The convenience and comfort offered by the online world has forced `customers to leave their traditional practices and magnet towards online interactions and engagement . According to a study conducted by McKinsey and Co. categories such as medicine, groceries , household supplies and personal care products have online shoppers exceeding 35% growth rate .

With that said , it is becoming the need of the hour that companies turn their focus on understanding customer needs and expectation in the online format and take prompt steps to ensure seamless online customer journeys .

Watch the video below to learn how Voxco can help you can enhance your overall customer experience.

https://www.youtube.com/watch?v=B_Sz8DEQEBc

What are Customer Retention Strategies?

Customer retention can be described as the process of turning existing customers into repeat buyers. It refers to the different measures organizations take to reduce customer attrition, and create healthy long term relationships with their customers. Customer retention strategies are the different initiatives and tactics used by organizations to retain existing customers, build customer loyalty, and improve customer lifetime value (CLV).

What is digital customer experience and how can you improve it ?

Any and every Business-to-Customer interaction that takes place through the online format is considered a part of digital customer experience. It can be as simple as posting a query on the company website or clicking on a call to action button that redirects you to a company touchpoint , as long as it being performed online , the customer will be considered a part of your digital target audience.

Understanding customer sentiment towards the company can be tedious especially when their communication with the company is restricted to interaction that does not ask for interpersonal engagement . As market footfalls decline , there are certain tools of gauging customer mindset that can prove useful for nuanced market research and informed decision making .

Define your target audience

There may be multiple people visiting your touchpoints, but not all them can be utilized to gather genuine feedback that’ll help improve your online functioning. The basic step of any market research is to understand , separate and segment your target audience into groups based on their similarities and differences to cater to each group specifically.

For example : If you’re a cosmetics company , your target audience may be women belonging to specific age groups . Studying each group separately helps to highlight the highs and lows of your overall online performance and the KPIs that have influenced each group to interact the way they do

Map your customer journey

Customers initiate interactions with a certain objective in mind. Mapping customer journeys puts these objectives into perspective by focusing on metrics such average touchpoints visited , ease of navigation , time spent per platform etc. The focus of any company is to make it easier for customers to reach their end result through minimal effort. This requires eliminating additional touchpoints that increase the length of customer journeys without having any substantial value addition.

CSAT ( Customer Satisfaction Score )

Short surveys that ask for customers to rate how satisfactory their digital customer experience was can go a long way. These surveys can be product or service specific or can ask customers to review their entire journey and boil it down to one rating. This can , however , lead to customers rating their satisfaction based entirely on one good or bad experience. It is difficult to strike a balance between multiple experiences which is why companies usually go for the former approach.

CSAT surveys can simply ask the customer to rate their experience immediately after an interaction along with an open ended question that requires them to provide a reasoning for the same.

This can project the efficacy of the current touchpoints in terms of how they offer the right choices that overlap with what customers are looking for. Further , the qualitative remarks highlight grey areas that are acting as roadblocks and positives that make customer experience pleasant.

NPS^®   ( Net Promoter Score^®  )

Net promoter Score^®  measures customer satisfaction in reference to the likelihood of customers referring the company or the brand to their friends and relatives. It is based on the principle that customers tend to make recommendation of companies when they feel that their own experience with the brand was upto the mark. It assesses the satisfaction levels of current customers along with the percentage of customers that can be nudged to indulge in unpaid promotions using word of mouth.This tool uses a 10 point rating scale question : Based on your own experience with the company , how likely are you to recommend the company and its offerings to your friends and family. The responses to this question helps in categorizing a customer as a promoter (9 and 10) , passive (6-8) or a detractor. The advantage of such a segmentation is to strategize and target these three groups differently:

• Promoters are pushed towards acting on their current mindset to promote the company to friends and family.
• Passives are marketed with brand awareness and comparison studies to inform how the company better meets their needs and is a better preference than the competitors.
• Detractors are asked to identify the experiences and aspects that they found unpleasant. This is then acted upon by inquiring how the company can improve upon them so as to prevent customers from churning.

CES ( Customer effort score )

Customer effort score measure the ease with which your customers are able to accomplish their tasks. This is a reflection on how a company is able to predict a need , and provide a quick solution which can be easily accessed by the customer without putting in much effort .

The degree of ease and simplicity gets converted into a score which correlates to their overall satisfaction. Low customer effort scores indicate that the customer is easily able to achieve their goals with the company through the online format without much hassle and so the customer satisfaction is high while a higher CES shows that the company needs to revamp their online interactions based on updated knowledge about customer requirements.

These tools are basic methods of obtaining customer satisfaction outlay and can be easily collected through surveys , interviews , focus groups and other commonly used research methods. Tracking your online customer means you need to focus on aspects such as average time taken , monitor touchpoints that have maximum customer engagement and accordingly modify your online strategy .

Simple steps that can improve digital customer experience

Gain feedback

Quantitative questions might help you identify that a customer is not satisfied but without a reasoning your decision making lacks direction. Make sure to see the pain points that are troubling your customers . Provide a platform where customers are allowed to express themselves freely .

Customers tend to prefer brands that maintain a practice of asking for their opinions and then acting upon them . It makes the customers feel valued and exudes a belief that the company is not just interested in improving their revenue.

Eliminate unnecessary areas

Mapping your customer journey will put you across many such areas that have little to no contribution in making the customer experiences smooth. Instead they are acting as an extra step which the customers need to take and the company needs to monitor. Removing these areas can help reduce online fatigue and effort taken by customers. The services provided by these platforms can be integrated on some other touchpoints that attracts maximum audience and where it can be of greater relevance.

Integrated services

Your customer will be much more happy if they know that the company offers solutions to all the customer problems at one place . Of course this can make filtering and searching for the right option , a difficult task for customers. But with the help of suitable categorization and customization , it can do wonders for the company and the customer.

Moreover , the availability of an omni-channel platform comforts the customer by assuring them that they are the right place and that they don’t have to jump through hoops to achieve a simple 5 minute task.

Indulge in online listening

Social media and communication platforms provide a space for customers to be candid . This is where they express themselves fully without any window dressing . Monitoring such content can help understand what customers and the general audience think about the company . It can also bring to light experiences or services which may have negatively impacted customer experiences.

Keeping an eye out for mentions and queries is also imperative . Doing so assists the company in addressing them promptly and minimizes damage to reputation.

With increasing digitization and rapid shift of customers to the online media , companies need to embrace this change and adapt themselves to current practices in order to maintain and grow their market share.

What is Text Analytics?

At the most basic level, text analytics is a technology focusing on deriving insights from verbatim comments. The free-form text goes through a process that mines it for meaning, translates it for the system, and then processes it for insights. It’s capable of discovering significant patterns within this text data, which is used for understanding what people are feeling in their responses, how often topics come up, and the context of the text.

