Clickable Prototype
Use a digital mock-up to test your concept with actual users.
What is Clickable Prototype?
A clickable prototype is an interactive digital mock-up that simulates your product's user interface and core functionality without requiring full development. Users can navigate through screens, click buttons, and experience key workflows as if using the actual product. This validation technique bridges the gap between static designs and a fully functional product, allowing you to test user interactions, information architecture, and feature prioritization at a fraction of the development cost.
Clickable prototypes are particularly valuable for testing desirability (do users want this?) and feasibility (can we build this effectively?) simultaneously. They provide quantitative data through user behavior analytics and qualitative insights through user feedback, making them one of the most reliable pre-development validation methods. Tools like Figma, InVision, or Marvel enable rapid creation and testing, while heat mapping and click tracking reveal how users actually interact with your proposed solution versus how you expect them to behave.
When to Use This Experiment
- Pre-development validation: Before investing in actual development, test if your solution resonates with users
- Complex user flows: When your product involves multi-step processes that need validation (e.g., onboarding, checkout, configuration)
- B2B software concepts: Testing enterprise software workflows and interfaces with potential corporate users
- Mobile app validation: Simulating app experiences on actual devices to test usability and navigation
- Feature prioritization: When deciding which features to build first based on user engagement and completion rates
- Investor presentations: Demonstrating product vision and user experience to potential investors or stakeholders
- User interface testing: Validating design decisions, layout choices, and interaction patterns before development
- Market research: Understanding user preferences between different design approaches or feature sets
How to Run This Experiment
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Define key user journeys: Identify 2-3 critical user flows you want to test (e.g., sign-up process, core feature usage, purchase flow). Map out each step users need to take to complete these journeys.
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Choose prototyping tools: Select tools based on your needs - Figma or Sketch for design-heavy prototypes, Marvel or InVision for rapid click-through experiences, or Framer for more advanced interactions. Consider your team's skills and budget.
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Create wireframes and screens: Design key screens for your user journeys, focusing on functionality over visual polish. Include navigation elements, forms, buttons, and content placeholders that users will interact with.
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Add interactivity and links: Connect screens with clickable hotspots, buttons, and navigation elements. Simulate realistic user flows including success states, error messages, and alternative paths users might take.
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Recruit target users: Find 10-15 participants who match your target customer profile. Use existing networks, social media, or platforms like UserTesting.com to recruit participants for testing sessions.
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Conduct user testing sessions: Run 30-45 minute sessions where users complete specific tasks while thinking aloud. Record sessions (with permission) and observe where users struggle, succeed, or deviate from expected behavior.
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Analyze interaction data: Review click-through rates, completion rates, and user paths. Identify common pain points, popular features, and areas where users drop off or get confused during the experience.
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Iterate based on insights: Refine your prototype based on user feedback and behavior data. Test critical changes with additional users to validate improvements before moving to development.
Pros and Cons
Pros
- Cost-effective validation: Test core concepts and user flows for under €500, avoiding expensive development mistakes
- High user engagement: Interactive elements provide more realistic feedback than static mockups or surveys
- Quantifiable metrics: Track completion rates, click patterns, and user behavior for data-driven decisions
- Rapid iteration: Make changes quickly based on user feedback without development constraints
- Stakeholder alignment: Clear visual representation helps team members and investors understand the product vision
Cons
- Limited functionality simulation: Cannot test complex backend processes, performance, or technical feasibility
- Prototype limitations: Users may not behave naturally knowing it's not a real product
- Time investment required: Creating comprehensive prototypes can take several days to weeks
- Technical constraints: Some interactions or integrations are difficult to simulate realistically
- Potential false confidence: Positive prototype feedback doesn't guarantee market success or technical viability
Real-World Examples
Slack used clickable prototypes extensively during their early development to test different approaches to team communication and channel organization. They created interactive mockups to validate their threading concept and @mention functionality with potential business users, which helped them prioritize features that became core to their success. The prototype testing revealed that users needed clearer visual indicators for unread messages, leading to their distinctive notification system.
Airbnb employed clickable prototypes to test their booking flow and host onboarding process before building these complex features. They discovered through prototype testing that hosts needed more guidance during the listing creation process, leading to their step-by-step listing wizard. The prototype also revealed that guests wanted more information upfront about cancellation policies, which influenced their booking interface design.
Spotify used interactive prototypes to test their music discovery features, including the concept that became 'Discover Weekly.' They created clickable mockups to test how users would interact with algorithmically generated playlists versus manually curated ones, measuring engagement and completion rates for different recommendation interfaces before committing to the development of their personalization algorithms.