How to use consumer surveys for brand awareness

How to use consumer surveys for brand awareness

What is a brand awareness survey?

Any product or service needs to differentiate itself from competitors and understand how they are performing in the market with the right audience. One way to measure how well they are standing out with their target audience, or whether the audience is correctly associating their brand with the product, service, or message they offer, is by tracking brand awareness. However, awareness can be hard to quantify making it difficult for teams to justify the expense associated with brand awareness campaigns. 

Brand awareness surveys are a helpful tool in measuring these efforts. These are consumer surveys that are specifically about consumer attitudes and recognition toward a brand, its competitors, and its products. 

How do businesses use consumer surveys to measure brand awareness?

Brand awareness surveys not only capture consumer sentiment but also offer a detailed breakdown of demographic audience data that can help brands ensure their message or product is resonating with the audience they are trying to reach. 

For example, if a lawn care brand was focusing on becoming top-of-mind among homeowners, they might ask consumers to list the lawn care brands that come to mind. Analyzing the results might reveal that homeowners age 45+ recognize their brand instantly, while younger homeowners identify a competitor more readily. They could use this information, along with attitudinal data, to develop a new campaign aimed at attracting younger audiences.

By using brand awareness consumer surveys, marketing and insights teams can get insights that help them understand how their brand is perceived by consumers and use that information to their advantage when it comes to pivoting or improving their positioning with target audiences. 

How to create a high-quality brand awareness survey

Like all surveys, structure brand awareness surveys clearly and logically to minimize bias or confusion. Although brands want consumers to recognize and choose their product over a competitor’s, it does not help them to present their brand as “superior” in the survey. Questions and the order they are presented should remain neutral in tone and logical in order. 

Brand awareness surveys often begin with an open-ended question asking consumers to identify brands within a specific category or products offered by a brand. Allowing consumers to freely list brands before they have seen any other questions where a name or logo might be included offers a more accurate portrayal of their ability to recall who is in the market. 

• • Please list all the lawn care brands that come to mind
  • Please list all the products that you associate with LawnBetter

Open-ended questions, while not always ideal for mobile environments, provide a non-leading way for consumers to recall whatever comes to mind when asked to think of a specific product category. 

Likert Scale questions asking consumers to rank their familiarity with a brand or competitive brand are also common near the beginning of a brand awareness survey. This helps establish the consumer’s recognition of the brand, although should follow any questions asking them to recall specific brands to get a more unbiased response. 

Brand awareness surveys also can include media, such as logos for recognition or association, or potentially a new advertisement that a brand is running in a specific geographic area. 

It is ideal to survey the target audience for a brand to determine how effective marketing efforts have been. However, target audiences can be broad or vary depending on the maturity of the brand, whether it is a new market or product launch, or if there has been an incident that inspired the brand awareness survey in the first place. 

If the brand recently launched in a new geographic area, monitoring recognition across a broad audience can help them decide who the right target audience is. Whereas if the brand or industry had recently been involved in a scandal, the focus would be on measuring the sentiment of potential customers who have shifted their perceptions as a result.

Survey template for brand awareness

To help you get started, use the Pollfish Brand Awareness survey template. Make sure to keep the best practices for creating survey questions, respondent experience, and platform in mind when developing your questionnaire for the best results.

by Ally

Likert scale questions: What are they and how do you write them?

Likert scale questions: What are they and how do you write them?

A Likert scale is a rating scale that lets respondents select answers ranging across a spectrum of choices to gain deeper insight into attitudes, beliefs, or opinions. 

How a Likert scale is different from a Rating Scale

A Likert scale was developed in 1932 by Rensis Likert, a psychologist, to better understand the feelings of respondents given a balanced set of choices. Likert scales are most often an odd-numbered series of options, between 5 to 7 answer choices, evenly distributed in weight and symmetry across the scale and ranging from one end of a spectrum to the other. The scale is popular in questionnaires and online surveys in collecting quantifiable data about subjects that are often difficult to analyze without observation, such as consumer attitudes, beliefs, and opinions.

