4 concept testing methods (and how to use them effectively)

Concept testing is a brilliant technique that helps you validate your ideas long before their launch. You discover what your users want and need, so you don’t invest valuable resources in building a homepage that confuses them or release a logo that doesn’t align with your company’s values.

But choosing the best concept testing method can feel daunting. You know you need to follow a structure or protocol to get reliable results, but you’re not sure what your options are or how to pick the right one.

This article dives deep into the 4 main concept testing methods—and breaks down the pros and cons of each. You’ll walk away knowing how to select the right type, so you can collect the best user insights to shape your project or product.

4 concept testing methods explained

Concept testing is a research method to collect user feedback on new products or design ideas. Whether you’re part of a user experience (UX), product, or marketing team, this process helps you determine what your users like (and dislike), so you can create products that convert better. 

Depending on your budget and needs, you might use 1:1 interviews, focus groups, or surveys to conduct concept testing. 

The 4 types of concept testing are: 

• Monadic testing

• Sequential monadic testing

• Comparative testing

• Protomonadic testing

Let’s look at each method, how to use it, and its benefits and drawbacks.

1. Monadic testing

In monadic testing, a single concept takes center stage. Testers evaluate a concept on its own merits, without comparing it to other options. 

If you want to test other concepts—and we recommend you do!—use a separate pool of testers who haven’t seen the first design, so your data reflects a more accurate representation of user preferences without interference from external factors.

For example, testers might say they like a logo’s design—until it’s in a line-up with the original, more familiar logo.

Monadic testing lets users focus on a single concept—e.g. “What do you think of this?” 

Monadic testing is great for most concept tests—from ad creative to tool names to product prototypes. But because it requires a bigger sample size, which takes more time to get, you may want to skip monadic testing for your low-fidelity concepts, like quick ad design sketches on paper, and save it for more detailed medium- or high-fidelity mock-ups.

When conducting monadic tests, keep these tips in mind:

• Find relevant testers who represent your target audience. For example, if a product is designed for a specific subset of your audience, use survey targeting to ensure you reach those users.

• Be consistent. Use the exact same process and questions when testing subsequent concepts with other testers. The only thing that should change is the design variant itself so you can make clear comparisons when you analyze your data.

• Read between the lines. When you analyze feedback, pay just as much attention to what testers don’t say. For example, if they don’t comment at all on a section of a wireframe, it might not stand out visually or add value.

Monadic testing benefits and challenges

Some benefits of monadic testing include: 

• You get in-depth insights on each concept to inform future iterations

• Testers only evaluate a single concept, which keeps the survey short and reduces test fatigue

• There’s less risk of order bias, which occurs when variant order affects the results

And some drawbacks of monadic testing are: 

• It may not reflect real-world circumstances; typically, people view products, marketing assets, and designs in the context of external factors

• Since each group only views a single concept, you need to find more testers to assess your other concepts

• You may need more time to gather enough insights and move on with your design and development process

💡 Pro tip: gather the sample size you need—fast—by placing your survey on the highest traffic page on your website. With Contentsquare Surveys, you can choose the on-site option that will attract the most respondents, like:

• Pop-over surveys that pop out at a certain point on the page

• Button surveys that users can opt to open

• Full-screen surveys that appear as an overlay in the middle of the page

If you have an engaged email list, that’s always a good delivery option. You can also create a link survey to be shown on a unique, dedicated URL, which you can email to your audience. Easy peasy. 

Consider survey type and delivery to quickly gather a large sample of responses

2. Sequential monadic testing

In sequential monadic testing, like in standard monadic testing, respondents see one concept at a time and answer questions about their preferences on that concept. But in the sequential monadic approach, researchers test multiple concepts with the same group of respondents. 

Seeing a stellar design first can influence a tester’s reaction to subsequent concepts. To minimize this bias, called order bias, researchers create 3 different test groups and show the concepts to each group in a different order. 

For example, if a direct-to-consumer (DTC) company is testing 3 packaging options for their new line of soap, they might show one group of testers Option A, B, and then C; another B, C, and then A; and a third C, B, and then A (as you see in the image above). Then, they compare the results between the groups.

Always test multiple concepts and ask users open-ended questions about them. This will help you compare and contrast different ideas, and dig into their preferences to find out which aspects they are most interested in—so you can apply these findings to your iterations.

— The Contentsquare team

💡Think of sequential monadic testing as presenting multiple monadic tests to the same audience. If you follow the same best practices you do for monadic, you’ll be golden. 

Sequential monadic testing benefits and challenges

Some benefits of sequential monadic testing include:

• Since each tester reviews multiple concepts, you don’t need a large sample size, which saves you time and reduces costs

• You gather the right amount of responses in a short amount of time, helping you make decisions and prioritize faster

And some drawbacks of sequential monadic testing are:

• You need active measures in place to combat bias 

• Surveys are longer, which can negatively affect completion rates

• If you limit survey length, you lead to fewer open and closed-ended questions, resulting in less in-depth data on concepts

💡 Pro tip: don’t make surveys feel like hard work. Instead, keep testers attentive during long concept tests by using visually interesting concept images or engaging language in your instructions and descriptions. If you’re struggling to refine your question text into something clear, concise and yet unbiased, let Contentsquare’s AI-question generator write it for you. 

