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.

1. 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.

2. 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.

3. 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.

4. 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.

‍

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.

1. 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.

2. 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.

3. 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).

4. 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?

5. 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 qualitative data collection method that involves observing and interacting with people in their natural environments. Rather than controlling variables in a lab or relying solely on digital inputs, field research places the researcher within the context they’re studying—allowing them to capture behavior, nuance, and social dynamics as they occur in real life.

This method is especially common in the social sciences—anthropology, sociology, and healthcare studies, to name a few—where bridging theory and practice is essential.

‍

Key Methods of Field Research

There are four primary ways researchers conduct field studies, each offering unique strengths depending on the study’s goals.

1. Ethnography

Ethnography involves immersive, long-term observation of a specific culture or community. The researcher becomes embedded in the environment, often living among the people being studied to understand their norms, behaviors, and day-to-day experiences. This method is a cornerstone of social anthropology.

2. Qualitative Interviews

This method gathers in-depth perspectives through open-ended interviews. Researchers might take an informal, conversational approach or use semi-structured formats. These interviews are designed to uncover both “what” people think and “why” they think that way.

3. Direct Observation

Here, the researcher takes a non-intrusive role, closely watching subjects in their environment without influencing the scene. This method provides strong ecological validity—capturing natural behavior as it unfolds.

4. Participant Observation

In this more involved method, the researcher actively engages in the group’s activities. This helps reduce participants’ awareness of being studied and offers a deeper look into the social interactions at play.

‍

How to Conduct Field Research: Key Steps

1. Build the right team: Start by assembling researchers with domain expertise relevant to your topic.
   
   
2. Choose the right method: Your timeline, budget, and study type will help determine whether interviews, observation, or ethnography is best.
   
   
3. Engage with the setting: Visit the environment, meet participants, and observe context firsthand.
   
   
4. Collect and analyze your data: Use qualitative coding tools or note-based systems to synthesize findings.
   
   
5. Communicate results clearly: Field research often informs policy, practice, or theory, so sharing insights effectively—through reports, papers, or presentations—is crucial.

‍

Why Conduct Field Research?

Field research is invaluable when:

• Context matters: You want to understand how real-world settings shape behavior.
  
  
• There’s a gap in available data: Secondary data or structured surveys may not provide the insight needed.
  
  
• You need rich, high-quality insight: Long-term observation often reveals insights participants themselves might overlook.

Field Research in Action: Notable Examples

• William Foote Whyte (1942): Lived among an Italian-American community in Boston to study neighborhood social structures.
  
  
• Elliot Liebow (1967): Spent 20 months observing African-American men in Washington, D.C., to explore life in urban poverty.
  
  
• C.J. Pascoe (2007): Conducted fieldwork in a high school to investigate how masculinity is constructed and expressed among teenagers.

‍

Advantages of Field Research

• High-quality data: Observations made in context reveal authentic human behavior.
  
  
• Uncovers new insights: Can expose cultural or social dynamics that structured surveys miss.
  
  
• Natural setting: Since there’s no artificial manipulation of variables, data tends to be more reflective of real life.

‍

Disadvantages of Field Research

• Time and cost intensive: Fieldwork often involves long durations and high operational costs.
  
  
• Small sample sizes: Because of the depth involved, researchers typically study fewer participants.
  
  
• Potential for bias: Immersive research can make it harder for researchers to stay fully objective.

‍

Conclusion

Field research offers unparalleled insight into real-world behaviors and social dynamics by placing researchers directly in the environment they’re studying. While it requires time, effort, and the right tools, the depth and authenticity of the data collected make it an invaluable method—especially in studies where context matters most. With modern tools like Voxco’s mobile offline surveys, conducting field research has never been more efficient or scalable.

‍

Need to collect data in the field? With Voxco’s mobile offline capabilities, you can conduct secure, reliable field research—anywhere, anytime. Book a demo to see how it works in action.