Although Likert scales are rating scales, the opposite is not necessarily true. While “Likert scale” is often used interchangeably to describe a rating scale, a Likert scale is a specific type of rating scale that exclusively focuses on a range of answers on a spectrum. A rating scale can consist of any number of rating choices, such as stars or numeric responses as are used in an NPS question type.

When you should use a Likert Scale

Likert scales are a particularly useful form of rating scale that can be used when observation isn’t an option. Website and mobile surveys, customer satisfaction questionnaires, and more allow researchers to gain insights on perceptions, behaviors, feelings, and more by asking respondents to self-report their reactions based on how they feel using the Likert scale. 

Some common uses for Likert scale rating question types include:

• • Customer Satisfaction surveys
  • Investigating the likelihood of action being taken
  • Gaining insights on beliefs or perceptions surrounding a specific topic
  • How frequently an action occurs

Likert scales make it easier to quantify these kinds of insights, so they can be an asset in analyzing large quantities of data. There are many more instances in which a Likert scale can be of use, as it is one of the most popular rating scales in use today. 

How to write Likert scale questions

Likert scales offer a balance of answer choices, so the scale should be symmetrical in weight. If, for example, one end is “extremely likely” the other end should be “extremely unlikely” or “not at all likely” with a neutral choice such as “neither likely nor unlikely” as the center option.

As with any survey, it’s important to follow best practices for writing good survey questions. Likert scales follow these same basic principles. 

Keep in mind that Likert scale answer choices are ordered, and should therefore not be shuffled. To reduce bias in a Likert scale, questions should be given with “reverse order” shuffling commands to keep the scale intact. This allows varied presentation of choices to respondents but doesn’t lead to confusion or misunderstandings.

In the Pollfish platform, you can select Likert scale answer variants from the “Predefined answers” selection to choose the scale that is the best fit for your question.

Examples of Likert scale questions

Customer satisfaction surveys

Likert scales can be used in customer satisfaction surveys to determine how customers felt about their experience, a product, or service. 

Example: How happy were you with your stay at our hotel?

• • Very satisfied
  • Somewhat satisfied
  • Neither satisfied nor dissatisfied
  • Somewhat dissatisfied
  • Very dissatisfied

Frequency of behaviors

If you are looking for how often a consumer purchases a product, takes an action or spends a certain amount of money, Likert scales can help uncover some of these behaviors.

Example: How often do you read articles on your phone vs in a newspaper?

• • Much more
  • Moderately more
  • About the same
  • Moderately less
  • Much less

Agreement statements

Perhaps the most popular Likert scale in survey questions is a scale of agreement-disagreement, where a respondent is asked to select the answer that best reflects their belief about a statement provided.

Example: Please select how much you agree or disagree with the following statement: Cats make better pets than dogs.

• • Strongly agree
  • Somewhat agree
  • Neither agree nor disagree
  • Somewhat disagree
  • Strongly disagree

Using a Likert scale in a matrix question type

Matrix style question types allow Likert scales to be used to ask about several different ideas at once. This can be a good option when the survey will otherwise be repetitive, measuring the same data for many similar ideas.

Likert scales offer researchers an easy scale to measure certain opinions on topics important to their customers and consumers and provide actionable insights that can be analyzed. To make the most of Likert scales using Pollfish, consider selecting answer choices from our Predefined answer selections or use our Crosstabs feature for advanced analysis when your survey is complete. 

by Ally

Getting familiar with conjoint analysis

Getting familiar with conjoint analysis

Conjoint-Analysis asks respondents to make a choice between multiple sets of criteria in a real-world scenario to help researchers understand which features are prioritized and considered during the decision-making phase.

What is Conjoint Analysis?

Conjoint-analysis is a quantitative research method that asks respondents to make selections between groups of attributes in a scenario against alternative scenarios made up of different attributes. It is considered more predictive of a buyer’s purchase behavior as it does not let respondents cherry-pick attributes or select the “ideal state” of a product in isolation. Instead, respondents are given sets of features in a product or package where they make trade-offs, indicating which features are most important and what they value most. Conjoint analysis survey questions are often used to evaluate features in products, pricing for packages, and different product offerings by a business. 

