey platform, systematic sampling can help streamline the research process and ensure reliable results.

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Table of ContentsYou can't just develop a new product without understanding the need or interest for it in the market. So how do begin with such research? Which research should you even conduct?This brings us to the topic, of exploratory research. Exploratory research helps us gain an understanding of a topic, defines the variables of the problem, and establishes a basis for a more specific research question.Read the article to learn what is exploratory research, its characteristics, & the methods used to perform it.

What is Exploratory Research?

Exploratory research investigates problems that are not clearly defined. It is conducted to gain insight into the existing problem, however, exploratory research does not provide a conclusive answer to these problems.A researcher starts with an idea that is general in nature and uses this as a means to recognize issues that can become the focus of future research. An important feature of exploratory research is that the researcher should keep an open mind and be willing to change the direction of their research as they collect more and more insightful data.Exploratory research uses the grounded theory approach also known as interpretive research. It aims to answer questions such as: “What is happening?” “Why is this happening?” “How is this happening?”For example; if a researcher wants to know how a particular filter is perceived by the target audience of their app, they can first find out which section uses their app. Then proceeding to find out which filters are most used, why they are used, and decide whether adding an additional filter similar to the existing ones will be a good idea.

What are the Characteristics of Exploratory Research?

Now that we have defined exploratory research, it is important to be familiar with its attributes. Exploratory research has several features that researchers need to learn to understand when to use it.The following are the characteristics of exploratory research:

1. They are not structured in nature.
2. Exploratory research is interactive, open-ended, and is usually easy on the budget of the organization.
3. It helps researchers uncover answers to questions such as; what is the problem being studied? What is the need for this study? What topics should be included in the study?
4. It is time-consuming and thus requires patience and persistence on the part of the researcher.
5. Exploratory research is broad, flexible, and adaptive in nature.
6. The researcher needs to go through all the information and data collected through the research.
7. Exploratory research needs to have an important cost or value, if not, then it is ineffective to carry out the research.
8. The researcher should have some theories that will help in supporting the findings uncovered during the exploratory research.
9. Exploratory research generally produces qualitative data.
10. In certain cases, where the sample of the study is large and data is collected through surveys and experimentation, explorative research can be quantitative.

Now, that we have cataloged the characteristics, the question is how to go about collecting the data for your exploratory research. The following section explains the two methods you can use to conduct your research.

Methods of Exploratory Research

Carrying out research on something that one has limited information about sounds and feels difficult, there are several methodologies that can help you to decide the best research design, how to go about collecting data, and the variables to study.There are two main methods of conducting exploratory research - primary research and secondary research. Under these two broad types, there are various methods that can be used depending upon the nature of your study.The data can be of quantitative or qualitative nature. Let’s look at each of the research methods in detail.

Primary Research Methods 

In the primary research, the information is collected directly from the respondents. This data can be collected from a group of people or just an individual.Primary research can be conducted by the researcher themselves or it can be carried out by a third party to conduct it instead. Primary research is usually done to explore a problem that needs in-depth analysis.

• Surveys:

Surveys or polls can be used to gather large amounts of data, usually from a predetermined group of respondents. It is one of the most popular quantitative research methods. Surveys or polls are used in exploratory research in order to explore the opinions, trends, or beliefs of the target population.Surveys can now be conducted online and thus be made more accessible, thanks to technology! Organizations, nowadays, have started offering shorter surveys and rewards to the respondents who fill them so that they can increase the response rates and gain more insights. Short surveys can be sent to respondents through text messages right after they make a purchase and are asked to fill it for a coupon/discount in return, so organizations can understand their views on the product under study.Voxco lets you conduct omnichannel surveys for gathering insightful market research data anywhere, anytime.

• Focus Groups:

Another widely used methodology in exploratory research is focus groups. In this method, a group of respondents are chosen and are asked to express their opinions on the topic of interest.One important consideration when making a focus group is to choose people who have a common background and similar experiences to get unified and consistent data.An example of a focus group would be when a researcher wants to explore what qualities consumers value when buying a laptop. This could be the display quality, battery life, brand value, or even the color. The researcher can make a focus group of people who buy laptops regularly and understand the dynamics a consumer considers when buying electronic devices.

