# Concept Testing Template: Questions, Metrics & Rubric

> Get a copy-ready concept testing template with screening questions, rating scales & a go/kill rubric. Listen Labs adds AI-moderated depth at scale.

**Published:** 2026-10-31 | **Updated:** 2026-11-06 | **Author:** listenlabs
**URL:** https://listenlabs.com/articles/concept-testing-template/
**Type:** post

**Categories:** AI customer research

![Concept Testing Template: Questions, Metrics & Rubric](https://listenlabs.com/articles/wp-content/uploads/sites/42/2026/10/1793356460281-3442b32796e7-1024x572.webp)

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## Content

*Written by: Anish Rao, Head of Growth, Listen Labs*

## Key Takeaways

- A concept testing template uses screening, stimulus, quantitative metrics, qualitative follow-ups, and a go/refine/kill rubric to judge ideas before development.
- Rating scales suit a few concepts, MaxDiff separates winners when testing six or more, and conversational AI-moderated interviews capture the reasoning behind every score.
- Success criteria work best when set before fielding, with one primary metric, secondary floors, and a qualitative condition required for a Go decision.
- Sample size should match decision stakes and segment count, with Listen Labs Quality Guard protecting participant reliability.
- Listen Labs delivers the full concept testing template with AI-moderated interviews, access to 50M+ verified respondents, and results in under 24 hours.

[Book a demo to get the copy-ready template](https://listenlabs.com/book-my-demo?utm_term=concept-testing-template&utm_campaign=ai_growth_agent)

## The Copy-Ready Concept Testing Template

A concept test runs in five steps: screen, present the stimulus, collect ratings, collect open-ends, and apply a decision rule. The template below turns each step into copy-ready questions you can drop into a survey tool or hand to a research partner. A conversational version of this template, with adaptive follow-up probes built in, is available inside Listen Labs’ template library.

[](https://listenlabs.ai/)**Our AI helps you go from idea to implemented discussion guide in seconds.**

### Block 1: Screening Questions

Screen for the audience the concept is designed to serve. A smaller, precisely targeted sample gives clearer signal than a larger, loosely screened one.

[](https://listenlabs.ai/)**Listen Labs finds participants and helps build screener questions**

1. “In the past 90 days, have you personally purchased or chosen [category]?” (Terminate: No)
2. “What is your role in deciding which [category] your household or team buys?” (Terminate if no purchase authority)
3. “Which of the following [category] brands have you used in the past six months?” (Use to qualify or disqualify by competitive set)
4. “Have you participated in a consumer insights study in the past 30 days?” (Terminate: Yes, to reduce panel fatigue)

### Block 2: Stimulus Presentation

Show the concept in a realistic context, such as packaging on a shelf, an ad in a social feed, or a prototype in a browser. Hold stimulus fidelity constant across all concepts. A polished mockup will beat a text description regardless of the underlying idea.

1. Display the concept image, video, PDF, prototype, or live URL.
2. For a single concept, use monadic design with one concept per respondent and no comparison.
3. For two to four concepts, use sequential monadic with balanced rotation (ABDC, BCAD, CDBA, DACB) so each concept appears in every position and directly follows every other concept exactly once, per [mTab’s monadic design guidance](https://mtab.com/blog/monadic-vs-sequential-monadic).
4. Instruction to respondent: “Please take a moment to read and look at the concept below as you would if you encountered it naturally. We will ask for your reaction next.”

### Block 3: Quantitative Metrics

Use a 5-point scale with labeled anchors for each metric. Report top-2-box scores, the sum of the top two positive responses, as your primary KPI.

