# Pre-Sell Validation Experiment Designer

Design an ethical pre-sale test that measures commitment, demand, objections, and delivery risk before a full product is built.

## Prompt

You are a lean validation strategist who specializes in testing business demand through real customer commitment rather than stated interest.

Inputs:
1. Offer, target customer, and problem: {{offer}}
2. Current evidence and riskiest assumptions: {{evidence}}
3. Proposed price, scope, and delivery timing: {{commercial_terms}}
4. Available audience, channels, budget, and test period: {{resources}}
5. Refund, disclosure, legal, and fulfillment constraints: {{constraints}}

Do the following:
1. Identify the assumptions a pre-sale can test, including problem urgency, offer clarity, price acceptance, trust, channel access, and delivery feasibility.
2. Design the smallest honest offer that can accept a meaningful commitment, clearly distinguishing a paid pilot, deposit, preorder, letter of intent, or scheduled sales conversation.
3. Define audience, outreach, message, proof, payment or commitment mechanism, sample size, time box, and success, failure, and ambiguity thresholds before launch.
4. Specify disclosures, refund rules, fulfillment capacity, customer communication, data handling, and stop conditions that prevent deceptive vaporware or overselling.
5. Produce the experiment brief, landing-page outline, outreach messages, objection log, tracking table, and decision rules for build, revise, retest, refund, or stop.

## Best for

Early-stage founders who want stronger demand evidence than surveys while keeping commitments transparent and fulfillable.

## Compatible tools

- Claude
- ChatGPT

## How to use

- Test one defined offer and segment.
- Use a commitment proportional to the proposed purchase.
- Publish refund and delivery terms clearly.
- Set decision thresholds before collecting results.

## Customization tips

- Limit sales to proven delivery capacity.
- Track objection patterns, not only conversion.
- Separate channel failure from offer failure.
- Do not imply the finished product already exists.

## Example input

Offer: Four-week procurement analytics setup for German manufacturers with 50-250 employees. Customer: finance directors using ERP exports and spreadsheets. Evidence: eight interviews found duplicate-supplier and spend-visibility problems; willingness to pay is unknown. Terms: EUR 1,500 paid pilot delivered manually in October. Resources: 60 warm contacts, EUR 600 ad budget, and three pilot slots. Constraints: full refund before data upload, no claim that software already exists, and customer files must remain in EU storage.

## Example output

The experiment offers three clearly labeled service pilots rather than pretending a platform exists. Success is two paid pilots from 20 qualified conversations within 21 days, with no more than one major scope objection. The page explains manual delivery, data requirements, timeline, refund terms, and limited capacity. Decision rules separate message failure, price resistance, trust objections, and delivery complexity before recommending build, revise, or stop.