For software to understand what people are saying in unstructured text, it needs to go through a structuring process that identifies key pieces of information, categorizes the information, and allows it to be interacted with to find patterns and other meanings. This translation allows the systems to discover the insights that are most important to your organization.

Since you’re frequently dealing with large volumes of data when you’re working with verbatim comments, the text analysis process needs to be as automated as possible for both data collection and analysis.

What is Text Analytic Software?

Text analytic software is a type of tool that performs text analysis. This term is often used interchangeably for several types of systems that work with text data in unstructured form. Deciding on the right type of analytics software for your organization depends on your overall goals for your text data, the resources you have available for implementation, and your existing systems.

• Text mining and text coding: This tool category focuses on processing the verbatim comments in your data sets, allowing you to categorize this information, sort it into different topics, and add labels to it.
• Text analytics: This software focuses on providing your organization with insights on the verbatim comments you have available. You can interact with your data to look for patterns, find common themes, and learn more about the sentiment behind each comment.
• Text data visualizations: After you have your data coded and analyzed, data visualization tools allow you to present your findings in easily understandable forms. This type of solution is excellent for presentations, reports, and similar functions.
• Custom-built tools: In some cases, you may have specialized requirements and use cases for text analysis that is not available through commercially available software. In these situations, custom-built tools using barebones APIs allow you to create exactly what you need to support your text analytic projects. However, this option is resource-intensive and requires an experienced development team and other specialists.
• All-in-one verbatim analysis software: You can get a complete platform that delivers all of the tools needed for your text analytics needs. These solutions combine text analytics, data visualizations, text mining, and text coding into one convenient platform. In most cases, this is the right choice for your organization.

The Benefits of Text Analysis

In the modern business world, you’re going to lose out if you don’t have text analysis to help you improve the experiences of your customers, employees, and other partners. Moving to a competitor is all too easy in many industries, but text analysis can give you the edge you need to respond to changes in the market, meet the expectations of everyone you’re working with, and create sustainable growth for your organization.

Here are some of the many benefits you can gain when you implement text analytic software:

• Gaining value from unstructured data: The sheer volume of feedback data available today is almost overwhelming, but it’s useless unless you can turn it into something that a computer can understand. Text analysis simplifies this process and makes it possible to work with some of the most valuable data you’ll ever have access to.
• Understand the experiences of customers and other key players: You can’t improve an experience until you truly understand what’s going on in the heads of the participants. The verbatim feedback gives you valuable insights into this process, at a scale that gives you information you can truly act on.
• Drive repeat customers: Another benefit of learning more about customer experiences is that you can increase loyalty by continually improving the interactions that everyone has. When you can build up a happy customer base, you increase your revenue and gain many other benefits.
• Gain more data for strategic decision-making: Data-driven decision-making is an important part of growing your business, as you can combine your experience with hard data to understand whether you’re making the right decisions. The more data you have available, the better.
• Discover what’s truly important for your experiences: You may be focused on the wrong areas in the experience, when customers, employees, and others may have other expectations. Align your investments with these expectations so you can make the most of your resources, while giving everyone what they’re looking for.
• Improve productivity: Manually working with unstructured data takes a lot of time, and is not realistic when considering the scale of text data available for many organizations. While manual processes may work at first, especially when you’re smaller, you’ll end up with insights slipping through the cracks, inconsistent processes that lead to errors, and other issues that make it difficult to scale. Text analytics tools make it more efficient and productive to work with this information.
• Surface new opportunities: You may not realize that there are new markets or product use cases just waiting for you until you start looking at your verbatim comments. This feedback can help you find new ways to grow your business and improve your products and services.

How Does Text Analysis Software Work

The exact process for text analysis depends on the type of solution you choose and the text that you’re working with, but there are a few common steps that this information goes through before you can start using it to make business decisions.

The first part of the process requires you to collect verbatim comments. This data collection process can involve many types of sources, since you can work with unstructured text data. Everything from social media comments to survey responses is fair game.

Once this data is collected, it needs to be mined and coded. This step prepares it for text analysis. The software will look at each comment, break down the meaning of the sentences, categorize it, sort it into different topics, and otherwise categorize this information.

From there, it’s ready for analysis. You can dive into the data to learn about important topics, the trends showing up in this information, and other key insights that can help your business grow. You choose different types of learning models for the system to effectively process this information, using advanced technology such as Natural Language Processing (NLP).

These insights can be transformed into data visualization, sent into other software for other types of analysis, and help in many areas of your organization. The vast majority of this entire process is automated, making it possible to scale text analysis.

What is Text Analysis Software Used For

Some of the most common use cases for text analytics software include:

• Voice of the Customer programs: Customers provide plenty of feedback, and text analysis software makes it easy to learn more about what they want out of your organization.
• Find growing problems: If many customers are running into issues with your products and services, you may not realize the sheer scale of the issue. Text analysis can show you the trends that indicate areas you need to fix.
• Enrich data from other sources: A commonly used metric for gauging customer satisfaction is a Net Promoter Score survey, but the insights you get from this approach make it difficult to understand the exact factors that influence this score. By using text analysis software to look at the open-ended feedback submitted alongside the survey, you can better understand why customers pick the responses that they do.
• Evaluating new products and services: Understand how customers respond to new products and services to determine whether you’re going in the right direction.

Common Text Analysis Tool Features

Each text analytics tool has its own range of capabilities, but some of the features that you might end up seeing in your selected software includes:

• Customized rulesets: You can create analysis rulesets that are customized to each use case that you’re working with. That way, you can focus on the exact type of analysis that is best suited for surfacing the insights that are most important for your business goals.
• Automatic translation: You don’t need to drop data from your verbatim comments simply because it’s not in your country’s native language. Text analysis tools often include automatic translation, which allows you to tap into these data sets as well.
• Convenient APIs: If you want to expand on the capabilities of text analysis tools or integrate them with other technology that your company uses, you can leverage these APIs to make it happen.
• Importing and exporting data between software: Easily move your data into and out of text analytics software.
• Developing dashboards: Convenient dashboards give you an at-a-glance look at text analytic insights. People in leadership positions can use these dashboards for strategic decision-making, or to get a big-picture view of business operations.
• Analyzing all text data: Both structured and unstructured data can be combined in many text analysis software, expanding the sources that you can work with.
• Real-time text analysis: Some solutions let you see insights in real-time, such as looking at trends in social media comments or customer support tickets.

Choosing Text Analysis Software

Picking the text analysis software that makes the most sense for your organization is based on many factors. When you’re evaluating this type of software, look at the capabilities, the type of data you work with most often, and what you need to get the most out of this information. By aligning your text analysis software needs with your business goals, you can set your company up for success.