Also, monitor your survey’s performance stats to see how well you’re engaging testers (Contentsquare monitors various data points on how users interact with your survey, including completion rate drop-offs and starts). If your completion rate seems low, take a look at the survey breakdown. You may need to shorten or revise questions that seem to be putting users off.

Contentsquare lets you view your survey’s performance statistics to increase engagement

3. Comparative testing

Sometimes, you have more than one great option and just need a second opinion (or a dozen second opinions). That’s where comparative testing comes in. 

Also known as comparison or preference testing, comparative testing is a method used to determine the relative appeal of 2 or more concepts. In other words, this approach looks at which design respondents prefer.

Contentsquare’s preference test template makes it easy to run comparative tests

In comparative testing, respondents view concepts side by side instead of one after another, as in sequential monadic testing. A survey might ask them to score or rank designs against fixed criteria, like originality or usefulness, or instruct them to indicate their preference and explain why.

When conducting comparative tests, keep these tips in mind: 

• Be flexible. Your test might not yield an outright ‘winner.’ Analyze user feedback about specific elements of both designs and then iterate.

• Test a limited number of concepts. For the best insights, test no more than 2 or 3 concepts at a time. Any more than that can confuse respondents and lead to inaccurate results.

• Follow up with another type of test. While comparative testing helps you understand which concept to develop further, monadic testing helps you dive deeper into a single concept. Try a monadic testing survey when testing your next iteration.

Comparative testing benefits and challenges

Some benefits of comparative testing include: 

• Testers can directly compare 2 or more designs

• You get clear data about what resonates most with users

• A small sample size works well, which is more cost-effective

And some drawbacks of comparative testing are: 

• It doesn’t provide in-depth feedback on the individual concepts or nuances in testers’ thinking

• It may not work well for complicated concepts because seeing 2 highly detailed images next to each other might confuse and overwhelm testers

💡 Pro tip: get your comparative tests up and running in minutes with Contentsquare’s  preference test template. Just upload a side-by-side image of your concepts, and you’re ready to roll. The template comes ready with 2 questions: 

1. Which design do you prefer?

2. What was your first impression of the design? 

While you have the option to add more questions, keeping the survey short means testers avoid survey fatigue, increasing completion rates.

If your Contentsquare plan doesn’t allow for image uploads, you can always link to a side-by-side image of your concepts on a webpage, like so: 

You can include hyperlinks (to images, if you choose) in your surveys, even if using the free tier

4. Protomonadic testing

Protomonadic testing combines elements of sequential monadic testing and comparative testing. Respondents view muttiple concepts separately and sequentially, and then choose their preference at the end. 

In this best-of-both-worlds approach, you accurately gauge the first impressions of each concept, and understand how they perform against each other.

Use Contentsquare Surveys to conduct protomonadic testing

Ask users for their first impressions of a design or concept with a rating scale question. Not only does this help quantify participants’ reactions, but it also makes it easy to tell—at a glance—how they perceive it (e.g., ‘dislike,’ ‘love,’ ‘hate,’ etc.) and where the main areas for improvement lie.

— The Contentsquare team

💡Note: for best results with protomonadic testing, follow the best practices of monadic and comparative testing. While it may seem like more work upfront to design a 2-for-1 test, you reap the benefits of richer, more reliable results.

Protomonadic testing benefits and challenges

Some benefits of protomonadic testing include:

• It allows for better comparisons between concepts since respondents have analyzed each concept before making a decisive judgment call

• You don’t need as many respondents as in monadic testing 

• You can ask a mixture of open and closed questions—to generate both qualitative and quantitative data (e.g. which option do you prefer? Why do you prefer that option?) 

And some drawbacks of protomonadic testing are:

• It can be more challenging to design since it combines 2 different types of tests

• With more questions, participants face survey fatigue, which can lower your completion rate

💡 Pro tip: use Contentsquare’s AI to analyze qualitative insights with ease.

Protomonadic testing can yield considerable quantitative and qualitative data. Many people feel confident analyzing numbers—the quantitative results from, say, a rating scale—but hesitate with how to approach qualitative results, like the long-text answers users give to explain their reasoning. Contentsquare Surveys offers AI features to help you understand the key takeaways from your qualitative data: 

• Automatic tagging categorizes your responses into the themes that keep emerging. You can add your own or allow AI to identify the key ideas for you.  

• Sentiment analysis tags the overall tone of a response as being positive, neutral or negative 

• An automated survey summary creates a report of your survey, including key takeaways, interesting quotes and suggestions for next steps to take based on its conclusions 

AI helps you analyze your qualitative data whilst avoiding human bias. Assess your qualitative survey data manually, and you risk placing an outsized amount of importance on the responses that agree with your existing hypotheses. 

Contentsquare’s AI survey response tagging feature in action 

Collect insights that lead to empathy and iteration

The 4 concept testing methods we explore in this article offer actionable data that empowers you to determine what your users want—so you can give it to them. However, each technique has its own unique benefits and drawbacks. 

To determine which method works for you, start by outlining your project’s goals and team’s constraints, like budget or time. Once you clearly understand your objective and parameters, then you can consider the pros and cons of each method, and find the one that fits best. 

No matter which method you choose, you’ll walk away with a better understanding of your ideal customer persona and ideas for a better product or design.

FAQs about concept testing methods