• Observation:

Observational research can either be quantitative or qualitative. This research is done to observe an individual and make inferences from their reactions to certain variables.This research does not require direct interaction with the participants. For instance, a researcher can simply record the observations of how people react at the launch of a new product.

• Interviews:

Surveys give you huge amounts of information in a relatively short period of time, but an interview with one person can give you the in-depth information which can otherwise be overlooked in surveys. Interviews are a methodology to collect data for qualitative research.Interviews can be carried out face-to-face or even on the telephone. Interviews usually contain open-ended questions so that enriching information is uncovered about the topic. For example; an interview with an employee on their job satisfaction can offer you valuable insights that would otherwise go unnoticed in the close-ended questions asked in a survey. [Related read: Primary Research]Voxco offers a complete suite of tools for market researchGet Market Research Toolkit

Secondary Research Methods:

In secondary research, information is gathered from primary research that has been published before. For instance, gathering information from case studies, newspapers, online blogs or websites, or government sources.

• Online Resources:

The quickest way to find information on any topic is through the internet. A huge amount of data is available on the internet that you can download and use whenever you need it. One important factor to consider when acquiring data online is to check the authenticity of the sources provided by the websites.For example, a researcher can find out the number of people using a preferred brand of clothing through a poll conducted by an independent website online.

• Literature review:

Reviewing the existing literature on a particular topic from online sources, libraries or commercial databases is the most inexpensive method of collecting data. The information in these sources can help a researcher discover a hypothesis that they can test.Here, sources can include information provided by newspapers, research journals, books, government documents, annual reports published by organizations, etc. However, the authenticity of the sources needs to be considered and examined.Government sources can provide authentic data but may require you to pay a nominal price to acquire it. Research agencies also produce data that you can acquire at a nominal cost, and this data tends to be quantitative in nature.Explore Voxco's MR Knowledge Hub

• Case studies:

Another way researchers can gather information for their exploratory research is by carefully analyzing the cases that have been through a similar problem the researcher wishes to study. These cases are important and critical in the business world, especially.The researcher should be cautious to review and analyze a case that is similar in regards to the variables of concern in the present study. This methodology is commonly used in the health sector, social sciences, and business organizations.For example; let’s assume that a researcher is interested in understanding how to effectively solve the problems of turnover in organizations. While exploring, he came across an organization that had high rates of turnover and was able to solve the problem by the end of the year. The researcher can study this case in detail and come up with methods that increased the chances of success for this organization.[Related read: Primary Vs. Secondary Research]

What are the Steps to Conduct Exploratory Research?

1. Identifying the problem area. The very first step is for the researcher to identify the area of research and the problem can be addressed by finding out ways to solve it.
2. Creating a hypothesis. If the researcher is aiming to solve a problem for which there are no prior studies, or the problem has not been resolved efficiently in previous research, then the researcher creates his/her own problem statement. This problem statement, also called a hypothesis, will be based on the questions that the researcher came up with while identifying the area of concern.
3. Advancing future research. Once the data for the current problem has been obtained, the researcher will continue the study through a descriptive investigation. Generally, qualitative methods are used for a detailed study of the data to find out if the information gathered through exploratory research is true or not.

Advantages of Exploratory Research

Exploratory research provides the researcher an opportunity to keep an open mind and explore the variables affecting their area of interest. Some of the advantages of exploratory research are:

• It allows researchers to be flexible and change their stance on the problem being studied as the research progresses.
• It is cost-effective.
• It lays a foundation and structure for future research.
• It can help researchers find out the causes of the problem being studied which can be elaborated on in future studies.

Now that we have listed the benefits, we can’t forget the limitations. It is important to learn about both before you jump into the research mode.Wondering what will be the cost of conducting survey research using Voxco?Get a free quote

Limitation of Exploratory Research

Exploratory research is not without its limitations.