If possible, try to go through trials and demos with a proof of concept that uses real-world text data. That way, you can see whether you are getting the right insights to meet your decision-making priorities, or if you need to reconsider the software capabilities that you’re looking at.

Getting the Most Out of Text Analysis Software

When you decide on text analysis software for your organization, make sure that you’re getting the most out of your investment. Identify key areas that could use the help of text analysis, such as your customer-facing programs. Look at your business goals and identify open-ended comments that could help you make better decisions in these areas. Consult with key stakeholders to determine what they want to get out of text analysis software, and involve them during the evaluation process to get buy-in for your selection.

During the implementation process, make sure that you have the right training resources so that employees know how to use the software, what types of insights they can get from it, and how the software makes it easier to arrive at these insights.

Collecting Data for Text Analysis Software

You have more open-ended data for text analysis software than you might think. Consider how many places that people can place comments or talk about the experiences they have with your company. Internally, you have order processing systems, customer relationship management software, customer support tools, marketing platforms, and sales tools that all contain significant data sets already.

Externally, social media is one of the most valuable sources for open-ended comments, although you can also discover more data on review sites, blogs, and other web pages. By bringing these data collections together into your text analysis software, you get a comprehensive view of all relevant feedback.

Sentiment Analysis

One term that you may encounter frequently when you’re looking at text analysis software is sentiment analysis . As this term implies, you can look at what a respondent is feeling in that comment. These emotions can be quite important for understanding what people mean in their comments, as there’s a lot of nuance that can completely change the meaning of text.

With sentiment analysis, the text analysis process moves beyond simply categorizing the text or providing a relatively literal understanding of the meaning. Instead, it goes deeper into this data to discover these emotions.

WordSpotting

Another common term in text analysis is wordspotting, which is also sometimes called keyword spotting. This happens early in the text analysis process, during text mining and text coding. The software looks for how many instances of words and phrases occur in this data, and can identify important keywords that frequently occur.

You can also define important keywords through custom rulesets, which allows you to sort through the data for this priority information.

Text Categorization

Text categorization happens early on in the text analysis process, and allows you to group comments into different categories. That way, you can see some of the most common trends in your data that come up.

These categories can show you what the priorities are among your customers, discover problem areas that need to be addressed, and show you what people are talking about frequently.

Topic Modeling

One way that text analysis software can categorize the text is through topic modeling. Rather than just looking for specific keywords, the software looks for an overall group of words that are related to the topic. Since verbatim comments can convey the same category through many different phrases, being able to let the software model topics and look for these groups can help you bring all of the relevant data together.

Text Analysis Compliance

Data privacy regulations and laws frequently govern what you can and can’t do with certain types of data. If you want to leverage your verbatim data sets through text analysis tools, you need to keep it compliant so your organization doesn’t incur any penalties.

For example, personally identifiable information is not needed to get the insights you need to make decisions in text analysis, since you’re looking at the overall data rather than one specific response. You can remove personally identifiable information in the data sets through the text analysis tool so you remain compliant.

Limits to Accuracy in Text Analysis

Natural Language Processing is an amazing technology, but human speech is incredibly complex and changes constantly. Text analysis software is not able to accurately analyze every single piece of feedback that concerns your organization, but it doesn’t need to to be useful.

Since you’re evaluating large data sets at scale, text analysis is able to deliver insights based on overarching trends and patterns within this data. If the system doesn’t quite pick up on the right connotation in a few individual responses, it doesn’t end up ruining the insights or compromising the data quality that is delivered.

Machine Learning for Topic Modeling

Machine learning, a type of artificial intelligence technology, is incredibly useful for topic modeling. Machine learning teaches the computer about the text that is relevant to topics, helps it learn how to identify topics, and guides the system in the modeling process. Without machine learning, which allows the system to continually learn from the data that is fed into the system, it would be impossible to handle text analysis at scale.

Natural Language Processing

Natural Language Processing is one of the most important parts of text analysis, as it allows computers to make sense of verbatim comments. Since your data sources for text feedback are typically unstructured, outside of multiple-choice surveys and similar sources, Natural Language Processing acts as that critical translation layer.

You can allow your customers to convey information as though they were speaking, and your text analysis software can work with that as-is. You end up having a lot more flexibility with this approach, which allows you to harness data sets that would otherwise be unavailable to you.

Implement Text Analysis Now

Starting with text analysis is simple when your organization works with Ascribe’s Verbatim Analytics Platform. Get powerful coding, analysis, and visualization tools to get the most out of your unstructured text today.

FAQs

• What is text analysis? Text analysis is the process of gaining key insights from text data, such as social media posts, survey responses, and comments.
• How do you do text analysis? Text analysis software is a specialized tool that takes unstructured text data, codes it, analyzes it, and then presents the insights in easily understandable forms.
• Why do we need text analytics? Text analytics is essential for truly understanding the thoughts, feelings, and expectations of customers, employees, and other partners in your business. Without text analytics, your organization would not be able to use large datasets of verbatim comments in analysis, as computers need this type of software to learn what people are saying in unstructured text.

Brands have popularly used rating scales to collect customer feedback on product or service reviews. Rating scale questions are so easy to recognize and understand that sometimes respondents don’t even need to read the question. We see smiley ratings or star ratings and immediately know what to do.

In this blog, we’ll discuss in detail the different types of rating scales and how you can use them in your surveys to collect customer feedback.

What is a Rating Scale?

Rating scales are among the most common survey question types used for online and offline surveys. They are close-ended questions with a set of categories as options for respondents. Rating scales help gather information on qualitative and quantitative attributes.

The most common examples of rating scales are the Likert scale, star rating, and slider. For example, when you visit an online shopping site, you see a rating scale question when it asks you to rate your shopping experience.

It is a popular choice for conducting market research. It can gather more relative information about a product or certain aspects of the product. The scale is commonly used to gain quantifiable feedback. It can be used to gain insight into a product’s performance, employee satisfaction or skill, customer service performance, etc.

What Are the Two Categories of the Rating Scale?

Rating scales can be classified into two categories: ordinal scale and interval scale. Some data are measured at the ordinal level and some at the interval level.

1. Ordinal Scale

An ordinal scale gathers data by putting them in a rank without a degree of difference.

2. Interval Scale

An interval scale measures data with an equal distance between two adjacent attributes.

Robust online survey tools should allow you to create interactive surveys with rating questions to keep the respondents engaged.

Now that we have learned what it is and the two categories of the collected data, let’s look into the different types.

What are the types of rating scales?

You can use this type of scale in your survey in six different ways. These six scales gather data based on the categories mentioned above.