Choice-based Conjoint-Analysis

Although there are several kinds of conjoint-analysis, the most commonly used technique is a choice-based conjoint analysis, which is used in many real-life purchasing scenarios. 

Rather than asking respondents to give their opinion about an isolated event, conjoint analysis allows you to measure two (or more) scenarios at a time against the alternatives available. Respondents are asked to make trade-offs between the presented scenarios, rather than choosing or customizing specific options. Each scenario includes a complete set of attributes or features to provide context that may influence their decision to choose one scenario over another. For example, respondents being asked to compare internet service packages against another based on what each package offers. 

These answers give researchers additional context about what is most important to their respondents when evaluating a complete package, rather than individual characteristics. 

An example of a conjoint analysis might be asking consumers to compare features of internet cable packages, then compare the packages overall.

A discrete choice conjoint analysis allows respondents to select between a series of products, or choose “none” which is considered more indicative of real-world behavior. For our example, if the respondent doesn’t see value in any of the presented options, they may choose not to buy an internet package from this company at all. 

Adaptive conjoint analysis

Another type of conjoint analysis is an Adaptive Conjoint Analysis, which requires software to use previous responses from respondents to tailor the questionnaire. Although it follows the same structure of asking respondents to make trade-offs to determine weights and information, it ultimately can result in smaller or more in-depth surveys about specific attributes. Adaptive Conjoint Analysis relies on a type of branching logic that eliminates attributes that are less relevant to the respondent and keeps their focus on fewer attributes throughout the survey. 

Weighting in conjoint analysis

Conjoint Analysis question options apply weights to the attributes in a grouping to elevate their importance. Weights are determined through a linear regression model that asks respondents to go through a series of configurations of a product or package. Using experimental design, these are then analyzed and weights are determined for the individual attributes. 

The values of the individual attributes that indicate the respondents’ trade-offs in a product are called part-worths, and they are used in determining the weight of each attribute in the grouping. 

In the internet package example, if “internet speed” was the most important attribute to respondents, it would be given a greater weight to indicate its significance in the respondent’s decision to choose this package. Alternatively, the contract length may be considered a trade-off for respondents wanting faster internet speeds, so the weight applied to “contract” would be given a weight that balances the “internet speed” attribute and makes the trade-off more comparable. These influence the choice the respondent makes and lets researchers discover the right balance between attributes offered in a conjoint selection.

Conjoint analysis terms and vocabulary

• Adaptive Conjoint Analysis: A digitally based conjoint analysis test that uses an algorithm specific to the respondent’s answers to tailor the questions to attributes that are relevant to the respondent.
• Attributes (Features): The criteria that make up a complete set of information to be compared in a conjoint analysis. In our internet packages example, the attributes are “Contract Length”, “Internet speed”, “channels provided”, etc.
• Choice-based conjoint Analysis: The most common type of conjoint-analysis that asks respondents to make a choice between two or more offerings made of a set of weighted attributes. 
• Discrete-choice conjoint analysis: A choice-based conjoint analysis that provides a “none of the above” option in addition to a series of options. It most clearly matches real-world purchase decisions by giving respondents the option not to select any of the choices.
• Experimental Design: A psychological practice of research design that refers to the distribution of respondents in different parts of an experiment. It often includes at least one experimental group and one control group.
• Full-profile: A conjoint analysis that explores all of the attributes. 
• Levels: Categories of specific attributes. In our internet packages example, levels for the attribute “Contract Length” are “12 months”, “18 months”, and “24 months.”
• Part-worths (Utilities): The partial value of a complete product, divided among its attributes to assign value to each part.
• Partial-Profile: A conjoint analysis that only explores a few attributes at a time.

Conjoint analysis offers greater depth into consumer purchasing decisions through weighting and assessing the data to see which features bring greater value to products. These insights can then be used to create new or better products, special offers, or inform competitive market opportunities.

by Ally

Using crosstabs in survey data analysis

Using crosstabs in survey data analysis

A common method for researchers to perform quantitative analysis of the relationship between two or more variables is cross-tabulation. Cross-tabulation, or crosstab (also abbreviated as “x-tabs”), is a matrix table typically used in multivariate market research and/or statistical analysis to analyze the relationship between variables in two or more categories. 