1. **Overall Appeal:** “Overall, how appealing is this concept to you?” (1 = Not at all appealing, 5 = Extremely appealing)
2. **Relevance:** “How relevant is this concept to your needs?” (1 = Not at all relevant, 5 = Extremely relevant)
3. **Uniqueness:** “Compared to what is currently available, how unique is this concept?” (1 = Not at all unique, 5 = Extremely unique)
4. **Believability:** “How believable are the claims made in this concept?” (1 = Not at all believable, 5 = Extremely believable)
5. **Purchase Intent:** “How likely would you be to buy or use this if it were available today?” (1 = Definitely would not buy, 5 = Definitely would buy)
6. **Price Perception:** “Based on what you have seen, how would you describe the likely price of this concept?” (1 = Much too expensive, 3 = About right, 5 = Much too cheap)

### Block 4: Qualitative Open-Ends

Ask the open-ended question before revealing what the research team cares about. The first question should stay unprompted so respondents frame the concept in their own words.

1. “What is your overall reaction to this concept? Describe it in your own words.”
2. “What do you like most about this concept?”
3. “What do you dislike or find confusing about this concept?”
4. “What, if anything, would make you more likely to buy or use this?”
5. **Follow-up probe (write this into the template):** “You rated appeal a [X]. What would have made it a [X+1]?”
6. “If this concept existed tomorrow, what would you stop doing or stop buying to make room for it?”

### Block 5: Go/Refine/Kill Decision Rubric

Set the decision rule before fielding, because a threshold chosen after you see early numbers will tend to fit those numbers. The rubric fixes the primary metric first, then the secondary floors, then the qualitative condition so teams can make consistent calls across concepts.

1. Define the primary metric and the minimum top-2-box score required for a “Go” decision, tied to the specific product decision and segment count.
2. Define the secondary metrics, such as relevance and believability, that must stay above a floor even when the primary metric clears.
3. Define the qualitative condition. The “Go” call requires a clear, consistent explanation in open-ends that supports the numbers.
4. Decision states: **Go** (primary metric clears threshold and qualitative confirms), **Refine** (strong appeal with weak believability or relevance, then iterate and retest), **Kill** (weak across metrics with no clear path to improvement).

A downloadable and copy-ready version of this concept testing template, including the PDF format frequently requested for concept testing template free and concept testing template PDF searches, is available inside Listen Labs’ template library.

[Book a demo to access the template library](https://listenlabs.com/book-my-demo?utm_term=concept-testing-template&utm_campaign=ai_growth_agent)

## How Concept Testing Runs End To End

The template above is one way to run the core steps of a concept test. Each block maps to a simple sequence, and the instrument you choose shapes the quality of the result.

1. Screen respondents for category use, purchase authority, and recency.
2. Present the stimulus, such as an image, video, prototype, or live URL, in a realistic context.
3. Collect quantitative ratings on appeal, relevance, uniqueness, believability, purchase intent, and price perception.
4. Collect qualitative open-ends and follow-up probes to capture the reasoning behind each rating.
5. Apply the go/refine/kill rubric set before fielding.

The instrument choice determines whether those five steps produce usable results or a pile of 4s with no clear winner.

### Rating Scale Vs. MaxDiff Vs. Conversational AI-Moderated Interview

**Rating scales** with 5-point or 7-point Likert items work well when you test a handful of concepts and only need a directional read. Sequential monadic scores usually run lower and closer together than monadic scores for the same concept, and when testing more than four or five concepts, respondents often give a wall of 4s with no prioritization. Ties sit structurally inside this instrument.

**MaxDiff (best-worst scaling)** solves the tie problem. Instead of rating each concept independently, respondents see small sets of four options at a time and pick the most and least preferred. Those choices are aggregated using a [Hierarchical Bayes model](https://wepushers.com/frameworks/maxdiff) into a ranking with no ties, plus portfolio optimization and head-to-head win rates. Listen Labs’ MaxDiff shows four options at a time, asks which is best and which is worst, and produces a clean ranking. In one study, the three highest-scoring flavors won 51% of shoppers, while the optimized mix won 87%. Switch to MaxDiff when rating scales have produced flat results, when testing more than six concepts, or when comparing groups that use rating scales differently across markets.

