A scannable bank of standard survey questions with their exact wording, response scales, when to use each, a common bias to avoid and the source where there is one. Copy any question with one click.
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The short answer
This bank lists 21 standard survey questions in 8 groups: satisfaction, loyalty, effort, product-market fit, concept testing, pricing, screening and demographics. Each gives the usual wording and scale, when to use it, a bias to avoid and its origin where one is published. It gives no benchmarks or norms.
How should you use these questions?
Each entry shows the wording most often used, the response scale that goes with it and what to watch for. To use one:
Replace the square brackets with your product, service or time frame, and change nothing else if you want to compare with earlier results.
Keep the scale as given. The wording and scale together define what is measured. A different scale gives different numbers, so scores are not comparable with other studies.
Check the source tag. "Published origin" means a paper or book introduced the question and is cited below. "Common practice" means the wording is standard in the industry but no single source defines it.
Pretest. Ask a handful of people from your audience to complete the survey and say what each question meant to them.
Satisfaction questions
Measure how well a product, service or interaction met expectations.
Overall satisfaction (CSAT)
Overall, how satisfied are you with [product, service or experience]?
Response scale
Very dissatisfied
Dissatisfied
Neither satisfied nor dissatisfied
Satisfied
Very satisfied
5-point labelled scale, scored 1 to 5.
When to use it
Straight after a specific purchase, delivery or support contact, or in a periodic relationship survey. Report the share choosing the top two answers, or the mean, and say which.
Bias to avoid
Leading wording and non-response. Do not add praise words such as "excellent" to the question, and report the response rate, because people with strong feelings are more likely to answer.
Origin
Common practice No single authoritative wording; the 5-point form is common practice. The American Customer Satisfaction Index asks overall satisfaction on a 10-point scale from 1 (very dissatisfied) to 10 (very satisfied).
Loyalty questions
Measure willingness to recommend, the most widely used loyalty question.
Likelihood to recommend (Net Promoter question)
How likely is it that you would recommend [company, product or service] to a friend or colleague?
Response scale
0 = Not at all likely to 10 = Extremely likely (11 points)
11-point scale. Standard scoring: 9 and 10 are promoters, 7 and 8 passives, 0 to 6 detractors; Net Promoter Score = % promoters minus % detractors, from -100 to +100.
When to use it
In a relationship survey, asked of customers at a regular interval and always in the same place in the questionnaire, so waves can be compared.
Bias to avoid
Order and context effects, and differences in how groups use the scale. Asking right after a complaint or after other satisfaction questions shifts answers, and raw scores from different countries or groups may not be comparable.
Source
Published origin Introduced by Reichheld (2003).
Effort questions
Measure how easy or hard it was for a customer to get something done.
Customer effort (original)
How much effort did you personally have to put forth to handle your request?
Response scale
1 = Very low effort
2
3
4
5 = Very high effort
5-point scale; the score is the average, and lower is better.
When to use it
After a customer service contact or self-service task, to find where customers work harder than they should.
Bias to avoid
Recall and timing: ask straight after the interaction, while it is fresh, and only of people who had one. Results depend on how "request" is understood, so name the interaction.
Source
Published origin Customer Effort Score was introduced in Dixon, Freeman and Toman (2010). The item wording is as widely reproduced.
Customer effort (version 2.0)
[Company] made it easy for me to handle my issue.
Response scale
Strongly disagree
Disagree
Somewhat disagree
Neither agree nor disagree
Somewhat agree
Agree
Strongly agree
7-point agreement scale. Higher is better, which reverses the direction of the original question.
When to use it
After a service interaction, when you want the company rather than the customer to own the ease. Do not compare its scores with the original 5-point question.
Bias to avoid
Acquiescence: agree-disagree statements invite agreement. Keep the statement short, name the interaction, and do not mix it with positively worded items without a check.
Source
Published origin Customer Effort Score 2.0, from Gartner (formerly the Corporate Executive Board), described in Dixon, Toman and DeLisi (2013).
Product-market fit questions
Find out how much people would miss a product, and why.
Product-market fit (Sean Ellis question)
How would you feel if you could no longer use [product]?
Response scale
Very disappointed
Somewhat disappointed
Not disappointed
Report the share answering "very disappointed". Ellis proposed 40% as a rule of thumb from his experience with start-ups; it is a heuristic, not a statistical norm.
When to use it
With people who have actually used the core product recently. Pair it with the follow-up questions below to learn why, and split the results by type of user.