• The answers of exploratory research are usually inconclusive. 
• Some of the data collected can be biased or subjective as it is mostly qualitative in nature. 
• Since exploratory research has a smaller sample size, there is hesitancy to generalize the findings to the whole population. 
• If data is collected through secondary sources there is a chance of the data being old or outdated.

Wrapping up;

Exploratory research helps you form the foundation of your research project. It lays down the groundwork for a research question you can explore in the future.Exploratory research is best used when you need insights on a problem or phenomenon before you begin to conduct further research.

Do you know less than one-third (29%) of insurance customers are satisfied with their current providers?29%Infact 21% of customers believe that insurers do not tailor their experiences at all.21%More than 30% of customers switch their insurers within a year after one poor claim experience!30%With 88% of insurance customers demanding more personalization from their providers and only 15% of customers satisifed with the existing digital experience, insurers need to put customer experience at the center of their business strategy. In this blog, we discuss how CX impacts insurance. Continue reading to find out more!We’re living in a new world – where digital proximity is driven by social distancing. As businesses prepare to go ‘contactless’ after the COVID-19 outbreak, it has become imperative for brands to reimagine their customer experience.The pandemic has upended the functioning of many industries and the insurance sector is no different. With low sales and few claims, insurance companies have now realized the need of accelerating digitization while diving deep into the core processes beyond automation. While many consumers are purchasing insurance products online, only 15% of them are satisfied with their insurers’ digital experience. Since the economic downturn has thoroughly impacted people’s lives and changed the perceptions of risk, customer relationships with their insurers matter more than ever.According to KPMG, COVID-19 may be a catalyst for greater innovation in the insurance sector, unlocking higher levels of customer experience. So, insurance companies that focus on providing best-in-class customer communication & response will future-proof themselves against any unexpected systemic disruptions.

Is it a challenge or opportunity?

The current scenario can come up as a challenge as well as an opportunity for insurers. If the insurers act appropriately now, they can experience a massive increase in sales even in an economic downturn. For instance, after the 2008 financial crisis, shareholders got up to 3X higher returns in companies that focused on customer experience.Innovating the improvements in CX is important but it can be challenging too. The insurers need to identify the online behavior of their customers. To ensure effective communication, insurers need to create customer journeys by using real-time & historical information. This includes harnessing existing information and making the most of it for the company's benefit.So the question arises - how to empower customers with a better experience through innovation around the core processes of insurance?

Customer care is paramount

As more than 30% of customers switch their insurers within a year after a bad claim experience, amplifying customer experience to win their trust has become the need of the hour. It is the key ingredient that enables brands to resonate more with their customers and develop an emotional connection. Building a long-lasting and strong relationship with your customers largely depends upon how you nurture them throughout their journey and the quality of the support being delivered.To establish this trustworthy bond with the policyholders, you have to ensure them that their claims will be paid. But these trying times have forced the customers to spectate certain anomalies as pandemics are not covered by insurance plans. The customers require a medium of reassurance where they feel heard.Quick service, diligent follow-ups, and a personalized experience are the basic requirements of all customers. Moreover, policyholders expect a bit more humanized experience now. As less than one-third (29%) of insurance customers are satisfied with their current providers, insurers need to adopt a more customer-centric approach to make them feel comfortable. From initial interaction to the inquiry-related terms, and payments & sales to claims, the insurers should deliver a compelling experience that resonates well with the customers' needs and desires. While every process has become digitized now, certain aspects such as policy planning or claim discussions still require the intervention of personnel who are empathic and patient.

Stabilize customers the right way

We witnessed how the economy experienced the brunt of COVID-19, small and medium-sized enterprises are no stranger to this. They’re struggling to gain footing and require powerful strategies that help them mitigate risks. As industry know-how has been crucial for commercial insurers until now, it’s time for them to actually reveal the value it adds to the customers, their business, and the local economy. The insurers’ industry knowledge and practical business skills can empower customers to streamline their workflows and the insurers’ can stabilize their own line of business too.By delivering exceptional customer care, insurers can carve a trustful relationship with their commercial customers that will extend even after the pandemic is over.

