Market Research Question Types – Which to Use? đ
You always want to right tool for the job, yes?
You can’t very well use a hammer to saw something in half… I mean, I guess you could just bash something in half with it, but you know what I mean – it’s not going to give you a good outcome.
Similarly, you don’t want to use the wrong question types for the job. Youâll never draw the right insights, even if the responses are technically accurate. By thoughtfully selecting formats that capture everything from broad strokes (leveraging single and multi selection) to nuanced shades of opinion (leveraging MaxDiff and Conjoint), you can unlock actionable insights that drive smarter decisions and bigger wins.
Here are the 16 main research question types to leverage in your surveys.
1) Single Selection đŻ
Single selection questions are the classic âpick one, any oneâ format, beloved by market researchers who appreciate simple, definitive answers. For instance, if gaming titan Nintendo wanted to survey Fortnite fans on which console theyâre most likely to purchase next, they could pose a single selection question such as:
Q: Which console will you buy in the next 2 years?
- Nintendo Console
- PlayStation Console
- Xbox Console
Responses might yield a clear-cut distributionâsay 45% Switch, 30% PlayStation, 25% Xboxâallowing Nintendo to see which console is winning the popularity contest. This question type is perfect for quick insights, but be aware that it can oversimplify complex opinions, so itâs best used when you need a decisive choice rather than a list of possibilities.
2) Multiple Selection đ
Multiple selection questions let respondents select more than one option, making them great for nuanced consumer behaviors. In a Zarona Cosmetics study, you might ask:
Q: Which product categories do you plan to purchase in the next month?
- Skincare
- Makeup
- Haircare
- Fragrances
- All of the Above
If they ran this question, they could discover that 60% choose Skincare and Makeup together, while only 25% go for Fragrances, revealing cross-category purchase intentions. Multiple selection helps you account for those moments when people canât choose just one itemâmuch like choosing only one streaming service feels next to impossible.
3) Open-Ended đŹ
Open-ended questions are perfect for capturing the âwhyâ behind consumer choices, letting respondents write freely without constraints. If Panther Foods wanted to explore consumer taste preferences for a new plant-based protein bar, they might ask:
Q: What flavors would you like to see in our next product, and why?
Potential responses (example free-text answers):
- âA spicy sriracha-lime blend for a bold kickâ
- âClassic chocolate-peanut butter with low sugarâ
- âTropical mango-coconut for a refreshing twistâ
Future respondents could provide elaborate feedbackâmaybe someone wants a sriracha-lime bar to match their adventurous palateâand youâd gather qualitative gems that no multiple-choice format could unearth. The downside? Coding and analyzing these open text answers can be as enjoyable as assembling a 2,000-piece jigsaw puzzle in the dark, so plan your analysis time wisely.
4) Numeric Open-Ended đ˘
Numeric open-ended questions capture straightforward number responses without the rigidity of preset ranges. Suppose Saiyuki Tech, a SaaS provider, wants to gauge how many hours per week enterprise software users spend in their project management tool. They could pose a numeric open-ended question like:
Q: How many hours do you estimate youâll spend using our platform each week?
(Answer is a free numeric entry, e.g., â2,â â15,â â40,â etc.)
If they ran this question, they might discover usage estimates anywhere from a modest 2 hours to a jaw-dropping 40, arming them with the data needed to plan feature development and user support. Itâs a great way to capture real-world usage figures without forcing respondents to squeeze their answer into a predefined bracket.
5) Description đ
A description question type (sometimes called a âtext displayâ) doesnât require a response; itâs primarily for presenting information or context right inside the survey. Picture Nintendo again, giving detailed info about a new gaming subscription plan before asking the next question, such as:
(Displayed Text Only):
- âOur new subscription plan includes exclusive skins, advanced multiplayer features, and monthly digital currency for in-game purchases.â
They might follow up with a question afterward to gauge interest or likelihood to subscribe. This ensures respondents have the background necessary to answer upcoming questions accurately, but it also tests your skill at writing concise explanations that donât cause eyes to glaze over.