Crosstabs in research analytics

Cross-tabulation is also known as simply “Crosstab”, but can also refer to a “contingency table” or “cross break.” They are a matrix-style table that allows the observation of one variable’s dependency on another and are commonly used to analyze relationships between categorical data, such as age, region, or frequency of an action. 

Even if you’re new to market research, you’ve likely encountered a cross-tabulation before. You can create them as pivot tables in Excel, or develop crosstab formats in popular analytics programs and most major market research tools. They have become an industry standard for processing many variables at once to determine conditional relationships between them. 

The power of crosstabs

What makes this method especially powerful for researchers is how variables are grouped into sub-categories to see how a dependent variable changes the results. 

Reduces confusion

Raw data can be tough to interpret for even the most advanced researcher. When data is displayed in a crosstab matrix, researchers can make sense of the relationships more easily. Crosstabs present a clearer interpretation of the data by showing percentages and frequencies that may change when contrasted with variables in other categories.

Offers a deeper look

Because of the depth offered by crosstabs, researchers can uncover relationships between variables or segments that may have been missed if presented in another format. Layers of data can be correlated, rather than just one or two categories due to the variable grouping.

Opportunities to involve the whole team

Manual statistical analysis can take a while and introduces an increased possibility of human error. For those who don’t have an advanced statistical analysis background, analyzing results can be intimidating and easily confused. The use of crosstabs structures this data into a more digestible format at the beginning of the analytics process. It provides an efficient layout for research professionals as well as a more actionable view for members of the team who lack advanced data analytics backgrounds.

Crosstab example

For example, in a survey about online habits, a variable might be how often a respondent shares content on social media. Another variable might be whether or not they read content that covers certain topics. 

Using a crosstab to correlate data from those answers, a researcher can see the number of respondents (count) that fell into each category based on the answer given. Crosstabs offer a level deeper than simply stating that those who read business and finance news are likely to share educational content, and instead are able to uncover that those who are reading Business and Finance news the most often are also the most likely to be sharing educational content the most often on social media.

Terms used in crosstabs

Crosstabs come with their own vocabulary specific to the layout. Understanding the specific language and elements used in crosstabs can be helpful for researchers looking to get more out of their analytics.

• Banners (or Cuts): The headers that name the categories of the data displayed by a column
• Categories: The way that the variables are grouped (for example, respondents who are “women” or  who “mostly agree with the statement ‘salads are healthy.’”)
• Columns: The cells that display data vertically
• Column-Percentage: A view of data that calculates the column data belonging to a particular row.
• Count (or Frequency): The total number of responses that fall into a row and column
• Crosstabs: Otherwise known as “cross-tabulation,” “data tabulation,” “cross break,” or contingency table” is the name of the table that researchers use to analyze categorical data.
• Fisher’s Exact Test: Another test for statistical significance that uses an exact deviation from the null value (rather than an approximation). Because of the exactness of the p-value, this is recommended for smaller sample populations (although can be applied to samples of all sizes)
• G-test: a test for determining a statistically significant likelihood of a variable’s dependence on another, sometimes considered to be more efficient than chi-square testing.
• Pearson’s chi-square test: a test for determining the statistical significance of a cross-tabulation by determining if the variables being compared are independent. It is a measure of how actual data compares to expected data.
• Percentage: The percentage of all responses that fall into a given row and column
• Rows: The cells that display data horizontally
• Row-Percentage: A view of data that calculates the row data belonging to a particular column.
• Stubs: The headers that name the categories of the data displayed by a row

For those looking to analyze their data using a crosstab, we suggest creating a Pollfish survey and exporting your results directly from the platform into this format. You can learn how on our post for using crosstabs in Pollfish.

by Ally

Validating concepts with Monadic Testing

Validating concepts with Monadic Testing

Researchers use monadic testing to gather feedback on a single concept from your target audience in a vacuum. The name originates from the Greek word “monos,” meaning alone. Another variation of this is a sequential monadic test— when an audience is questioned about two or more concepts in an isolated survey environment.