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.

‍

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

• Monadic 
• Sequential Monadic
• Comparative
• Proto-monadic

Whether you’re testing a hypothesis, understanding customer behavior, or exploring a new idea, your research approach will influence the quality of data you collect — and how easily you can analyze it.

The two primary approaches in research are qualitative and quantitative. While both are valuable, each is suited to different goals and contexts. In this blog, we’ll compare the two, break down their methods and analysis techniques, and help you decide which is best for your next project.

‍

What Is Quantitative Research?

Quantitative research focuses on measurable data — using numbers, statistics, and structured questions to identify patterns, test relationships, and validate hypotheses. Because it produces numerical output, this type of research is often presented using graphs, charts, and data tables.

Common examples:

• Closed-ended survey questions
  
  
• Polls and rating scales
  
  
• Observational counts
  
  
• Experimental data
  
  

This approach is ideal when you need large-scale validation or comparisons across segments.

‍

What Is Qualitative Research?

Qualitative research explores non-numeric, text-based information to understand opinions, perceptions, motivations, and behaviors. It allows participants to express themselves freely, making it useful for discovering deeper insights or uncovering new perspectives.

Common examples:

• Open-ended survey questions
  
  
• One-on-one interviews
  
  
• Focus groups
  
  
• Observational research
  
  
• Text, video, or audio analysis
  
  

This approach is best for exploring themes, developing hypotheses, or understanding the “why” behind behavior. You can learn more about how quant researchers are approaching open-end analysis in this blog on becoming a quallie.

‍

Key Differences Between Qualitative and Quantitative Research 

‍

Research Methods Overview

Quantitative Research Methods:

• Closed-ended questions: Provide predefined answer options to facilitate analysis.
  
  
• Experiments: Test cause-and-effect relationships through controlled conditions.
  
  
• Observations: Measure quantifiable behavior (e.g., foot traffic, temperature).
  
  
• Polls and rating scales: Gather large-scale opinions on specific items.
  
  
• Telephonic/online surveys: Structured questionnaires distributed via phone or digital platforms.
  
  

Qualitative Research Methods:

• Open-ended questions: Allow respondents to express their thoughts freely.
  
  
• Interviews: In-depth, one-on-one conversations to explore perspectives.
  
  
• Focus groups: Guided discussions among a small group of participants.
  
  
• Ethnography: Observational research conducted within communities or cultural groups.
  
  
• Document review: Analysis of written material relevant to the research topic.
  
  

‍

How Data Is Analyzed

Quantitative Research:

• Uses statistical analysis to identify trends, relationships, and differences.
  
  
• Generates data that can be summarized in tables, charts, and dashboards.
  
  
• Common techniques include descriptive stats, regression analysis, and inferential tests.
  
  

Qualitative Research:

• Involves organizing and interpreting open-ended responses.
  
  
• Relies on thematic analysis, discourse analysis, or visual tools like word clouds.
  
  
• Tools like Ascribe help automate coding and sentiment analysis of large-scale verbatim data.
  
  

‍

Advantages and Limitations

Quantitative Research

Advantages:

• Produces reliable, generalizable results
  
  
• Easier to analyze using structured tools
  
  
• Useful for confirming hypotheses or tracking metrics over time
  
  

Limitations:

• Doesn’t capture nuance or emotional context
  
  
• Limited by pre-defined questions and answer choices
  
  
• Requires large sample sizes for statistical significance
  
  

‍

Qualitative Research

Advantages:

• Captures in-depth insights and human perspectives
  
  
• Allows flexibility in data collection
  
  
• Helps identify issues and generate hypotheses
  
  

Limitations:

• Time- and resource-intensive
  
  
• Analysis can be complex and subjective
  
  
• Smaller sample sizes may limit generalizability
  
  

‍

Choosing the Right Approach

Choosing between qualitative and quantitative research depends on your objectives:

• Want to test or validate a hypothesis? → Choose quantitative
  
  
• Need to explore a topic or understand behavior? → Choose qualitative
  
  
• Looking for both validation and exploration? → Consider a mixed-methods approach
  
  

Researchers must also weigh resources, timelines, and data analysis capabilities when selecting a methodology. Ultimately, the most effective research approach is the one that delivers the insights you need — clearly, accurately, and efficiently.