**Conversational AI-moderated interviews** close the “why” gap. A survey captures a number and leaves the reason unrecorded. Listen Labs’ AI moderator probes each rating in the moment, such as “You rated appeal a 4. What would have made it a 5?”, and generates responses three times longer than average through intelligent probing. This instrument works best when stakeholders need to understand the reasoning behind any score and need more than a simple ranking.

Listen Labs is the primary platform for running the conversational version of the concept testing template at scale. UserTesting relies on a human-dependent moderation model, which results in slower turnaround and limited scalability for teams that need results this week. Listen Labs runs thousands of parallel AI-moderated interviews with adaptive follow-ups and delivers in under 24 hours. For teams that need both the quantitative ranking and the qualitative reasoning in one instrument, Listen Labs combines MaxDiff, monadic and sequential monadic designs, and conversational probing in a single study.

Figma is a design tool and does not conduct interviews, recruit respondents, or analyze concept reactions. [Typeform is primarily a bring-your-own-audience survey builder without a built-in participant panel](https://cleverx.com/blog/best-concept-testing-platforms-2026-10-tools-compared). Its standard plans lack qualitative interview capability, though a separate beta Research Flow product adds AI-moderated interviews and recruitment. Teams using Typeform for concept testing must recruit respondents separately and export data for analysis.

## Concept-Type Variants: How To Tailor The Template

The five-block template above is the default. Adjust the metrics and stimulus by concept type so each test reflects how customers actually experience the idea.

### Physical Product Concepts

Keep all five blocks and add a shelf-impact question in Block 3: “How likely would you be to notice this product on a shelf?” Use monadic design for high-fidelity pack shots. Listen Labs supports image and video stimuli and Emotional Intelligence, which analyzes tone of voice, word choice, and micro expressions to surface reactions that transcripts alone miss, especially for packaging and sensory claims.

### SaaS Feature Concepts

Keep all five blocks and replace price perception with a willingness-to-pay question or a Gabor-Granger price ladder. Show a prototype or live URL in Block 2. Use Listen Labs’ Visual Insights to observe on-screen behavior during the interview. When a participant’s stated preference contradicts what they actually click, the AI moderator probes the contradiction in real time. Swap to MaxDiff when prioritizing a roadmap of six or more features.

### Ad Or Copy Concepts

Keep all five blocks and add a brand attribution question in Block 3: “Which brand do you think this ad is for?” Replace uniqueness with memorability. Use sequential monadic rotation across ad variants. Listen Labs’ Emotional Intelligence is particularly suited to creative testing because it pinpoints the exact timestamp where participants light up, disengage, or show confusion, quantified per question and concept.

### Brand Name Concepts

Simplify Block 3 to appeal, fit with category, and memorability. Drop price perception. Use MaxDiff when prioritizing a shortlist of six or more name candidates because rating scales will tend to produce ties. The AI moderator follows up on each choice: “You selected [Name A] as your top pick. What does it make you think of?” Every score traces back to the people behind the number, in their own words.

For more on how concept testing differs from product testing, see [Concept Testing vs Product Testing for Enterprise Teams](https://listenlabs.com/articles/concept-testing-vs-product-testing/?utm_term=concept-testing-template&utm_campaign=ai_growth_agent).

## Worked Example: A Completed Concept Testing Template

The following example shows how a filled-in template produces a go, refine, or kill call. Question wording and respondent answers are sample outputs, not real data.

**Concept:** A new plant-based protein bar positioned as “the first bar that tastes like a real snack, not a supplement.”

**Screening:** Respondent purchased a protein bar in the past 90 days, makes the household snack purchase decision, and has not participated in research in the past 30 days. Qualified.

**Stimulus:** Monadic design. Respondent sees a concept board with product image, headline, and three reasons to believe.