1. Numeric scale.
2. Verbal scale.
3. Slider scale.
4. Likert scale.
5. Graphic scale.
6. Descriptive scale.

We have explained these six rating scale types in detail to help you determine the right time to use the right question.

1. Numeric rating scale or NRS

A numeric rating scale uses numbers to identify the items in the scale. In this scale, not all numbers need an attribute attached to them.

For instance, you can ask your survey respondents to rate a product from 1 to 5 on a scale. You can assign ‘1’ as totally dissatisfied and ‘5’ as totally satisfied.

2. Verbal rating scale or VRS:

Verbal scales are used for pain assessment. Also known as verbal pain scores and verbal descriptor scale compiles a number of statements describing pain intensity and duration.

For instance, when you go to a dentist, you are asked to rate the intensity of your tooth pain. At that time, you receive a scale with items like “none,” “mild,” “moderate,” “severe,” and “very severe.”

3. Visual analog scale or Slider scale:

The idea behind VAS is to let the audience select any value from the scale between two endpoints. In the scale, only the endpoints have attributes allotted to numbers, and the rest of the scale is empty.

Often just called a slider scale, the audience can rate whatever they want without being restricted to particular characteristics or rank.

For example, a scale rating ranges from extremely easy to extremely difficult, with no other value allotted.

4. Likert scale:

A Likert scale is a useful tool for effective market research to receive feedback on a wide range of psychometric attributes. The agree-disagree scale is particularly useful when your intention is to gather information on frequency, experience, quality, likelihood, etc.

For example, a Likert scale is a good tool for evaluating employee satisfaction with company policies.

5. Graphic rating scale:

Instead of numbers, imagine using pictures, such as stars or smiley faces to ask your customers and audience to rate. The stars and smiley faces can generate the same value as a number.

6. Descriptive scale:

In certain surveys or research, a numeric scale may not help much. A descriptive scale explains each option for the respondent. It contains a thorough explanation for the purpose of gathering information with deep insights.

These are the six types of rating questions that you can use in your surveys to make it an engaging and fun experience for the survey takers.

How to create a rating scale survey?

While rating scale questions are simple to create and easy to understand, there are certain factors you need to be mindful of to ensure it doesn’t confuse the respondents. Let’s look at some tips to learn how you can create a rating survey.

1. Determine the scale –

The first step is identifying the right scale for your survey question and its scale points/response options. The scale should reflect the purpose of the research, and the scale points should resonate with the idea of the selected scale.

The aim is to ensure that the respondents can interpret the meaning and purpose of your rating questions easily and accurately.

2. Implement the right scale –

The six rating scale types should help you understand which rating scale you should use in your survey.

If you can’t determine which scale best fits the research, then consider running a test with the rating questions you wish to use. Evaluate the result to see which scale helps you collect the intended data.

3. Use a consistent rating scale –

Maintain consistency in your survey by using the same order/value in scale points. The best way to do this is by assigning the lowest end of the scale as 1 and the highest end as 5. This order is easy to follow as it shows that the more you move to the right, the bigger the numbers.

This not only helps your survey respondents but also allows for easier analysis.

4. Balance positive and negative options –

A balanced scale helps minimize response bias and non-response bias. This ensures that the respondent has the option to simply opt for the middle value and not show any priority. In contrast, for others, the security of the middle value influences them to provide honest opinions.

5. Let one question focus on one idea –

Prevent combining multiple concepts in a single question. This will confuse the respondents and muddle your result.

What are the advantages of using rating scales in surveys?

We have discussed the factors that contribute to the popularity of this question type, among others.

• It is a simple and easy-to-understand question type for both the researcher and the audience.
• It doesn’t take too much of the respondents’ time.
• There are various types of scales to help you create an engaging survey.
• In terms of marketing surveys, this scale is a valuable tool for data analysis. It can gain product review for evaluation and a further improvement in marketing strategy.

What are the disadvantages of rating scales?

Let’s also see the disadvantages of using this scale in surveys.

• It does not help collect the reason behind a customer review.
• It gets access to the overall experience but not the reason behind the audience’s perception.
• In the case of VRS, the scale may oftentimes overestimate the patient’s pain experience. In addition, patients with limited vocabulary may not understand the statements in a verbal descriptor scale.

When should you use the rating scale in your survey questionnaire?

The rating scale allows you to gather a large volume of quantifiable responses. The data you gather can help you identify patterns in feedback and determine what needs priority.

Here are a few instances where you can use rating scale questions in your survey:

#1 You can use it to gather information on a particular topic.

For example, you can use it in market research surveys to collect the following data:

• Customer reviews about an app they are using.
• Customer satisfaction with the delivery service of a courier company.
• Likelihood of recommending a café to a friend.
• Rate a list of brands from least to most favorite.

#2 You can use it to gather information about a service or product from the target audience for the purpose of comparison and analysis.

For instance, if you are planning to start a business, this type of scale will provide you with awareness of the current market demand. With the information gained, you can strategize your scheme.

#3 You can measure the frequency to assess how often a survey respondent engages in certain behaviors.

For instance, in healthcare research, you can use a rating scale to measure how frequently your patients partake in exercise or certain health behaviors.

#4 You can identify the importance and priority respondents assign to certain products.

For example, you can use it to understand your target market’s preference and level of importance for a product available in the market.

Examples of Rating Scale Survey Questions

Before we conclude our blog, let’s look at some rating question examples you can use in your survey questionnaires:

1. Customer Satisfaction Rating Scale Questions –

• How satisfied are you with the newly launched live customer support chat service on our app?
• How likely are you to refer our podcast app to others?

2. Product feedback Rating Scale Questions–

• Rate the quality of our latest product. (1- poor and 5 – excellent)
• How easy was it to use the new doc scanner app?

3. Event Experience Rating Scale Questions–

• How would you rate the organization of our music festival event? (1-poorly organized, 5- extremely well organized)
• How likely are you to attend our summer event in the future?

Wrapping up;

This sums up all you need to know about rating scales. However, here are some tips to ensure that your scale questions are easy to understand for the audience.

1. Assign clear and precise labels to the endpoints of your scale.
2. Avoid response bias by including both endpoints in your question ( satisfied/dissatisfied, easy/not easy, likely/not likely, etc.).
3. It’s better to make ‘1’ the negative and the other end ‘5’ or ‘10’ as the positive point.
4. Use odd scales to offer a neutral point for the audience.

Various rating scale question types are available in Voxco’s market research software, each serving a different purpose. Hence, before selecting a scale, it is important to figure out the purpose of the survey and the kind of information you wish to gain.