‍

Your brand name is the first point of contact for your target audience. It conveys the essence of the brand’s value, personality, and promise. It helps distinguish your brand from competitors and builds trust with customers.

In this blog, we will dive into the steps of effective brand name testing and explore how a strong brand name aligns with the brand’s objectives and identity.

What is Brand Name Testing?

Your company’s brand name will play a significant role in shaping your brand image and will also act as an identifier that distinguishes your brand from competitors. As your brand name can play such a big role in shaping your brand image, it is important to pick a name that will resonate with your target audience and will fit the brand image that you want to portray. This is where brand name research comes into the picture.

Brand name testing involves presenting your target audience with a variety of brand name options and asking them to provide feedback on each one. This research helps you make an informed decision while choosing a brand name that is best suited to your company using qualitative and quantitative backing.

Why is Brand Name Testing Important?

Brand name testing helps determine a name that resonates with the target market. The process offers several key benefits. Let’s look into some ways it is important to evaluate potential brand names.

1. It can significantly impact brand perception. A well-chosen name can make your brand more appealing and memorable. Conversely, a poorly chosen name can lead to a lack of trust in the brand.
2. A strategically evaluated brand name that aligns with brand values will help build strong brand-consumer relationships and drive loyalty over time.
3. Choosing a brand name without testing with the target audience will lead to a lack of interest and engagement with the brand. This can also lead to misinterpretations and damage brand credibility.

Also read: What is Concept Testing

Steps to Take to Conduct Brand Name Testing

Now that we have explored the importance of brand name research, let’s look at how to pick a brand name.

1. Pick the brand names you want to test:

The first step in brand name testing is to create a list of brand names that you want to test. These brand names should be ones that you and your coworkers are already confident of. The number of brand names you choose to include will determine the kind of survey design you select.

One effective tool to aid in the early stages of selecting prospective names is using a brand name generator. This tool helps generate name options that align well with your brand’s identity and maximizes creativity, especially before moving into formal testing.

These are some of the most common survey designs:

Monadic Testing:

This research design involves showing participants a single stimulus in isolation. It gives you a measure of how appealing a certain brand name is without skewing the respondent’s perception with the influence of other stimuli/brand names.

Sequential Monadic Design:

This research design involves asking respondents for feedback on multiple stimuli. However, each brand name shown must be evaluated separately rather than evaluation through comparison.

2. Select the parameters by which you will assess the brand names:

The next step is to select the parameters, or metrics, by which you want respondents to assess your brand name. These are a few metrics by which you can measure how good a brand name is:

• Uniqueness: Does the brand name stand out against competitors, or is it easily confused with other brand names?
• Pronounceability: Is the brand name easy to pronounce, or is it common for people to mispronounce it?
• Appeal: Is the brand name appealing to your target audience?
• Purchase Intent: Does the brand name motivate people to purchase the brand’s products/services?

The metric(s) you select should depend on what your goals are for your brand name. If you want a brand name that is memorable and stands out, the most valuable metric for you to measure would be uniqueness.

3. Gather data for brand name testing:

Once you’ve chosen the metrics by which you want your brand name assessed, you can create your brand name testing survey and send it out to your target audience.

Leverage a robust survey software that empowers you to design interactive brand name surveys. Use image questions, ranking, or rating question types to gather a holistic view of potential brand names. Customize the survey with brand colors and fonts to showcase the brand image to the respondents, helping them share insightful feedback.