Transform the transactional approach into a comprehensive support one

There's no doubt that point of contact with customers is a part of the transactional business approach for most insurers. To change the impression of insurance as a “necessary evil”, companies should adopt a model that supports a wider ecosystem of life rather than the incident-driven ones. By doing so, it will help to add value in this tough time.As 21% of customers believe that providers do not tailor their experiences at all, insurers need to make their brand synonymous with care at every stage of the customer journey. For instance, refunding auto premiums because of minimized driving, abolishing deductibles for people impacted by COVID-19, or even fast-tracking claims can play a pivotal role in demonstrating care and empathy.The onset of coronavirus pandemic has exposed the vulnerabilities and stress points of customers as well as insurers. However, it has also come up as a long-sought opportunity for driving the necessary change. Do you want to re-engineer your existing operations with improved CX? Voxco can help you gain complete control of Customer Experience with insights into customer feedback, satisfaction with the claim process, and more.Check out our Customer Experience Hub and get a jump start on measurably improving the quality of your CX initiatives.

Create Omnichannel Surveys with Voxco

Voxco is trusted by Top 50 Market Research firms, Global Brands & Universities in 40 countries. Voxco offers full omnichannel capability including CATI, Predictive Dialler, Online surveys, offline CAPI, and Panel Management. Check out Voxco Offerings below: 

Voxco Online

Create engaging omnichannel online surveys with advanced skip patterns, multi-media files, automatic device detection and more.Read more about Voxco Online Survey Software

Voxco Analytics

Create one click summaries, visual dashboards, uncover key trends and easily share the report with teams.Read more about Voxco Survey Analytics 

Voxco CATI

Maximize CATI ROI with advanced features, hosting options, seamless telephony integration, and flexible pricing.Read more on Voxco CATI survey software

Voxco IVR

Voxco IVR can be used as a standalone, self completion survey option or in combination with other data collection modes.Read more about Voxco IVR Survey Software Book Demo

In today’s time where customer experience is intrinsic to the business success of most brands, charitable and non-profit organizations are still struggling to understand their customer needs. According to Stanford Social Innovation Review, 88% of leaders in the social innovation sphere consider “customer feedback” as important but find it difficult to implement.

There are around 10 million non-profit and non-governmental organizations in the world. As donations are the primary source of funding for these organizations, it becomes more important for them to enhance their donor experience. Moreover, two-thirds of donors either donate to organizations they know or the ones recommended by their family/ friends (without doing any research before giving). Considering the massive boost in donations related to disaster relief, i.e. from $212,000 in 2015 to $862,000 in 2017 (approx 300% of median cash), the growth of nonprofits has always been on the rise.

Therefore, nonprofits need to adopt the business model of the profit-world and start treating their donors/stakeholders as customers. Let’s dig deeper to understand it further:

Download Now:  5 Customer Experience Survey Templates

Customer experience is ubiquitous

There’s no doubt that commercial businesses always strive to please their customers. Likewise, nonprofit organizations need to focus on building strong relationships with their donors as they fund their mission. So, the big brands and nonprofits sail in the same boat – as neither of them can ignore their customer experience. Since the donors of a nonprofit program do not receive any service in exchange for money (like customers who get a product/service for some dollars), managing a successful relationship with donors can be an intricate process. With the continuous increase of nonprofit organizations out there, how will you grab your donors’ attention and strengthen their commitment to your organization?

As nonprofit customers can be seen wearing multiple hats – as a donor, volunteer, client, stakeholder, or an advocate, there’s a need for a better sophisticated model as compared to the one used for simple transactional experience. In fact, the volunteers may prove to be the ideal donors- by donating twice than the non-volunteers, if engaged through efficient volunteering. Thus, the notion of CX becomes more crucial for nonprofits.

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Prioritizing customer-centricity in nonprofits

Connect with your donors emotionally

Start by delighting your donors in a personalized way. While 92% of marketers find personalization helpful in brand building, 87% of them consider it effective for lead generation.  Choosing innovative and out-of-the-box methods to “wow” your donors can drive engagement with your nonprofit organization. Throwing in exciting surprises, tweeting a personalized thank you note, or sending a humorous email to infuse some fun could be a great idea.