Research design is a blueprint for your entire research process. It helps you seamlessly navigate through the complexities of sampling, data collection, analysis, and interpretation. Whether you are venturing into the world of social sciences or conducting market research, understanding the elements and intricacies of the methodologies behind research will help you conduct the study with more clarity and confidence.

In this blog, we’ll explore the characteristics and types of research methodology to help you understand how to design your research process.

What is research design?

According to the definition of research design, it refers to the framework of market research methods and techniques that are chosen by a researcher. The design that is chosen by the researchers allow them to utilise the methods that are suitable for the study and to set up their studies successfully in the future as well.

Research design offers a variety of options. It can be qualitative, quantitative, or mixed. Under these designs, researchers can choose from various research methods such as experimental studies, surveys, correlational studies, or quasi-experimental review studies. There are also sub-types of research methods including experimental design, defining research problems, and descriptive studies.

Research designs are influenced by the research problem a company chooses to work on. This problem serves as the determining factor in the choice of research design, highlighting the logical sequence of steps in conducting a research study.

The market research study’s design phase is when the researchers determine the tools to be used and how they will be used. Good research usually ensures minimum levels of bias in the data collection method to improve both the internal and external validity of the research. The desired outcome of experimental research is to have a design that will result in the least amount of error in the study.

What are the elements of research design?

Some essential elements of research designs are highlighted below:

1. Research purpose:

A research design cannot be decided without an accurate purpose or problem statement.

2. Appropriate sampling:

This includes determining the appropriate sampling methods, correct sample size, and key characteristics of the population. Tools like a market research panel can simplify this step by giving you access to vetted and willing survey participants.

3. Data collection methods:

The process of gathering data from participants is a critical element of research design. This step involves selecting what data to collect, the right mode of data collection, and the tools used (be it card sorting tools, or other tools) for the purpose. Voxco offers three modes of data collection - online, CATI, and mobile-offline.

4. Data analysis:

Research designs include data analysis and interpretation. This element includes deciding which statistical method to use to analyze the data to mitigate any error or bias in research results.

5. Types of methodology:

This step includes determining the best among the several types of research methodology. Different research designs require different settings for the conduction of a study.

6. Setting up time frame:

Another element is to outline the general timeline it will take to conduct a study using different research methods.

7. Integrity:

Using an accurate research design will help your study be successful. Research studies that are successful and include the least amount of error provide important insights that are free of bias.

8. Ethical considerations:

It must also ensure adhering to ethical considerations such as informed consent, confidentiality, and anonymity.

What are the main characteristics of research design?

To better understand how you can design your own research process, let’s take a look at the main characteristics of the subject.

01. Neutrality before research initiation:

When you are planning to study a phenomenon, you may have an assumption about the kind of data you are expecting to collect. However, the results you find from the study should not be driven by bias and must be neutral. In order to understand the opinions on the obtained results, you can discuss it with multiple people and consider the points made by individuals who agree with the results obtained.

02. Reliability of research design:

When you replicate an already conducted market research, you expect similar results. Decide the type of research questions you are going to ask through your surveys and define that in your research design. This will help set a standard for the results. Only if your design is reliable it will help you obtain the expected results.

03. Validity of insights:

You need to ensure that the survey questionnaire you are using is valid. Validity refers to the fact that the research tool you use measures what it purports to measure. Only valid tools will help researchers in gathering accurate results for their study.

04. Generalizability of research findings:

The outcome of your research design should be generalizable to a wider population. Good research design findings are generalizable to everyone, and they indicate that if your survey were to be replicated on any subgroup of the population, it would yield similar results.

A good research design balances all the above characteristics. Researchers must also understand the different research design types to choose from. This understanding will help them implement the most accurate research design for their study.

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What are the different types of research design?

Broadly, there are two types of research design types:

1. Qualitative research design
2. Quantitative research design

Quantitative Research Design:

Quantitative research is the process of collecting and analyzing numerical data. It is generally used to find patterns, averages, predictions, and cause-effect relationships between the variables being studied. It is also used to generalize the results of a particular study to the population in consideration.

Quantitative research is widely used in science, both in the natural and social sciences. It provides actionable insights that are essential for company growth.

Qualitative Research Design:

Qualitative research is a method used for market research that aims to obtain data through open-ended questions and conversations with the intended consumers.

This method aims to establish not only “what” people think but also “how” they came to that opinion and “why” they think so.

What are the subtypes of research design?

We can further explore research design in five sub-types based on the objective, methodology, and focus.

01. Descriptive research design

Descriptive research refers to the methods that describe the characteristics of the variables under study. This methodology focuses on answering questions relating to “what” than the “why” of the research subject. The primary focus of descriptive research is to simply describe the nature of the demographics under the study instead of focusing on the “why”.

Descriptive research is called an observational research method, as none of the variables in the study are influenced during the research process. If the problem is unclear enough to conduct a descriptive analysis, researchers can use exploratory research methods first.

02. Experimental research design

Experimental research, also called experimentation, is conducted using a scientific approach with two or more variables. The first variable is a constant that can be manipulated to see the differences caused by the second variable. Most studies using quantitative research methods are experimental in nature.

Experimental research helps you in gathering the necessary data for you to make better decisions about your proposed hypothesis. The success of experimental research usually confirms that the change observed in the variable under study is solely based on the manipulation of the independent variable.

Experimental research design is the most practical and accurate kind of research method that helps establish causation. This research design is used in social sciences to understand and observe human behavior. The behavior is observed by placing humans in two groups so that researchers can make comparisons.

03. Correlational research design

A correlation refers to an association or a relationship between two entities.

Correlational research studies how one entity impacts the other and what are changes are observed when either one of them changes. This research method is carried out to understand naturally occurring relationships between variables.

Hence, at least two groups are required to conduct correlational quantitative research successfully. The variables in this study are not under the researcher's control; the researcher is simply trying to establish whether or not a relationship between two variables exists.

Since correlational studies only explain whether there is a relationship between two groups, they do not establish causation. Thus, it is not recommended to draw conclusions solely based on correlational studies; just because two variables are in sync does not mean they are interrelated or that one variable is causing the changes in the other variable!

A numeric correlation coefficient determines the strength of the relationship between two variables and ranges from -1 to +1. If the correlation coefficient obtained is -1, it indicates a perfect negative relationship between the two variables, i.e., as one variable increases (age), the other variable decreases (purchase of sports products).

If the correlation coefficient of a study is found to be +1, it indicates a perfect positive relationship between the two variables, whereas one variable increases (age) and the other variable also increases (purchasing beauty-enhancing products).

04. Diagnostic research design

In a diagnostic research design, the researcher is trying to evaluate the cause of a specific problem or phenomenon.