Monadic testing

A monadic test is a survey that investigates a single concept in depth with a target audience, broken into at least two groups. It is commonly used in product and concept testing, such as testing creative for a campaign or testing pricing or features for a product. These surveys tend to be short, but extremely focused. 

Because respondents are only being asked for information about a single concept, monadic testing can go in-depth on the subject without creating a lengthy questionnaire. For example, in a monadic test for ad creative, the respondent might first be asked to give their opinion on a specific advertisement. Follow-up questions could include what they liked about the piece, the colors, the images, text, or emotions and feelings they experienced when looking at it. 

If a researcher wants to evaluate multiple concepts, they will need to create multiple samples of the same audience, as each group will still only be reviewing a single concept. If in the earlier example there were three advertisements to be tested, there would be three surveys, each featuring a different ad and asking the same follow-up questions to determine specific details about it. Each survey would be sent to an audience sample that is made up of the same targeting criteria. The results would then be compared and analyzed to determine which creative was preferred by members of the target audience.

Benefits and drawbacks of Monadic Testing

Monadic tests expose respondents to one stimulus independent of contextual information. This gives researchers insight into a respondent’s thoughts about the product or concept when no external distractions are present and can supply some in-depth, clean data to confirm the idea when there are no constraints.

For example, a monadic test might reveal that respondents felt favorably towards a product, while they might express negative ideas about it given information about the brand or when compared to similar products. This lets researchers know that the issue may be related to brand perception or competitive solutions, rather than the product itself. 

Because monadic tests exist to investigate a single concept, they can also go in-depth without becoming too long. This is especially helpful in formats like mobile surveys, which should be kept short enough to captivate the attention of respondents on the go.

A drawback to monadic testing is typically cost, as it requires more respondents to create multiple samples fitting the same targeting criteria to complete the survey in isolation. This can also be challenging when the audience targeting is very narrow and harder to find enough respondents to create two complete samples.

Sequential Monadic Testing

A sequential monadic test (sometimes known as a paired test) is a monadic test that asks respondents about two or more concepts in the same isolated environment. The concepts are often randomized to reduce survey bias but follow the same series of questions about each concept to determine key information. 

An example might be asking groups of respondents in the target audience for opinions on an existing feature and a proposed new feature. One group might be shown the current feature first, while another group is shown the new feature first, then the existing one. Information is gathered to see which feature performed better with the target audience and if exposure to the existing feature played a role in the respondents’ reception of the new concept.

Benefits and drawbacks of Sequential Monadic Testing

Sequential monadic tests are often used when only a small target audience is available or when research budgets are lower. They can be more cost-effective since more concepts can be evaluated in one round, and fewer respondents are needed overall to maintain accurate results. This also means that sequential monadic tests are likely to be completed first.

However, sequential monadic tests are open to various kinds of survey bias. They can be affected by the Interaction Effect, when a respondent who is biased toward earlier concepts, rates others based on a comparison they are keeping in mind. For example, if a respondent sees an ad they like at the beginning, they may view the following ads relative to the first concept and be less likely to say they like them.

They are also at risk of order bias unless the concepts are shuffled regularly to ensure that respondents aren’t regularly being exposed to the concepts in the same order.

Questionnaires also tend to be longer with sequential monadic tests, as more concepts are introduced. This may mean lower completion rates, or if the questionnaire is kept short, the depth of the questions themselves may be limited.

Comparison Testing

Monadic testing and sequential monadic testing differ from comparison tests in that the audience is not being asked to compare different concepts, simply to supply feedback about them, often diving deeper into the concepts as a result.

Comparison testing specifically asks the respondents to compare two or more concepts against one another, such as ranking items in a list or choosing the “best” item presented.

Benefits and drawbacks to comparison testing

Comparison testing supplies easily digestible results. When respondents share that they like a product the best, or would pay more for a specific feature, it’s clear which concept is the winner. 

However, comparison testing rarely supplies context. For example, if there was a specific detail that made the feature worth more to respondents, a comparison test would not reveal that information to researchers. If the detail is then missed when the feature is developed, it may not be the right feature or worth it to respondents to pay for it.