Share the impact of contributions made

Communicating the impact of donor contributions is an essential aspect of your marketing plan. As donors contribute to your cause, let them know how their money is being used and the changes imparted by your organization. Ask donors how they feel about the impact created by their donations and if the difference was inline with their expectations.

Help your staff surpass expectations

When customer advocacy is ingrained into your organization’s culture, everyone onboard focuses on being adventurous yet creative to become a happiness hero for their clients. Encouraging your employees to embed personalized communication can help drive donor loyalty. Giving your staff the freedom to perform not only improves your fundraising strategies but also makes your donors feel valued.

Give your donors a voice

Soliciting feedback from donors helps to understand them better and strengthens engagement across your nonprofit organization. Also, it bolsters the donor’s belief in self-advocacy by giving them a prominent seat at the decision table. Conducting omnichannel surveys can help you uncover donor trends and follow a data-driven approach to donor satisfaction. Moreover, it will empower you to personalize communication with donors and interact in a targeted way.

Like in any business relationship, delivering exceptional service is essential for an organization to meet its mission and vision. As the nonprofits are scrutinized largely, it’s imperative to treat your donors/stakeholders right which makes them feel valued.

In case you’re exploring some options for social research, we empower nonprofits conduct social research surveys and track the impact of their studies globally. With over 45 years of experience servicing the social research space, Voxco’s robust and flexible platform is known to support complex sampling strategies and sophisticated programming needs. Our powerpacked solution manages data of varying volumes and complexity with ease.

Get in touch with Voxco for best-in-class solutions to transform every touchpoint into a positive donor experience.

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Text mining, also known as text data mining or text analytics, refers to the process of deriving high-quality information from text. Leveraging techniques and tools from both AI (artificial intelligence) and NLP, text mining involves the discovery of patterns, trends, and insights in text data. Text mining is widely used in various fields, including marketing, business intelligence, healthcare, finance, to make sense of large amounts of unstructured text and derive actionable insights. 

How Text Mining Applications Benefit Your Company

Text mining can provide numerous benefits to a company across various departments and functions. Here are some of the key ways it can add value:

1. Customer Insights and Sentiment Analysis
2. Market Research and Competitive Analysis
3. Improving Customer Service
4. Enhancing Product Development
5. Boosting Marketing Efforts
6. Human Resources and Employee Insights
7. Knowledge Management
8. Operational Efficiency

By leveraging text mining, companies can unlock valuable insights from unstructured text data, leading to improved decision-making, enhanced customer experiences, and increased operational efficiency.

What Are the Main Steps in the Text Mining Process?

Text mining typically includes the following tasks:

1. Information Retrieval: Extracting relevant information from large text collections, such as documents, emails, web pages, and social media posts.
2. Natural Language Processing (NLP): Using computational techniques to analyze and understand human language. NLP includes tasks like tokenization, part-of-speech tagging, named entity recognition, and sentiment analysis.
3. Text Categorization: Automatically classifying text into predefined categories or topics. This can be used for organizing documents, spam detection, and more.
4. Text Clustering: Grouping similar documents or text segments together based on their content. This helps in identifying themes and patterns within large text datasets.
5. Sentiment Analysis: Determining the sentiment expressed in a piece of text, such as positive, negative, or neutral. This is commonly used in social media monitoring and customer feedback analysis.
6. Topic Modeling: Discovering abstract topics within a collection of documents. Techniques like Latent Dirichlet Allocation (LDA) are commonly used for this purpose.
7. Information Extraction: Extracting specific pieces of information, such as names, dates, and relationships, from unstructured text.
8. Summarization: Creating concise summaries of large texts to highlight the most important points.
9. Text Visualization: Using graphical representations to help understand and interpret text data, such as word clouds and topic maps.

Text Mining Examples in Marketing

There are many use cases available for text mining. If you were in marketing, for example, here are some of the most common use cases you might consider.

