Value Proposition Test - Broken Promise
In Brief
The broken promise smoke test is a virality experiment that measures how much natural word-of-mouth potential a product idea has before anything is built. You share a landing page with a small group of 10-15 people and explicitly ask them not to tell anyone. Then you track whether new sign-ups appear from people outside your original group. The output is a referral rate: if sign-ups exceed the number of people you contacted, the idea is compelling enough that people broke a social promise to share it.
Common Use Case
You have three different product ideas and limited time to build. You create a simple landing page for each, share it with a small group of contacts, and ask them not to tell anyone. A week later, one page has sign-ups from people you never contacted. That idea has natural word-of-mouth potential worth pursuing.
Helps Answer
- Does this idea address a need people feel strongly enough to share?
- Are people excited enough to tell their friends about it?
- Which of several ideas generates the most organic sharing?
- What kind of person gets most excited about this concept?
- Is there natural word-of-mouth potential before we build anything?
Description
Broken promise 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 social capital: prospects pledge to share or recommend before access is granted.
This smoke test puts up a small social barrier (the social pressure of a personal promise) to see if anyone would consider the product idea worthy of sharing regardless of how developed it is. It tests the virality coefficient, or the “Referral” in Pirate Metrics’ AARRR model. It is arguably a social variation of the landing page MVP.
This test is most relevant for early-stage founders, particularly when testing a variety of ideas. It helps identify which of a number of ideas actually have relative merit. Ultimately, it helps focus your resources on an attractive and buzzworthy product.
In terms of the mechanics, it works similar to a landing page. There is a dedicated web page with a call-to-action. On the thank-you page or in follow-up communication, the copy explicitly asks the registrant NOT to share the idea with others. After some time passes, you compare the original list of people you contacted with the actual list of signups. Anyone who wasn’t in your original list of people you told about the idea is counted as a referral.
Referral rate = (total number of signups in a time period/total number of original people contacted)
If this is above 100 percent, then you are seeing referrals.
With this smoke test, you aren’t testing conversion rate but referral rate. Has anyone referred the product despite being told that it’s not to be shared?
To the extent possible, it’s also worth noting who is being referred. This provides additional insight on the ideal target profile for that product idea. These referrals should ideally be interviewed to discover more about them.
Some founders use AI-simulated personas to pre-test messaging before running the real experiment — a useful sanity check for obvious weaknesses, but not a substitute. The entire point of this smoke test is measuring real human behavior: whether real people break a real social promise because they find the idea too compelling to keep secret. AI cannot simulate genuine social pressure or authentic word-of-mouth enthusiasm.
How to
Prep
1. Define the “promise” and how you’ll revoke it.
Decide what exclusive access you’re offering (early invite, beta seat, founding-member status, a private waitlist) and write the explicit ask not to share. Both halves matter: the offer creates the social capital, and the explicit “please keep this to yourself” is what makes a downstream signup count as a broken promise rather than just a forwarded link. Draft the language you’ll use on the thank-you page or follow-up email so the request is unambiguous.
2. Identify and recruit a representative seed group.
Pick 10-15 people who actually look like your target customer, not friends and family who already love everything you do. Mix in people you know less well — colleagues of colleagues, members of relevant communities — so the network downstream of each seed contains real prospects, not just your existing fan club. Keep an exact list of who you contacted; you will need it to count referrals later.
3. Pre-commit to a referral-rate threshold and pick a traffic source.
Decide before launch what referral rate constitutes a go signal (e.g., signups exceeding 100% of seeds, or a specific count like 30% of seeds producing referrals). Picking the cutoff after you see results is how confirmation bias creeps in. Also decide how you’ll attribute traffic — UTM-tagged share links per seed, a unique landing page per seed, or an explicit “how did you hear about us” field — so you can tell referrals apart from the original group.
4. Stand up the landing page and tracking before you send anything.
Have the page, signup form, attribution mechanism, and a way to compare “list of seeds” vs. “list of signups” all wired up in advance. Running the test for a week and discovering you can’t tell who came from where is the most common way this experiment fails.
Execution
- Send out a few early landing pages/code-free MVPs to a group of 10-15 people.
- Ask them not to share with anyone.
- Track email signups or a different CTA (follows, Facebook page likes).
- See which (if any) of the pages gets signups outside of the group you sent it to (broken promise = formula for growth).
(adapted from Art of Cleverness)
Analysis
Interpretation is framed by how you originally formulated the experiment (i.e., what is the cutoff value for the referral rate required to make a go/no-go decision?). Also, be clear on exactly who you count, both in the original and referred groups. For example, immediate family like your mom doesn’t count (as well as anyone she refers) unless she really is representative of your target market.
- Confirmation bias Founders can pre-select recipients who already love the idea, which inflates the apparent referral rate. Send to a representative sample, not just enthusiasts.
- Moving-the-goalpost bias Setting (or adjusting) the referral-rate cutoff after you see results is how a weak signal gets reframed as a green light. As @LaunchTomorrow puts it: figure out your cutoff rate for referrals before you run a broken promise smoke test.
- Survivorship bias on referrers A handful of enthusiastic sharers can mask broad indifference. As @simonelucidi87 puts it, “It’s only hard to market an idea if your idea or product isn’t loved” — but two loud advocates aren’t the same as a loved idea. Look at the rate, not just the loudest voice.
Learn more
Case Studies
Gmail invites auctioned on eBay
Robinhood
Before launch, Robinhood shared its commission-free trading concept via a landing page and referral link. Despite no product existing, the waitlist grew to nearly 1 million users.
Harry’s
The razor startup emailed a small group of personal contacts about their pre-launch landing page and asked them to keep it quiet. In seven days, the campaign collected over 100,000 emails — 77% of them arriving via referral — suggesting the value proposition was too compelling not to share.
Further reading
- The Art of Cleverness — Mike Chen, “Use the Broken-Promise Strategy to validate your startup”
- Andrew Chen, The Cold Start Problem, HarperBusiness, 2021 — scarcity, waitlist, and invite-only mechanics in early viral growth (Clubhouse and other case studies).
- Steve Blank — Customer Development Manifesto
- Eric Ries (2011) — The Lean Startup, Chapter 6 “Test”
- Tim Ferriss blog — Jeff Raider on Harry’s pre-launch campaign
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