This research design is used to understand more in detail the factors that are creating problems in the company. Diagnostic research design includes three steps:

Step -1: The inception of the issue – When did the issue arise? In what situations is the issue more evident?

Step -2: Diagnosis of the issue – What is the underlying cause of the issue? What is influencing the issue to worsen?

Step -3: Solution for the issue – What is working in curing the issue? Under what situations does the problem seem to become less evident?

05. Explanatory research design

Explanatory research design uses the ideas and thoughts of a researcher on one subject to be the guiding point for future studies, it is also used in exploring theories further. The research focuses on explaining the unexplored patterns of phenomena and elaborates on the details pertaining to the research questions such as; what, why, and how.

Conclusion

A clear research design provides a direction guiding your process with a clear objective and questions to investigate the topic of interest. Research design ensures the validity and reliability of the research findings and confirms that one can replicate the result even for future research. An appropriately created and executed research design helps you draw meaningful conclusions.

What is Convenience Sampling?

A type of non-probability sampling, Convenience Sampling involves collecting samples from the population that is closer to the researcher. It is also known as accidental sampling, opportunity sampling, or grab sampling because the researcher can use the respondents who are conveniently available at the researcher’s reach. Convenience sampling can be used in the best market research tools available.

Gathering samples from the entire community is not always possible, at those times researchers use convenience sampling. The process is uncomplicated, prompt, and because it uses an audience of close contact, it is economical as well.

The sample includes people who are in the researcher’s close proximity such as workplace, school, club, apartment complex, etc. The factor that whether the sample represents the entire population is not taken under consideration. However, with this sampling technique, you can gather opinions, habits, reviews, etc. in an easy and simple way.

Examples of Convenience Sampling

In business and Market research, convenience sampling provides data from the perspective of the audience about the brand image and reputation. It is also used to obtain opinions about newly launched products or on a small-scale project.

• Let’s say a student is planning to open a food truck outside a college campus. They need to collect opinions based on the student’s choice of food to create their menu. The student will ask their friends and other students around campus to collect the data. margin of error calculator.
• You may have come across people outside a mall or convenience store with pamphlets and questionnaire surveys. This is also an example of a convenience sample, the people with pamphlets ask the people on the street to participate in the survey. The researcher may not know these people but they are available within their reach at the moment. You can use paper surveys or a mobile offline survey software.
• You need to create an online survey on the best mobile phones and the desired feature for your online blog. You create a survey with relevant questions and send them to your email and phone contact and share the link on your social media accounts. This way people from your daily contact can respond to the survey and you can gather the data in an easy manner.

When can you use Convenience Sampling?

Convenience sampling has certain issues, such as you cannot generalize the result to a larger population. However, in some cases, it is the only option that can give you the result. Sometimes, it is the only method when you cannot get a list of respondents or a large population. Convenience sampling is easy to conduct. Also when you need results in a short time and have a low budget, it is the method that can save you.

For instance, if your company has 3 offices and you are conducting a survey on how the employees feel about their wages. It is not possible for you to go through the entire body of employees of all the 3 offices. So, you grab the employee in your office and the ones you come across to conduct the survey. Hence, the alternate name, ‘grab sampling’.

In American universities, the convenience sampling survey method was used to understand the association between perceptions of unethical consumer behavior with demographic factors. Understand how to collect relevant information using demographic survey template.

What are the advantages of using Convenience Sampling?

Provides results quickly:

In cases when time is limited and you need to collect data fast, convenience sampling is used by many researchers. The simplicity factor of this non-probability sampling makes it a quick and easy procedure, unlike other non-probability samplings.

Cheap method of sampling:

Money is another factor it saves. A researcher includes the people who are in close proximity to the researcher, hence it is a cost-effective market research tool. The researcher can generate data with little to no investment. Students with low or no budget can use convenience sampling because they can make use of the people in their contact to obtain data for their survey.

Easy to use:

The respondents in convenience sampling are readily available to the researcher. The members of the sample can be friends, families, employees, regular customers, and random people on crowded streets. Therefore, the responders are accessible to the researcher, and collection of data, as a result, is an easy task.

Provides Qualitative Information:

On certain issues, it can provide in-depth information. For example, you can add a survey with the bill presented in your restaurant. The customers can fill the survey and give you their opinion, comments, and review about your restaurant. This way you can gather information relevant to the success of your restaurant with the help of convenience sampling.

Disadvantage of Convenience Sampling

Does not produce a representative result

Convenience sampling is a type of market research which uses a small part of the population to make assumptions about the entire population. However, generalization of the result to the larger population is not always possible. The convenience sampling result may vary widely depending on the scale of the population. For small-scale projects, a large sample size and data may provide representative results.

Biased

The result in convenience sampling can be biased because some people may take part in the survey and some may not. This can disturb the purpose of the survey and make the result futile.

The biased result can be prevented by using probability sampling along with convenience sampling. This can help derive more accurate results.

Efficiently analyzing Convenience Sampling

It is mostly recommended to use probability sampling. But, when convenience sampling is the only option follow the tips to have more efficient results.

• With a large sample size, the method of cross-validation can be used on one-half of the data. To see if the result is a match you can compare the result of the first half with the second half of the data.
• When conducting a sample it is advised to take multiple samples during the period of the research. This way you may be able to produce reliable results.
• Repeating the research several times can bring you closer to be results that can be generalized to a large population.

What is Field Research

Field Research is a method of collecting qualitative data with the aim to understand, observe, and interact with people in their natural setting. It requires specialized market research tools. The goal is to understand how a subject behaves in a specific setting to identify how different variables in this setting may be interacting with the subject. Field research is used most in the field of social science, such as anthropology and health care professions, as in these fields it is vital to create a bridge between theory and practice.

Methods of Field Research

There are 4 main methods of conducting field research, and they are as follows:

1. Ethnography

Ethnography is a kind of fieldwork that aims to record and analyse a particular culture, society, or community. This method defines social anthropology, and it usually involves the complete immersion of an anthropologist in the culture and everyday life of the community they are trying to study.

     2. Qualitative Interviews

The goal of qualitative interviews is to provide a researcher with a breadth of information that they can sift through in order to make inferences of their sample group. It does so through interviews by directly asking participants questions. There are three types of qualitative interviews; informal, conversational, and open ended.

    3. Direct observation

This method of field research involves researchers gathering information on their subject through close visual inspection in their natural setting. The researcher, and in this case the observer, remains unobtrusive and detached in order to not influence the behavior of their subject.

    4. Participant Observation

In this method of field research, the researchers join people by participating in certain group activities relating to their study in order to observe the participants in the context of said activity.

