Create a Ranking Survey + Free Template

Overview: A ranking survey measures preferences by asking respondents to order a list of items, either all of them or just their top choices (e.g., top three). Results include a ranking score, first-place counts, and a distribution showing how each item performed overall.

Getting Started: Launch a ranking survey in seconds using this template. Using our drag-and-drop editor, you can fully customize the questions. Need help? Our team can handle the entire project for you.

Crafting the list of attributes is the most important step in designing a ranking survey. Each item should be unique, clearly worded, and not too similar to the others. Keep the phrasing simple. If an option includes too many words or technical terms, it becomes harder for respondents to evaluate and rank the choices.

Avoid including too many attributes because it can overwhelm respondents and reduce the quality of the results. A list of more than 10 items demands excessive cognitive effort, making it hard for people to compare and order each.

Consider your survey’s goals and how the ranking data will drive you toward them—supplemental questions sharpen this focus. They add additional data points that you can use when analyzing results. For example, if doing a market research study, you may want to include a question about household income, then you can segment preference by income.

Once your ranking question is drafted, add it to the survey where you see fit. Depending on how many supplemental questions you have, we recommend placing the ranking question toward the beginning of your survey, ideally on page one, and then adding supplemental questions on page two. This way, if respondents get to page two and exit the survey, you still capture the critical ranking data.

If you're using a survey panel, you may have no choice but to put supplemental questions on page one and the ranking question after. In this case, people are being compensated for their responses, so drop-off rates are less of an issue.

When you add a ranking question to your survey there are some options you can toggle:

• Require an answer (can not proceed until answered).
• Define how many attributes to rank, for example, top three or top five.
• Clear the selected choices so respondents can start over.

There are two survey ranking question types you can choose from: click ranking or drag and drop. Below are the details of each along with interactive examples. Depending on your project one type might be more beneficial than others. Both types of ranking questions display the results the same.

Click-to-rank questions are perfect for quickly determining what matters, like in a customer feedback survey. This ranking question lets respondents order preferences with a tap, delivering fast, actionable insights without dragging options.

This type is ideal when you know your audience will mainly be on mobile devices. Scrolling a large list and dragging on a mobile device may not be an ideal option, leading to user frustration and possibly drop-off rates.

This type is also ideal when lists are smaller, like five to ten items, and you're asking to rank a top three.

A drag-and-drop ranking question is ideal when you're doing more advanced research, and respondents may need to take more time evaluating options. Drag-and-drop makes it easy to reorder and visualize preferences.

This type is ideal when you know your audience will mainly be on desktop devices, as it is much easier to drag and drop, especially large lists, with a larger screen and mouse.

This type is also ideal when lists are more extensive, roughly ten items or more.

You can create skip and display logic rules based on ranking questions. Ranking logic is essential for market research surveys to ask follow-up questions. For example, you could use display logic to ask a Gabor Granger for each item a respondent ranked, giving you further insights into the monetary value of a respondent's preference.

While ranking questions can be highly beneficial, they come with a few important limitations:

• No measure of preference strength: A major drawback is the inability to quantify how much more someone prefers one item over another. For example, if "Banana" and "Chocolate" are the top two flavors, there's no way to tell if people like Banana slightly more—or 100 times more. That missing context can lead to poor prioritization, especially in product development or marketing decisions.
• Survey fatigue and cognitive overload: Evaluating a long list of items can overwhelm respondents. Even with simplified input types like click ranking, people must consider the entire list before making choices. This mental load can introduce errors, especially when more than 10 attributes are ranked.
• Primacy bias: Respondents tend to rank items that appear first more favorably, particularly when fatigued or on mobile devices. This order bias can skew results, making it seem like certain items are more important than they actually are.

A solution to these issues is MaxDiff survey. Instead of asking respondents to rank everything at once, MaxDiff presents small, randomized sets of attributes (e.g., 5 out of 10) and asks for the most and least important in each set. Over multiple sets, this approach generates more precise, reliable data.

Using the same food flavor example, MaxDiff would force respondents to directly compare "Banana" and "Chocolate" in context—giving you a clearer picture of which option has stronger preference weight. The end result is a more defensible, data-driven insight into what truly matters most.

The ranking score is a weighted calculation. Items ranked first are given a higher value or "weight." Each option's score is calculated by summing the weighted values. For example, if there are five options, the weighted sum for an option a respondent placed in the first position (1) would be worth 5. The points are summarized, and the item with the highest points is ranked first.

The results include how often an item was ranked first and display the ranking distribution with a small bar chart. The color-coding of the ranking distribution makes it easy to see net top/bottom rank attributes.

The Excel export will display each attribute as a column with the respondent's ranking. The column would be blank if a respondent did not rank an attribute.

The sample data below are the results of the click ranking survey used by the food manufacturer. Each attribute has a row with the ranked distribution, first-place counts, and total score.

A feature unique to SurveyKing is the ability to create a segment report for a ranking question. This report type is helpful to drill down into the data and spot hidden relationships. For example, you might include a question in your survey that asks for the respondents' gender. You could then create a segment report (or a cross-tabulation report) by gender. The results would include the table shown above for both "Male" and "Female". You may notice "Males" prefer a particular attribute that females do not prefer or vice versa.