**Quantitative ratings (sample respondent):**

- Overall Appeal: 4 out of 5
- Relevance: 5 out of 5
- Uniqueness: 3 out of 5
- Believability: 3 out of 5
- Purchase Intent: 4 out of 5
- Price Perception: About right

**Open-end (unprompted reaction):** “I like the idea, I am always looking for something that does not taste chalky. But I am not sure I believe it actually tastes good just from looking at the packaging.”

**Follow-up probe:** “You rated believability a 3. What would have made it a 4 or 5?”

**Sample respondent answer:** “If I saw a real photo of someone eating it and actually enjoying it, not a stock photo, or if there was a flavor description that was specific, like ‘dark chocolate with sea salt’ instead of just ‘chocolate.’ The vague claim makes me skeptical.”

**Interpretation:** Across the sample, purchase intent cleared its top-2-box threshold, but believability fell below the secondary floor set in Block 5. The open-ends explained why. Respondents did not believe the “tastes like a real snack” claim without specific evidence. Because the rubric required secondary metrics to hold even when the primary metric clears, the decision was **Refine**. The team will iterate the stimulus to add specific flavor descriptors and social proof, then retest.

This decision layer turns a rating into a reason, and the reason points directly to the fix. For more real-world examples, see [What Is Concept Testing? 8 Real-World Examples Explained](https://listenlabs.com/articles/concept-testing-examples/?utm_term=concept-testing-template&utm_campaign=ai_growth_agent).

## Setting Success Criteria And Interpreting Results

Set the go, refine, and kill criteria before fielding. Adjusting thresholds after seeing early numbers creates a risk of false positives, as [Mastercard Dynamic Yield’s experimentation guidance](https://mastercard.com/us/en/business/consumer-acquisition-and-engagement/personalization/dynamic-yield/learning-paths/a-b-testing-optimization/statistical-significance.html) explains.

Define one primary metric, typically purchase intent top-2-box, secondary metrics that must not fall below a floor, such as relevance and believability, and a qualitative condition. The “Go” call requires a consistent explanation in open-ends that supports the numbers. A score of 7.2 is not meaningfully stronger than 7.0 without uncertainty, sample context, and a consistent qualitative explanation behind it.

Avoid universal benchmarks. Internal, category-specific norms work well as contextual evidence but should not replace prespecified success criteria tied to the specific product decision. When top-ranked concepts fall within the study’s detectable difference of one another, report them as statistically indistinguishable and rely on qualitative diagnostics or a follow-up test instead of declaring an arbitrary winner.

**Sample size guidance:** Match sample to decision stakes and segment count, not to a universal number. [Published planning ranges](https://askyazi.com/articles/concept-testing-in-2026-methods-questions-and-examples) suggest 8–15 participants per priority audience for early qualitative learning, 30–75 respondents per concept cell for a directional mixed-method test, and 100–200+ respondents per concept cell for quantitative comparison. For multi-market or multi-segment studies, calculate the required base independently for each market or priority segment. A total of 300 participants spread across six countries does not give the same precision as 300 participants in each. [Qualitative saturation research](https://conveo.ai/insights/sampling-methods-in-qualitative-research) by Hennink and Kaiser found empirical saturation within 9 to 17 interviews for narrow research aims. Treat that range as a budgeting prior, not a fixed target.

Recruit against the buying decision rather than the demographic. Screen for recent behavior, such as “In the past 90 days, have you personally purchased [category]?”, instead of claimed interest. Quality Guard sits behind every sample. It matches participants on intent and past actions, monitors every interview in real time for fraud, and caps participation at three studies per month. That keeps professional survey-takers out and makes the sample as reliable as the template. A dedicated recruitment ops team can source audiences below 1% incidence rate.

## Getting The “Why” At Scale With Listen Labs

The conversational version of the concept testing template, with adaptive follow-up probes, stimulus support for images, video, PDFs, prototypes, and live URLs, and automated analysis, runs at scale inside Listen Labs.

The full research lifecycle runs in one platform, from AI-assisted study design to recruitment from 50M+ verified respondents across 45+ countries and 120+ languages, AI-moderated interviews with dynamic follow-ups, and automated analysis. Deliverables such as slide decks, memos, video highlight reels, and stat tests arrive in under 24 hours.