Bias to avoid
Selection and survivorship. Surveying only the most active users, or counting only those who answer, flatters the result. Define who is asked and report how many answered.
Source
Published origin Developed by Sean Ellis and described, with the 40% rule of thumb, in Vohra (2018).
Product-market fit follow-up: who benefits
What type of people do you think would most benefit from [product]?
Response scale
Open text
Open-ended; read the answers for the words people use to describe the product's ideal user.
When to use it
Straight after the "how would you feel" question, to learn who people think the product is for.
Bias to avoid
Projection. Answers describe who the respondent imagines, so check them against who actually uses the product.
Source
Published origin Follow-up question used by Vohra (2018).
Product-market fit follow-up: main benefit
What is the main benefit you receive from [product]?
Response scale
Open text
Open-ended; group the answers into themes and count them.
When to use it
To find the value people say they get, in their words, from those who would be very disappointed to lose it.
Bias to avoid
Leading by example: do not list possible benefits in the question, or answers will echo your list.
Source
Published origin Follow-up question used by Vohra (2018).
Product-market fit follow-up: improvement
How can we improve [product] for you?
Response scale
Open text
Open-ended; read answers from people who would be "somewhat disappointed" for what holds them back.
When to use it
To find what would move people who like the product to love it.
Bias to avoid
Loud minority: a few detailed answers can dominate. Count themes rather than quoting the most vivid reply.
Source
Published origin Follow-up question used by Vohra (2018).
Concept testing questions
Test reactions to an idea, a concept or a statement about it.
Purchase intent
If [product] were available at [price], how likely would you be to buy it?
Response scale
Definitely would buy
Probably would buy
Might or might not buy
Probably would not buy
Definitely would not buy
5-point intent scale as commonly worded. Often summarised as the top-two-box share.
When to use it
In a concept test, after respondents have seen a clear description of the product and its price.
Bias to avoid
Hypothetical bias. Stated intent overstates what people go on to buy, so read it as a way to compare concepts tested the same way, not as a sales forecast. Research has adjusted stated intent to predict trial of new products.
Origin
Common practice The 5-point intent scale is standard industry practice with no single origin. On how stated intent relates to actual purchase, see Jamieson and Bass (1989).
Agreement with a statement (Likert-type)
To what extent do you agree or disagree with the following statement? [Statement]
Response scale
Strongly disagree
Disagree
Neither agree nor disagree
Agree
Strongly agree
5-point agreement scale, scored 1 to 5. Use one idea per statement.
When to use it
For concept diagnostics ("This product is relevant to me", "The description is easy to understand") and attitude batteries.
Bias to avoid
Acquiescence: some respondents agree with anything. Mix positively and negatively worded statements and avoid double-barrelled statements that ask about two things at once.
Source
Published origin The agreement format comes from Likert (1932).
Pricing questions
Questions for the Van Westendorp and Gabor-Granger pricing methods.
Van Westendorp: too cheap
At what price would you consider the product to be priced so low that you would feel the quality couldn't be very good?
Response scale
A price (a number, in the currency of the study)
Asked together with the three other Van Westendorp questions, in a fixed order. Use the Van Westendorp calculator to analyse the answers.
When to use it
In a price sensitivity study with people who would consider the product, after a full description of it.
Bias to avoid
Anchoring. Do not show a reference price or example prices, and describe the product the same way to everyone.
Source
Published origin Van Westendorp (1976). The wording is as commonly reproduced; exact wording varies between practitioners.
Van Westendorp: cheap (a bargain)
At what price would you consider the product to be a bargain, a great buy for the money?
Response scale
A price (a number, in the currency of the study)
The answer should be above the "too cheap" price; the calculator can leave out respondents whose four answers are not in rising order.
When to use it
As the second of the four Van Westendorp questions.
Bias to avoid
Order effects. Ask the four questions in the same order for every respondent, and tell them the answers should differ.
Source
Published origin Van Westendorp (1976); wording as commonly reproduced.
Van Westendorp: getting expensive
At what price would you consider the product starting to get expensive, so that it is not out of the question, but you would have to give some thought to buying it?
Response scale
A price (a number, in the currency of the study)
The answer should be above the "bargain" price.
When to use it
As the third of the four Van Westendorp questions.
Bias to avoid
Confusion with the next question. This wording separates "getting expensive" from "too expensive", so keep it whole rather than shortening it.
