Value Proposition Test - Pocket

In Brief

A pocket smoke test is an informal demo where you carry a physical prototype or mobile app mockup and pull it out during a conversation with a prospect. You show the prototype, observe how the person naturally reacts and tries to use it, and ask follow-up questions. The output is qualitative feedback on whether the solution concept makes sense, what features matter, and how users expect to interact with the product. It works best for physical goods, hardware, or any product with a tangible form factor.

Description

Pocket smoke tests are part of the Value Proposition Test family — methods that test demand for a promise by asking participants to commit money, time, data, or actions. Here the commitment is attention and reaction: the prospect engages with the prototype in a live conversation and signals fit (or rejection) through what they say and do.

The method fits products with a tangible form factor — physical goods, hardware, mobile apps, or any concept a person can pick up, hold, or tap on a screen. Treat the prototype as a conversation piece, not a finished product. You are focusing on the main “happy case”: is it something users want, and could it believably address their problem? It’s fine for the prototype to be ugly, as long as you use it to learn something you didn’t already know. By showing a specific solution to a voiced problem, you can go deeper than an interview and observe how the user expects to use the product.

This kind of test only works once you leave your desk. As Steve Blank puts it, “there are no facts inside your building, so get outside.” A pocket smoke test is the lowest-overhead way to act on that — carry the prototype with you and let real reactions, not internal opinions, decide what to build next.

How to

Prep AI Prompt

1. Build the minimum viable prototype.

Only build enough to start a conversation:

• Physical products: Foam, cardboard, 3D-printed, or assembled from off-the-shelf parts. Ugly is fine — you’re testing the concept, not the finish.
• Software/apps: A clickable Figma prototype, a few screens in a slideshow, or a live demo on your phone or laptop. It doesn’t need to work end-to-end — just enough to show the core flow.
• Services: A one-page description, a mock invoice, or a sample deliverable.

2. Identify 10–15 people to demo to.

They should have the problem you’re solving. Sources: existing users, prospects from interviews, conference attendees, coworking space contacts. Avoid friends and family unless they’re genuinely in your target market.

3. Prepare your observation checklist.

Decide what you’re watching for before you demo:

• Do they understand what it does without you explaining?
• Do they try to interact with it in the way you intended?
• Do they ask about price, availability, or next steps? (Strong buying signal.)
• Do they compare it to something they already use? (Tells you your competition.)

Analysis AI Prompt

1. Tally reactions across demos.

After 10+ demos, categorize responses: How many understood it without explanation? How many asked about price or next steps? How many compared it to something they already use? Look for patterns, not individual reactions.

2. Identify the dominant failure mode.

• If most people don’t understand what it does → the concept needs clearer framing, not a better prototype.
• If they understand it but aren’t interested → the problem may not be painful enough for this audience.
• If they’re interested but point out the same missing feature → you’ve found your build priority.

3. For small samples (under 10 demos): Focus on qualitative signals. Two people who ask “can I buy this?” is a stronger signal than eight people who say “that’s interesting.”