6) Slider đ
Slider questions are a sleek way to capture the intensity or degree of an opinion on a continuum. If 3D Printing Solutions in the Manufacturing sector wants to measure how confident potential B2B customers are in 3D-printed prototypes versus traditionally tooled prototypes, they might ask:
Q: How confident are you in the durability of 3D-printed components?
- Slider scale from 0 (Not at all confident) to 100 (Extremely confident)
Future results might cluster around the 70â80 range if the market is fairly confident but not 100% convinced yet. Itâs an engaging format for participants, though analyzing 101 potential data points (0 to 100) can be more complex than a simple 5-point scale.
7) Rating Stars â
Rating star questions replicate that familiar online review feel, making them visually appealing and intuitive for respondents. If Rockstar Energy wants a quick measure of satisfaction for a new flavor, they could ask:
Q: How would you rate our new Mango-Lime energy drink?
- 1 Star (Very Dissatisfied)
- 2 Stars
- 3 Stars
- 4 Stars
- 5 Stars (Very Satisfied)
If they ran this question among Red Bull fans, they might see an average rating of 4.2 out of 5 stars, suggesting that the new flavor is a hit. The star format adds a friendly, consumer-focused vibe to the survey, though it does reduce nuance to a single aggregated rating.
8) Ranking đ
Ranking questions let respondents order multiple items based on preference or importance. If Nintendo wants to see which game features matter most to eSports fansâlike gameplay difficulty, character customization, online community, or visual qualityâthey could ask:
Q: Please rank the following features in order of importance (1 = Most Important, 4 = Least Important):
- Gameplay difficulty
- Character customization
- Online community
- Visual quality
The future results might reveal that 60% rank âonline communityâ as number one, overshadowing âvisual quality,â which might come as a surprise. While the ranking format is super-helpful for prioritizing product features, remember that analyzing ties or near-ties can feel like solving a Rubikâs Cube blindfolded.
9) Matrix Table đď¸
Matrix table questions let you bundle multiple items and rating scales into a single, compact grid. Imagine Glorvon in the Fintech space evaluating multiple service attributes by asking:
Q: Please rate each of the following on a scale from âVery Poorâ to âVery Good.â
- User interface
- Transaction fees
- Speed of transfers
- Customer support
If they ran this question, they could see how each element stacks up across the same scale, painting a holistic picture of service satisfaction. The matrix format is super-efficient, though it can sometimes overwhelm respondents if it looks like a giant, data-hungry bingo card.
10) Constant Sum â
Constant sum questions ask respondents to allocate a fixed number of points among different categories, revealing relative priorities. If Nintendo wants to see how PlayStation owners might split their gaming budget, they could pose a constant sum question like:
Q: You have 100 tokens to allocate across the following categories. How would you distribute them?
- Subscription services
- New game titles
- In-game items
- Gaming accessories
Future responses might show that 40 tokens go to new game titles, 30 to subscription services, 20 to accessories, and 10 to in-game items, revealing exactly where peopleâs money is likely to flow. While the data can be incredibly telling, be prepared for a slight learning curve from respondents who might wonder why they canât just choose everything.
11) Drill Down đľď¸
The coffee origin example above is probably not something people would be able to answer, but it showcases the logic of the question well. Drill-down questions guide respondents through nested options, ensuring a more personalized response path. For instance, if Panther Foods wants to narrow down snack preferences, they might use the following structure:
Q1: Do you prefer savory or sweet snacks?
- Savory
- Sweet
Q2 (if Savory): Which do you prefer?
- Chips
- Crackers
Q2 (if Sweet): Which do you prefer?
- Cookies
- Candy
If they ran this question, theyâd be able to see exactly how many participants prefer savory over sweet, and then which sub-category each group leans toward. This approach is extremely helpful for exploring hierarchical or layered product lineups, though coding skip logic can sometimes feel like youâre herding cats.