4. Analyse results to select a brand name:

The final step is to analyze the responses collected in order to evaluate each brand name. This is the most important step, as it involves selecting a brand name that will appeal to your target audience and match the brand perception you want to create.

How brand name surveys can help you find the best options?

Your brand name sticks to the audience’s experience, emotion, and memory. Getting it right can help you nurture a strong brand image and reputation.

1. Brand name testing allows you to understand how your target consumers perceive each potential name. Through surveys, you can gauge factors like brand recall, likability, and uniqueness.
2. Testing multiple names allows you to assess how each name resonates with the market and stands out from competitors. By comparing each name, you can determine the uniqueness of each name.
3. The feedback from your target market can help you understand how to narrow down your options and make it more strong.
4. The process can help mitigate the risk of choosing a name that could have a negative impact on brand reputation. By gathering customer feedback, you can identify and address any potential issues with the options before the official launch.

Why Choose Voxco for your Brand Name Testing Surveys?

These are a few reasons why you should choose Voxco’s Survey Software to conduct your Brand Name Testing Surveys:

• Omnichannel Survey Solution

Our Omnichannel survey software allows you to create your brand name testing survey on a centralized platform before sending it through all channels. This saves time re-programming surveys for multiple channels. Additionally, all collected data is presented on a single integrated platform, allowing you to compare feedback received through all channels.

• Voxco IVR and Voxco Dialer

You can use our survey software in amalgamation with additional tools, such as Voxco Dialer and Voxco IVR, for the most seamless and efficient omnichannel survey experience.

• Powerful Dashboards

Voxco’s powerful and dynamic dashboards provide you with real-time visual presentations of your brand-tracking survey responses. This can help you make a quicker decision while picking your brand name, as our dashboard presents data from across all channels on one unified platform.

• Voxco Audience:

With Voxco’s market research panel, you can gather data from willing respondents representing the total population. With Voxco Audience, you can create your own sample audience to conduct brand name analysis.

A coding management system can organize and quantify comments from your customers, prospects, or other constituents.  Coding is all about gleaning actionable insight from textual comments.  Coding originated in the survey research industry and its terminology and technique come from that industry.

The survey research industry has elevated the art coding customer comments to a science.  These techniques are valuable to any company that wants to have an accurate understanding of what their customers are telling them – and what company does not?

To understand how you can use these techniques to keep your finger on the pulse of your customers, let us first introduce some concepts and terminology.

What is Coding?

Open and Closed End Questions

Think about the surveys you have seen.  There are two basic types of questions, such as these:

1. On a scale of 0 to 10, how likely are you to recommend our business to a friend or colleague?
2. Why did you give us that rating?

The first of these is called a closed-end question because the possible answers are known in advance.  The second is called an open-end question.  There are an infinite number of possible responses to the question.

Verbatims

Perhaps surprisingly, we do not call the text that the respondent types for an open-end question an “answer”.  Instead, we call it a verbatim.  Yes, I know that “verbatim” is not a noun, but it has been adopted as one in the survey research industry.  Why do we not call the text an answer?  Well, because we cannot do much with the text alone. 

Coding

Imagine you have 5,000 responses to the short survey above and your boss asks you: What percentage of our customers rated us below 8 because of a product delivery problem?  Perhaps you would read each of the verbatims from the second question and make notes on a piece of paper, then tally up the results.  If you are more methodical you might put the verbatims in Excel and make a column for “Delivery Problem” and put a 1 in the column if the verbatim mentions a delivery problem.  As you do this, you would probably add other columns for other issues mentioned, perhaps “Product Quality” and “Technical Support”.  That way when your boss asks you tomorrow for how many customers gave us a negative rating for technical support, you could have the answer instantly, just by summing the Technical Support column.

In survey research we call what you just did in Excel Coding.  The columns such as Delivery Problem and Technical Support are called Codes, but you can see that in a certain sense we could also call them Answers.  When you put a 1 in the Technical Support column, you mean that this customer mentioned technical support as an issue.  

