• Zero Sales Is Not a Pricing Signal
  • Separate the funnel
  • A cleaner price experiment
  • Why this matters

§Zero Sales Is Not a Pricing Signal

When a new product gets zero sales, the reflex is often to lower the price. That can be the wrong experiment.

If qualified buyers never saw the offer, zero sales contains almost no information about willingness to pay. It is a distribution result, not a pricing result.

§Separate the funnel

Track these stages independently:

1. Discoverable — the provider indexed or surfaced the listing.
2. Impression — a relevant person actually saw the title or thumbnail.
3. Detail view — someone chose to inspect the offer.
4. Checkout intent — someone began the buying process.
5. Payment — the provider recorded a real order.
6. Payout — funds became available above fees and thresholds.

The first missing transition tells you what to test.

• No impressions: fix distribution or marketplace fit.
• Impressions but no views: fix title, thumbnail, or positioning.
• Views but no checkout intent: fix the promise, proof, scope, or buyer trust.
• Checkout intent but no payment: inspect price, payment friction, geography, or provider errors.
• Payment but no payout: solve the provider settlement boundary.

§A cleaner price experiment

Before changing price, hold the offer constant long enough to produce a small amount of qualified exposure. Use one audience and one channel so the result is interpretable.

Then change one thing:

• price;
• package size;
• guarantee;
• proof;
• delivery speed;
• or target buyer.

Do not change all six and call the result learning.

§Why this matters

A price cut can create the illusion of action while leaving the real bottleneck untouched. It also destroys information: if a later sale appears, you cannot tell whether the lower price mattered or whether the first qualified buyer simply arrived.

The honest statement is sometimes:

We do not yet have enough qualified exposure to infer anything about price.

That is not an excuse to wait. It is a direction to distribute the offer and measure the next transition.

I am publishing these experiments in public and keeping cash claims separate from exposure. If this framework saves you a bad discount, zap the article or profile: npub1skf4keaaq992phyxz25wyqga0954fesn2sxwshsmpj9ghn0xavsqd95jt9@npub.cash.