Steps to conduct Field Research

The following are some key steps taken in conducting field research:

1. Identifying and obtaining a team of researchers who are specialized in the field of research of the study.
2. Identifying the right method of field research for your research topic. The various methods of field research are discussed above. A lot of factors will play a role in deciding what method a researcher chooses, such as duration of the study, financial limitations, and type of study.
3. Visiting the site/setting of the study in order to study the main subjects of the study.
4. Analyzing the data collected through field research.
5. Constructively communicating the results of the field research, whether that be through a research paper or newspaper article etc.

Reasons to conduct Field Research

The following are a few reasons as to why field research is conducted, typically via market research tools:

1. To understand the context of studies: field research allows researchers to identify the setting of their subjects to draw correlations between how their surroundings may be affecting certain behaviors.
2. To acquire in-depth and high quality data:  Field research provides in-depth information as subjects are observed and analysed for a long period of time.
3. When there is a lack of data on a certain subject: field research can be used to fill gaps in data that may only be filled through in-depth primary research.

Examples of Field Research

• The following are real studies conducted using field research in order to answer questions about human behavior in certain settings:
  • William Foote Whyte used participant observation in his 1942 study to answer the question “How is the social structure of a local “slum” organized?”.  The study involved over 3 years of participation and observations among an Italian community in Boston’s North End.
  • Liebow’s study in 1967 involved twenty months of participation and observations among an African American community in Washington, DC, to answer the question “How do the urban poor live?”.
  • American sociologist, Cheri Jo Pascoe, conducted eighteen months of observations and interviews in a racially diverse working-class high school to answer the question “How is masculinity constructed by and among high school students, and what does this mean for our understanding of gender and sexuality?”.

Advantages of Field Research

1. Can yield detailed data as researchers get to observe their subjects in their own setting.
2. May uncover new social facts: Field research can be used to uncover social facts that may not be easily discernible, and that the research participants may also be unaware of.

No tampering of variables as methods of field research are conducted in natural settings in the real world. Voxco's mobile offline research software is a powerful tool for conducting field research.

Disadvantages of Field Research

• Expensive to collect: most methods of field research involve the researcher to immerse themselves into new settings for long periods of time in order to acquire in-depth data. This can be expensive.
• Time consuming: Field research is time consuming to conduct.
• Information gathered may lack breadth: Field research involves in-depth studies and will usually tend to have a small sample group as researchers may be unable to collect in-depth data from large groups of people.

Concept Testing is a market research method used by Companies to evaluate concepts or ideas before it is launched in the market. A target audience is surveyed on the concepts to gauge the interest, acceptance, and willingness of the customers to engage with the concept (product, service, advertisement).The responses collected from the audience help the company make an informative decision before the launch. When a brand is preparing to launch a new product or redesign an old product, they conduct Concept Testing to identify the likes and dislikes of the target market.

Importance:

• Concept testing allows the brand to see how well the product will perform if it is launched in the market. 
• It helps gain insight into the improvements and changes which are needed. 
• Concept testing helps identify how different segments of audiences prefer different features. 
• The data collected from customers prevents the company from investing in concepts that may not be accepted by the customers. 
• Concept testing prevents a company from investing in bad concepts based on assumptions. 

Concept Testing Methods

There are four most commonly used methods of Concept Testing.

Monadic Concept Testing:

In monadic concept testing, a single concept is evaluated by the respondents. If there is more than one concept, the respondents are divided into multiple groups. Each group is then shown one concept to analyze.This means that each respondent only comes across one concept. This allows conducting an in-depth survey. Make sure to keep the survey short and follow up if required.

Sequential Monadic Concept Testing:

In a sequential monadic test, the respondents are asked to evaluate each of the concepts. The respondents are divided into multiple groups and each group is shown the concepts in random sequence. The random sequence prevents the respondents from forming any biased opinions.Multiple concepts are evaluated with a small sample group which saves time and resources for the company. The risk is that the survey questionnaire may end up being long because multiple concepts are tested in one round.[elementor-template id="38118"]

Comparative Concept Testing:

For Comparative Concept Testing, respondents are asked to evaluate between multiple options to select the best Concept. The survey is simple, the brand asks which concept or idea is better and the winning concept is finalized for the launch.

Proto-monadic Concept Testing:

It is a combination of comparative and monadic concept testing. The respondents are asked to select the best concept. Then they are asked questions to evaluate the selected concept.The comparative concept testing alone cannot provide the reason for the respondents’ preferred choice. The second evaluation using the monadic test helps provide the necessary reason. It helps gather information on the various aspects, features, or attributes of the preferred concept.

Application of Concept Testing in Market Research

Concept testing helps businesses identify the best and the bad ideas. It saves a company from launching a bad concept in the market and faces loss. Concept testing is thus a crucial step before any ad campaign, logo, product, service, etc. are launched.These are some scenarios you can use in your Concept Testing.Identify Market: You need to have a good understanding of the market to target the right audience with the right concept. Concept Testing helps understand the reason why a different segment of audience likes different concepts. The knowledge of different demographic segments helps develop successful market strategies.Pricing: When you want to launch a new product or get an opinion on the prices of your products you can gather customer feedback. It can help you make decisions on how you should change the price or charge your products.Marketing message: With concept testing, you can identify what kind of marketing message resonates with your target audience. It helps you to understand how you can attract and influence your target customers to consider your brand for future business.Branding: You can also use concept testing for deciding logo, website design, color, etc. You can ask the respondent to select the effective idea and understand their reason for their choice.

Best Practices for Concept Testing

For any subtle adjustment whether it is on pricing or features, conduct Concept Testing. By identifying the different aspects of the concept you can focus on the key features. Concept testing can provide a clear view about which concepts need improvement and which need to be dropped. Conduct concept testing for each change made in the product as per customer feedback. Collecting customer’s perspectives on the newly changed concept is the way to ensure that your data stays up-to-date. The ongoing process of concept testing helps you track all the latest trends about customer’s needs and wants.Learn from the previously collected data by comparing it to the new data. Previous data is filled with information that can help you improve new concepts for testing. You can look into past research to identify which method of testing works effectively.The introduction is an important part of the survey because it gives the audience the idea of what the purpose of the survey is. You need to make sure that the concept is described in simple language. The introduction should include the concept, benefits, and key differentiators of the product.The survey design for Concept Testing should be simple. The choice of answers should be easy to understand. Using questions like Likert Scales gives a coherent structure and a smooth flow to the survey. It is also easy to analyze the data collected from a Likert Scale.[elementor-template id="38078"]

FAQs

What is Concept Testing in Market Research?

Concept Testing in Market Research involves using surveys to evaluate the target audience’s acceptance and willingness to buy the new product concept. The new concept is tested before it is introduced in the market to gauge customer’s reaction to the features, price, and other important aspects.