By coding the verbatims as you have done in Excel you have turned the qualitative data (the verbatims) into quantitative data (the codes).

Sources of Verbatims

In the example above we talked about a simple two question survey.  You may have recognized this as a classic Net Promoter Score (NPS®) survey.  This is a good place to start when learning about coding, because the technique of verbatim coding was developed and refined in the survey research industry.

But traditional surveys are certainly not the only source of verbatims for coding.  Verbatim coding is applicable whenever you want to get actionable insights from comments.  This includes such sources as:

• NPS surveys
• Employee satisfaction surveys
• Help desk inquiries
• Call center transcriptions
• CRM systems

Verbatim coding is appropriate whenever you need to quantify comments from constituents.

Reasons for Coding

If we think of coding as tagging comments with codes that attribute meaning to the comment, we see that there are two primary reasons for coding.

Quantification

Coding turns qualitative data into quantitative data.  Once our NPS survey is coded we can easily ask questions such as:

• What percentage of net detractors mentioned product delivery?
• How does the number of net detractors mentioning product delivery in Q1 compare with Q2?
• How does the number of net detractors mentioning product delivery in the Central Region compare with the Eastern Region?

Note that in the second and third example above we assume that we have some additional information.  In the second example we assume we know the time the comment was given.  This comes along for free; we just need to make a record of the date of receipt of the comment.  The third example assumes we know the region the customer is in.  We could of course get this information by asking a closed end Region question in the NPS survey.  But hopefully we can get this information, and much more, by using our CRM system to augment the data with known information about the customer.

Quantification and analysis of verbatims in this fashion is the bread and butter of the survey research industry.  But these techniques are equally applicable for in-house analysis – and far less costly than engaging a research company.

Classification and Indexing

Even after they are coded, verbatims remain a very rich source of insight into your customers thoughts and attitudes.  With coded verbatims you can get your hands on the verbatims most applicable for a business question you are considering.  For example, a product manager with a delivery problem in the Central Division might, and probably should, read a selection of verbatims from that region that mention delivery.  Once coded, finding these verbatims is trivial.

Classification of verbatims by coding allows product managers and others to keep their fingers on the pulse of the customer using the rich texture of verbatim comments, targeted at specific areas of interest by coding.

Coding Methodologies

We started by looking at coding verbatims using Excel.  That is not a contrived example.  There are small survey research companies that do just that.  But there are far more convenient and productive techniques than using Excel.

Human Coding

In the survey research industry verbatim coding is a profession.  People trained in coding read and code every verbatim in open end survey questions.  The industry uses Ascribe Coder to code over 200 million verbatims each year.

Human coding produces the highest accuracy and allows for nuanced differences in meaning between codes.  The construction of a well-designed code frame, which is the set of codes for a question, is part of the art of human coding.  Human coders have full control over the form of the code frame and can deliver results tailored to a specific survey research objective.

The accuracy and control afforded by human coding comes at an associated cost of labor and turn-around time.

Automated Coding

Advances in natural language processing today allow fully automated coding.  Ascribe CX Inspector can code thousands of verbatims in minutes, with no human involvement.  CX Inspector constructs a set of groups automatically by analysis of the verbatims and classifies the verbatims into those groups.  The groups are directly analogous to codes, although they are created by the machine rather than by hand.

Blended Approaches

To be fair, a market researcher would not call the fully automated approach verbatim coding because:

• It does not use a code frame constructed by the researcher
• The accuracy is lower than human coding

With the addition of some human labor, the results of fully automated coding can be massaged to resemble human coding more closely.  In CX Inspector you can edit the automatically created groups by merging and renaming them.  This gives you control similar to the code frame in Ascribe Coder while retaining the speed advantage of automated classification of verbatims.  The resulting groups can be saved as a taxonomy and used on multiple projects, just as you might use a code frame in Ascribe Coder for multiple projects.