Protomonadic Survey Design

Protomonadic survey design refers to a monadic test that is followed by a comparison test. This is most often used with sequential monadic testing, where respondents are asked to evaluate several individual concepts, then choose the concept they prefer at the end.

Protomonadic research can be useful when validating a sequential monadic test, as it shows whether the preferred concept from the comparison is consistent with the feedback received about each concept.

Common uses for monadic tests (and variations of it) are ad-testing, product testing, concept testing, package testing, and more.

Monadic tests are a simple, yet effective, way for researchers to better understand the reasoning and details of a single concept so they can pivot their strategy, prioritize specific features, or replicate elements that tested positively across future projects.

by Ally

Qualitative vs Quantitative Survey Questions

Quantitative vs Qualitative survey questions

Want a quick summary? Check out the infographic!

Research is developed using quantitative and qualitative research methods to gain a complete understanding of the target audience’s needs, challenges, wants, willingness to take action, and more. However, the right time to use either method (or use both together) can vary depending on your research goals and needs. 

Difference between Quantitative and Qualitative Research

Quantitative research is about collecting information that can be expressed numerically. Researchers often use it to correlate data about specific demographics, such as Gen Z being more likely to focus on their finances than Millennials. Quantitative research is usually conducted through surveys or web analytics, often including large volumes of people to ensure trends are statistically representative. 

Even when the survey audience is very large, quantitative research can be targeted towards a specific audience, usually determined by demographic information such as age, gender, geographic location.

Qualitative research focuses on personalized behavior, such as habits or motivations behind their decisions. This can be gathered through contextual inquiries or interviews to learn more about feelings, attitudes, and habits that are harder to quantify but offer important additional context to support statistical data.

When quantitative and qualitative research are paired, a complete set of data can be gathered about the target audience’s demographics, experience, attitudes, behaviors, wants and needs.

by Ally

How to find survey respondents

How to find survey respondents

You have questions that need answers, all you need now is an audience to complete your survey. But how do you find survey respondents? And how do you make sure they are a good match for the questions you will be asking? 

We’ll dive into the pros and cons of some of the most common ways of finding survey respondents that should be considered before choosing the best approach for you.

Harness the power of social media

Most people have one form or another of staying connected digitally, whether it’s Facebook, LinkedIn, Reddit, or others. Consider that each of these places offers a built-in audience that you can survey.

Pros

• Social media audiences are free to reach out to.
• There’s no limit to how many potential respondents you could have.
• Some platforms, like Instagram, offer quick polls that may work for quick, simplistic surveys.

Cons

• Social media was not designed for survey distribution, and your network is not large or randomized enough to serve as an accurate sample audience. 
• Targeting is extremely limited. Even if you join a group that is specific to your survey (for example, if you want to reach parents of toddlers, you may join a group for mothers sharing parenting tips), you will run into audience bias, as those who are actively supporting a brand or idea on social media tend to be more vocal than the average member of the group.
• No control. You won’t be able to verify information about who takes your survey, or how many times. If your survey is a link, respondents could share or email it outside of your desired target market. 
• Limited functionality. Even using proprietary survey abilities, like Instagram’s polling feature, you're limited in what you can ask. The platform is not complex enough for a full questionnaire or nuanced reporting.

Do-it-yourself with survey research tools

DIY research tools are one of the easiest and most affordable ways to reach respondents at scale. Most come with the option to buy responses from an audience as part of the package, and there are many different survey tools to choose from based on your needs.

Pros

• DIY research tools find participants and manage the distribution for you, which takes all the work of finding respondents off your plate.
• Most of the tools available are incredibly affordable. Services like Pollfish begin at just $1 per completed response and results can come in less than a day.
• Targeting is MUCH better than any comparable option for this price point. Survey tools vet respondents to verify demographic information, ensuring that surveys are matched to the right audience.
• There are many more question types, meaning you can gather data from a variety of different angles. Customize your questionnaire to specific respondents using branching or add media to offer better context.