Source
Published origin Van Westendorp (1976); wording as commonly reproduced.
Van Westendorp: too expensive
At what price would you consider the product to be so expensive that you would not consider buying it?
Response scale
A price (a number, in the currency of the study)
The answer should be above the "getting expensive" price.
When to use it
As the fourth of the four Van Westendorp questions.
Bias to avoid
Hypothetical bias: these are stated limits, not behaviour. Do not read them as the price at which sales would stop.
Source
Published origin Van Westendorp (1976); wording as commonly reproduced.
Gabor-Granger: would you buy at this price
Would you buy [product] at [price]?
Response scale
Yes
No
Asked at several prices; the share saying yes at each price gives the demand curve. Use the Gabor-Granger calculator to analyse it.
When to use it
To compare demand at a set of prices for a product people understand, one respondent seeing several prices.
Bias to avoid
Order and anchoring. The first price shown anchors later answers, so vary the starting price or order across respondents, and remember that stated intent overstates purchase.
Source
Published origin The approach comes from Gabor and Granger (1966), who studied price limits in consumers' minds; the question wording is as commonly used.
Screening and quality questions
Choose who takes part and catch careless answers.
Industry exclusion screener
Do you, or does anyone in your household, work in any of the following industries? Please select all that apply.
Response scale
Market research
Advertising or public relations
Media or journalism
[The industry of the product being studied]
None of these
Close the survey politely if anyone selects an option other than "None of these". Randomise the order of the options but keep "None of these" last.
When to use it
At the start of a study, to keep out people whose work could bias their answers or who might pass on the results.
Bias to avoid
Giving away the qualifying answer. Always include "None of these" and a mix of options, so respondents cannot guess which answer lets them through.
Origin
Common practice Standard industry practice; there is no single authoritative wording.
Category usage screener
Which of the following have you [bought or used] in the past [3 months]? Please select all that apply.
Response scale
[Target category]
[Other category]
[Other category]
None of these
Admit only people who select the target category. Put it among other categories and keep "None of these" last.
When to use it
To find recent buyers or users of a category without revealing the topic of the study.
Bias to avoid
Topic disclosure. A screener that lists only the target category tells respondents what to say to qualify.
Origin
Common practice Standard industry practice; there is no single authoritative wording.
Instructed-response attention check
To show that you are reading carefully, please select "Agree" for this statement.
Response scale
Strongly disagree
Disagree
Neither agree nor disagree
Agree
Strongly agree
Place it among a battery that uses the same scale. Remove or review respondents who choose a different answer.
When to use it
In longer online surveys, to find respondents who answer without reading.
Bias to avoid
Attention checks can change how people answer what follows (Hauser and Schwarz, 2015). Use one or two, keep them plain, and do not use them to punish people on a single slip.
Source
Published origin Instructed-response items are described in Meade and Craig (2012); instructional manipulation checks in Oppenheimer, Meyvis and Davidenko (2009).
Demographics questions
Describe who answered, so results can be split and weighted.
Age
What is your age? (or: In what year were you born?)
Response scale
A number of years (or a birth year)
Prefer not to say
Ask for the exact age or year of birth and group it later.
When to use it
In almost every consumer study, to split and weight results.
Bias to avoid
Fixed age bands chosen in advance cannot be re-cut. Ask for the exact value and band it in analysis; offer "Prefer not to say" and put it near the end.
Origin
Common practice Common practice; there is no single authoritative wording.
Gender
What is your gender?
Response scale
Woman
Man
Non-binary
Prefer to self-describe: [text]
Prefer not to say
There is no universal standard; options vary by country and purpose. To identify transgender respondents, the GenIUSS two-step approach asks sex assigned at birth and then current gender identity.
When to use it
To describe the sample and, where it matters, weight it.
Bias to avoid
Forced choice. Leaving out options pushes people into answers that do not describe them; offer a way to self-describe and to decline.
Origin
Common practice No universal standard. For research on gender minorities see the GenIUSS Group (2014).
Education
What is the highest level of education you have completed?
Response scale
Less than secondary school
Secondary school or equivalent
Some further or higher education, no degree
Bachelor's degree or equivalent
Master's degree or equivalent
Doctorate or equivalent
Prefer not to say
Categories differ by country. Match the national standard, or the International Standard Classification of Education (ISCED 2011) for international work.
When to use it
To describe the sample and to compare with census or national statistics.