Working in the other direction, Ascribe Coder provides the Coding Assistant.  Coding Assistant uses natural language processing to suggest verbatims to code based on prior human coding.  Coding Assistant confers much of the speed advantage of fully automated coding, while retaining full human control of the results.

Conclusion

Verbatim coding was developed by the survey research industry, but its usefulness is not confined to that industry.  Verbatim coding is applicable whenever you need to organize and quantify comments from your constituents.  Tools such as Ascribe Coder and CX Inspector allow you to reap the benefits of verbatim coding without engaging a third party supplier.

When it comes to phone surveys (CATI), response rates aren’t just about the questions you ask — they’re about how you show up for respondents. And that starts with trust.

In an era where skepticism toward data collection is growing, trust has become the deciding factor in whether someone completes your survey — or hangs up.

According to the GRBN Global Trust Survey 2024, 33% of respondents globally express trust in market research companies, while 26% do not, resulting in a Net Trust Index (NTI) of +7. This indicates a modest improvement from previous years, but also highlights that a significant portion of the public remains skeptical.

Here are five ways your team can earn that trust from the first ring to the final thank-you.

‍

1. Listen First — Then Offer a Callback

Many interviewers are trained to get through the introduction quickly and get into the script. But successful calls often begin by doing the opposite: listening.

Is the respondent busy? Do they sound hesitant? Are they open to continuing?

If not, offer a clear, respectful callback — and make sure your software supports this. Whether it’s a hard callback (right person, wrong time) or a soft callback (uncertain fit), giving respondents a chance to engage on their terms can go a long way in building early trust.

🔎 Tip: Choose CATI software with flexible callback queues, shift-based scheduling, and custom messaging for better call handling.

‍

2. Be Strategic with Demographic Questions

Many surveys begin with sociodemographic screening, but asking personal questions too early — like age, income, or religion — can turn people off before the real conversation starts.

Instead:

• Ease into sensitive questions later in the call
  
  
• Focus first on questions relevant to the respondent’s experiences
  
  
• Use routing logic to only ask what’s necessary
  
  

If a survey is only partially completed, weighting it appropriately still allows you to extract value without compromising data integrity.

‍

3. Make It a Conversation, Not an Interrogation

Phone surveys should feel like a dialogue — not a script being read aloud.

Train interviewers to:

• Match the respondent’s tone
  
  
• Speak clearly and with empathy
  
  
• Use open-ended probes when appropriate
  
  

Supervisors should routinely monitor call quality, not just for compliance, but for tone and rapport. Your CATI software should support real-time monitoring and reporting on productivity, drop-off points, and call feedback — so teams can improve quickly.

🧠 Motivated, well-trained interviewers are often the biggest driver of trust — and completions.

‍

4. Reassign Callbacks to High-Converting Interviewers

Not every callback is created equal. When follow-up calls are needed, assign them to your most experienced or high-converting interviewers — especially if the first contact was soft or hesitant.

These interviewers are more skilled at:

• Building quick rapport
  
  
• Reframing the value of the survey
  
  
• Making the interaction feel human, not transactional
  
  

⚠️ Keep in mind: many respondents decline participation because of time constraints — but a strong interviewer can often help re-engage someone who actually does want to share their opinion.

‍

5. Equip Your Team with the Right Tools

Even the best-trained interviewer can’t succeed without the right software behind them.

To inspire trust, your CATI platform should offer:

• Callback management by time slot or shift
  
  
• Interviewer assignment by language, location, or experience
  
  
• Complex quota management
  
  
• Integrated phone and web survey workflows (for mixed-mode studies)
  
  
• On-premise or cloud dialers and IVR support
  
  

A reliable platform reduces friction and helps interviewers stay focused on what matters most: the respondent.

‍

Final Thoughts

Trust can’t be faked — and in phone surveys, it can’t be rushed. Building trust means listening more, pushing less, and creating a sense of mutual respect from the first moment of contact.