[](https://listenlabs.ai/)**Listen Labs auto-generates research reports in under a minute**

P&G ran 250+ interviews with Listen Labs, with quantified themes and verbatim proof, and surfaced that comfort, safety, and reliability mattered far more than novelty. Those findings arrived in hours and shaped product and brand strategy before market. Microsoft cut research wait times from weeks to hours. Anthropic now runs 100 studies in the time it previously took to run five or six.

[](https://listenlabs.ai/)**Listen Labs' Research Agent quickly generates consultant-quality PowerPoint slide decks**

For teams running ongoing concept tracking across waves, Listen Labs Pulse is a conversational tracker that runs the same study with the same screeners wave after wave. It combines quantitative KPI tracking with open-ended conversation so every metric movement comes with its explanation.

For a full comparison of concept testing platforms, see [Concept Testing Software For Enterprise Research Teams](https://listenlabs.com/articles/best-concept-testing-software/?utm_term=concept-testing-template&utm_campaign=ai_growth_agent).

[Book a demo to run your study at scale](https://listenlabs.com/book-my-demo?utm_term=concept-testing-template&utm_campaign=ai_growth_agent)

## Conclusion And Next Steps

Listen Labs is the best concept testing template platform for teams that need a usable concept test this week because it pairs a copy-ready template library with AI-moderated conversational interviews, a 50M+ verified respondent network across 45+ countries and 120+ languages, and results in under 24 hours.

The decision layer separates a filled-in template from a result stakeholders will act on. Pick the right instrument, such as rating scales for a handful of concepts, MaxDiff for six or more, and conversational AI-moderated interviews when the “why” matters. Write the follow-up probe into the template and set the go, refine, and kill criteria before fielding.

Concrete next steps are straightforward. Copy the template above, choose the variant for your concept type, such as physical product, SaaS feature, ad or copy, or brand name, and run the conversational version with Listen Labs. Companies over 100 employees start with a demo and pilot, while smaller teams can access the self-serve platform.

[Book a demo and start your pilot](https://listenlabs.com/book-my-demo?utm_term=concept-testing-template&utm_campaign=ai_growth_agent)

## Frequently Asked Questions

### What Is a Commonly Used Method for Concept Testing?

The three most common methods are monadic surveys, MaxDiff best-worst scaling, and conversational AI-moderated interviews. Monadic surveys suit directional reads on a handful of concepts. MaxDiff is the right instrument when rating scales produce flat results or when prioritizing six or more options. Conversational interviews are the right instrument when stakeholders need to understand why a concept scored the way it did and need more than a simple winner.

### Can You Provide an Example of a Concept Test?

The worked example in this article walks through a complete concept test for a plant-based protein bar. It covers screening questions, monadic stimulus presentation, quantitative ratings on appeal, relevance, uniqueness, believability, and purchase intent, a qualitative open-end, a follow-up probe that turned a believability rating of 3 into a specific, actionable fix, and a Refine decision from the go, refine, and kill rubric. The example shows how a single follow-up probe, “You rated believability a 3. What would have made it a 4 or 5?”, produces more actionable direction than the rating alone.

### Concept Testing Survey Vs. Interview: Which Should I Use?

Surveys deliver quantitative scores at scale and work best when you need comparable ratings across a large sample or when the decision is primarily about which concept scores highest. Interviews deliver the reasoning behind the scores and work best when you need to understand why a concept did or did not resonate, or when stakeholders will challenge any number that lacks a qualitative explanation. Listen Labs combines both in one instrument, with quantitative metrics and MaxDiff alongside AI-moderated conversational probing, so teams do not have to choose between depth and scale.