• Learning about positive, negative, and neutral reactions from your audience: Sentiment analysis is an excellent tool for marketers as it allows you to quickly see what the reception is to the topic that you’re studying. When you have a good understanding of your audience’s reactions, you can tailor your marketing based on that information.
• Categorizing survey responses: Group survey responses into broad topics or get granular with it, depending on your needs. You can focus on the areas that are most important for a particular campaign. Recurring themes may require closer examination, so you can conduct more studies that focus specifically in those areas to get more information.
• Translating and scoring survey results: Are you working with more than one language on your survey responses? You don’t need to translate that as a separate step before it goes into your text mining application. Simply choose a software that supports the languages you see the most and it can automate the process.
• Gauging interest in a new concept: Even when you do your best at developing a concept that should appeal to your audience, sometimes the latest project just falls flat. You can start to troubleshoot why that happened by using text mining and open ended survey questions to see what your audience is thinking about the latest products, services, and company moves. By gauging the interest in a new concept before you move forward with the project, you can handle development much more cost-effectively. This helps you avoid particularly high-profile failures, as a small study may end up with respondents that are more on-board with the concept than a more representative sample of your audience.
• Understanding the customer experience: Do you know why your customers feel the way that they do about your customer experience? It’s not enough to know if they are happy or not. You need to know the why behind it if you want to excel at marketing. Text mining gives you the why so that you can continually improve the experience and the marketing tools that support it.
• Discovering your customer satisfaction ratings and the meaning behind them: Your audience gives you a lot of feedback on whether they’re happy or not, you just need a way to analyze it. Use text mining to look through customer service records to identify customers who may be open to purchasing again, those that are upset with the company and need attention, and others that may need a push to move away from being ambivalent in either direction.
• Tracking the success of new products and services: You want to know how well your new products and services are doing now, not weeks or months from now. Automating the analysis through a text mining tool means that you can get near a real-time understanding of how well a product launch is going.
• Finding new business opportunities: Open ended survey responses allow you to find replies that are outside of the norm. Sometimes your customers have adopted a product or service for a use case that never came up in research studies. Expanding horizontally or vertically may be possible based on this data, which can offer an excellent approach to building your business.
• Using customer service data for marketing strategies: Your customer service data is a marketing goldmine, but it’s often overlooked due to the logistical challenges of processing the information. Text mining eliminates these concerns and allows you to find out more about your customers, what they like, dislike, and how to keep them loyal and happy.
• Providing hard data for reports and presentations: If you need a way to make your case to upper management, having powerful visualizations in helpful reports and presentations is one way to make it happen. Text mining creates structure out of unstructured data, so you’re able to use it in this fashion. Customizable dashboards are another way to easily access the data in a form that’s user-friendly for most marketers. When you can easily work with the data, that makes it more accessible to power all types of marketing efforts.
• Improving the value of social media comments: People are more than happy to comment on social media posts, but harnessing that data is hard if you’re doing it manually and have a relatively active page. Text mining makes this process more efficient and allows you to leverage such a large and frequently updated data set. Consistently looking at your social media comments is also a good way to stay ahead of any public relations problems you may encounter. You can execute your crisis communications plan as soon as you start seeing negative comments pop up.
• Creating performance benchmarks for marketing campaigns: Get more benchmarking metrics for your marketing campaigns so you can study how customer sentiment changes over time, the ways they react to new campaigns, and isolating the characteristics that lead to a successful marketing effort.
• Powering Voice of the Customer programs: Voice of the Customer programs are greatly improved when you have a cost-effective and productive way of working with audience feedback.

Whether you’re using text mining for a one-off study or an ongoing series, your team will benefit from its implementation. It takes some time to fine-tune the results for your use cases, but once you get it dialed in, you’re going to wonder how you ever did without it.

Choose Ascribe For Your Text Analysis Needs

Ascribe has two advanced text analytics solutions to meet your business needs. CX Inspector is a text analysis solution that quickly unlocks actionable insights from large data sets with unstructured or open end responses and creates charts to visualize the results. Coder, another text analytics solution, is the leading verbatim coding platform designed to improve the efficiency of coding. Contact us for more information or request a demo with your data.

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 combine open-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.