12) Net Promoter ScoreÂŽ (NPS) đ
The NPS question focuses on one thing: how likely your respondents are to recommend your brand, product, or service. If Nintendo wants to gauge brand loyalty among Nintendo Switch owners, they could ask:
Q: On a scale from 0 to 10, how likely are you to recommend Nintendo to a friend or colleague?
- 0 = Not at all likely
- 10 = Extremely likely
If they ran this question, theyâd subtract the percentage of Detractors (0â6) from the percentage of Promoters (9â10), ignoring the Passives (7â8), to get the famous NPS. While NPS wonât tell you every detail about why your brand is beloved or hated, itâs a great snapshot of brand advocacyâjust donât forget to pair it with open-ended follow-ups for context.
13) A/B Test âď¸
A/B tests compare two optionsâlike two ad creatives or two product labelsâto see which resonates more. For example, Saiyuki Tech could show a group of enterprise software users two different web app dashboards, each with unique designs or feature sets:
Q: Which web app dashboard do you prefer?
- Dashboard A (Minimalist layout)
- Dashboard B (Robust sidebar)
If they ran this test, they might find that 65% prefer Dashboard A for its minimalist layout, while 35% stick with Dashboard B. This method is data-driven, straightforward, and perfect for incremental improvementsâthough sometimes youâll learn that both versions are equally disliked (a humbling experience indeed).
14) Conjoint đ
Conjoint analysis helps you figure out which combination of features or attributes your market values most. If Nintendo wants to figure out the perfect bundle for a new âeSports subscription,â it could vary price, exclusive skins, early access to games, and monthly in-game currency in different hypothetical packages, for example:
Q: Which subscription package would you be most likely to purchase?
- Package A: Mid-range price, Exclusive skins, No in-game currency
- Package B: High price, Early access to games, Monthly in-game currency
- Package C: Low price, Fewer exclusive skins, Occasional bonus items
By analyzing which package is chosen most often, Nintendo could learn that 70% of Fortnite fans prefer a mid-tier price with exclusive skins and minimal in-game currency. Conjoint analysis is incredibly revealing but can get complicated fastâlike trying to juggle flaming torches while riding a unicycleâso itâs best to be methodical in your design.
15) MaxDiff đ
MaxDiff stands for Maximum Difference Scaling, and itâs used to identify the most and least important attributes from a set. Panther Foods might use MaxDiff to test packaging elements by asking:
Q: For the attributes listed below, choose which one is MOST important to you and which one is LEAST important to you.
- Color scheme
- Product claims
- Brand logo size
- Tagline
If they ran this question, respondents might consistently pick âbrand logo sizeâ as least important, while âproduct claimsâ stands out as the top priority. This method is excellent for pinpointing relative preferences, though interpreting the results might take a bit more brainwork than a simple rating scale.
16) Pricing Study (Van Westendorp) đ˛
The Van Westendorp Price Sensitivity Meter is a classic approach to determine consumersâ price thresholds: too cheap, cheap, expensive, and too expensive. If Nintendo was launching a new premium controller, they could ask eSports fans something like:
Q: At what price point would you consider this controller to be:
- Too cheap?
- Cheap?
- Expensive?
- Too expensive?
If they ran this question, theyâd find a rangeâperhaps $40 to $60âwhere most potential buyers consider the controller acceptable, helping them pinpoint an optimal price. Though it wonât address competitor pricing or brand perception directly, it gives a straightforward sense of where your price might be too low or too high.
With these question types in your research arsenal, youâre equipped to capture feedback in ways that are both diverse and data-rich. Whether youâre working with a major player like Nintendo or a niche brand exploring emerging markets, customizing your question format is the key to deeper insights.
When youâre ready to apply these methods for real, remember that the right survey platform or research partner can elevate your outcomes significantly.
Written by Troy Harrington
Troy serves as the product evangelist for Pollfish. Leveraging 13 years of marketing leadership experience, he drives awareness of Pollfish's valuable insights capabilities, enabling brands to make better decisions.

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