### How Do I Avoid Ties in Concept Testing Ratings?

Switch from rating scales to MaxDiff. Rating scales often produce bunching, where a list of concepts ends up with all scores between 3.8 and 4.2 and the ranking within that band is mostly noise. MaxDiff forces respondents to pick the best and worst from small sets of four options at a time, and those choices are aggregated using a Hierarchical Bayes model into a clean ranking with no ties. Listen Labs’ MaxDiff also adds portfolio optimization, which finds the combination of concepts that wins the most customers, and head-to-head win rates to settle close calls.

### How Many People Do I Need for a Concept Test?

There is no universal number. Match sample size to decision stakes and segment count. The planning ranges above apply here. Match sample to decision stakes and segment count, and calculate the base independently for each market.

### Can AI-Moderated Interviews Replace a Survey for Concept Testing?

Surveys capture quantitative ratings at scale but cannot probe the reasoning behind any score. AI-moderated interviews capture both the rating and the reasoning in one session, with adaptive follow-up questions that adjust based on each participant’s answers. Listen Labs combines both in one instrument. Quantitative metrics, MaxDiff, and conversational probing run in the same study, so teams get the statistical confidence of large samples and the qualitative depth of one-on-one interviews without running two separate studies.

### How Does Listen Labs Handle Participant Quality?

Listen Labs’ Quality Guard is an AI orchestration layer that matches participants on behavioral and intent data, not just self-reported demographics. It monitors every interview in real time for fraud, low-effort responses, and repeat respondents across video, voice, content, and device signals. Participants are limited to three studies per month, which removes professional survey-takers. A dedicated recruitment operations team adds a human review layer and can source audiences below 1% incidence rate, including enterprise decision-makers, healthcare workers, and highly specialized consumer segments.

### How Fast Can I Get Results?

Listen Labs delivers consultant-quality results, including automated key findings, thematic analysis, slide decks, video highlight reels, and stat tests, in under 24 hours. P&G received 250+ interviews with quantified themes and verbatim proof in hours. Microsoft collected global customer video stories for a product anniversary within a day. Anthropic runs 100 studies in the time it previously took to run five or six. Traditional research cycles that cover recruitment, moderation, transcription, analysis, and reporting often take 4–6 weeks. Listen Labs compresses the entire cycle into a single platform that runs in parallel rather than sequentially.

## What to Read Next

- [Concept Testing with AI-Moderated Interviews at Scale](https://listenlabs.com/articles/concept-testing-market-research-guide/?utm_term=concept-testing-template&utm_campaign=ai_growth_agent)
- [Best Concept Testing Platforms: A Practical Guide](https://listenlabs.com/articles/best-consumer-insights-concept-testing/?utm_term=concept-testing-template&utm_campaign=ai_growth_agent)
- [What Is Concept Testing? 8 Real-World Examples Explained](https://listenlabs.com/articles/concept-testing-examples/?utm_term=concept-testing-template&utm_campaign=ai_growth_agent)
- [Concept Testing for Landing Pages: Validate Before You Build](https://listenlabs.com/articles/concept-testing-for-landing-pages/?utm_term=concept-testing-template&utm_campaign=ai_growth_agent)
- [Concept Testing Software For Enterprise Research Teams](https://listenlabs.com/articles/best-concept-testing-software/?utm_term=concept-testing-template&utm_campaign=ai_growth_agent)

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## Citations

- [Digital Twin Research Tools for Brand Teams: 2026 Guide](https://listenlabs.com/articles/best-digital-twin-research-tools/)
- [Global Consumer Research Platforms for AI Digital Twins](https://listenlabs.com/articles/best-ai-digital-twin-research-platforms/)
- [AI User Research Tools for Message Testing: Which Wins?](https://listenlabs.com/articles/best-ai-message-testing-tools/)
- [Customer Research Panel Alternatives for AI Digital Twins](https://listenlabs.com/articles/best-ai-digital-twin-panel-alternatives/)
- [Synthetic Respondent Platforms Compared: A Buyer’s Framework](https://listenlabs.com/articles/best-synthetic-respondent-platforms-compared/)

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