The result? Higher response rates, better data quality, and stronger relationships with the people behind your research.

Need a platform that helps your team build trust — and boost completions?
Book a demo to see how we support researchers with powerful tools for real-time productivity, quality monitoring, and seamless call management.

‍

When elections are underway, exit polls often dominate the headlines — offering early insights even before the official results are tallied. But what exactly are exit polls, and how reliable are they? Here's what you need to know.

What Is an Exit Poll?

An exit poll is a survey conducted with voters immediately after they leave their polling station. Instead of asking who people plan to vote for — like a traditional opinion poll — exit polls ask who they actually voted for. This key difference makes exit polls a powerful tool for media organizations, research firms, and political analysts who want to project election outcomes before the final count is complete.

Exit polls not only forecast winners; they also help unpack why voters made their choices, offering a glimpse into the broader social, economic, and political forces shaping the election.

Typically, exit polls are conducted by private research firms on behalf of media outlets, academic institutions, or consortiums like the National Election Pool (NEP) in the United States.

How Are Exit Polls Conducted?

Running a credible exit poll requires more than just stopping voters on the sidewalk. Here's a closer look at the process:

• Sampling: Pollsters select a representative sample of polling stations across regions and demographics.
  
  
• Questionnaires: Voters are asked to complete anonymous surveys covering who they voted for, demographic details (like age, race, gender), and sometimes why they made their choice.
  
  
• Timing and Frequency: Interviewers typically survey every nth voter (e.g., every third or fifth) throughout the day to maintain a randomized sample.
  
  
• Data Collection: Voters usually fill out the surveys themselves to maintain confidentiality and minimize interviewer bias.
  
  
• Adjustments: Pollsters track refusals and estimate demographic gaps to adjust results accordingly.
  
  
• Tallying Results: Data is processed and analyzed quickly to provide preliminary projections — often well before the official results are available.
  
  

Example: 

In the U.S., Edison Research conducts exit polls for the NEP. Their interviewers collect 100–150 responses per polling location, tracking refusals and visibly estimating demographic information to correct for non-response bias.

Are Exit Polls Reliable?

Exit polls are a important — but they aren’t foolproof. Here's why:

Strengths:

• Offer early indicators of election outcomes.
  
  
• Provide deep demographic and issue-based insights into voter behavior.
  
  
• Serve as a safeguard against potential irregularities in vote counts.
  

• Non-Response Bias: Some voters decline to participate, potentially skewing results.
  
  
• Early Voting & Absentee Ballots: Exit polls generally can't capture voters who voted by mail or early, which can distort projections — especially in close races.
  
  
• Human Error: Sampling mistakes, timing issues, or question wording can introduce inaccuracies.
  
  

A famous example of exit polling error occurred during the 1992 UK General Election, when polls incorrectly predicted a hung Parliament — only for the Conservative Party to secure a clear victory.

Today, reputable organizations continually refine their exit polling methods to account for changing voting patterns and demographics. But while exit polls are valuable, final certified election results always remain the ultimate authority.

Why Exit Polls Matter Beyond Election Night

Exit polls don’t just satisfy election-night curiosity. They also:

• Illuminate long-term political trends and voter priorities.
  
  
• Help researchers understand shifts in public opinion across different groups.
  
  
• Detect discrepancies that could signal election irregularities.
  
  
• Inform post-election strategy for political parties, advocacy groups, and policymakers.
  
  

In a world racing for relevance, where understanding public sentiment is more critical (and more complex) than ever, exit polls offer a powerful — if imperfect — way to "answer anything" about electoral behavior. If you're interested in how polling methods are continuing to evolve, don't miss our blog on Lessons from Past Elections: Adapting Polling Methods for Tomorrow.

‍

To Conclude

Whether you’re analyzing voter sentiment, evaluating polling strategies, or running studies of your own, success today hinges on being able to answer complex questions clearly and quickly. Exit polls are just one example of how high-quality, timely data can change the conversation — and the outcome.

