Knit combines quant clarity, video-based qual depth, and AI-driven speed — all validated by expert researchers — so you can make smarter concept decisions with confidence.

Knit's Concept Testing

## Smarter concept decisions, powered by real voices

Knit’s concept testing framework combines the scale of quant, the depth of qualitative video insights, and the speed of AI-driven analysis — all validated by experienced researchers. Get instant quantitative clarity on key measures, while video feedback surfaces the emotion and nuance behind consumer reactions.

Every study is backed by methodological best practices and expert review, ensuring rigor and reliability. The outcome is clear, confident decision-making and rapid iteration — smarter choices powered by consumers’ real voices and validated by experts.

## Benefits

### Why leading brands go with Knit's Concept Testing

Knit combines quant clarity, video-powered qual, and AI-driven analysis with expert oversight — delivering faster, more reliable insights that move decisions forward.

### Integrated Quant + Qual

Hard numbers and emotional nuance captured within the same survey — so you can gauge key performance indicators and hear consumer feedback all together.

### Story-first, data-deep

Fast toplines for stakeholder-ready narratives with one-click access to detailed, defendable data.

### AI-Powered + Human-Approved

Automated analysis and reporting paired with expert review for richer, more reliable, and assuredly actionable insights.

Knit’s Unique Advantage

## Video-Based Qual

Knit adds a layer of human emotion on top of quant:

➊ Hear tone, see facial reactions  
➋ Capture what they say, and how they say it  
➌ Enriches data with emotion, depth, and context  
➍ Bonus: Video showreels bring the voice of the consumer to your stakeholders

## Key Features

### Features built for better concept decisions

Discover the tools that turn your concept testing data into clear, confident stories and decisions.

### Scorecard Visualization

Conditionally formatted tables that highlight the strongest as well as the underperforming concepts across key measures.

### Stimulus Evaluation Block

A flexible, stimulus-specific block enabling consistent evaluation across concepts while allowing broader questions to be asked before or after the stimulus.

### Flexible Formats

Supporting single-exposure (monadic), multi-exposure (sequential monadic), pre-post formats, and a wide range of stimuli types.

### Calculated Fields

Auto-generated metrics like Top 2 Box, Bottom 2 Box, and top/bottom attributes and themes — fully customizable to fit the needs of each study.

From creative to packaging, Knit’s concept testing adapts to every stage of your brand’s journey — delivering fast, rigorous insights powered by real consumer voices.

#### Creative Testing

Refine ads and messaging with consumer-driven clarity.

#### New Product Development

Validate new product ideas before launch with confidence.

#### Branding and Positioning

Test brand narratives to ensure relevance and resonance.

#### Service Offering Validation

Gauge demand and fine-tune services for market fit.

#### App & Website UX Concepts

Optimize digital experiences through real user feedback.

#### Packaging Design

Evaluate packaging appeal, usability, and purchase impact.

Knit provided the agility we needed to test creative quickly, learn, and adjust before launch. It’s a critical tool for our brand strategy moving forward.

## Turn ideas into confident decisions

Discover how you can test concepts faster, with deeper insights and greater confidence.

## Frequently Asked Questions

Concept testing can feel complex, but it doesn’t have to be. This FAQ section covers the details behind Knit’s approach — from monadic vs. sequential designs to video-based qual — helping you see why leading brands trust Knit to guide smarter decisions.

1. How is Knit’s concept testing different from traditional methods?  
2. How quickly can I get results from a concept test with Knit?  
3. What types of concepts can I test on Knit?  
4. What methodologies does Knit support?  
5. How many respondents do I need for a valid test?  
6. Can Knit handle multiple concepts at once?  
7. What outputs will I get from a Knit concept test?  
8. How does Knit ensure rigor and reliability in AI-powered research?  
9. Can I run concept testing with international or niche audiences?