Bias to avoid
Comparability. Categories that do not match the census or national classification make weighting and comparison harder; copy the official categories where you will compare.
Origin
Common practice No universal wording. For international comparison, see the ISCED 2011 classification.
When should you write your own question?
A standard question is a good default, not the right choice every time. Write your own, or adapt carefully, when:
You are measuring something specific to your product that no standard question covers, such as a particular feature or a regional behaviour.
The audience is different from the one the question was built for, such as children, people with limited literacy or another language. Translate and pretest rather than just reuse the wording.
The topic is sensitive. Questions about health, money or identity need more care than a standard item gives.
You need to compare with an existing tracker. Then keep the old wording exactly, even if you would write it differently today.
Frequently asked questions
Can I change the wording of a standard question?
Small changes, such as the product name or the time frame, are normal. Changing the scale or the meaning changes what is measured, and the results can no longer be compared with other uses of the question, or with your own earlier waves.
Why are there no benchmarks or norms?
Scores depend on the industry, the audience, the mode, the timing and the exact question, and published "averages" often compare different things. This bank lists none. The one rule of thumb it mentions, Sean Ellis's 40% for the product-market fit question, is attributed and labelled as a heuristic.
Are these questions validated?
They are in wide use and documented, but how well a question works depends on how you use it. Pretest it with people from your audience, and read the bias note for each.
How many questions should a survey have?
As few as will answer the decisions you need to make. Every question spends respondents' attention, and long surveys lose people. Start from the decision and add only the questions that inform it.
The question is published (Reichheld, 2003). The names Net Promoter, NPS and Net Promoter Score are registered trademarks of their owners, so check their guidance before using the names commercially.
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Where the standard wording and scales come from. Each source was looked up and checked on the date shown.
Fornell, C., Johnson, M. D., Anderson, E. W., Cha, J. and Bryant, B. E. (1996). The American Customer Satisfaction Index: Nature, purpose, and findings. Journal of Marketing, 60(4), 7-18. checked October 5, 2026
Reichheld, F. F. (2003). The one number you need to grow. Harvard Business Review, 81(12), 46-54. checked October 5, 2026
Dixon, M., Freeman, K. and Toman, N. (2010). Stop trying to delight your customers. Harvard Business Review, 88(7-8), 116-122. checked October 5, 2026
Dixon, M., Toman, N. and DeLisi, R. (2013). The Effortless Experience: Conquering the New Battleground for Customer Loyalty. New York: Portfolio/Penguin. (Introduces Customer Effort Score 2.0.) checked October 5, 2026
Vohra, R. (2018). How Superhuman built an engine to find product/market fit. First Round Review (review.firstround.com). Describes the survey Sean Ellis developed, its follow-up questions and the 40% rule of thumb. checked October 5, 2026
Jamieson, L. F. and Bass, F. M. (1989). Adjusting stated intention measures to predict trial purchase of new products: A comparison of models and methods. Journal of Marketing Research, 26(3), 336-345. checked October 5, 2026
Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 22(140), 1-55. checked October 5, 2026
Van Westendorp, P. H. (1976). NSS Price Sensitivity Meter (PSM): A new approach to study consumer perception of price. Proceedings of the 29th ESOMAR Congress, Venice, 139-167. checked October 5, 2026
Gabor, A. and Granger, C. W. J. (1966). Price as an indicator of quality: Report on an enquiry. Economica, 33(129), 43-70. doi:10.2307/2552272checked October 5, 2026
Meade, A. W. and Craig, S. B. (2012). Identifying careless responses in survey data. Psychological Methods, 17(3), 437-455. doi:10.1037/a0028085checked October 5, 2026
Oppenheimer, D. M., Meyvis, T. and Davidenko, N. (2009). Instructional manipulation checks: Detecting satisficing to increase statistical power. Journal of Experimental Social Psychology, 45(4), 867-872. checked October 5, 2026
Hauser, D. J. and Schwarz, N. (2015). It's a trap! Instructional manipulation checks prompt systematic thinking on "tricky" tasks. SAGE Open, 5(2). doi:10.1177/2158244015584617checked October 5, 2026
GenIUSS Group (2014). Best practices for asking questions to identify transgender and other gender minority respondents on population-based surveys. Los Angeles: The Williams Institute, UCLA School of Law. checked October 5, 2026
UNESCO Institute for Statistics (2012). International Standard Classification of Education: ISCED 2011. Montreal: UNESCO-UIS. checked October 5, 2026
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