When Siena College needed to make three million calls and deliver real-time results, they turned to Voxco. With our powerful platform and expert support, they made it happen. Book a demo today to find out how we can help you meet your biggest goals, too.

Employee satisfaction plays a critical role in overall organizational success. Happy, motivated employees are more likely to stay longer, perform better, and positively represent your company — both to customers and potential hires. But how do you know how satisfied your employees actually are?

That’s where employee satisfaction surveys come in. When designed well, they provide honest, actionable insights into what your teams are thinking — and what might be holding them back.

In this blog, we’ll walk through:

• What employee satisfaction surveys are
  
  
• Why they’re essential
  
  
• 10 key questions to include in your next survey
  
  

‍

What Is an Employee Satisfaction Survey?

An employee satisfaction survey is a structured questionnaire used to assess how employees feel about their jobs, the workplace environment, management, compensation, career growth, and more.

These surveys go beyond simple ratings — they explore how aligned employees feel with your organization’s goals, whether they’re motivated, and if they feel supported in their roles.

‍

Why You Should Conduct Employee Satisfaction Surveys

Organizations that proactively gather employee feedback are better positioned to improve morale, reduce turnover, and create a healthier workplace culture. Here’s how regular satisfaction surveys help:

1. Identify Skill Gaps and Training Needs

Surveys can reveal where employees feel underprepared or unsupported in their roles — helping HR and managers design relevant training or mentorship programs.

2. Encourage Honest, Anonymous Feedback

When designed to protect privacy, satisfaction surveys offer a safe space for employees to share their true thoughts — even about sensitive issues like recognition, fairness, or leadership effectiveness.

3. Improve Retention and Reduce Turnover

Understanding and addressing the factors behind disengagement helps reduce costly employee exits. Satisfaction surveys are an early warning system for burnout, misalignment, or unmet expectations.

‍

10 Insightful Employee Satisfaction Survey Questions

The questions you ask matter. Below are 10 proven questions that can help you gather meaningful data and take the right next steps.

1. Do you feel you have opportunities to learn and develop new skills?

This helps measure whether employees feel like they’re growing — or if they’re seeking development elsewhere.

2. Does your manager support you in completing your work effectively?

Good leadership enables great work. This question uncovers whether employees feel guided or left unsupported.

3. Do you have positive relationships with your coworkers?

Team dynamics are crucial to satisfaction. Poor collaboration or tension can significantly impact morale.

4. Do you feel fairly compensated for your role?

Salary is just one piece of satisfaction, but it’s a big one. This question can flag issues around pay equity or unmet expectations.

5. How likely are you to recommend this organization as a great place to work?

This employee Net Promoter Score (eNPS)-style question gives a clear snapshot of overall satisfaction and loyalty.

6. Do you feel your opinions are valued by leadership?

Employees are more engaged when they feel heard. This question identifies whether feedback loops are strong or broken.

7. How would you rate your current work-life balance?

Stress, burnout, and lack of flexibility often surface here. This question helps organizations better understand employee well-being.

8. Where do you see yourself within this organization in the near future?

This offers insight into long-term commitment, internal mobility, and whether employees feel optimistic about their growth path.

9. Do you feel recognized for your work and contributions?

Lack of recognition is a common reason for disengagement. This helps identify whether appreciation is being communicated effectively.

10. Do you have a clear understanding of the company’s goals and vision?

Clarity drives alignment. This question ensures your team isn’t just working — they know what they’re working toward.

‍

Final Takeaway

Employee satisfaction surveys are more than a feedback tool — they’re a blueprint for building a better, more aligned organization. By asking the right questions, you can uncover blind spots, reduce turnover, and strengthen engagement across teams.

Just remember: collecting data is only step one. What matters most is what you do next — turning insights into action.

‍