What is a Concept Statement?

A Concept Statement in concept testing is the description of the concept that helps visualize the end product/ service.

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There are four basic methods a brand can conduct Concept Testing:

• Monadic 
• Sequential Monadic
• Comparative
• Proto-monadic

Quantitative and Qualitative research :Which one to prefer?

Research is a great way of gathering data and information to enhance understanding about a variety of issues and ideas. It is imperative to know the kind of research to go for, in order to satisfy a purpose and obtain valuable data in the most convenient manner. While deciding the research type, one also needs to consider how this selection will impact analysis and contribute by providing input to make informed decisions. A research methodology may be easy to conduct but may not generate sufficient key insights or may not be feasible in terms of time, effort and resource investment but may be easy to evaluate and conclude. There should be a certain balance between the conduction and conclusion aspect of research, to pick the correct choice.The two broad categories of research are quantitative and qualitative research.Quantitative research deals with numbers and statistics to describe test and draw conclusions about variables. Such a data can be mathematically and statistically analyzed. It is also viable to present such type of research data in the form of charts and graphs for enhanced understanding. This type of research mainly focuses on testing relationships and hypothesis by gathering maximum information to make a meaningful conclusion. Examples of such type of research include observations, closed-ended type questions in surveys and experimental data.Qualitative research, on the other hand, focuses on descriptive and text-based data to make observations, understand ideas and concepts, gather insights and social perceptions. It is aimed at grasping how people view things around them using an unstructured and unrestricting method of research that allows people to elaborate their viewpoints. Unlike quantitative research, qualitative research is not focused on hard numbers and figures and is analyzed using text based analysis tools. Examples of qualitative research are open-ended questions, interviews, group discussions, video and audio recordings among others

QUANTITATIVEQUALITATIVENumber and figure basedText basedTests relationship and hypothesisIt is used for making observations and enhancing conceptual understandingAnalyzed using statistical and data analysis toolsEvaluated by using summarizing techniques and text based analysisResearched using closed-ended questionsResearched using open-ended questionsPresented using graphs, charts and diagramsTechniques such as Word cloud helps in capturing and presenting key insightsRequires large number of participantsLimited number of participants are neededObjective in natureSubjective in natureLanguage based reportingStatistical reportingExample: interviews and focus groupsExample: Structured questions and observations01

Methodologies

Qualitative researchQualitative research is conducted using focus groups, in-depth interviews, ethnography, documents, reviews and open-ended survey questions. All of these methods allow participants to elaborate and clarify their opinions and thoughts as well as study behavior in specific circumstances. Though it may seem a cumbersome process, collecting qualitative data helps in understand respondent mindset and making assumptions and observations based on authentic information.Focus groups: Discussions between participants with relevant knowledge base to gather holistic data on the research topic.Interviews: One to one dialogue to gather point of views and respondent’s thoughts about products, ideas and concepts.Open-ended survey questions: Unstructured questions meant to gather feedback and respondent’s unrestricted opinion.Documents: Second hand information on research topics to grasp topics using sourced data.Ethnography: An observation style based research involving participation in a community to note behavior and activitiesReviews: Studying and reviewing written piecesQuantitative researchQuantitative research is gathered using closed-ended questions, observations, experiments and different survey methods. These research methods mainly focus on bringing cause and effect relation as well as proving the validity of hypothesis by gathering input to support or deny the same.Closed-ended questions: Questions with limited answer options to assist categorizing and analyzing.Observations: Observing and noting numeric variables like temperature.Experiments: Establishing correlation between variables through controlled conditioning.Telephonic surveys: Gathering structures data through telephonic conversation.Polls: Polling questions and statements to assess agreement, rating and choice.

Analysis

Qualitative researchTexts and language used in qualitative research is highly variable and cannot be uniformly understood. Text based summarizing and interpretation techniques are used to highlight key areas.Thematic analysis: A latent approach that tries to uncover the underlying meaning behind written information by following a series of steps that involve understanding, highlighting, assigning themes and codes and finally writing up the takeaways from the gathered qualitative information as a whole by supporting each of these takeaways using phrases and texts from the first-hand data. It also tries to establish whether or not the purpose of the research has been satisfied based on the data collected.Word cloud: Word cloud is a summarizing tool used for overviewing the key words that texts contain. This usually promotes a follow up discussion on reasons that lead to the use of a word or phrase in relevance to the research topic.Discourse analysis: Understanding communication, linguistics and structure of qualitative data. This technique studies speech, sentence structures, conversational indicators and frame of references to understand how people interact in a social setting.Quantitative researchQuantitative research is based on facts and figures and so, it becomes relatively easier to make sense out of it using data and statistical analysis tools. These tools can be used to summarize and understand nature of relationship between variables. The analysis results describe the data using certain mathematical concepts that can easily be applied due to the structured nature of the data collection process. Descriptive analysis provides holistic and concise figures which combine results from individual responses to provide an overall picture of the collected responses.Such a statistical summary can then be presented in the form of graphs, pie charts, bar diagrams, line charts and other forms of data representation method which makes it easy to comprehend. Such statistical summaries can then be used to validate or invalidate hypothesis. This gives a more realistic and reliable approach for testing theories.

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Limitations

Qualitative research

• Highly varied and complex data makes it difficult to analyze as compared to quantitative data 
• It becomes difficult to assess reliability and validity given the lack of rigidity in answering
• Respondents may not be clear and elaborative in expressing themselves completely and so researchers may sometimes have to follow up to increase their understanding on particular answers.
• It requires a lot of time and money to hire professionals and conduct in-depth interviews.

Quantitative research

• Structured nature of the research limits answer choices

• Analysis of quantitative research requires expertise to decide the right kind of tool and conduct the process properly to generate meaningful results.

• Does not allow researcher to gather insights and study behavior.

• Requires large number responses to make the study substantive which requires a lot of resource investment.

Advantages

Qualitative research

• Aids understanding of participant mindset and reasoning
• Elaborative and unrestricted
• Establishes observations and enhances relationship understanding between variables
• Assists in gathering feedback for identifying gaps and positives.
• Narrative nature of the research makes the study and its results more authentic

Quantitative research

• Minimizes chances of ambiguities and confusion
• Relatively accurate 
• Easy to analyze using systematic tools
• Proves theories based on supportive information
• Easy to present and convey to third parties 
• Easy to summarize, categorize and interpret due to pre-defined structure.

The researcher has to carefully consider the purpose, resource availability and decisions to be made before going ahead with a particular research method. Researchers looking to make observations and establish theories should go for a qualitative approach, while testing and proving these theories is more feasible using a quantitative approach.Free Market Research ToolkitFill out this form to access 5 market research survey templates + 2 MR guide.