 Cons

• If you’re not well-versed in research, some survey tools can be harder to use. Choose options that offer live support when possible, to help you through tough spots.
• Although many DIY survey tools are affordable, they do come with a price tag. Make sure you know how much your survey will cost before you launch, and don’t get tricked by survey tools that charge for the audience and the questionnaire builder separately.
• Delivery method is important with DIY surveys—make sure your tool lets you meet respondents where they are: on their smartphones. Mobile survey delivery ensures broader reach and faster results than other methods.

Buy a list of email addresses

Buying a list is an older, email-based approach to conducting surveys. You can buy a list of email addresses from a company that specializes in building these based on psychographic or demographic criteria and manage the distribution of it yourself.

Pros

• Email lists for B2B audiences can be targeted to specific titles and companies you’re looking to reach. 

Cons

• Email lists are expensive, slow, and require a lot of collaboration with the company you bought them from to distribute the survey.
• That B2B audience you paid a premium for? They’re getting too many emails as it is. The odds of them responding to your survey are extremely low.
• If you’re reaching an audience in Europe, list-purchasing will violate the consent rules of GDPR, which could result in significant fines.

Pay-per-click

You can pay for distribution methods to get your survey in front of more people. Google AdWords or sponsoring your survey on a social media post could broaden your audience digitally. 

Pros

• You will broaden and randomize the reach of your survey beyond what you would be capable of using social media or emailing a list.
• You have more control over targeting than you would with social media or an email list (although not more than DIY Survey tools)

Cons

• You’ll get a lot of impressions, but PPC cannot promise click-through rates.
• Expect to pay a lot for clicks when you get them, whether it’s the audience you want. PPC cannot guarantee that the audience you targeted is the only people who will try to complete the survey.

Hire an agency

If all else fails, you can always hire a market research agency. MRAs are made up of professional researchers who can hold your hand through the process, which may be necessary for especially complex projects.

Pros

• You can be completely hands-off. The agency will find survey respondents and manage the entire process for you.
• You’ll have the expertise of professional researchers leading your project, which might be worth the price tag for an especially complex research initiative.
• Your results will be packaged nicely. Instead of allowing you to be overwhelmed by analytics, your research team will present a report of the findings.

Cons

• Like all agencies, MRAs come with a heavy price tag. Expect to pay a premium for high-touch service.
• MRAs are slower than other options. Depending on the urgency of your project, this may not be feasible, especially when instant insights are available.
• Expect a contract with your agency and be prepared to take on the cost and risk of signing on for a full project.

Survey respondents are everywhere if you know where to look. Remember to always consider not only the size of the audience but the ability to reach a specific segment that is representative of the target population for best results. 

Pollfish reaches an audience of over 670 million real consumers engaged on their mobile devices, with advanced targeting and distribution all in one. We’ll help you find a global audience for your survey and get professional support along the way. 

by Ally

How to write good survey questions

How to write good survey questions

Good survey questions lead to good data. But what makes a survey question "good" and when is the right time to use specific types?

At Pollfish, we have distributed tens of thousands of surveys and manually review them all, so we know a thing or two about writing good survey questions. Our experts have compiled a list of the essentials below into a sort-of questionnaire template to make sure you have what you need to create great surveys and get the highest-quality data. 

1. Have a goal in mind.

Consider what you’re trying to learn by conducting this survey. Do you have an idea that you want to validate, or are you hoping that you can disprove an assumption under which you have been operating? Surveys work best when they focus on one specific goal. When building the questionnaire for your survey, it is important to offer questions that support your goal. 

2. Eliminate Jargon.

Just because a concept is clear to you doesn’t mean your target audience is always on the same page. A well-designed questionnaire contains good survey questions to be sure. But they also use plain language (no jargon) to explain concepts or acronyms that customers may be unfamiliar with and offer an opt-out for those who are unsure. Don’t be afraid to use more than one question or offer an example to ensure clarity on complex information in your questionnaire template— a confused audience leads to frustration and low-quality responses.

3. Make answer choices clear and distinct.

When multiple-choice answers are presented, the respondent must make a selection. If these responses overlap or are confusing for the respondent, the quality of the data decreases because they aren’t sure what is being asked of them. Make sure answers are distinct and specific whenever possible so the respondent can confidently choose the best answer.

