Pop-Up Store experiment

Tomáš Veselý - podpořen AI
1 day ago
4 min read

We're building a comprehensive knowledge library about product development as part of our mission. The library is for anyone looking to make better decisions — primarily decisions about product development. Whether you're an inventor, a product manager, or a Chief Product Officer, using the right research methods and experiments increases your chances of building the right things for the right audience. Today we'll introduce the Pop-Up Store validation method.

When to Use This Experiment?

The Pop-Up Store works well in the early stage, when you need to verify real interest in a product or service before investing in permanent infrastructure. The method makes sense when the customer experience depends on a specific setting and situation — often a physical product or a simulated digital service.

Deciding whether to build a permanent solution at all (a brick-and-mortar store, a venue, a production line) is costly and hard to reverse.
The product's value can only be experienced on-site, in a real usage situation.
The target is a large, homogeneous market where it makes sense to measure demand on a small sample.
There's a way to temporarily simulate the entire experience without full physical or digital infrastructure.

Basic Experiment Principles

The core of the experiment is to build a physical pop-up stand, kiosk, or temporary store where, in one place and over a short period, you either deliver a digital service or offer a physical product — and watch whether people in a real situation actually want it.

Define the assumption. Pin down the one critical assumption the solution rests on, typically that real demand exists in the given market and that people are willing to use the product or pay for it.
Choose a place and a window. Pick a real location and a narrow timeframe where the target audience already moves naturally — a festival, a market, a busy street, or a conference — so the pop-up catches people in an authentic situation.
Build the pop-up. Set up a temporary operation without permanent infrastructure. For a digital service, this might be a kiosk with a tablet or terminal where people try the service right there; for a physical product, a stand with real goods and staff.
Offer a sample. Lower the barrier to entry using the Offer Sample method. Give passersby a free taste or a short demo so they experience part of the value first-hand. A free sample also triggers the principle of reciprocity and opens the door to a follow-up request to sign up or buy.
Collect data. Track footfall (acquisition) and how many people actually try the offer (activation), but the focus is on qualitative feedback and validating market interest . What people say, how they react, what they ask about, and what unexpected uses emerge. The signal to scale up is genuine, repeated interest and positive responses that persist even after the initial curiosity fades; lukewarm reactions and indifference, by contrast, suggest the demand isn't there yet.
Identify the risks. Results are skewed by the novelty effect, the temporary, one-of-a-kind nature of a pop-up attracts the curious, whose interest may not reflect long-term demand. Footfall is also often driven by an attractive location rather than the product itself. A short window and a small sample give directional, not statistically robust, evidence, and the result is tied to a specific place and audience, so it may not hold elsewhere.

Real-World Experiment Example

Link to research: Airbnb Founders: Brian Chesky, Nathan Blecharczyk, and Joe Gebbia

In October 2007, Brian Chesky and Joe Gebbia couldn't afford their rent in San Francisco. A major design conference was in town and the hotels were sold out. They had the idea of renting out air mattresses in the living room of the apartment they were leasing — and throwing in breakfast on top. They named the service "Air Bed and Breakfast."

Instead of asking whether anyone would use such a service, they built a simple website, inflated three mattresses, and offered a night's stay for $80. It was a complete service in a real situation, but only for a few days and in one place. The first night, three strangers slept over. Together they paid $240.

Three guests on a small sample confirmed that people are willing to sleep on a stranger's mattress when the hotels are full. This temporary experiment in a real environment gave the founders their first concrete proof of demand and became the foundation of the company that later grew into Airbnb.

What Can Be Tested With This Experiment?

The method's main strength is validating real customer behavior in a real situation and their genuine interest in trying the product. You can validate, for example:

Existence of demand: whether anyone wants the offer at all when they encounter it live. The signal is how many people stop on their own and take up the offer instead of walking past.
Reaction to the core value: whether people are drawn to the very thing the whole solution rests on. The signal is what they react to, what they ask about, and what brings them back — and what they ignore.
Willingness to pay: whether the offer is valuable enough for people to part with money, not just praise. The signal is how many of those who try it actually buy at a real price (for Airbnb, three paying guests for $240).
Product feasibility: whether the team can deliver the service at all under real conditions and without permanent infrastructure. The signal is where the operation grinds — queues, outages, or steps the team can't keep up with.
Unexpected uses: how people use the product differently than expected. The signal is recurring behavior patterns and prompts that lead to adjusting the offer.
Key features: which features of the temporary product matter most to users, and which they simply overlook.