4. Give users an “other” option.

Make sure that, in a multiple-choice sequence, you’ve given respondents the chance to opt out of the question if it doesn’t apply to them or if none of their answers fit. Provide an option like “no opinion,” “neutral,” or “none of the above.” You can also offer the option to select “other” and provide an open-ended response that can give you more context.

5. Avoid “yes/ no” screening questions.

Screening questions help you connect with a qualified audience at the beginning of the survey. When respondents select a qualified response, they will enter the rest of the survey. However, people are biased toward choosing “yes” or a positive response when presented with a yes/ no question, even if their real opinion is more neutral. To reduce bias, provide a list of answer choices with no indication that one is preferred over the others. 

6. Don’t ask two questions at once.

Each question should focus on obtaining a specific piece of information. When you ask two questions at once using “and” or “or,” you’re introducing another question, which may have a different answer. This will have one of two results: either you’ll confuse your respondents, who are forced to choose the right answer to one question; or your respondents will confuse you with their answers. Either way, make sure you write simple survey questions asking for separate pieces of information as separate questions. 

7. Use skip logic when applicable.

Skip logic, or branching, allows you to create multiple question paths based on an earlier answer. This means more qualified respondents will be asked to answer more in-depth questions and reduces answers like “don’t know” or “no opinion” later. 

8. Use different question types.

Respondents offer better and more thoughtful answers when they are engaged. And that means asking several types of research questions. Use ranking, matrix, open-ended, or multiple-choice questions to stimulate them and keep them interested, especially in a longer questionnaire. Different question types not only keep the respondents engaged (which can increase your completion rates) but can also elicit different responses.

9. Shuffle answer choices for ranking, matrix, and multiple-choice questions. 

We are naturally inclined towards the first information we are presented with—the top answer— in a series of answer options. Shuffling the order of the answer choices reduces bias in responses. However, for answers that relate to one another—such as a Likert scale or timeline—it’s helpful to keep them in an order that flows logically to avoid confusion or misreading.

10. Add media or images to provide helpful context.

Media—such as images, video, or audio clips—provides another level of clarity to your survey questions. You can use these either to give added context to the question or offer media as an answer choice.

11. Always keep the audience in mind.

Remember as you are writing the questions to always keep your target audience in mind. The audience can be as broad as the “general population” or as narrow as you need it to be. The important thing is to know who you want to target so you can communicate with them effectively.

Regardless of the types of survey questions you select, questions should be short, clear, and to the point, but also engage the respondent through multi-media and question types. Remember that the less confused your respondents are, the clearer your data will be. 

These best practices will help you write good survey questions on any platform, including our own. If you have additional questions specific to Pollfish, check out our resource center or reach out to our Support team to learn more.

by Ally

What is a Rating Question? Definition, Examples & More

What is a rating question?

Rating questions (See also: slider questions) allow participants to weigh, or assign numerical values to answers via a graphical interface —using a simple 1-5 star rating system, or 0-100 scale where a higher number is a better score.

Rating Questions are particularly effective on mobile, due to the graphic user interface, and simple tap-to-enter, or drag-and-drop functionality.

Rating questions can be used to evaluate a variety of topics or stimuli, including statements, images, or videos.

Adding rating questions to a mobile survey

Since most mobile survey users prefer simple gestures that can be performed with one hand, these questions are favorites among respondents for their ease of use because of their tapping or drag-and-drop capabilities.

The information you gain is on one parameter—their preference for a particular piece of content.

You can use a series of survey rating questions to understand preferences for different topics and then compare the results.

You may also consider including different types of survey questions to avoid participants falling into a response pattern.

Rating Questions Examples

Here are the rating questions examples you can use when you create a survey with Pollfish.

Star Ratings

Select a rating out of 5 stars, as seen on Yelp or Roger Ebert. This is the simplest form of rating question and provides the cleanest data set. But the nature of the question reduces the depth of response you can get from it.

Slider Ratings

A scale of 1-100 will allow users to rate performance. This question type provides a wider distribution of responses.

Single-Selection Matrix Question 

Rate your choices on a matrix of possible options. By reducing to one response per line, you can ask for a rating on multiple features or products inside the same question.

by Michael Harbolt